diff --git a/.github/workflows/gigaam.yml b/.github/workflows/gigaam.yml new file mode 100644 index 0000000..79d88a1 --- /dev/null +++ b/.github/workflows/gigaam.yml @@ -0,0 +1,145 @@ +name: CI + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + workflow_dispatch: + +env: + FORCE_COLOR: 1 + +jobs: + test: + name: Test (Python ${{ matrix.python-version }}) + runs-on: ubuntu-latest + timeout-minutes: 45 + + strategy: + fail-fast: false + matrix: + python-version: ["3.10", "3.11", "3.12"] + + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' + + - name: Install system dependencies + run: | + sudo apt-get update + sudo apt-get install -y wget ffmpeg libsndfile1 libasound2-dev + ffmpeg -version + + - name: Download test audio file + run: | + wget -O example.wav https://cdn.chatwm.opensmodel.sberdevices.ru/GigaAM/example.wav + + - name: Cache pip dependencies + uses: actions/cache@v4 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-pip-${{ matrix.python-version }}-${{ hashFiles('pyproject.toml') }} + restore-keys: | + ${{ runner.os }}-pip-${{ matrix.python-version }}- + ${{ runner.os }}-pip- + + - name: Show disk usage before install + run: df -h + + - name: Install Python dependencies + run: | + python -m pip install --upgrade pip setuptools wheel + pip install --no-cache-dir torch==2.8.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cpu + pip install --no-cache-dir -e ".[longform,tests]" + + - name: Show disk usage after install + run: df -h + + - name: Run model loading tests + run: | + pytest -v tests/test_loading.py -m partial --tb=short + + - name: Run audio loading tests + run: | + pytest -v tests/test_reading.py --tb=short + + - name: Run batching tests + run: | + pytest -v tests/test_batching.py --tb=short + + - name: Run longform tests + env: + HF_TOKEN: ${{ secrets.HF_TOKEN }} + run: | + pytest -v tests/test_longform.py --tb=short + + - name: Run onnx tests + run: | + pytest -v tests/test_onnx.py --tb=short + + - name: Run all tests with coverage + if: matrix.python-version == '3.10' + env: + HF_TOKEN: ${{ secrets.HF_TOKEN }} + run: | + pytest --cov=gigaam --cov-report=xml --cov-report=term-missing tests/ + + - name: Upload coverage to Codecov + if: matrix.python-version == '3.10' + uses: codecov/codecov-action@v4 + with: + file: ./coverage.xml + flags: unittests + name: codecov-umbrella + fail_ci_if_error: false + + lint: + name: Lint and Format Check + runs-on: ubuntu-latest + timeout-minutes: 10 + + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.10' + cache: 'pip' + + - name: Cache pip dependencies + uses: actions/cache@v4 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-pip-lint-${{ hashFiles('pyproject.toml') }} + restore-keys: | + ${{ runner.os }}-pip-lint- + + - name: Install linters + run: | + pip install --upgrade pip + pip install flake8 black isort mypy types-requests + + - name: Check code formatting with black + run: | + black --check --diff gigaam/ tests/ + + - name: Check imports with isort + run: | + isort --check-only --diff gigaam/ tests/ + + - name: Lint with flake8 + run: | + flake8 --ignore=E203,W503,W504 --max-line-length=120 --statistics gigaam/ tests/ + + - name: Type check with mypy + run: | + mypy gigaam/ --ignore-missing-imports --no-strict-optional diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c9c59ca --- /dev/null +++ b/.gitignore @@ -0,0 +1,14 @@ +__pycache__ +build +*.egg-info +*.wav +.DS_Store +*tmp* +onnx +.venv +mlx_convert/.venv +mlx_convert/gigaam-v3-ctc-mlx/ +mlx_convert/gigaam-v3-ctc-mlx-fp32/ +mlx_convert/gigaam-v3-rnnt-mlx/ +mlx_convert/ggml-small.bin +mlx_convert/debug_*.py \ No newline at end of file diff --git a/GigaAM License_NC.pdf b/GigaAM License_NC.pdf deleted file mode 100644 index d9a6f7f..0000000 Binary files a/GigaAM License_NC.pdf and /dev/null differ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..8d9a2e7 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 GigaChat Team + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md index 04c8604..509abd6 100644 --- a/README.md +++ b/README.md @@ -1,85 +1,184 @@ # GigaAM: the family of open-source acoustic models for speech processing -![plot](./gigaam_scheme.svg) +
-## Table of contents +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](./LICENSE) +[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/) +[![arXiv](https://img.shields.io/badge/arXiv-2506.01192-b31b1b.svg)](https://arxiv.org/abs/2506.01192) +[![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-Models-yellow.svg)](https://huggingface.co/ai-sage/GigaAM-v3) +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salute-developers/GigaAM/blob/main/colab_example.ipynb) -* [GigaAM](#gigaam) -* [GigaAM for Speech Recognition](#gigaam-for-speech-recognition) - * [GigaAM-CTC](#gigaam-ctc) - * [GigaAM-RNNT](#gigaam-rnnt) -* [GigaAM-Emo](#gigaam-emo) -* [License](./GigaAM%20License_NC.pdf) -* [Links](#links) +
-## GigaAM +
-GigaAM (**Giga** **A**coustic **M**odel) is a [Conformer](https://arxiv.org/pdf/2005.08100.pdf)-based [wav2vec2](https://arxiv.org/pdf/2006.11477.pdf) foundational model (around 240M parameters). We trained GigaAM on nearly 50 thousand hours of diversified speech audio in the Russian language. +![plot](./assets/gigaam_scheme.svg) -Resources: -* [Model weights](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/ssl_model_weights.ckpt) -* [Encoder config](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/encoder_config.yaml) -* [Colab example](https://colab.research.google.com/github/salute-developers/GigaAM/blob/main/examples/notebooks/GigaAM_Model_Usage_Example.ipynb) -* [Docker example](./examples/README.md) +## Latest News +* 2025/11 — GigaAM-v3: **30%** WER reduction on new data domains; GigaAM-v3-e2e: end-to-end transcription support (**70:30** win in Side-by-Side vs Whisper-large-v3) +* 2025/06 — Our [research paper on GigaAM](https://arxiv.org/abs/2506.01192) was accepted to InterSpeech 2025! +* 2024/12 — [MIT License](./LICENSE), GigaAM-v2 (**-15%** and **-12%** WER Reduction for CTC and RNN-T models, respectively), [ONNX export support](#onnx-export-and-inference) +* 2024/05 — GigaAM-RNNT (**-19%** WER Reduction), [long-form inference using external Voice Activity Detection](#model-inference) +* 2024/04 — GigaAM Release: GigaAM-CTC ([SoTA Speech Recognition model for the Russian language](#model-performance)), [GigaAM-Emo](#model-performance) +--- -## GigaAM for Speech Recognition +## Setup -We fine-tuned the GigaAM encoder for Speech Recognition with two different decoders: -* GigaAM-CTC was fine-tunined with [Connectionist Temporal Classification](https://www.cs.toronto.edu/~graves/icml_2006.pdf) and character-based tokenizer. -* GigaAM-RNNT was fine-tuned with [RNN Transducer loss](https://arxiv.org/abs/1211.3711) and subword tokenizer. +### Requirements +- Python ≥ 3.10 +- [ffmpeg](https://ffmpeg.org/) installed and added to your system's PATH -Both models were trained using [the NeMo toolkit](https://github.com/NVIDIA/NeMo) on publicly available Russian labeled data: +### Install the GigaAM Package -| dataset | size, hours | weight | -| --- | --- | --- | -| [Golos](https://arxiv.org/pdf/2106.10161.pdf) | 1227 | 0.6 | -| [SOVA](https://github.com/sovaai/sova-dataset) | 369 | 0.2 | -| [Russian Common Voice](https://arxiv.org/pdf/1912.06670.pdf) | 207 | 0.1 | -| [Russian LibriSpeech](https://arxiv.org/pdf/2012.03411.pdf) | 93 | 0.1 | +```bash +# Clone the repository +git clone https://github.com/salute-developers/GigaAM.git +cd GigaAM +# Install the package requirements +pip install -e . -Resources: -* ### GigaAM-CTC: - * [Model weights](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/ctc_model_weights.ckpt) - * [Model config](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/ctc_model_config.yaml) - * [Colab example](https://colab.research.google.com/github/salute-developers/GigaAM/blob/main/examples/notebooks/GigaAM_CTC_Model_Usage_Example.ipynb) - * [Docker example](./examples/README.md) -* ### GigaAM-RNNT: - * [Model weights](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/rnnt_model_weights.ckpt) - * [Model config](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/rnnt_model_config.yaml) - * [Colab example](https://colab.research.google.com/github/salute-developers/GigaAM/blob/main/examples/notebooks/GigaAM_RNNT_Model_Usage_Example.ipynb) - * [Docker examples](./examples/README.md) +# (optionally) Verify the installation: +pip install -e ".[tests]" +pytest -v tests/test_loading.py -m partial # or `-m full` to test all models +``` -The following table summarizes the performance of different models in terms of Word Error Rate on open Russian datasets: +--- -| model | parameters | [Golos Crowd](https://arxiv.org/abs/2106.10161) | [Golos Farfield](https://arxiv.org/abs/2106.10161) | [OpenSTT Youtube](https://github.com/snakers4/open_stt) | [OpenSTT Phone calls](https://github.com/snakers4/open_stt) | [OpenSTT Audiobooks](https://github.com/snakers4/open_stt) | [Mozilla Common Voice](https://arxiv.org/pdf/1912.06670.pdf) | [Russian LibriSpeech](https://arxiv.org/pdf/2012.03411.pdf) | -| --- | --- | --- | --- | --- | --- | --- | --- | --- | -| [Whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) | 1.5B | 17.4 | 14.5 | 21.1 | 31.2 | 17.0 | 5.3 | 9.0 | -| [NVIDIA Ru-FastConformer-RNNT](https://huggingface.co/nvidia/stt_ru_fastconformer_hybrid_large_pc) | 115M | 2.6 | 6.6 | 23.8 | 32.9 | 16.4 | 2.7 | 11.6 | -| GigaAM-CTC | 242M | 3.1 | 5.7 | 18.4 | 25.6 | 15.1| 1.7 | 8.1 | -| GigaAM-RNNT | 243M | 2.3 | 4.4 | 16.7 | 22.9 | 13.9 | 0.9 | 7.4 | +## GigaAM overview -## GigaAM-Emo +GigaAM is a [Conformer](https://arxiv.org/pdf/2005.08100.pdf)-based foundational model (220-240M parameters) pre-trained on diverse Russian speech data. It serves as the backbone for the entire GigaAM family, enabling state-of-the-art fine-tuned performance in speech recognition and emotion recognition. More information about GigaAM-v1 can be found in our [post on Habr](https://habr.com/ru/companies/sberdevices/articles/805569). We fine-tuned the GigaAM encoder for ASR using [CTC](https://www.cs.toronto.edu/~graves/icml_2006.pdf) and [RNNT](https://arxiv.org/abs/1211.3711) decoders. GigaAM family includes three lines of models -GigaAM-Emo is an acoustic model for Emotion Recognition. We fine-tuned the GigaAM Encoder on the [Dusha](https://arxiv.org/pdf/2212.12266.pdf) dataset. +| | Pretrain Method | Pretrain (hours) | ASR (hours) | Available Versions | +| :--- | :--- | :--- | :--- | :---: | +| **v1** | [Wav2vec 2.0](https://arxiv.org/abs/2006.11477) | 50,000 | 2,000 | `v1_ssl`, `emo`, `v1_ctc`, `v1_rnnt` | +| **v2** | [HuBERT–CTC](https://arxiv.org/abs/2506.01192) | 50,000 | 2,000 | `v2_ssl`, `v2_ctc`, `v2_rnnt` | +| **v3** | HuBERT–CTC | 700,000 | 4,000 | `v3_ssl`, `v3_ctc`, `v3_rnnt`, `v3_e2e_ctc`, `v3_e2e_rnnt` | -Resources: -* [Model weights](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/emo_model_weights.ckpt) -* [Model config](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/emo_model_config.yaml) -* [Colab example](https://colab.research.google.com/github/salute-developers/GigaAM/blob/main/examples/notebooks/GigaAM_Emo_Model_Usage_Example.ipynb) -* [Docker example](./examples/README.md) +Where `v3_e2e_ctc` and `v3_e2e_rnnt` support punctuation and text normalization. -The following table summarizes the performance of different models on the [Dusha](https://arxiv.org/pdf/2212.12266.pdf) dataset: +## Model Performance -| | | Crowd | | | Podcast | | -| --- | --- | --- | --- | --- | --- | --- | -| | Unweighted Accuracy | Weighted Accuracy | Macro F1-score | Unweighted Accuracy | Weighted Accuracy | Macro F1-score | -| [DUSHA](https://arxiv.org/pdf/2212.12266.pdf) baseline
([MobileNetV2](https://arxiv.org/abs/1801.04381) + [Self-Attention](https://arxiv.org/pdf/1805.08318.pdf)) | 0.83 | 0.76 | 0.77 | 0.89 | 0.53 | 0.54 | -| [АБК](https://aij.ru/archive?albumId=2&videoId=337) ([TIM-Net](https://arxiv.org/pdf/2211.08233.pdf)) | 0.84 | 0.77 | 0.78 | 0.90 | 0.50 | 0.55 | -| GigaAM-Emo | 0.90 | 0.87 | 0.84 | 0.90 | 0.76 | 0.67 | +`GigaAM-v3` training incorporates new internal datasets: callcenter, music, speech with atypical characteristics, and voice messages. As a result, the models perform on average **30%** better on these new domains while maintaining the same quality as `GigaAM-v2` on public benchmarks. In end-to-end ASR comparisons of `e2e_ctc` and `e2e_rnnt` against Whisper (judged via independent LLM-as-a-Judge side-by-side) GigaAM models win by an average margin of **70:30**. Our emotion recognition model `GigaAM-Emo` outperforms existing models by **15%** Macro F1-Score. + +For detailed results, see [here](./evaluation.md). + +--- + +## Usage + +### Model inference + +**Note:** ASR with `.transcribe` function is applicable for audio **only up to 25 seconds**. To enable `.transcribe_longform` install the additional [pyannote.audio](https://github.com/pyannote/pyannote-audio) dependencies + +
+Longform setup instruction + +* Generate [Hugging Face API token](https://huggingface.co/docs/hub/security-tokens) +* Accept the conditions to access [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) files and content + +```bash +pip install -e ".[longform]" +# optionally run longform testing +pip install -e ".[tests]" +HF_TOKEN= pytest -v tests/test_longform.py +``` +
+ +
+ + +```python +import gigaam + +# Load test audio +audio_path = gigaam.utils.download_short_audio() +long_audio_path = gigaam.utils.download_long_audio() + +# Audio embeddings +model_name = "v3_ssl" # Options: `v1_ssl`, `v2_ssl`, `v3_ssl` +model = gigaam.load_model(model_name) +embedding, _ = model.embed_audio(audio_path) +print(embedding) + +# ASR +model_name = "v3_e2e_rnnt" # Options: any model version with suffix `_ctc` or `_rnnt` +model = gigaam.load_model(model_name) +transcription = model.transcribe(audio_path) +print(transcription) + +# and long-form ASR +import os +os.environ["HF_TOKEN"] = +utterances = model.transcribe_longform(long_audio_path) +for utt in utterances: + transcription, (start, end) = utt["transcription"], utt["boundaries"] + print(f"[{gigaam.format_time(start)} - {gigaam.format_time(end)}]: {transcription}") + +# Emotion recognition +model = gigaam.load_model("emo") +emotion2prob = model.get_probs(audio_path) +print(", ".join([f"{emotion}: {prob:.3f}" for emotion, prob in emotion2prob.items()])) +``` + +### Loading from Hugging Face + +> **Note:** Install requirements from the [example](./colab_example.ipynb). + +```python +from transformers import AutoModel + +model = AutoModel.from_pretrained("ai-sage/GigaAM-v3", revision="e2e_rnnt", trust_remote_code=True) +``` + +### ONNX Export and Inference + +> **Note:** GPU support can be enabled with `pip install onnxruntime-gpu==1.23.*` if applicable. + +1. Export the model to ONNX using the `model.to_onnx` method: + ```python + onnx_dir = "onnx" + model_version = "v3_ctc" # Options: any version + + model = gigaam.load_model(model_version) + model.to_onnx(dir_path=onnx_dir) + ``` + +2. Run ONNX inference: + ```python + from gigaam.onnx_utils import load_onnx, infer_onnx + + sessions, model_cfg = load_onnx(onnx_dir, model_version) + result = infer_onnx(audio_path, model_cfg, sessions) + print(result) # string for ctc / rnnt, np.ndarray for ssl / emo + ``` + +These and more advanced (e.g. custom audio loading, batching) examples can be found in the [Colab notebook](https://colab.research.google.com/github/salute-developers/GigaAM/blob/main/colab_example.ipynb). + +--- + +## Citation + +If you use GigaAM in your research, please cite our paper: + +```bibtex +@inproceedings{kutsakov25_interspeech, + title = {{GigaAM: Efficient Self-Supervised Learner for Speech Recognition}}, + author = {Aleksandr Kutsakov and Alexandr Maximenko and Georgii Gospodinov and Pavel Bogomolov and Fyodor Minkin}, + year = {2025}, + booktitle = {{Interspeech 2025}}, + pages = {1213--1217}, + doi = {10.21437/Interspeech.2025-1616}, + issn = {2958-1796}, +} +``` ## Links + +* [[arxiv] GigaAM: Efficient Self-Supervised Learner for Speech Recognition](https://arxiv.org/abs/2506.01192) +* [[habr] GigaAM-v3: открытая SOTA-модель распознавания речи на русском](https://habr.com/ru/companies/sberdevices/articles/973160/) * [[habr] GigaAM: класс открытых моделей для обработки звучащей речи](https://habr.com/ru/companies/sberdevices/articles/805569) +* [[youtube] Как научить LLM слышать: GigaAM 🤝 GigaChat Audio](https://www.youtube.com/watch?v=O7NSH2SAwRc) * [[youtube] GigaAM: Семейство акустических моделей для русского языка](https://youtu.be/PvZuTUnZa2Q?t=26442) * [[youtube] Speech-only Pre-training: обучение универсального аудиоэнкодера](https://www.youtube.com/watch?v=ktO4Mx6UMNk) diff --git a/README_ru.md b/README_ru.md index f4148a2..3d458af 100644 --- a/README_ru.md +++ b/README_ru.md @@ -1,84 +1,182 @@ -# GigaAM: семейство акустических моделей для обработки звучащей речи +# GigaAM: семейство открытых акустических моделей для обработки речи -![plot](./gigaam_scheme.svg) +
-## Содержание +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](./LICENSE) +[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/) +[![arXiv](https://img.shields.io/badge/arXiv-2506.01192-b31b1b.svg)](https://arxiv.org/abs/2506.01192) +[![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-Models-yellow.svg)](https://huggingface.co/ai-sage/GigaAM-v3) +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salute-developers/GigaAM/blob/main/colab_example.ipynb) -* [GigaAM](#gigaam) -* [GigaAM для распознавания речи](#gigaam-для-распознавания-речи) - * [GigaAM-CTC](#gigaam-ctc) - * [GigaAM-RNNT](#gigaam-rnnt) -* [GigaAM-Emo](#gigaam-emo) -* [Лицензионное соглашение](./GigaAM%20License_NC.pdf) -* [Ссылки](#ссылки) +
-## GigaAM +
-GigaAM (**Giga** **A**coustic **M**odel) — фундаментальная акустическая модель, основанная на [Conformer](https://arxiv.org/pdf/2005.08100.pdf) энкодере (около 240M параметров). Мы предобучали GigaAM в [wav2vec2](https://arxiv.org/pdf/2006.11477.pdf) режиме на 50 тысячах часов разнообразных русскоязычных данных. +![plot](./assets/gigaam_scheme.svg) -Материалы -* [Model weights](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/ssl_model_weights.ckpt) -* [Encoder config](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/encoder_config.yaml) -* [Пример использования в colab](https://colab.research.google.com/github/salute-developers/GigaAM/blob/main/examples/notebooks/GigaAM_Model_Usage_Example.ipynb) -* [Пример использования в docker](./examples/README.md) +## Последние обновления +* **2025/11** — GigaAM-v3: снижение WER на **30%** на новых доменах данных; GigaAM-v3-e2e: end-to-end распознавание речи (**70:30** в side-by-side сравнении против Whisper-large-v3) +* **2025/06** — Наша [научная статья о GigaAM](https://arxiv.org/abs/2506.01192) принята на InterSpeech 2025! +* **2024/12** — [MIT-лицензия](./LICENSE), GigaAM-v2 (**снижение WER на 15% и 12%** для CTC и RNN-T моделей), [поддержка экспорта в ONNX](#конвертация-в-onnx-и-использование-графа) +* **2024/05** — GigaAM-RNNT (**снижение WER на 19%**), [распознавание речи на длинных аудиозаписях с помощью внешней VAD-модели](#основные-функции) +* **2024/04** — Релиз GigaAM: GigaAM-CTC ([Лучшая открытая модель для распознавания речи на русском языке](#качество-моделей)), [GigaAM-Emo](#качество-моделей) +--- +## Установка -## GigaAM для распознавания речи -Мы дообучали GigaAM энкодер для задачи распознавания речи с двумя разными декодерами: -* GigaAM-CTC была дообучена с [CTC](https://www.cs.toronto.edu/~graves/icml_2006.pdf) функцией потерь и посимвольной токенизацией. -* GigaAM-RNNT была дообучена с [RNN-T](https://arxiv.org/abs/1211.3711) функцией потерь и subword-токенизацией. +### Требования +- Python ≥ 3.10 +- [ffmpeg](https://ffmpeg.org/) установлен и добавлен в переменную PATH системы -Для обучения обеих моделей использовался [фреймворк NeMo](https://github.com/NVIDIA/NeMo) и следующие открытые данные: +### Установка пакета GigaAM -| dataset | size, hours | weight | -| --- | --- | --- | -| [Golos](https://arxiv.org/pdf/2106.10161.pdf) | 1227 | 0.6 | -| [SOVA](https://github.com/sovaai/sova-dataset) | 369 | 0.2 | -| [Russian Common Voice](https://arxiv.org/pdf/1912.06670.pdf) | 207 | 0.1 | -| [Russian LibriSpeech](https://arxiv.org/pdf/2012.03411.pdf) | 93 | 0.1 | +```bash +# Клонировать репозиторий +git clone https://github.com/salute-developers/GigaAM.git +cd GigaAM -Материалы: -* ### GigaAM-CTC: - * [Model weights](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/ctc_model_weights.ckpt) - * [Model config](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/ctc_model_config.yaml) - * [Пример использования в colab](https://colab.research.google.com/github/salute-developers/GigaAM/blob/main/examples/notebooks/GigaAM_CTC_Model_Usage_Example.ipynb) - * [Пример использования в docker](./examples/README.md) -* ### GigaAM-RNNT: - * [Model weights](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/rnnt_model_weights.ckpt) - * [Model config](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/rnnt_model_config.yaml) - * [Пример использования в colab](https://colab.research.google.com/github/salute-developers/GigaAM/blob/main/examples/notebooks/GigaAM_RNNT_Model_Usage_Example.ipynb) - * [Пример использования в docker](./examples/README.md) +# Установить зависимости +pip install -e . -В таблице ниже приведены оценки Word Error Rate различных моделей на открытых русскоязычных наборах данных: +# (опционально) Проверить установку: +pip install -e ".[tests]" +pytest -v tests/test_loading.py -m partial # или `-m full` для тестирования всех моделей +``` -| model | parameters | [Golos Crowd](https://arxiv.org/abs/2106.10161) | [Golos Farfield](https://arxiv.org/abs/2106.10161) | [OpenSTT Youtube](https://github.com/snakers4/open_stt) | [OpenSTT Phone calls](https://github.com/snakers4/open_stt) | [OpenSTT Audiobooks](https://github.com/snakers4/open_stt) | [Mozilla Common Voice](https://arxiv.org/pdf/1912.06670.pdf) | [Russian LibriSpeech](https://arxiv.org/pdf/2012.03411.pdf) | -| --- | --- | --- | --- | --- | --- | --- | --- | --- | -| [Whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) | 1.5B | 17.4 | 14.5 | 21.1 | 31.2 | 17.0 | 5.3 | 9.0 | -| [NVIDIA Ru-FastConformer-RNNT](https://huggingface.co/nvidia/stt_ru_fastconformer_hybrid_large_pc) | 115M | 2.6 | 6.6 | 23.8 | 32.9 | 16.4 | 2.7 | 11.6 | -| GigaAM-CTC | 242M | 3.1 | 5.7 | 18.4 | 25.6 | 15.1| 1.7 | 8.1 | -| GigaAM-RNNT | 243M | 2.3 | 4.4 | 16.7 | 22.9 | 13.9 | 0.9 | 7.4 | +--- -## GigaAM-Emo +## Обзор GigaAM -GigaAM-Emo — акустическая модель для определения эмоций. Мы доучивали GigaAM на датасете [Dusha](https://arxiv.org/pdf/2212.12266.pdf). +GigaAM - фундаментальная акустическая модель на базе архитектуры [Conformer](https://arxiv.org/pdf/2005.08100.pdf) (220–240 млн параметров), предобученная на разнообразных русскоязычных данных. Она служит основой для всего семейства GigaAM и обеспечивает высокое качество при дообучении на задачи распознавания речи и распознавания эмоций. Подробнее о GigaAM-v1 можно узнать в нашей [статье на Хабре](https://habr.com/ru/companies/sberdevices/articles/805569). Для задач автоматического распознавания речи (ASR) мы дообучили энкодер GigaAM с декодерами на основе [CTC](https://www.cs.toronto.edu/~graves/icml_2006.pdf) и [RNNT](https://arxiv.org/abs/1211.3711). Семейство GigaAM включает три поколения моделей: -Материалы: -* [Model weights](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/emo_model_weights.ckpt) -* [Model config](https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/emo_model_config.yaml) -* [Пример использования в colab](https://colab.research.google.com/github/salute-developers/GigaAM/blob/main/examples/notebooks/GigaAM_Emo_Model_Usage_Example.ipynb) -* [Пример использования в docker](./examples/README.md) +| | Метод предобучения | Объём предобучения (часы) | Объём данных ASR (часы) | Доступные версии | +| :--- | :--- | :--- | :--- | :---: | +| **v1** | [Wav2vec 2.0](https://arxiv.org/abs/2006.11477) | 50 000 | 2 000 | `v1_ssl`, `emo`, `v1_ctc`, `v1_rnnt` | +| **v2** | [HuBERT–CTC](https://arxiv.org/abs/2506.01192) | 50 000 | 2 000 | `v2_ssl`, `v2_ctc`, `v2_rnnt` | +| **v3** | HuBERT–CTC | 700 000 | 4 000 | `v3_ssl`, `v3_ctc`, `v3_rnnt`, `v3_e2e_ctc`, `v3_e2e_rnnt` | +Версии `v3_e2e_ctc` и `v3_e2e_rnnt` поддерживают пунктуацию и нормализацию текста. -В таблице ниже приведены метрики качества открытых моделей на датасете [Dusha](https://arxiv.org/pdf/2212.12266.pdf): +## Качество моделей -| | | Crowd | | | Podcast | | -| --- | --- | --- | --- | --- | --- | --- | -| | Unweighted Accuracy | Weighted Accuracy | Macro F1-score | Unweighted Accuracy | Weighted Accuracy | Macro F1-score | -| [DUSHA](https://arxiv.org/pdf/2212.12266.pdf) baseline
([MobileNetV2](https://arxiv.org/abs/1801.04381) + [Self-Attention](https://arxiv.org/pdf/1805.08318.pdf)) | 0.83 | 0.76 | 0.77 | 0.89 | 0.53 | 0.54 | -| [АБК](https://aij.ru/archive?albumId=2&videoId=337) ([TIM-Net](https://arxiv.org/pdf/2211.08233.pdf)) | 0.84 | 0.77 | 0.78 | 0.90 | 0.50 | 0.55 | -| GigaAM-Emo | 0.90 | 0.87 | 0.84 | 0.90 | 0.76 | 0.67 | +В обучение `GigaAM-v3` были включены новые внутренние наборы данных: колл-центр, музыка, речь с атипичными характеристиками и голосовые сообщения. В результате модели в среднем демонстрируют улучшение на **30%** (по метрике WER) на новых доменах при уровне качества `GigaAM-v2` на публичных бенчмарках. В сравнении end-to-end моделей (`e2e_ctc` и `e2e_rnnt`) с Whisper (оценка проводилась с использованием внешней LLM в формате side-by-side) модели GigaAM выигрывают в соотношении **70:30**. Наша модель распознавания эмоций `GigaAM-Emo` превосходит существующие аналоги на **15%** по метрике Macro F1-Score. + +С более подробными результатами можно ознакомиться [здесь](./evaluation.md). + +--- + +## Использование + +### Основные функции + +**Важно:** функция `.transcribe` для ASR применима только к аудиофайлам **до 25 секунд**. Для использования `.transcribe_longform` необходимо установить дополнительные зависимости [pyannote.audio](https://github.com/pyannote/pyannote-audio). + +
+Инструкция по настройке распознавания длинных аудио + +* Сгенерируйте [токен API Hugging Face](https://huggingface.co/docs/hub/security-tokens) +* Примите условия для получения доступа к контенту [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) + +```bash +pip install -e ".[longform]" +# опционально: запустить тесты для длинной транскрибации +pip install -e ".[tests]" +HF_TOKEN=<ваш hf токен> pytest -v tests/test_longform.py +``` +
+ +
+ + +```python +import gigaam + +# Загрузка тестового аудио +audio_path = gigaam.utils.download_short_audio() +long_audio_path = gigaam.utils.download_long_audio() + +# Аудио-эмбеддинги +model_name = "v3_ssl" # Варианты: `v1_ssl`, `v2_ssl`, `v3_ssl` +model = gigaam.load_model(model_name) +embedding, _ = model.embed_audio(audio_path) +print(embedding) + +# Распознавание речи +model_name = "v3_e2e_rnnt" # Варианты: любые версии с суффиксами `_ctc` или `_rnnt` +model = gigaam.load_model(model_name) +transcription = model.transcribe(audio_path) +print(transcription) + +# Распознавание на длинном аудио +import os +os.environ["HF_TOKEN"] = "" +utterances = model.transcribe_longform(long_audio_path) +for utt in utterances: + transcription, (start, end) = utt["transcription"], utt["boundaries"] + print(f"[{gigaam.format_time(start)} - {gigaam.format_time(end)}]: {transcription}") + +# Распознавание эмоций +model = gigaam.load_model("emo") +emotion2prob = model.get_probs(audio_path) +print(", ".join([f"{emotion}: {prob:.3f}" for emotion, prob in emotion2prob.items()])) +``` + +### Загрузка из Hugging Face + +> Используйте установку зависимостей из [примера](./colab_example.ipynb). + +```python +from transformers import AutoModel + +model = AutoModel.from_pretrained("ai-sage/GigaAM-v3", revision="e2e_rnnt", trust_remote_code=True) +``` + +### Конвертация в ONNX и использование графа + +> GPU будет использоваться после установки `pip install onnxruntime-gpu==1.23.*` (если доступно). + +1. Экспорт модели в ONNX с помощью метода `model.to_onnx`: + ```python + onnx_dir = "onnx" + model_version = "v3_ctc" # Варианты: любая версия модели + + model = gigaam.load_model(model_version) + model.to_onnx(dir_path=onnx_dir) + ``` + +2. Запуск с использованием ONNX: + ```python + from gigaam.onnx_utils import load_onnx, infer_onnx + + sessions, model_cfg = load_onnx(onnx_dir, model_version) + result = infer_onnx(audio_path, model_cfg, sessions) + print(result) # str для ctc / rnnt версий, np.ndarray для ssl / emo + ``` + +Эти и более продвинутые примеры (кастомная загрузка аудио, батчинг) доступны в [Colab notebook](https://colab.research.google.com/github/salute-developers/GigaAM/blob/main/colab_example.ipynb). + +--- + +## Citation + +Если применяете GigaAM в своих исследованиях, используйте ссылку на нашу статью: + +```bibtex +@inproceedings{kutsakov25_interspeech, + title = {{GigaAM: Efficient Self-Supervised Learner for Speech Recognition}}, + author = {Aleksandr Kutsakov and Alexandr Maximenko and Georgii Gospodinov and Pavel Bogomolov and Fyodor Minkin}, + year = {2025}, + booktitle = {{Interspeech 2025}}, + pages = {1213--1217}, + doi = {10.21437/Interspeech.2025-1616}, + issn = {2958-1796}, +} +``` ## Ссылки +* [[arxiv] GigaAM: Efficient Self-Supervised Learner for Speech Recognition](https://arxiv.org/abs/2506.01192) +* [[habr] GigaAM-v3: открытая SOTA-модель распознавания речи на русском](https://habr.com/ru/companies/sberdevices/articles/973160/) * [[habr] GigaAM: класс открытых моделей для обработки звучащей речи](https://habr.com/ru/companies/sberdevices/articles/805569) +* [[youtube] Как научить LLM слышать: GigaAM 🤝 GigaChat Audio](https://www.youtube.com/watch?v=O7NSH2SAwRc) * [[youtube] GigaAM: Семейство акустических моделей для русского языка](https://youtu.be/PvZuTUnZa2Q?t=26442) * [[youtube] Speech-only Pre-training: обучение универсального аудиоэнкодера](https://www.youtube.com/watch?v=ktO4Mx6UMNk) diff --git a/assets/gigaam_scheme.svg b/assets/gigaam_scheme.svg new file mode 100644 index 0000000..747271a --- /dev/null +++ b/assets/gigaam_scheme.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/assets/sbs_results.png b/assets/sbs_results.png new file mode 100644 index 0000000..11d21e5 Binary files /dev/null and b/assets/sbs_results.png differ diff --git a/colab_example.ipynb b/colab_example.ipynb new file mode 100644 index 0000000..03d4b46 --- /dev/null +++ b/colab_example.ipynb @@ -0,0 +1,882 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "n-Zq_26Ma2Dx" + }, + "source": [ + "## GigaAM from GitHub" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HyTZ5vTXbgkO" + }, + "source": [ + "### Installing reqs and testing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3MW_PBRka4w9", + "outputId": "84bd793d-e0dd-4b89-aa08-01d4721752ae" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cloning into 'gigaam'...\n", + "remote: Enumerating objects: 504, done.\u001b[K\n", + "remote: Counting objects: 100% (12/12), done.\u001b[K\n", + "remote: Compressing objects: 100% (10/10), done.\u001b[K\n", + "remote: Total 504 (delta 3), reused 7 (delta 2), pack-reused 492 (from 1)\u001b[K\n", + "Receiving objects: 100% (504/504), 2.75 MiB | 5.95 MiB/s, done.\n", + "Resolving deltas: 100% (298/298), done.\n", + "/content/gigaam\n" + ] + } + ], + "source": [ + "! git clone https://github.com/salute-developers/GigaAM.git\n", + "%cd GigaAM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nFpMODKsVqrt" + }, + "outputs": [], + "source": [ + "! pip install -e .[tests]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8qsuHA6RY926", + "outputId": "b26805c3-3886-4128-d3bd-210fc939d698" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m============================= test session starts ==============================\u001b[0m\n", + "platform linux -- Python 3.12.12, pytest-8.4.2, pluggy-1.6.0 -- /usr/bin/python3\n", + "cachedir: .pytest_cache\n", + "rootdir: /content/gigaam\n", + "plugins: cov-7.0.0, hydra-core-1.3.2, anyio-4.11.0, typeguard-4.4.4, langsmith-0.4.42\n", + "collected 16 items / 12 deselected / 4 selected \u001b[0m\n", + "\n", + "tests/test_loading.py::test_model_revision_partial[emo] \u001b[32mPASSED\u001b[0m\u001b[33m [ 25%]\u001b[0m\n", + "tests/test_loading.py::test_model_revision_partial[v2_ssl] \u001b[32mPASSED\u001b[0m\u001b[33m [ 50%]\u001b[0m\n", + "tests/test_loading.py::test_model_revision_partial[v3_ctc] \u001b[32mPASSED\u001b[0m\u001b[33m [ 75%]\u001b[0m\n", + "tests/test_loading.py::test_model_revision_partial[v3_e2e_rnnt] \u001b[32mPASSED\u001b[0m\u001b[33m [100%]\u001b[0m\n", + "\n", + "\u001b[33m=========== \u001b[32m4 passed\u001b[0m, \u001b[33m\u001b[1m12 deselected\u001b[0m, \u001b[33m\u001b[1m2 warnings\u001b[0m\u001b[33m in 167.17s (0:02:47)\u001b[0m\u001b[33m ===========\u001b[0m\n" + ] + } + ], + "source": [ + "! pytest -v tests/test_loading.py -m partial --disable-warnings" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oBkEJhSGbmGL" + }, + "source": [ + "### Model inference" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HqL9ZolqfihI" + }, + "source": [ + "##### Loading arguments" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "snrIrMfOfN0Z" + }, + "outputs": [], + "source": [ + "import gigaam\n", + "\n", + "model = gigaam.load_model(\n", + " \"v3_e2e_rnnt\", # model version: see readme#gigaam-family at GitHub for description\n", + " fp16_encoder=True, # enabled by default on CUDA, **IGNORED ON CPU (using fp32)**\n", + " use_flash=False, # disabled by default, boost performance for large tensors\n", + " device=None, # use cuda if available by default, can be set to `cpu` / `cuda`\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "peg7n2kqfkkW" + }, + "source": [ + "##### Inference methods" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "D8vK2SEWVq0o", + "outputId": "7f87497e-de4e-44a1-8394-6f735c292a34" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embeds: tensor([[[-0.2824, 0.3641, 0.4503, ..., -0.4728, -0.4027, -0.2415],\n", + " [ 0.1607, -0.4995, -0.0565, ..., -0.6242, -0.2318, -0.2053],\n", + " [-1.1857, -1.0032, -0.6091, ..., -0.5142, -0.3736, -0.2652],\n", + " ...,\n", + " [ 0.0187, -0.3757, -0.8965, ..., 0.1718, 0.0567, 0.1307],\n", + " [ 0.2691, -0.0672, -0.5010, ..., -1.4433, -1.4832, -1.4515],\n", + " [-1.5648, -1.6692, -1.2828, ..., 0.5110, 0.4826, 0.0133]]],\n", + " device='cuda:0', grad_fn=)\n", + "\n", + "Transcription: Ничьих не требуя похвал, Счастлив уж я надеждой сладкой, Что дева с трепетом любви Посмотрит, может быть, украдкой На песни грешные мои. У лукоморья дуб зелёный.\n", + "\n", + "Emotions: angry: 0.000, sad: 0.002, neutral: 0.923, positive: 0.075\n" + ] + } + ], + "source": [ + "# Load test audio\n", + "audio_path = gigaam.utils.download_short_audio()\n", + "\n", + "# Audio embeddings\n", + "model_name = \"v2_ssl\" # Options: `v1_ssl`, `v2_ssl`, `v3_ssl`\n", + "model = gigaam.load_model(model_name)\n", + "embedding, _ = model.embed_audio(audio_path)\n", + "print(\"Embeds:\", embedding)\n", + "\n", + "# ASR\n", + "model_name = \"v3_e2e_rnnt\" # Options: any model version with suffix `_ctc` or `_rnnt`\n", + "model = gigaam.load_model(model_name)\n", + "transcription = model.transcribe(audio_path)\n", + "print(\"\\nTranscription:\", transcription)\n", + "\n", + "# Emotion recognition\n", + "model = gigaam.load_model(\"emo\")\n", + "emotion2prob = model.get_probs(audio_path)\n", + "print(\"\\nEmotions:\", \", \".join([f\"{emotion}: {prob:.3f}\" for emotion, prob in emotion2prob.items()]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AP3QqjPpwZYl" + }, + "source": [ + "### ONNX convertation and inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NnKh1tnjwYZw", + "outputId": "8786532b-a4b9-4fe2-8936-a7db4e57b38d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Succesfully ported onnx v2_ssl_encoder to onnx/v2_ssl_encoder.onnx.\n", + "Succesfully ported onnx v3_e2e_rnnt_encoder to onnx/v3_e2e_rnnt_encoder.onnx.\n", + "Succesfully ported onnx v3_e2e_rnnt_decoder to onnx/v3_e2e_rnnt_decoder.onnx.\n", + "Succesfully ported onnx v3_e2e_rnnt_joint to onnx/v3_e2e_rnnt_joint.onnx.\n", + "Succesfully ported onnx emo to onnx/emo.onnx.\n" + ] + } + ], + "source": [ + "onnx_dir = \"onnx\"\n", + "\n", + "gigaam.load_model(\"v2_ssl\").to_onnx(dir_path=onnx_dir)\n", + "gigaam.load_model(\"v3_e2e_rnnt\").to_onnx(dir_path=onnx_dir)\n", + "gigaam.load_model(\"emo\").to_onnx(dir_path=onnx_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uk2zWgmIwt5n", + "outputId": "e3850b76-c20d-4f60-c49b-2ca8053b8d08" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embeds: [[[-0.2815268 0.36459672 0.45034242 ... -0.47318697 -0.40278506\n", + " -0.24017537]\n", + " [ 0.16104491 -0.49883014 -0.05703727 ... -0.6232359 -0.23104468\n", + " -0.20491666]\n", + " [-1.1852155 -1.002929 -0.608784 ... -0.5141611 -0.37379324\n", + " -0.2654637 ]\n", + " ...\n", + " [ 0.01822925 -0.3753832 -0.8963473 ... 0.17242727 0.05693132\n", + " 0.1306364 ]\n", + " [ 0.26943898 -0.06628752 -0.5013158 ... -1.4431518 -1.4824082\n", + " -1.4502244 ]\n", + " [-1.5649999 -1.669118 -1.2826786 ... 0.511411 0.48322338\n", + " 0.01353467]]]\n", + "\n", + "Transcription: Ничьих не требуя похвал, Счастлив уж я надеждой сладкой, Что дева с трепетом любви Посмотрит, может быть, украдкой На песни грешные мои. У лукоморья дуб зелёный.\n", + "\n", + "Emotions: [[7.7093333e-05 2.2028047e-03 9.2327267e-01 7.4447356e-02]]\n" + ] + } + ], + "source": [ + "from gigaam.onnx_utils import load_onnx, infer_onnx\n", + "\n", + "sessions, model_cfg = load_onnx(onnx_dir, \"v2_ssl\")\n", + "print(\"Embeds:\", infer_onnx(audio_path, model_cfg, sessions))\n", + "\n", + "sessions, model_cfg = load_onnx(onnx_dir, \"v3_e2e_rnnt\")\n", + "print(\"\\nTranscription:\", infer_onnx(audio_path, model_cfg, sessions))\n", + "\n", + "sessions, model_cfg = load_onnx(onnx_dir, \"emo\")\n", + "print(\"\\nEmotions:\", infer_onnx(audio_path, model_cfg, sessions))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "adf16N9yaxGo" + }, + "source": [ + "### Longform\n", + "\n", + "As `.transcribe` function input lenght is limited by 25 seconds, we need `.transcribe_longform` with audio segmentation based on `pyannote/segmentation-3.0` voice activity detection pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NiX5t2CHb1QB" + }, + "outputs": [], + "source": [ + "! pip install -e .[longform]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Jq3NiXRR1nts", + "outputId": "76b5606a-56a8-47e7-9ae8-d9c911313ed4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content/gigaam\n" + ] + } + ], + "source": [ + "# in colab the session might need restarting here\n", + "%cd GigaAM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DB8qh_X1bUL_", + "outputId": "387aea4b-382f-4348-9eae-8b51da2c7026" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m============================= test session starts ==============================\u001b[0m\n", + "platform linux -- Python 3.12.12, pytest-8.4.2, pluggy-1.6.0 -- /usr/bin/python3\n", + "cachedir: .pytest_cache\n", + "rootdir: /content/gigaam\n", + "plugins: cov-7.0.0, hydra-core-1.3.2, anyio-4.11.0, typeguard-4.4.4, langsmith-0.4.42\n", + "collected 7 items \u001b[0m\n", + "\n", + "tests/test_longform.py::test_segmentation_functionality[30.0] \u001b[32mPASSED\u001b[0m\u001b[32m [ 14%]\u001b[0m\n", + "tests/test_longform.py::test_segmentation_functionality[60.0] \u001b[32mPASSED\u001b[0m\u001b[32m [ 28%]\u001b[0m\n", + "tests/test_longform.py::test_segmentation_functionality[120.0] \u001b[32mPASSED\u001b[0m\u001b[32m [ 42%]\u001b[0m\n", + "tests/test_longform.py::test_transcribe_longform[v3_ctc] \u001b[32mPASSED\u001b[0m\u001b[32m [ 57%]\u001b[0m\n", + "tests/test_longform.py::test_transcribe_longform[v3_e2e_rnnt] \u001b[32mPASSED\u001b[0m\u001b[33m [ 71%]\u001b[0m\n", + "tests/test_longform.py::test_longform_consistency[v3_ctc] \u001b[32mPASSED\u001b[0m\u001b[33m [ 85%]\u001b[0m\n", + "tests/test_longform.py::test_segmentation_edge_cases \u001b[32mPASSED\u001b[0m\u001b[33m [100%]\u001b[0m\n", + "\n", + "\u001b[33m======================== \u001b[32m7 passed\u001b[0m, \u001b[33m\u001b[1m1 warning\u001b[0m\u001b[33m in 56.39s\u001b[0m\u001b[33m =========================\u001b[0m\n" + ] + } + ], + "source": [ + "! HF_TOKEN=\"\" pytest -v tests/test_longform.py --disable-warnings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZFlpV4VXapk7", + "outputId": "17a36be8-d1db-4ce8-cf33-5812b93c5f14" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[00:00:00 - 00:16:80]: Вечерня отошла давно, Но в кельях тихо и темно; Уже и сам игумен строгий Свои молитвы прекратил И кости ветхие склонил, Перекрестясь на одр убогий. Кругом и сон, и тишина; Но церкви дверь отворена.\n", + "[00:17:07 - 00:32:54]: Трепещет луч лампады, И тускло озаряет он И тёмную живопись икон, и возглащённые оклады. И раздаётся в тишине То тяжкий вздох, то шёпот важный, И мрачно дремлет в тишине старинный свод.\n", + "[00:32:95 - 00:49:30]: Глухой и влажный Стоят за клиросом чернец и грешник, Неподвижны оба. И шёпот их — Как глаз из гроба, И грешник бледен, как мертвец — Монах. Несчастный! Полно, перестань!\n", + "[00:49:81 - 01:05:65]: Ужасна исповедь злодея, Заплачена тобою дань Тому, Кто в злобе пламенея Лукавого грешника блюдёт И к вечной гибели ведёт. Смирись, опомнись. Время, время. Раскаянье, покров\n", + "[01:05:94 - 01:10:88]: Я разрешу тебя, грехов сложи мучительное бремя.\n" + ] + } + ], + "source": [ + "import os\n", + "import warnings\n", + "warnings.simplefilter(\"ignore\")\n", + "\n", + "import gigaam\n", + "\n", + "os.environ[\"HF_TOKEN\"] = \"\"\n", + "\n", + "long_audio_path = gigaam.utils.download_long_audio()\n", + "model = gigaam.load_model(\"v3_e2e_rnnt\")\n", + "\n", + "utterances = model.transcribe_longform(long_audio_path)\n", + "for utt in utterances:\n", + " transcription, (start, end) = utt[\"transcription\"], utt[\"boundaries\"]\n", + " print(f\"[{gigaam.format_time(start)} - {gigaam.format_time(end)}]: {transcription}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oBwAxFger9vB" + }, + "source": [ + "### More advanced examples" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FNPBdICGsqio" + }, + "source": [ + "##### Another way to load audio" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fmwFIE9qaf7y", + "outputId": "9d85b25e-0346-4306-cf3c-131a6a63af1f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Ничьих не требуя похвал, Счастлив уж я надеждой сладкой, Что дева с трепетом любви Посмотрит, может быть, украдкой На песни грешные мои. У лукоморья дуб зелёный.', 'Ничьих не требуя похвал, Счастлив уж я надеждой сладкой, Что дева с трепетом любви Посмотрит, может быть, украдкой На песни грешные мои. У лукоморья дуб зелёный.']\n" + ] + } + ], + "source": [ + "import librosa\n", + "import torch\n", + "\n", + "from gigaam.utils import AudioDataset\n", + "from gigaam.preprocess import SAMPLE_RATE as SR\n", + "\n", + "fname = gigaam.utils.download_short_audio()\n", + "wav = gigaam.load_audio(fname)\n", + "# arrays can be not equal after possible resampling, but close enough\n", + "wav_ = torch.from_numpy(librosa.load(fname, sr=SR, mono=True)[0])\n", + "\n", + "wav_tns, lengths = AudioDataset.collate([wav, wav_])\n", + "with torch.no_grad():\n", + " encoded, encoded_len = model(\n", + " wav_tns.to(model._device).to(model._dtype), lengths.to(model._device)\n", + " )\n", + " print(model.decoding.decode(model.head, encoded, encoded_len))\n", + "\n", + "# outputs expected to be equal" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kIeaDNXYsw44" + }, + "source": [ + "##### Longform & batch > 1\n", + "\n", + "We'll demonstrate batching with an example for longform inference. Here a long audio is split into independent segments - making it ideal for batching." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LMqA7Kbosu1k" + }, + "outputs": [], + "source": [ + "import os\n", + "import soundfile as sf\n", + "from pathlib import Path\n", + "\n", + "import gigaam\n", + "from gigaam.preprocess import SAMPLE_RATE as SR\n", + "from gigaam.vad_utils import segment_audio_file\n", + "\n", + "\n", + "os.environ[\"HF_TOKEN\"] = \"\"\n", + "\n", + "exp_dir = Path(\"tmp_inference_example\")\n", + "exp_dir.mkdir(exist_ok=True)\n", + "long_fname = gigaam.utils.download_long_audio()\n", + "model = gigaam.load_model(\"v3_e2e_rnnt\")\n", + "\n", + "# Saving parts as files\n", + "with torch.inference_mode():\n", + " segments, boundaries = segment_audio_file(long_fname, sr=SR, device=model._device)\n", + "wav_paths = []\n", + "for i, segment in enumerate(segments):\n", + " wav_paths.append(str(exp_dir / f\"{Path(long_fname).stem}_{i}{Path(long_fname).suffix}\"))\n", + " sf.write(wav_paths[-1], segment, SR)\n", + "\n", + "# Load wavs: you can use their paths or load with librosa-like method as above\n", + "wavs_np = [librosa.load(wav_path, sr=SR, mono=True)[0] for wav_path in wav_paths]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "C6OcwpVqt05n" + }, + "source": [ + "Finally, run batched inference. In our case the input audio is fairly short, `batch_size` can be decreased for longer inputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xGeCADORttRY", + "outputId": "2cff3399-663c-497a-d41a-2d028a3de65c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[00:00:00 - 00:16:80]: Вечерня отошла давно, Но в кельях тихо и темно; Уже и сам игумен строгий Свои молитвы прекратил И кости ветхие склонил, Перекрестясь на одр убогий. Кругом и сон, и тишина; Но церкви дверь отворена.\n", + "[00:17:07 - 00:32:54]: Трепещет луч лампады, И тускло озаряет он И тёмную живопись икон, и возглащённые оклады. И раздаётся в тишине То тяжкий вздох, то шёпот важный, И мрачно дремлет в тишине старинный свод.\n", + "[00:32:95 - 00:49:30]: Глухой и влажный Стоят за клиросом чернец и грешник, Неподвижны оба. И шёпот их — Как глаз из гроба, И грешник бледен, как мертвец — Монах. Несчастный! Полно, перестань!\n", + "[00:49:81 - 01:05:65]: Ужасна исповедь злодея, Заплачена тобою дань Тому, Кто в злобе пламенея Лукавого грешника блюдёт И к вечной гибели ведёт. Смирись, опомнись. Время, время. Раскаянье, покров\n", + "[01:05:94 - 01:10:88]: Я разрешу тебя, грехов сложи мучительное бремя.\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "dataset = AudioDataset(wavs_np) # or AudioDataset(wav_paths)\n", + "batch_size = len(dataset)\n", + "dataloader = torch.utils.data.DataLoader(\n", + " dataset, batch_size=batch_size, collate_fn=dataset.collate, shuffle=False\n", + ")\n", + "\n", + "pred_texts = []\n", + "for wav_tns, lengths in dataloader:\n", + " wav_tns, lengths = wav_tns.to(model._device).to(model._dtype), lengths.to(model._device)\n", + " with torch.no_grad():\n", + " encoded, encoded_len = model(wav_tns, lengths)\n", + " pred_texts.extend(model.decoding.decode(model.head, encoded, encoded_len))\n", + "\n", + "for (start, end), text in zip(boundaries, pred_texts):\n", + " print(f\"[{gigaam.format_time(start)} - {gigaam.format_time(end)}]: {text}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4ipQYBdYcQH0" + }, + "source": [ + "## GigaAM from Hugging Face" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_FJqALokc5vx" + }, + "source": [ + "### Reqs and files loading: now by hand" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5EAqie1xco_I" + }, + "outputs": [], + "source": [ + "! pip install numpy==2.* torch==2.8.* torchaudio==2.8.* transformers==4.57.* \\\n", + " pyannote.audio==4.0 torchcodec==0.7 numba>=0.62 \\\n", + " onnx==1.19.* onnxruntime==1.23.* \\\n", + " hydra-core==1.3.* omegaconf==2.3.* \\\n", + " sentencepiece tqdm\n", + "! pip install --force-reinstall numpy # make transformers work fine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9nMLdr0VvIGj" + }, + "outputs": [], + "source": [ + "! wget https://cdn.chatwm.opensmodel.sberdevices.ru/GigaAM/example.wav\n", + "! wget https://cdn.chatwm.opensmodel.sberdevices.ru/GigaAM/long_example.wav" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 267, + "referenced_widgets": [ + "2e3f6159ef5e407e8f1fb9bca32162b7", + "4b76d95921354dc785fe6193f2925317", + "c23e0e9417cc44a7bb653a7a56b954d0", + "ea66c0bd776e4845834237d8c5d4837b", + "5441e4df284e49408cb8be1271039aa4", + "ac4f18edb19b468f963e771fdb1dc12e", + "6f3018569f1a400fb15e4eda9eb5ec8e", + "bd2462f8e23f4bb1ba8d50bb73c64699", + "fff6474730784e089f6d956b8e2bd1a0", + "d61a763ea9884978aef9261c818f3c83", + "3c95709c357f4869812159549a0edffb", + "ea8dd50349f342c6b0b53cfab3e4437a", + "d7820d5e55964d4cac1989638bb70a6d", + "e30d397d613b48e9916d5f2a0e3acd8e", + "164df35c2a65427e800b6a17fdb90438", + "593ccb48450e43ae9df220123bfc2c85", + "69c0084f5032462687d770528342c803", + "c046db1ead5c4b309d7e3e127661ccc4", + "6bbccc048f264a7a91bdcc520d25e492", + "1b1adb6e50da4cb7a94a01d1315331f6", + "83fb6d82fab347708f7dd10b98340a1a", + "3fe5ac311b244176b2d61c49c20986e7", + "604c907ea10344c996e6d5d09896d7bf", + "31aaa1dc03e042a8820692d86b198eb3", + "920b77ca466643528c108e56cdd65aa5", + "6ce4197e475b44ca8ddd47f1c9fc4393", + "693b1d83cb7044afa437ce811f5b864a", + "62178bcb8395445baf7ec87d43ff1956", + "5034511b0e9c41adb9de422f60be5db9", + "23272696a2b34ac4883abb80b2f80be1", + "65d81f90e2014bdc8af2348a16a22919", + "38fa41796aea4b7ebe2f552a76691f30", + "43b54378f65347bb97b8a9958fe0e141", + "c4c239fbddbd49428c53e99e4c6d7395", + "94b3977244d84269a9b12ee836d1eb81", + "040db1bc88cc4798a257d30d61e22f0d", + "3fde9901a2254aa79c42c9b510e2f293", + "8b04a773cc3a46508992d4145864f9bc", + "ed0e21bd550b4fd38d47a55377eedb6c", + "909ae2153eb04d3394bad1298787b56e", + "cf99eeeb1b394df4af5adaa674aabffc", + "29909a4fc3fa48c2b34f59840f7889c3", + "510a7cbe840140118b8c850545a11aab", + "f4443110254141e8a1366a3d7a54ef93" + ] + }, + "id": "UCY_7Ltya2Wt", + "outputId": "a5924f29-ea27-4181-aa56-dbef579875f4" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2e3f6159ef5e407e8f1fb9bca32162b7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "config.json: 0%| | 0.00/1.87k [00:00 Note: `fp16` encoder on CUDA can be used with `model.model.encoder = model.model.encoder.half()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3OlYBRz0dVcA", + "outputId": "b5cfac2c-0d94-452f-acbb-c69cbd0155ed" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embed: tensor([[[-0.2815, 0.3646, 0.4503, ..., -0.4732, -0.4028, -0.2402],\n", + " [ 0.1610, -0.4988, -0.0570, ..., -0.6232, -0.2310, -0.2049],\n", + " [-1.1852, -1.0029, -0.6088, ..., -0.5142, -0.3738, -0.2655],\n", + " ...,\n", + " [ 0.0182, -0.3754, -0.8963, ..., 0.1724, 0.0569, 0.1306],\n", + " [ 0.2694, -0.0663, -0.5013, ..., -1.4432, -1.4824, -1.4502],\n", + " [-1.5650, -1.6691, -1.2827, ..., 0.5114, 0.4832, 0.0135]]],\n", + " grad_fn=)\n", + "\n", + "Transcription: Ничьих не требуя похвал, Счастлив уж я надеждой сладкой, Что дева с трепетом любви Посмотрит, может быть, украдкой На песни грешные мои. У лукоморья дуб зелёный.\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "device = torch.device(\"cpu\")\n", + "repo_name = \"ai-sage/GigaAM-v3\"\n", + "\n", + "# Audio embeddings\n", + "model_name = \"ssl\"\n", + "model = AutoModel.from_pretrained(repo_name, revision=model_name, trust_remote_code=True).to(device)\n", + "embedding, _ = model.embed_audio(\"example.wav\")\n", + "print(\"Embed:\", embedding)\n", + "\n", + "# ASR\n", + "model_name = \"e2e_rnnt\" # Options: rnnt, ctc, e2e_rnnt, e2e_ctc\n", + "model = AutoModel.from_pretrained(repo_name, revision=model_name, trust_remote_code=True).to(device)\n", + "transcription = model.transcribe(\"example.wav\")\n", + "print(\"\\nTranscription:\", transcription)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ek1gKS6_cW-G", + "outputId": "39308dfb-f63d-4eb5-d944-30bcf6d7e462" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Longform transcription:\n", + "\n", + "[0.0000 - 16.8047]: Вечерня отошла давно, Но в кельях тихо и темно; Уже и сам игумен строгий Свои молитвы прекратил И кости ветхие склонил, Перекрестясь на одр убогий. Кругом и сон, и тишина; Но церкви дверь отворена.\n", + "[17.0747 - 32.5491]: Трепещет луч лампады, И тускло озаряет он И тёмную живопись икон, и возглащённые оклады. И раздаётся в тишине То тяжкий вздох, то шёпот важный, И мрачно дремлет в тишине старинный свод.\n", + "[32.9541 - 49.3060]: Глухой и влажный Стоят за клиросом чернец и грешник, Неподвижны оба. И шёпот их — Как глаз из гроба, И грешник бледен, как мертвец — Монах. Несчастный! Полно, перестань!\n", + "[49.8122 - 65.6578]: Ужасна исповедь злодея, Заплачена тобою дань Тому, Кто в злобе пламенея Лукавого грешника блюдёт И к вечной гибели ведёт. Смирись, опомнись. Время, время. Раскаянье, покров\n", + "[65.9447 - 70.8891]: Я разрешу тебя, грехов сложи мучительное бремя.\n" + ] + } + ], + "source": [ + "import os\n", + "os.environ[\"HF_TOKEN\"] = \"\"\n", + "\n", + "model = AutoModel.from_pretrained(repo_name, revision=\"e2e_rnnt\", trust_remote_code=True)\n", + "utterances = model.transcribe_longform(\"long_example.wav\")\n", + "print(\"Longform transcription:\\n\")\n", + "for utt in utterances:\n", + " transcription, (start, end) = utt[\"transcription\"], utt[\"boundaries\"]\n", + " print(f\"[{start:.4f} - {end:.4f}]: {transcription}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LBG1U5TOrB24", + "outputId": "5f8e0707-a6ed-4e4f-fc45-78c31465e8fd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Succesfully ported onnx v3_e2e_rnnt_encoder to hf_onnx/v3_e2e_rnnt_encoder.onnx.\n", + "Succesfully ported onnx v3_e2e_rnnt_decoder to hf_onnx/v3_e2e_rnnt_decoder.onnx.\n", + "Succesfully ported onnx v3_e2e_rnnt_joint to hf_onnx/v3_e2e_rnnt_joint.onnx.\n" + ] + } + ], + "source": [ + "model.to_onnx(\"hf_onnx\")" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/evaluation.md b/evaluation.md new file mode 100644 index 0000000..2c02925 --- /dev/null +++ b/evaluation.md @@ -0,0 +1,77 @@ +## ASR + +Word Error Rate (%) across all validation sets for ASR models: + + +| Set Name | V3 CTC | V3 RNNT | E2E CTC*| E2E RNNT*| V2 CTC | V2 RNNT | V1 CTC | V1 RNNT | T-One + LM | Whisper*| +|:------------------------|-------:|--------:|-----------:|------------:|-------:|--------:|-------:|--------:|-----------:|--------:| +| Golos Farfield | 4.5 | 3.9 | 6.1 | 5.5 | 4.3 | 4.0 | 5.8 | 4.8 | 12.2 | 16.4 | +| Golos Crowd | 2.8 | 2.4 | 9.7 | 9.1 | 2.5 | 2.3 | 3.1 | 2.3 | 5.7 | 19.0 | +| Russian LibriSpeech | 4.7 | 4.4 | 6.4 | 6.4 | 5.2 | 5.2 | 7.5 | 7.7 | 6.2 | 9.4 | +| Mozilla Common Voice 19 | 1.3 | 0.9 | 3.2 | 3.0 | 1.5 | 0.9 | 8.4 | 8.0 | 5.2 | 5.5 | +| Natural Speech | 7.8 | 6.9 | 9.6 | 8.5 | 10.8 | 10.3 | 12.6 | 11.4 | 14.5 | 13.4 | +| Disordered Speech | 20.6 | 19.2 | 22.8 | 23.1 | 28.0 | 27.5 | 37.5 | 40.8 | 51.0 | 58.6 | +| Callcenter | 10.3 | 9.5 | 13.3 | 12.6 | 13.6 | 12.9 | 15.5 | 15.0 | 13.5 | 23.1 | +| OpenSTT Phone Calls | 18.6 | 17.4 | 20.0 | 19.1 | 20.7 | 19.8 | 23.0 | 21.1 | 19.8 | 27.4 | +| OpenSTT Youtube | 11.6 | 10.6 | 12.7 | 11.8 | 13.9 | 13.0 | 16.0 | 14.7 | 21.9 | 17.8 | +| OpenSTT Audiobooks | 8.7 | 8.2 | 10.3 | 9.3 | 10.8 | 10.3 | 12.7 | 11.7 | 13.4 | 14.3 | +| **Average** | **9.1**| **8.3** | 12.0 | 11.2 | 11.1 | 10.6 | 14.2 | 13.8 | 16.3 | 21.0 | + +\* with post-processing applied (removing punctuation and capitalization, replacing numerals, etc.) + +## End-to-end ASR + +Side-by-side (SBS) comparison of **v3_e2e_ctc** (blue) and **v3_e2e_rnnt** (red) against Whisper outputs across domains, using 500 random samples per test set. Each model was evaluated independently against Whisper by an LLM-as-a-Judge with randomized prompt ordering. + +![plot](./assets/sbs_results.png) + +For the creation of training labels with punctuation and text normalization, we used [GigaChat Max Audio](https://habr.com/ru/companies/sberdevices/articles/904894/): this model was provided with both the original audio and transcription from the training corpus, alongside few-shot examples, to produce normalized text with precise punctuation. In contrast, using Whisper in forced decoding mode adds only punctuation (not normalization). Although this alternative reduces hallucinations, it results in especially poor comma accuracy, and training on such labels further degrades overall model performance. + +| | Normalization | F1(,) ↑ | F1(.) ↑ | F1(?) ↑ | WER ↓ | CER ↓ | +|:----------------------------------|:------------------|----------:|----------:|----------:|--------:|--------:| +| GigaChat Max Audio | Full | 84.2 | 85.6 | 74.9 | 18.4 | 10.9 | +| Whisper Punctuator | punctuation only | 62.2 | 85.0 | 77.7 | 0.0 | 0.0 | +| GigaAM from Whisper labels | punctuation only | 50.3 | 84.1 | 77.7 | 12.0 | 7.8 | +| GigaAM-e2e-ctc | Full | 83.7 | 86.7 | 78.6 | 16.0 | 8.7 | +| GigaAM-e2e-rnnt | Full | 84.5 | 86.7 | 79.8 | 14.2 | 8.8 | + +## Emotion recognition + +GigaAM-Emo was trained on the [Dusha](https://arxiv.org/pdf/2212.12266.pdf) dataset + +| | | Crowd | | | Podcast | | +| --- | --- | --- | --- | --- | --- | --- | +| | Unweighted Accuracy | Weighted Accuracy | Macro F1-score | Unweighted Accuracy | Weighted Accuracy | Macro F1-score | +| [DUSHA](https://arxiv.org/pdf/2212.12266.pdf) baseline
([MobileNetV2](https://arxiv.org/abs/1801.04381) + [Self-Attention](https://arxiv.org/pdf/1805.08318.pdf)) | 0.83 | 0.76 | 0.77 | 0.89 | 0.53 | 0.54 | +| [АБК](https://aij.ru/archive?albumId=2&videoId=337) ([TIM-Net](https://arxiv.org/pdf/2211.08233.pdf)) | 0.84 | 0.77 | 0.78 | **0.90** | 0.50 | 0.55 | +| GigaAM-Emo | **0.90** | **0.87** | **0.84** | **0.90** | **0.76** | **0.67** | + +## Attention type + +We benchmarked attention implementations (one layer and full encoder) on CUDA. Custom implementation runs naive tensor multiplication. [SDPA](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) is enabled by default, as it offers better robustness across varying input dimensions. `flash_attn` is only beneficial for long sequences with `batch_size >> 1` ([example for `bs > 1`](./colab_example.ipynb)). + +* Only Attention Mechanism (Time (ms) ± std) + +| bs, seq_len | Custom | SDPA | Flash | +|-------------|---------------|-------------|--------------| +| 1, 10s | 0.03 ± 0.00 | 0.03 ± 0.00 | 0.05 ± 0.03 | +| 8, 20s | 0.15 ± 0.01 | 0.14 ± 0.01 | 0.66 ± 0.14 | +| 128, 30s | 3.60 ± 0.10 | 3.59 ± 0.04 | 1.40 ± 0.06 | + +* Full Encoder Inference (Time (ms) ± std) + +| bs, seq_len | Custom | SDPA | Flash | +|-------------|---------------|--------------|--------------| +| 1, 10s | 10.14 ± 0.17 | 10.06 ± 0.12 | 11.57 ± 0.25 | +| 8, 20s | 15.84 ± 0.07 | 15.90 ± 0.02 | 25.26 ± 0.26 | +| 128, 30s | 324.53 ± 0.17 | 324.48 ± 0.09| 293.80 ± 0.89| + + +## Test Coverage + +We maintain high unit test coverage to ensure reliability and ease of refactoring. Coverage currently stands at 91%, measured with `pytest-cov` (`flash-attn` is not covered, as it requires GPU execution). + +Command to reproduce (testing will take some time) +```bash +HF_TOKEN= pytest --cov=gigaam --cov-report=term-missing -v tests/ +``` diff --git a/examples/Dockerfile b/examples/Dockerfile deleted file mode 100644 index d86fd62..0000000 --- a/examples/Dockerfile +++ /dev/null @@ -1,13 +0,0 @@ -FROM nvcr.io/nvidia/nemo:23.10 - -RUN mkdir -p /workspace/data -WORKDIR /workspace/data - -RUN wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/{ssl_model_weights.ckpt,emo_model_weights.ckpt,ctc_model_weights.ckpt,rnnt_model_weights.ckpt,ctc_model_config.yaml,emo_model_config.yaml,encoder_config.yaml,rnnt_model_config.yaml,tokenizer_all_sets.tar,example.wav,long_example.wav} -RUN tar -xf tokenizer_all_sets.tar && rm tokenizer_all_sets.tar - -RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 -RUN pip install Cython -RUN pip install git+https://github.com/NVIDIA/NeMo.git@1fa961ba03ab5f8c91b278640e29807079373372#egg=nemo_toolkit[all] -RUN pip install -U soundfile -RUN pip install pyannote.audio==3.2.0 \ No newline at end of file diff --git a/examples/README.md b/examples/README.md deleted file mode 100644 index e6bf8c0..0000000 --- a/examples/README.md +++ /dev/null @@ -1,126 +0,0 @@ -* [Virtual environment](#virtual-environment) -* [Docker](#docker) -* For long-form inference: - * generate [Hugging Face API token](https://huggingface.co/docs/hub/security-tokens) - * accept the conditions to access [pyannote/voice-activity-detection](https://huggingface.co/pyannote/voice-activity-detection) files and content - * accept the conditions to access [pyannote/segmentation](https://huggingface.co/pyannote/segmentation) files and content - - -## Virtual environment -```bash -apt install python3-dev -apt install python3-venv -apt install ffmpeg libavcodec-extra -``` - -```bash -python3.10 -m venv venv && . venv/bin/activate -pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 -pip install Cython -pip install -U wheel -pip install git+https://github.com/NVIDIA/NeMo.git@1fa961ba03ab5f8c91b278640e29807079373372#egg=nemo_toolkit[all] -pip install pyannote.audio==3.2.0 -mkdir ./data -wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/{ssl_model_weights.ckpt,emo_model_weights.ckpt,ctc_model_weights.ckpt,rnnt_model_weights.ckpt,ctc_model_config.yaml,emo_model_config.yaml,encoder_config.yaml,rnnt_model_config.yaml,tokenizer_all_sets.tar,example.wav,long_example.wav} -P ./data && tar -xf ./data/tokenizer_all_sets.tar --directory ./data/ && rm ./data/tokenizer_all_sets.tar - -# GigaAM -python ssl_inference.py --encoder_config ./data/encoder_config.yaml \ - --model_weights ./data/ssl_model_weights.ckpt --device cuda --audio_path ./data/example.wav - -# encoded signal shape: torch.Size([1, 768, 283]) - -# GigaAM-CTC -python ctc_inference.py --model_config ./data/ctc_model_config.yaml \ - --model_weights ./data/ctc_model_weights.ckpt --device cuda --audio_path ./data/example.wav - -# transcription: ничьих не требуя похвал счастлив уж я надеждой сладкой что дева с трепетом любви посмотрит может быть украдкой на песни грешные мои у лукоморья дуб зеленый - -# GigaAM-CTC long-form -python ctc_longform_inference.py --model_config ./data/ctc_model_config.yaml \ - --model_weights ./data/ctc_model_weights.ckpt --device cuda \ - --audio_path ./data/long_example.wav --hf_token - -# [00:00:00 - 00:16:83]: вечерня отошла давно но в кельях тихо и темно уже и сам эгумин строгий свои молитвы прекратил и кости ветхие склонил перекрестясь на одр убогий кругом и сон и тишина но церкви дверь отворена -# [00:17:10 - 00:32:61]: трепещет луч лампады и тускло озаряет он и темную живопись икон и возлощенные оклады и раздается в тишине то тяжкий вздох то шепот важный и мрачно дремлет в вашине старинный свод -# ... - -# GigaAM-RNNT -python rnnt_inference.py --model_config ./data/rnnt_model_config.yaml \ - --model_weights ./data/rnnt_model_weights.ckpt --tokenizer_path ./data/tokenizer_all_sets \ - --device cuda --audio_path ./data/example.wav - -# transcription: ничьих не требуя похвал счастлив уж я надеждой сладкой что дева с трепетом любви посмотрит может быть украдкой на песни грешные мои у лукоморья дуб зеленый - -# GigaAM-RNNT long-form -python rnnt_longform_inference.py --model_config ./data/rnnt_model_config.yaml \ - --model_weights ./data/rnnt_model_weights.ckpt --tokenizer_path ./data/tokenizer_all_sets \ - --device cuda --audio_path ./data/long_example.wav --hf_token - -# [00:00:00 - 00:16:83]: вечерня отошла давно но в кельях тихо и темно уже и сам игумин строгий свои молитвы прекратил и кости ветхие склонил перекрестясь на одр убогий кругом и сон и тишина но церкви дверь отворена -# [00:17:10 - 00:32:61]: трепещет луч лампады и тускло озаряет он и темну живопись икон и возлащенные оклады и раздается в тишине то тяжкий вздох то шепот важный и мрачно дремлет в вышине старинный свод -# ... - -# GigaAM-Emo -python emo_inference.py --model_config ./data/emo_model_config.yaml \ - --model_weights ./data/emo_model_weights.ckpt --device cuda --audio_path ./data/example.wav - -# angry: 0.000, sad: 0.002, neutral: 0.923, positive: 0.074 -``` - -## Docker - -```bash -docker build -t gigaam_image . - -# GigaAM -docker run -v $PWD:/workspace/gigaam --gpus all gigaam_image \ - python /workspace/gigaam/ssl_inference.py --encoder_config /workspace/data/encoder_config.yaml \ - --model_weights /workspace/data/ssl_model_weights.ckpt \ - --device cuda --audio_path /workspace/data/example.wav - -# encoded signal shape: torch.Size([1, 768, 283]) - -# GigaAM-CTC -docker run -v $PWD:/workspace/gigaam --gpus all gigaam_image \ - python /workspace/gigaam/ctc_inference.py --model_config /workspace/data/ctc_model_config.yaml \ - --model_weights /workspace/data/ctc_model_weights.ckpt \ - --device cuda --audio_path /workspace/data/example.wav - -# transcription: ничьих не требуя похвал счастлив уж я надеждой сладкой что дева с трепетом любви посмотрит может быть украдкой на песни грешные мои у лукоморья дуб зеленый - -# GigaAM-CTC longform -docker run -v $PWD:/workspace/gigaam --gpus all gigaam_image \ - python /workspace/gigaam/ctc_longform_inference.py --model_config /workspace/data/ctc_model_config.yaml \ - --model_weights /workspace/data/ctc_model_weights.ckpt --device cuda \ - --audio_path /workspace/data/long_example.wav --hf_token - -# [00:00:00 - 00:16:83]: вечерня отошла давно но в кельях тихо и темно уже и сам эгумин строгий свои молитвы прекратил и кости ветхие склонил перекрестясь на одр убогий кругом и сон и тишина но церкви дверь отворена -# [00:17:10 - 00:32:61]: трепещет луч лампады и тускло озаряет он и темную живопись икон и возлощенные оклады и раздается в тишине то тяжкий вздох то шепот важный и мрачно дремлет в вашине старинный свод -# ... - -# GigaAM-RNNT -docker run -v $PWD:/workspace/gigaam --gpus all gigaam_image \ - python /workspace/gigaam/rnnt_inference.py --model_config /workspace/data/rnnt_model_config.yaml \ - --model_weights /workspace/data/rnnt_model_weights.ckpt --tokenizer_path /workspace/data/tokenizer_all_sets \ - --device cuda --audio_path /workspace/data/example.wav - -# transcription: ничьих не требуя похвал счастлив уж я надеждой сладкой что дева с трепетом любви посмотрит может быть украдкой на песни грешные мои у лукоморья дуб зеленый - -# GigaAM-RNNT longform -docker run -v $PWD:/workspace/gigaam --gpus all gigaam_image \ - python /workspace/gigaam/rnnt_longform_inference.py --model_config /workspace/data/rnnt_model_config.yaml \ - --model_weights /workspace/data/rnnt_model_weights.ckpt --tokenizer_path /workspace/data/tokenizer_all_sets \ - --device cuda --audio_path /workspace/data/long_example.wav --hf_token - -# [00:00:00 - 00:16:83]: вечерня отошла давно но в кельях тихо и темно уже и сам игумин строгий свои молитвы прекратил и кости ветхие склонил перекрестясь на одр убогий кругом и сон и тишина но церкви дверь отворена -# [00:17:10 - 00:32:61]: трепещет луч лампады и тускло озаряет он и темну живопись икон и возлащенные оклады и раздается в тишине то тяжкий вздох то шепот важный и мрачно дремлет в вышине старинный свод -# ... - -# GigaAM-Emo -docker run -v $PWD:/workspace/gigaam --gpus all gigaam_image \ - python /workspace/gigaam/emo_inference.py --model_config /workspace/data/emo_model_config.yaml \ - --model_weights /workspace/data/emo_model_weights.ckpt \ - --device cuda --audio_path /workspace/data/example.wav - -# angry: 0.000, sad: 0.002, neutral: 0.923, positive: 0.074 -``` diff --git a/examples/ctc_inference.py b/examples/ctc_inference.py deleted file mode 100644 index 3bf4112..0000000 --- a/examples/ctc_inference.py +++ /dev/null @@ -1,83 +0,0 @@ -import argparse - -import torch -import torchaudio -from nemo.collections.asr.models import EncDecCTCModel -from nemo.collections.asr.modules.audio_preprocessing import ( - AudioToMelSpectrogramPreprocessor as NeMoAudioToMelSpectrogramPreprocessor, -) -from nemo.collections.asr.parts.preprocessing.features import ( - FilterbankFeaturesTA as NeMoFilterbankFeaturesTA, -) - - -class FilterbankFeaturesTA(NeMoFilterbankFeaturesTA): - def __init__(self, mel_scale: str = "htk", wkwargs=None, **kwargs): - if "window_size" in kwargs: - del kwargs["window_size"] - if "window_stride" in kwargs: - del kwargs["window_stride"] - - super().__init__(**kwargs) - - self._mel_spec_extractor = torchaudio.transforms.MelSpectrogram( - sample_rate=self._sample_rate, - win_length=self.win_length, - hop_length=self.hop_length, - n_mels=kwargs["nfilt"], - window_fn=self.torch_windows[kwargs["window"]], - mel_scale=mel_scale, - norm=kwargs["mel_norm"], - n_fft=kwargs["n_fft"], - f_max=kwargs.get("highfreq", None), - f_min=kwargs.get("lowfreq", 0), - wkwargs=wkwargs, - ) - - -class AudioToMelSpectrogramPreprocessor(NeMoAudioToMelSpectrogramPreprocessor): - def __init__(self, mel_scale: str = "htk", **kwargs): - super().__init__(**kwargs) - kwargs["nfilt"] = kwargs["features"] - del kwargs["features"] - self.featurizer = ( - FilterbankFeaturesTA( # Deprecated arguments; kept for config compatibility - mel_scale=mel_scale, - **kwargs, - ) - ) - - -def _parse_args(): - parser = argparse.ArgumentParser( - description="Run inference using GigaAM-CTC checkpoint" - ) - parser.add_argument("--model_config", help="Path to GigaAM-CTC config file (.yaml)") - parser.add_argument( - "--model_weights", help="Path to GigaAM-CTC checkpoint file (.ckpt)" - ) - parser.add_argument("--audio_path", help="Path to audio signal") - parser.add_argument("--device", help="Device: cpu / cuda") - return parser.parse_args() - - -def main(model_config: str, model_weights: str, device: str, audio_path: str): - model = EncDecCTCModel.from_config_file(model_config) - - ckpt = torch.load(model_weights, map_location="cpu") - model.load_state_dict(ckpt, strict=False) - model = model.to(device) - model.eval() - - transcription = model.transcribe([audio_path])[0] - print(f"transcription: {transcription}") - - -if __name__ == "__main__": - args = _parse_args() - main( - model_config=args.model_config, - model_weights=args.model_weights, - device=args.device, - audio_path=args.audio_path, - ) diff --git a/examples/ctc_longform_inference.py b/examples/ctc_longform_inference.py deleted file mode 100644 index cb4a680..0000000 --- a/examples/ctc_longform_inference.py +++ /dev/null @@ -1,201 +0,0 @@ -import argparse -from io import BytesIO -from typing import List, Tuple - -import numpy as np -import torch -import torchaudio -from nemo.collections.asr.models import EncDecCTCModel -from nemo.collections.asr.modules.audio_preprocessing import ( - AudioToMelSpectrogramPreprocessor as NeMoAudioToMelSpectrogramPreprocessor, -) -from nemo.collections.asr.parts.preprocessing.features import ( - FilterbankFeaturesTA as NeMoFilterbankFeaturesTA, -) -from pyannote.audio import Pipeline -from pydub import AudioSegment - - -class FilterbankFeaturesTA(NeMoFilterbankFeaturesTA): - def __init__(self, mel_scale: str = "htk", wkwargs=None, **kwargs): - if "window_size" in kwargs: - del kwargs["window_size"] - if "window_stride" in kwargs: - del kwargs["window_stride"] - - super().__init__(**kwargs) - - self._mel_spec_extractor = torchaudio.transforms.MelSpectrogram( - sample_rate=self._sample_rate, - win_length=self.win_length, - hop_length=self.hop_length, - n_mels=kwargs["nfilt"], - window_fn=self.torch_windows[kwargs["window"]], - mel_scale=mel_scale, - norm=kwargs["mel_norm"], - n_fft=kwargs["n_fft"], - f_max=kwargs.get("highfreq", None), - f_min=kwargs.get("lowfreq", 0), - wkwargs=wkwargs, - ) - - -class AudioToMelSpectrogramPreprocessor(NeMoAudioToMelSpectrogramPreprocessor): - def __init__(self, mel_scale: str = "htk", **kwargs): - super().__init__(**kwargs) - kwargs["nfilt"] = kwargs["features"] - del kwargs["features"] - self.featurizer = ( - FilterbankFeaturesTA( # Deprecated arguments; kept for config compatibility - mel_scale=mel_scale, - **kwargs, - ) - ) - - -def audiosegment_to_numpy(audiosegment: AudioSegment) -> np.ndarray: - """Convert AudioSegment to numpy array.""" - samples = np.array(audiosegment.get_array_of_samples()) - if audiosegment.channels == 2: - samples = samples.reshape((-1, 2)) - - samples = samples.astype(np.float32, order="C") / 32768.0 - return samples - - -def format_time(seconds: float) -> str: - hours = int(seconds // 3600) - minutes = int((seconds % 3600) // 60) - seconds = seconds % 60 - full_seconds = int(seconds) - milliseconds = int((seconds - full_seconds) * 100) - - if hours > 0: - return f"{hours:02}:{minutes:02}:{full_seconds:02}:{milliseconds:02}" - else: - return f"{minutes:02}:{full_seconds:02}:{milliseconds:02}" - - -def segment_audio( - audio_path: str, - pipeline: Pipeline, - max_duration: float = 22.0, - min_duration: float = 15.0, - new_chunk_threshold: float = 0.2, -) -> Tuple[List[np.ndarray], List[List[float]]]: - # Prepare audio for pyannote vad pipeline - audio = AudioSegment.from_wav(audio_path) - audio_bytes = BytesIO() - audio.export(audio_bytes, format="wav") - audio_bytes.seek(0) - - # Process audio with pipeline to obtain segments with speech activity - sad_segments = pipeline({"uri": "filename", "audio": audio_bytes}) - - segments = [] - curr_duration = 0 - curr_start = 0 - curr_end = 0 - boundaries = [] - - # Concat segments from pipeline into chunks for asr according to max/min duration - for segment in sad_segments.get_timeline().support(): - start = max(0, segment.start) - end = min(len(audio) / 1000, segment.end) - if ( - curr_duration > min_duration and start - curr_end > new_chunk_threshold - ) or (curr_duration + (end - curr_end) > max_duration): - audio_segment = audiosegment_to_numpy( - audio[curr_start * 1000 : curr_end * 1000] - ) - segments.append(audio_segment) - boundaries.append([curr_start, curr_end]) - curr_start = start - - curr_end = end - curr_duration = curr_end - curr_start - - if curr_duration != 0: - audio_segment = audiosegment_to_numpy( - audio[curr_start * 1000 : curr_end * 1000] - ) - segments.append(audio_segment) - boundaries.append([curr_start, curr_end]) - - return segments, boundaries - - -def _parse_args(): - parser = argparse.ArgumentParser( - description="Run long-form inference using GigaAM-CTC checkpoint" - ) - parser.add_argument("--model_config", help="Path to GigaAM-CTC config file (.yaml)") - parser.add_argument( - "--model_weights", help="Path to GigaAM-CTC checkpoint file (.ckpt)" - ) - parser.add_argument("--audio_path", help="Path to audio signal") - parser.add_argument( - "--hf_token", help="HuggingFace token for using pyannote Pipeline" - ) - parser.add_argument("--device", help="Device: cpu / cuda") - parser.add_argument("--fp16", help="Run in FP16 mode", default=True) - parser.add_argument( - "--batch_size", help="Batch size for acoustic model inference", default=10 - ) - return parser.parse_args() - - -def main( - model_config: str, - model_weights: str, - device: str, - audio_path: str, - hf_token: str, - fp16: bool, - batch_size: int = 10, -): - # Initialize model - model = EncDecCTCModel.from_config_file(model_config) - - ckpt = torch.load(model_weights, map_location="cpu") - model.load_state_dict(ckpt, strict=False) - model = model.to(device) - if device != "cpu" and fp16: - model = model.half() - model.preprocessor = model.preprocessor.float() - model.eval() - - # Initialize pyannote pipeline - pipeline = Pipeline.from_pretrained( - "pyannote/voice-activity-detection", use_auth_token=hf_token - ) - pipeline = pipeline.to(torch.device(device)) - - # Segment audio - segments, boundaries = segment_audio(audio_path, pipeline) - - # Transcribe segments - transcriptions = [] - if device != "cpu" and fp16: - with torch.autocast(device_type="cuda", dtype=torch.float16): - transcriptions = model.transcribe(segments, batch_size=batch_size) - else: - transcriptions = model.transcribe(segments, batch_size=batch_size) - - for transcription, boundary in zip(transcriptions, boundaries): - print( - f"[{format_time(boundary[0])} - {format_time(boundary[1])}]: {transcription}\n" - ) - - -if __name__ == "__main__": - args = _parse_args() - main( - model_config=args.model_config, - model_weights=args.model_weights, - device=args.device, - audio_path=args.audio_path, - hf_token=args.hf_token, - fp16=args.fp16, - batch_size=args.batch_size, - ) diff --git a/examples/emo_inference.py b/examples/emo_inference.py deleted file mode 100644 index 0d8dd4e..0000000 --- a/examples/emo_inference.py +++ /dev/null @@ -1,81 +0,0 @@ -import argparse -from typing import List, Union - -import hydra -import soundfile -import torch -from omegaconf import DictConfig, ListConfig, OmegaConf - - -class SpecScaler(torch.nn.Module): - def forward(self, x: torch.Tensor) -> torch.Tensor: - return torch.log(x.clamp_(1e-9, 1e9)) - - -class GigaAMEmo(torch.nn.Module): - def __init__(self, conf: Union[DictConfig, ListConfig]): - super().__init__() - self.id2name = conf.id2name - self.feature_extractor = hydra.utils.instantiate(conf.feature_extractor) - self.conformer = hydra.utils.instantiate(conf.encoder) - self.linear_head = hydra.utils.instantiate(conf.classification_head) - - @property - def device(self): - return next(self.parameters()).device - - def forward(self, features, features_length=None): - if features.dim() == 2: - features = features.unsqueeze(0) - if not features_length: - features_length = torch.ones(features.shape[0], device=self.device) * features.shape[-1] - encoded, _ = self.conformer(audio_signal=features, length=features_length) - encoded_pooled = torch.nn.functional.avg_pool1d( - encoded, kernel_size=encoded.shape[-1] - ).squeeze(-1) - - logits = self.linear_head(encoded_pooled) - return logits - - def get_probs(self, audio_path: str) -> List[List[float]]: - audio_signal, _ = soundfile.read(audio_path, dtype="float32") - audio_tensor = torch.tensor(audio_signal).float().to(self.device) - features = self.feature_extractor(audio_tensor) - logits = self.forward(features) - probs = torch.nn.functional.softmax(logits, dim=1).detach().tolist() - return probs - - -def _parse_args(): - parser = argparse.ArgumentParser( - description="Run inference using GigaAM-Emo checkpoint" - ) - parser.add_argument("--model_config", help="Path to GigaAM-Emo config file (.yaml)") - parser.add_argument( - "--model_weights", help="Path to GigaAM-Emo checkpoint file (.ckpt)" - ) - parser.add_argument("--audio_path", help="Path to audio signal") - parser.add_argument("--device", help="Device: cpu / cuda") - return parser.parse_args() - - -def main(model_config: str, model_weights: str, device: str, audio_path: str): - conf = OmegaConf.load(model_config) - model = GigaAMEmo(conf) - ckpt = torch.load(model_weights, map_location="cpu") - model.load_state_dict(ckpt, strict=False) - model = model.to(device) - model.eval() - with torch.no_grad(): - probs = model.get_probs(audio_path)[0] - print(", ".join([f"{model.id2name[i]}: {p:.3f}" for i, p in enumerate(probs)])) - - -if __name__ == "__main__": - args = _parse_args() - main( - model_config=args.model_config, - model_weights=args.model_weights, - device=args.device, - audio_path=args.audio_path, - ) diff --git a/examples/notebooks/GigaAM_CTC_Model_Usage_Example.ipynb b/examples/notebooks/GigaAM_CTC_Model_Usage_Example.ipynb deleted file mode 100644 index f1eabc7..0000000 --- a/examples/notebooks/GigaAM_CTC_Model_Usage_Example.ipynb +++ /dev/null @@ -1,1162 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "0Q99xpSOdHEM" - }, - "source": [ - "### Installing and importing dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "2am02YkxKBJ5", - "outputId": "344cb595-c9ba-4d9e-a970-2d486392ff36" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting wget\n", - " Downloading wget-3.2.zip (10 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Building wheels for collected packages: wget\n", - " Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9656 sha256=bbb22603efb56b962ad6a42332b0daf8d137a1446f5e46d6af4c4c2fb216fbdd\n", - " Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n", - "Successfully built wget\n", - "Installing collected packages: wget\n", - "Successfully installed wget-3.2\n", - "Reading package lists... Done\n", - "Building dependency tree... Done\n", - "Reading state information... 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"Building wheels for collected packages: causal-conv1d, antlr4-python3-runtime, progress, clip, fasttext, kaldi-python-io, nemo_toolkit, rouge-score, sox, distance, docopt, intervaltree, asciitree, cdifflib\n", - " Building wheel for causal-conv1d (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for causal-conv1d: filename=causal_conv1d-1.2.2.post1-cp310-cp310-linux_x86_64.whl size=103643300 sha256=2bba8823ae89bd79c2d067978e0e533fab8298f69855bfc5d199828b278cf66c\n", - " Stored in directory: /root/.cache/pip/wheels/22/a7/db/0c9482dec3707ad23181b0eb2da40e4b8f26aaed49752fc49f\n", - " Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=3475219dd9a159d2f8bb2612ab31b18a5bbfc6afd74b2893a59926eb5e69b04f\n", - " Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n", - " Building wheel for progress (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - 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"Successfully built causal-conv1d antlr4-python3-runtime progress clip fasttext kaldi-python-io nemo_toolkit rouge-score sox distance docopt intervaltree asciitree cdifflib\n", - "Installing collected packages: trampoline, pydub, progress, plac, pangu, opencc, ninja, ijson, docopt, distance, clip, braceexpand, asciitree, antlr4-python3-runtime, aniso8601, addict, xxhash, webdataset, trimesh, textdistance, sox, smmap, shellingham, setproctitle, sentry-sdk, ruamel.yaml.clib, rapidfuzz, pytest-runner, pypinyin, pynini, pybind11, portalocker, pathspec, parameterized, onnx, omegaconf, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numcodecs, mypy-extensions, marshmallow, markdown2, loguru, lilcom, lightning-utilities, libcst, latexcodec, kornia-rs, kaldiio, kaldi-python-io, jmespath, jedi, isort, intervaltree, ftfy, fasteners, faiss-cpu, einops, docutils, docker-pycreds, dill, decord, cytoolz, colorama, click, cdifflib, attrdict, zarr, sacremoses, sacrebleu, ruamel.yaml, resampy, pytest-mock, pypinyin-dict, PyMCubes, pyloudnorm, pybtex, pyannote.core, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, Levenshtein, jiwer, hydra-core, gitdb, fiddle, fasttext, einops-exts, botocore, black, typer, texterrors, s3transfer, rouge-score, pybtex-docutils, nvidia-cusolver-cu12, lhotse, gitpython, flask-restful, diffusers, wandb, transformers, sphinxcontrib-bibtex, pyannote.database, g2p-en, datasets, boto3, torchsde, torchmetrics, torchdiffeq, sentence-transformers, pyannote.metrics, nerfacc, nemo_toolkit, nemo-text-processing, kornia, causal-conv1d, accelerated-scan, timm, pytorch-lightning, taming-transformers, open-clip-torch\n", - " Attempting uninstall: docutils\n", - " Found existing installation: docutils 0.18.1\n", - " Uninstalling docutils-0.18.1:\n", - " Successfully uninstalled docutils-0.18.1\n", - " Attempting uninstall: click\n", - " Found existing installation: click 8.1.7\n", - " Uninstalling click-8.1.7:\n", - " Successfully uninstalled click-8.1.7\n", - " Attempting uninstall: typer\n", - " Found existing installation: typer 0.9.4\n", - " Uninstalling typer-0.9.4:\n", - " Successfully uninstalled typer-0.9.4\n", - " Attempting uninstall: transformers\n", - " Found existing installation: transformers 4.41.0\n", - " Uninstalling transformers-4.41.0:\n", - " Successfully uninstalled transformers-4.41.0\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n", - "weasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed Levenshtein-0.22.0 PyMCubes-0.1.4 accelerated-scan-0.2.0 addict-2.4.0 aniso8601-9.0.1 antlr4-python3-runtime-4.9.3 asciitree-0.3.3 attrdict-2.0.1 black-24.4.2 boto3-1.34.113 botocore-1.34.113 braceexpand-0.1.7 causal-conv1d-1.2.2.post1 cdifflib-1.2.6 click-8.0.2 clip-0.2.0 colorama-0.4.6 cytoolz-0.12.3 datasets-2.19.1 decord-0.6.0 diffusers-0.28.0 dill-0.3.8 distance-0.1.3 docker-pycreds-0.4.0 docopt-0.6.2 docutils-0.17.1 einops-0.8.0 einops-exts-0.0.4 faiss-cpu-1.8.0 fasteners-0.19 fasttext-0.9.2 fiddle-0.3.0 flask-restful-0.3.10 ftfy-6.2.0 g2p-en-2.1.0 gitdb-4.0.11 gitpython-3.1.43 hydra-core-1.3.2 ijson-3.2.3 intervaltree-3.1.0 isort-5.13.2 jedi-0.19.1 jiwer-2.5.2 jmespath-1.0.1 kaldi-python-io-1.2.2 kaldiio-2.18.0 kornia-0.7.2 kornia-rs-0.1.3 latexcodec-3.0.0 lhotse-1.23.0 libcst-1.4.0 lightning-utilities-0.11.2 lilcom-1.7 loguru-0.7.2 markdown2-2.4.13 marshmallow-3.21.2 multiprocess-0.70.16 mypy-extensions-1.0.0 nemo-text-processing-1.0.2 nemo_toolkit-2.0.0rc1 nerfacc-0.5.3 ninja-1.11.1.1 numcodecs-0.12.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 omegaconf-2.3.0 onnx-1.16.1 open-clip-torch-2.24.0 opencc-1.1.6 pangu-4.0.6.1 parameterized-0.9.0 pathspec-0.12.1 plac-1.4.3 portalocker-2.8.2 progress-1.6 pyannote.core-5.0.0 pyannote.database-5.1.0 pyannote.metrics-3.2.1 pybind11-2.12.0 pybtex-0.24.0 pybtex-docutils-1.0.3 pydub-0.25.1 pyloudnorm-0.1.1 pynini-2.1.5 pypinyin-0.51.0 pypinyin-dict-0.8.0 pytest-mock-3.14.0 pytest-runner-6.0.1 pytorch-lightning-2.2.5 rapidfuzz-2.13.7 resampy-0.4.3 rouge-score-0.1.2 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.8 s3transfer-0.10.1 sacrebleu-2.4.2 sacremoses-0.1.1 sentence-transformers-2.7.0 sentry-sdk-2.3.1 setproctitle-1.3.3 shellingham-1.5.4 smmap-5.0.1 sox-1.5.0 sphinxcontrib-bibtex-2.6.2 taming-transformers-0.0.1 textdistance-4.6.2 texterrors-0.4.4 timm-1.0.3 torchdiffeq-0.2.3 torchmetrics-1.4.0.post0 torchsde-0.2.6 trampoline-0.1.2 transformers-4.40.2 trimesh-4.4.0 typer-0.12.3 wandb-0.17.0 webdataset-0.2.86 xxhash-3.4.1 zarr-2.18.2\n" - ] - } - ], - "source": [ - "!pip install wget\n", - "!apt-get install sox libsndfile1 ffmpeg\n", - "!pip install matplotlib>=3.3.2\n", - "\n", - "!python -m pip install git+https://github.com/NVIDIA/NeMo.git@1fa961ba03ab5f8c91b278640e29807079373372#egg=nemo_toolkit[all]\n", - "!python -m pip install pyannote.audio==3.2.0" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "WLLmJFoeBWt7" - }, - "outputs": [], - "source": [ - "import torch\n", - "import torchaudio\n", - "from nemo.collections.asr.models import EncDecCTCModel\n", - "from nemo.collections.asr.modules.audio_preprocessing import (\n", - " AudioToMelSpectrogramPreprocessor as NeMoAudioToMelSpectrogramPreprocessor,\n", - ")\n", - "from nemo.collections.asr.parts.preprocessing.features import (\n", - " FilterbankFeaturesTA as NeMoFilterbankFeaturesTA,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8oUuhRnadATl" - }, - "source": [ - "### Downloading config, weights and audio example" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OD99UH_9U8UW", - "outputId": "dd3cb10c-66cd-42f6-cbd2-8e9e738a172a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2024-05-28 07:16:36-- https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/example.wav\n", - "Resolving n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)... 37.230.193.192\n", - "Connecting to n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)|37.230.193.192|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 361324 (353K) [application/octet-stream]\n", - "Saving to: ‘example.wav’\n", - "\n", - "example.wav 100%[===================>] 352.86K 646KB/s in 0.5s \n", - "\n", - "2024-05-28 07:16:38 (646 KB/s) - ‘example.wav’ saved [361324/361324]\n", - "\n" - ] - } - ], - "source": [ - "import locale\n", - "\n", - "locale.getpreferredencoding = lambda: \"UTF-8\"\n", - "\n", - "# Loading weights, config and example wav for CTC-model\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/ctc_model_weights.ckpt\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/ctc_model_config.yaml\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/example.wav\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/long_example.wav" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7l6p192UWtLe" - }, - "source": [ - "### Adding modules for features extraction" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "gzdGDuz3ZN2V" - }, - "outputs": [], - "source": [ - "class FilterbankFeaturesTA(NeMoFilterbankFeaturesTA):\n", - " def __init__(self, mel_scale: str = \"htk\", wkwargs=None, **kwargs):\n", - " if \"window_size\" in kwargs:\n", - " del kwargs[\"window_size\"]\n", - " if \"window_stride\" in kwargs:\n", - " del kwargs[\"window_stride\"]\n", - "\n", - " super().__init__(**kwargs)\n", - "\n", - " self._mel_spec_extractor: torchaudio.transforms.MelSpectrogram = (\n", - " torchaudio.transforms.MelSpectrogram(\n", - " sample_rate=self._sample_rate,\n", - " win_length=self.win_length,\n", - " hop_length=self.hop_length,\n", - " n_mels=kwargs[\"nfilt\"],\n", - " window_fn=self.torch_windows[kwargs[\"window\"]],\n", - " mel_scale=mel_scale,\n", - " norm=kwargs[\"mel_norm\"],\n", - " n_fft=kwargs[\"n_fft\"],\n", - " f_max=kwargs.get(\"highfreq\", None),\n", - " f_min=kwargs.get(\"lowfreq\", 0),\n", - " wkwargs=wkwargs,\n", - " )\n", - " )\n", - "\n", - "\n", - "class AudioToMelSpectrogramPreprocessor(NeMoAudioToMelSpectrogramPreprocessor):\n", - " def __init__(self, mel_scale: str = \"htk\", **kwargs):\n", - " super().__init__(**kwargs)\n", - " kwargs[\"nfilt\"] = kwargs[\"features\"]\n", - " del kwargs[\"features\"]\n", - " self.featurizer = (\n", - " FilterbankFeaturesTA( # Deprecated arguments; kept for config compatibility\n", - " mel_scale=mel_scale,\n", - " **kwargs,\n", - " )\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pAS_1RpLWpjh" - }, - "source": [ - "### Transcribation example witch CTC-model" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "D5dm-IG_KvY0", - "outputId": "f04c3f8f-8ee3-43ca-8a91-bae19f9bbcf7" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[NeMo I 2024-05-28 20:34:54 features:305] PADDING: 0\n" - ] - } - ], - "source": [ - "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "\n", - "model = EncDecCTCModel.from_config_file(\"./ctc_model_config.yaml\")\n", - "ckpt = torch.load(\"./ctc_model_weights.ckpt\", map_location=\"cpu\")\n", - "model.load_state_dict(ckpt, strict=False)\n", - "model.eval()\n", - "model = model.to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nlrRmRPvKxYq", - "outputId": "5dc3722a-3795-4e18-f12f-bbad168bd48f" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Transcribing: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.26it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "['ничьих не требуя похвал счастлив уж я надеждой сладкой что дева с трепетом любви посмотрит может быть украдкой на песни грешные мои у лукоморья дуб зеленый']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.transcribe([\"example.wav\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5HoUmN9FaFKa" - }, - "source": [ - "### Long-form transcription example\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from io import BytesIO\n", - "from typing import List, Tuple\n", - "\n", - "import numpy as np\n", - "from pyannote.audio import Pipeline\n", - "from pydub import AudioSegment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Function for segmentation" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def audiosegment_to_numpy(audiosegment: AudioSegment) -> np.ndarray:\n", - " \"\"\"Convert AudioSegment to numpy array.\"\"\"\n", - " samples = np.array(audiosegment.get_array_of_samples())\n", - " if audiosegment.channels == 2:\n", - " samples = samples.reshape((-1, 2))\n", - "\n", - " samples = samples.astype(np.float32, order=\"C\") / 32768.0\n", - " return samples\n", - "\n", - "\n", - "def segment_audio(\n", - " audio_path: str,\n", - " pipeline: Pipeline,\n", - " max_duration: float = 22.0,\n", - " min_duration: float = 15.0,\n", - " new_chunk_threshold: float = 0.2,\n", - ") -> Tuple[List[np.ndarray], List[List[float]]]:\n", - " # Prepare audio for pyannote vad pipeline\n", - " audio = AudioSegment.from_wav(audio_path)\n", - " audio_bytes = BytesIO()\n", - " audio.export(audio_bytes, format=\"wav\")\n", - " audio_bytes.seek(0)\n", - "\n", - " # Process audio with pipeline to obtain segments with speech activity\n", - " sad_segments = pipeline({\"uri\": \"filename\", \"audio\": audio_bytes})\n", - "\n", - " segments = []\n", - " curr_duration = 0\n", - " curr_start = 0\n", - " curr_end = 0\n", - " boundaries = []\n", - "\n", - " # Concat segments from pipeline into chunks for asr according to max/min duration\n", - " for segment in sad_segments.get_timeline().support():\n", - " start = max(0, segment.start)\n", - " end = min(len(audio) / 1000, segment.end)\n", - " if (\n", - " curr_duration > min_duration and start - curr_end > new_chunk_threshold\n", - " ) or (curr_duration + (end - curr_end) > max_duration):\n", - " audio_segment = audiosegment_to_numpy(\n", - " audio[curr_start * 1000 : curr_end * 1000]\n", - " )\n", - " segments.append(audio_segment)\n", - " boundaries.append([curr_start, curr_end])\n", - " curr_start = start\n", - "\n", - " curr_end = end\n", - " curr_duration = curr_end - curr_start\n", - "\n", - " if curr_duration != 0:\n", - " audio_segment = audiosegment_to_numpy(\n", - " audio[curr_start * 1000 : curr_end * 1000]\n", - " )\n", - " segments.append(audio_segment)\n", - " boundaries.append([curr_start, curr_end])\n", - "\n", - " return segments, boundaries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Initializing pyannote VAD pipeline and using it for segmentation" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Lightning automatically upgraded your loaded checkpoint from v1.1.3 to v2.2.4. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint ../libs_cache/pyannote_cache/models--pyannote--segmentation/snapshots/059e96f964841d40f1a5e755bb7223f76666bba4/pytorch_model.bin`\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model was trained with pyannote.audio 0.0.1, yours is 3.2.0. Bad things might happen unless you revert pyannote.audio to 0.x.\n", - "Model was trained with torch 1.7.1, yours is 2.3.0+cu121. Bad things might happen unless you revert torch to 1.x.\n" - ] - } - ], - "source": [ - "HF_TOKEN = \"\"\n", - "\n", - "# Initialize pyannote pipeline\n", - "pipeline = Pipeline.from_pretrained(\n", - " \"pyannote/voice-activity-detection\", use_auth_token=HF_TOKEN\n", - ")\n", - "pipeline = pipeline.to(torch.device(device))\n", - "\n", - "# Segment audio\n", - "segments, boundaries = segment_audio(\"./long_example.wav\", pipeline)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Transcribing: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 3.23it/s]\n" - ] - } - ], - "source": [ - "# Transcribing segments\n", - "BATCH_SIZE = 10\n", - "transcriptions = model.transcribe(segments, batch_size=BATCH_SIZE)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[00:00:00 - 00:16:83]: вечерня отошла давно но в кельях тихо и темно уже и сам эгумин строгий свои молитвы прекратил и кости ветхие склонил перекрестясь на одр убогий кругом и сон и тишина но церкви дверь отворена\n", - "\n", - "[00:17:10 - 00:32:61]: трепещет луч лампады и тускло озаряет он и темную живопись икон и возлощенные оклады и раздается в тишине то тяжкий вздох то шепот важный и мрачно дремлет в вашине старинный свод\n", - "\n", - "[00:32:95 - 00:49:33]: глухой и влажный стоят за клиросом чернец и грешник неподвижны оба и шепот их как глаз из гроба и грешник бледен как мертвец монах несчастный полно перестань\n", - "\n", - "[00:49:82 - 01:05:74]: ужасна исповедь злодея заплачена тобою дань тому кто в злобе пламенее лукаво грешника блюдет и к вечной гибели ведет смирись опомнись время время раскаянья покров\n", - "\n", - "[01:05:97 - 01:10:90]: я разрешу тебя грехов сложи мучительное бремя\n", - "\n" - ] - } - ], - "source": [ - "def format_time(seconds):\n", - " hours = int(seconds // 3600)\n", - " minutes = int((seconds % 3600) // 60)\n", - " seconds = seconds % 60\n", - " full_seconds = int(seconds)\n", - " milliseconds = int((seconds - full_seconds) * 100)\n", - "\n", - " if hours > 0:\n", - " return f\"{hours:02}:{minutes:02}:{full_seconds:02}:{milliseconds:02}\"\n", - " else:\n", - " return f\"{minutes:02}:{full_seconds:02}:{milliseconds:02}\"\n", - "\n", - "\n", - "for transcription, boundary in zip(transcriptions, boundaries):\n", - " boundary_0 = format_time(boundary[0])\n", - " boundary_1 = format_time(boundary[1])\n", - " print(f\"[{boundary_0} - {boundary_1}]: {transcription}\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/examples/notebooks/GigaAM_Emo_Model_Usage_Example.ipynb b/examples/notebooks/GigaAM_Emo_Model_Usage_Example.ipynb deleted file mode 100644 index 271de74..0000000 --- a/examples/notebooks/GigaAM_Emo_Model_Usage_Example.ipynb +++ /dev/null @@ -1,955 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "oREzT-effoFr" - }, - "source": [ - "### Installing and importing dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yxU1SOPfWxab", - "outputId": "e9b2c73a-d3d4-4ba9-8ce1-3527c95c4d3f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting wget\n", - " Downloading wget-3.2.zip (10 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Building wheels for collected packages: wget\n", - " Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9656 sha256=2e82f0e3a185ee764cf0a1eef86b3f525139a342d3630e878d05860de80d6dee\n", - " Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n", - "Successfully built wget\n", - "Installing collected packages: wget\n", - "Successfully installed wget-3.2\n", - "Reading package lists... Done\n", - "Building dependency tree... Done\n", - "Reading state information... 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"Successfully built causal-conv1d antlr4-python3-runtime progress clip fasttext kaldi-python-io nemo_toolkit rouge-score sox distance docopt intervaltree asciitree cdifflib\n", - "Installing collected packages: trampoline, pydub, progress, plac, pangu, opencc, ninja, ijson, docopt, distance, clip, braceexpand, asciitree, antlr4-python3-runtime, aniso8601, addict, xxhash, webdataset, trimesh, textdistance, sox, smmap, shellingham, setproctitle, sentry-sdk, ruamel.yaml.clib, rapidfuzz, pytest-runner, pypinyin, pynini, pybind11, portalocker, pathspec, parameterized, onnx, omegaconf, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numcodecs, mypy-extensions, marshmallow, markdown2, loguru, lilcom, lightning-utilities, libcst, latexcodec, kornia-rs, kaldiio, kaldi-python-io, jmespath, jedi, isort, intervaltree, ftfy, fasteners, faiss-cpu, einops, docutils, docker-pycreds, dill, decord, cytoolz, colorama, click, cdifflib, attrdict, zarr, sacremoses, sacrebleu, ruamel.yaml, resampy, pytest-mock, pypinyin-dict, PyMCubes, pyloudnorm, pybtex, pyannote.core, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, Levenshtein, jiwer, hydra-core, gitdb, fiddle, fasttext, einops-exts, botocore, black, typer, texterrors, s3transfer, rouge-score, pybtex-docutils, nvidia-cusolver-cu12, lhotse, gitpython, flask-restful, diffusers, wandb, transformers, sphinxcontrib-bibtex, pyannote.database, g2p-en, datasets, boto3, torchsde, torchmetrics, torchdiffeq, sentence-transformers, pyannote.metrics, nerfacc, nemo_toolkit, nemo-text-processing, kornia, causal-conv1d, accelerated-scan, timm, pytorch-lightning, taming-transformers, open-clip-torch\n", - " Attempting uninstall: docutils\n", - " Found existing installation: docutils 0.18.1\n", - " Uninstalling docutils-0.18.1:\n", - " Successfully uninstalled docutils-0.18.1\n", - " Attempting uninstall: click\n", - " Found existing installation: click 8.1.7\n", - " Uninstalling click-8.1.7:\n", - " Successfully uninstalled click-8.1.7\n", - " Attempting uninstall: typer\n", - " Found existing installation: typer 0.9.4\n", - " Uninstalling typer-0.9.4:\n", - " Successfully uninstalled typer-0.9.4\n", - " Attempting uninstall: transformers\n", - " Found existing installation: transformers 4.41.0\n", - " Uninstalling transformers-4.41.0:\n", - " Successfully uninstalled transformers-4.41.0\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n", - "weasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed Levenshtein-0.22.0 PyMCubes-0.1.4 accelerated-scan-0.2.0 addict-2.4.0 aniso8601-9.0.1 antlr4-python3-runtime-4.9.3 asciitree-0.3.3 attrdict-2.0.1 black-24.4.2 boto3-1.34.113 botocore-1.34.113 braceexpand-0.1.7 causal-conv1d-1.2.2.post1 cdifflib-1.2.6 click-8.0.2 clip-0.2.0 colorama-0.4.6 cytoolz-0.12.3 datasets-2.19.1 decord-0.6.0 diffusers-0.28.0 dill-0.3.8 distance-0.1.3 docker-pycreds-0.4.0 docopt-0.6.2 docutils-0.17.1 einops-0.8.0 einops-exts-0.0.4 faiss-cpu-1.8.0 fasteners-0.19 fasttext-0.9.2 fiddle-0.3.0 flask-restful-0.3.10 ftfy-6.2.0 g2p-en-2.1.0 gitdb-4.0.11 gitpython-3.1.43 hydra-core-1.3.2 ijson-3.2.3 intervaltree-3.1.0 isort-5.13.2 jedi-0.19.1 jiwer-2.5.2 jmespath-1.0.1 kaldi-python-io-1.2.2 kaldiio-2.18.0 kornia-0.7.2 kornia-rs-0.1.3 latexcodec-3.0.0 lhotse-1.23.0 libcst-1.4.0 lightning-utilities-0.11.2 lilcom-1.7 loguru-0.7.2 markdown2-2.4.13 marshmallow-3.21.2 multiprocess-0.70.16 mypy-extensions-1.0.0 nemo-text-processing-1.0.2 nemo_toolkit-2.0.0rc1 nerfacc-0.5.3 ninja-1.11.1.1 numcodecs-0.12.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 omegaconf-2.3.0 onnx-1.16.1 open-clip-torch-2.24.0 opencc-1.1.6 pangu-4.0.6.1 parameterized-0.9.0 pathspec-0.12.1 plac-1.4.3 portalocker-2.8.2 progress-1.6 pyannote.core-5.0.0 pyannote.database-5.1.0 pyannote.metrics-3.2.1 pybind11-2.12.0 pybtex-0.24.0 pybtex-docutils-1.0.3 pydub-0.25.1 pyloudnorm-0.1.1 pynini-2.1.5 pypinyin-0.51.0 pypinyin-dict-0.8.0 pytest-mock-3.14.0 pytest-runner-6.0.1 pytorch-lightning-2.2.5 rapidfuzz-2.13.7 resampy-0.4.3 rouge-score-0.1.2 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.8 s3transfer-0.10.1 sacrebleu-2.4.2 sacremoses-0.1.1 sentence-transformers-2.7.0 sentry-sdk-2.3.1 setproctitle-1.3.3 shellingham-1.5.4 smmap-5.0.1 sox-1.5.0 sphinxcontrib-bibtex-2.6.2 taming-transformers-0.0.1 textdistance-4.6.2 texterrors-0.4.4 timm-1.0.3 torchdiffeq-0.2.3 torchmetrics-1.4.0.post0 torchsde-0.2.6 trampoline-0.1.2 transformers-4.40.2 trimesh-4.4.0 typer-0.12.3 wandb-0.17.0 webdataset-0.2.86 xxhash-3.4.1 zarr-2.18.2\n" - ] - } - ], - "source": [ - "!pip install wget\n", - "!apt-get install sox libsndfile1 ffmpeg\n", - "!pip install matplotlib>=3.3.2\n", - "\n", - "!python -m pip install git+https://github.com/NVIDIA/NeMo.git@1fa961ba03ab5f8c91b278640e29807079373372#egg=nemo_toolkit[all]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "_Utv8kLRW9Js" - }, - "outputs": [], - "source": [ - "from typing import List, Union\n", - "\n", - "import hydra\n", - "import soundfile as sf\n", - "import torch\n", - "from omegaconf import DictConfig, ListConfig, OmegaConf" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZLslfbEfXQIE" - }, - "source": [ - "### Model for emotions classification" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "paEKSFFVXPqC" - }, - "outputs": [], - "source": [ - "class SpecScaler(torch.nn.Module):\n", - " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", - " return torch.log(x.clamp_(1e-9, 1e9))\n", - "\n", - "\n", - "class GigaAMEmo(torch.nn.Module):\n", - " def __init__(self, conf: Union[DictConfig, ListConfig]):\n", - " super().__init__()\n", - " self.id2name = conf.id2name\n", - " self.feature_extractor = hydra.utils.instantiate(conf.feature_extractor)\n", - " self.conformer = hydra.utils.instantiate(conf.encoder)\n", - " self.linear_head = hydra.utils.instantiate(conf.classification_head)\n", - "\n", - " def forward(self, features, features_length=None):\n", - " if features.dim() == 2:\n", - " features = features.unsqueeze(0)\n", - " if not features_length:\n", - " features_length = torch.ones(features.shape[0]) * features.shape[-1]\n", - " features_length = features_length.to(features.device)\n", - " encoded, _ = self.conformer(audio_signal=features, length=features_length)\n", - " encoded_pooled = torch.nn.functional.avg_pool1d(\n", - " encoded, kernel_size=encoded.shape[-1]\n", - " ).squeeze(-1)\n", - "\n", - " logits = self.linear_head(encoded_pooled)\n", - " return logits\n", - "\n", - " def get_probs(self, audio_path: str) -> List[List[float]]:\n", - " audio_signal, _ = sf.read(audio_path, dtype=\"float32\")\n", - " features = self.feature_extractor(\n", - " torch.tensor(audio_signal).float().to(next(self.parameters()).device)\n", - " )\n", - " logits = self.forward(features)\n", - " probs = torch.nn.functional.softmax(logits).detach().tolist()\n", - " return probs" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7UFpN0Ghc244" - }, - "source": [ - "### Downloading config, weights and audio example" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "jFZJGISjcmHW", - "outputId": "74a2a71e-2dba-4551-c2cb-737eaa35bfa4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2024-05-28 07:10:07-- https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/emo_model_weights.ckpt\n", - "Resolving n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)... 37.230.193.192\n", - "Connecting to n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)|37.230.193.192|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 968409626 (924M) [application/octet-stream]\n", - "Saving to: ‘emo_model_weights.ckpt’\n", - "\n", - "emo_model_weights.c 100%[===================>] 923.55M 7.48MB/s in 1m 45s \n", - "\n", - "2024-05-28 07:11:53 (8.82 MB/s) - ‘emo_model_weights.ckpt’ saved [968409626/968409626]\n", - "\n", - "--2024-05-28 07:11:54-- https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/emo_model_config.yaml\n", - "Resolving n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)... 37.230.193.192\n", - "Connecting to n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)|37.230.193.192|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 765 [application/octet-stream]\n", - "Saving to: ‘emo_model_config.yaml’\n", - "\n", - "emo_model_config.ya 100%[===================>] 765 --.-KB/s in 0s \n", - "\n", - "2024-05-28 07:11:54 (252 MB/s) - ‘emo_model_config.yaml’ saved [765/765]\n", - "\n", - "--2024-05-28 07:11:54-- https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/example.wav\n", - "Resolving n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)... 37.230.193.192\n", - "Connecting to n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)|37.230.193.192|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 361324 (353K) [application/octet-stream]\n", - "Saving to: ‘example.wav’\n", - "\n", - "example.wav 100%[===================>] 352.86K 715KB/s in 0.5s \n", - "\n", - "2024-05-28 07:11:56 (715 KB/s) - ‘example.wav’ saved [361324/361324]\n", - "\n" - ] - } - ], - "source": [ - "import locale\n", - "\n", - "locale.getpreferredencoding = lambda: \"UTF-8\"\n", - "\n", - "# Loading weights, config and example wav for CTC-model\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/emo_model_weights.ckpt\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/emo_model_config.yaml\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/example.wav" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NZ6-O2M0fxDY" - }, - "source": [ - "### Model instantiating and inference" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "plXt8297d5km", - "outputId": "537acb90-f6a1-4a73-ea66-0d3fe6fd9a3a" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[NeMo W 2024-05-28 07:20:12 nemo_logging:349] :32: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n", - " probs = torch.nn.functional.softmax(logits).detach().tolist()\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "angry: 0.000, sad: 0.002, neutral: 0.923, positive: 0.074\n" - ] - } - ], - "source": [ - "model_config = \"emo_model_config.yaml\"\n", - "model_weights = \"emo_model_weights.ckpt\"\n", - "audio_path = \"example.wav\"\n", - "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "\n", - "conf = OmegaConf.load(model_config)\n", - "model = GigaAMEmo(conf)\n", - "ckpt = torch.load(model_weights, map_location=\"cpu\")\n", - "model.load_state_dict(ckpt, strict=False)\n", - "model = model.to(device)\n", - "model.eval()\n", - "with torch.no_grad():\n", - " probs = model.get_probs(audio_path)[0]\n", - "print(\", \".join([f\"{model.id2name[i]}: {p:.3f}\" for i, p in enumerate(probs)]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "nmFvC_GfkasV" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/notebooks/GigaAM_Model_Usage_Example.ipynb b/examples/notebooks/GigaAM_Model_Usage_Example.ipynb deleted file mode 100644 index c6eb2d4..0000000 --- a/examples/notebooks/GigaAM_Model_Usage_Example.ipynb +++ /dev/null @@ -1,881 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "gpuType": "T4" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "source": [ - "### Installing and importing dependencies" - ], - "metadata": { - "id": "aqymJFVQhere" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "mJ5zzajTbzRX", - "outputId": "a6e8f1cc-5ef7-43e2-824e-39133c8f3f98" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting wget\n", - " Downloading wget-3.2.zip (10 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Building wheels for collected packages: wget\n", - " Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9656 sha256=fb233af0965c5da90b8babdcb0fbd51095c2a135ec877618013ed9078dced85b\n", - " Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n", - "Successfully built wget\n", - "Installing collected packages: wget\n", - "Successfully installed wget-3.2\n", - "Reading package lists... Done\n", - "Building dependency tree... Done\n", - "Reading state information... 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"Building wheels for collected packages: causal-conv1d, antlr4-python3-runtime, progress, clip, fasttext, kaldi-python-io, nemo_toolkit, rouge-score, sox, distance, docopt, intervaltree, asciitree, cdifflib\n", - " Building wheel for causal-conv1d (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for causal-conv1d: filename=causal_conv1d-1.2.2.post1-cp310-cp310-linux_x86_64.whl size=103643300 sha256=2bba8823ae89bd79c2d067978e0e533fab8298f69855bfc5d199828b278cf66c\n", - " Stored in directory: /root/.cache/pip/wheels/22/a7/db/0c9482dec3707ad23181b0eb2da40e4b8f26aaed49752fc49f\n", - " Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=5ba620ca9da88d714c879b4a21820b9bdebd36fb76051b0b48a375e2e4f0fcb7\n", - " Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n", - " Building wheel for progress (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - 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" Building wheel for intervaltree (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for intervaltree: filename=intervaltree-3.1.0-py2.py3-none-any.whl size=26096 sha256=e831b80cc0232f925c293997e7be035697c58f5a834060f1b1f6a097fa5502b7\n", - " Stored in directory: /root/.cache/pip/wheels/fa/80/8c/43488a924a046b733b64de3fac99252674c892a4c3801c0a61\n", - " Building wheel for asciitree (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for asciitree: filename=asciitree-0.3.3-py3-none-any.whl size=5034 sha256=483b21d8a257179dcd00b430bc43c6fce9f97d0442b44433ac2794d3030a48e1\n", - " Stored in directory: /root/.cache/pip/wheels/7f/4e/be/1171b40f43b918087657ec57cf3b81fa1a2e027d8755baa184\n", - " Building wheel for cdifflib (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for cdifflib: filename=cdifflib-1.2.6-cp310-cp310-linux_x86_64.whl size=27681 sha256=a4929dc925e36d0e71a89e124ce85f0ada2ec5862708eb18c9136da35649ebc1\n", - " Stored in directory: /root/.cache/pip/wheels/87/a7/fd/8061e24ed08689045cb6d1ca303768dc463b20a5a338174841\n", - "Successfully built causal-conv1d antlr4-python3-runtime progress clip fasttext kaldi-python-io nemo_toolkit rouge-score sox distance docopt intervaltree asciitree cdifflib\n", - "Installing collected packages: trampoline, pydub, progress, plac, pangu, opencc, ninja, ijson, docopt, distance, clip, braceexpand, asciitree, antlr4-python3-runtime, aniso8601, addict, xxhash, webdataset, trimesh, textdistance, sox, smmap, shellingham, setproctitle, sentry-sdk, ruamel.yaml.clib, rapidfuzz, pytest-runner, pypinyin, pynini, pybind11, portalocker, pathspec, parameterized, onnx, omegaconf, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numcodecs, mypy-extensions, marshmallow, markdown2, loguru, lilcom, lightning-utilities, libcst, latexcodec, kornia-rs, kaldiio, kaldi-python-io, jmespath, jedi, isort, intervaltree, ftfy, fasteners, faiss-cpu, einops, docutils, docker-pycreds, dill, decord, cytoolz, colorama, click, cdifflib, attrdict, zarr, sacremoses, sacrebleu, ruamel.yaml, resampy, pytest-mock, pypinyin-dict, PyMCubes, pyloudnorm, pybtex, pyannote.core, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, Levenshtein, jiwer, hydra-core, gitdb, fiddle, fasttext, einops-exts, botocore, black, typer, texterrors, s3transfer, rouge-score, pybtex-docutils, nvidia-cusolver-cu12, lhotse, gitpython, flask-restful, diffusers, wandb, transformers, sphinxcontrib-bibtex, pyannote.database, g2p-en, datasets, boto3, torchsde, torchmetrics, torchdiffeq, sentence-transformers, pyannote.metrics, nerfacc, nemo_toolkit, nemo-text-processing, kornia, causal-conv1d, accelerated-scan, timm, pytorch-lightning, taming-transformers, open-clip-torch\n", - " Attempting uninstall: docutils\n", - " Found existing installation: docutils 0.18.1\n", - " Uninstalling docutils-0.18.1:\n", - " Successfully uninstalled docutils-0.18.1\n", - " Attempting uninstall: click\n", - " Found existing installation: click 8.1.7\n", - " Uninstalling click-8.1.7:\n", - " Successfully uninstalled click-8.1.7\n", - " Attempting uninstall: typer\n", - " Found existing installation: typer 0.9.4\n", - " Uninstalling typer-0.9.4:\n", - " Successfully uninstalled typer-0.9.4\n", - " Attempting uninstall: transformers\n", - " Found existing installation: transformers 4.41.0\n", - " Uninstalling transformers-4.41.0:\n", - " Successfully uninstalled transformers-4.41.0\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n", - "weasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed Levenshtein-0.22.0 PyMCubes-0.1.4 accelerated-scan-0.2.0 addict-2.4.0 aniso8601-9.0.1 antlr4-python3-runtime-4.9.3 asciitree-0.3.3 attrdict-2.0.1 black-24.4.2 boto3-1.34.113 botocore-1.34.113 braceexpand-0.1.7 causal-conv1d-1.2.2.post1 cdifflib-1.2.6 click-8.0.2 clip-0.2.0 colorama-0.4.6 cytoolz-0.12.3 datasets-2.19.1 decord-0.6.0 diffusers-0.28.0 dill-0.3.8 distance-0.1.3 docker-pycreds-0.4.0 docopt-0.6.2 docutils-0.17.1 einops-0.8.0 einops-exts-0.0.4 faiss-cpu-1.8.0 fasteners-0.19 fasttext-0.9.2 fiddle-0.3.0 flask-restful-0.3.10 ftfy-6.2.0 g2p-en-2.1.0 gitdb-4.0.11 gitpython-3.1.43 hydra-core-1.3.2 ijson-3.2.3 intervaltree-3.1.0 isort-5.13.2 jedi-0.19.1 jiwer-2.5.2 jmespath-1.0.1 kaldi-python-io-1.2.2 kaldiio-2.18.0 kornia-0.7.2 kornia-rs-0.1.3 latexcodec-3.0.0 lhotse-1.23.0 libcst-1.4.0 lightning-utilities-0.11.2 lilcom-1.7 loguru-0.7.2 markdown2-2.4.13 marshmallow-3.21.2 multiprocess-0.70.16 mypy-extensions-1.0.0 nemo-text-processing-1.0.2 nemo_toolkit-2.0.0rc1 nerfacc-0.5.3 ninja-1.11.1.1 numcodecs-0.12.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 omegaconf-2.3.0 onnx-1.16.1 open-clip-torch-2.24.0 opencc-1.1.6 pangu-4.0.6.1 parameterized-0.9.0 pathspec-0.12.1 plac-1.4.3 portalocker-2.8.2 progress-1.6 pyannote.core-5.0.0 pyannote.database-5.1.0 pyannote.metrics-3.2.1 pybind11-2.12.0 pybtex-0.24.0 pybtex-docutils-1.0.3 pydub-0.25.1 pyloudnorm-0.1.1 pynini-2.1.5 pypinyin-0.51.0 pypinyin-dict-0.8.0 pytest-mock-3.14.0 pytest-runner-6.0.1 pytorch-lightning-2.2.5 rapidfuzz-2.13.7 resampy-0.4.3 rouge-score-0.1.2 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.8 s3transfer-0.10.1 sacrebleu-2.4.2 sacremoses-0.1.1 sentence-transformers-2.7.0 sentry-sdk-2.3.1 setproctitle-1.3.3 shellingham-1.5.4 smmap-5.0.1 sox-1.5.0 sphinxcontrib-bibtex-2.6.2 taming-transformers-0.0.1 textdistance-4.6.2 texterrors-0.4.4 timm-1.0.3 torchdiffeq-0.2.3 torchmetrics-1.4.0.post0 torchsde-0.2.6 trampoline-0.1.2 transformers-4.40.2 trimesh-4.4.0 typer-0.12.3 wandb-0.17.0 webdataset-0.2.86 xxhash-3.4.1 zarr-2.18.2\n" - ] - } - ], - "source": [ - "!pip install wget\n", - "!apt-get install sox libsndfile1 ffmpeg\n", - "\n", - "!python -m pip install git+https://github.com/NVIDIA/NeMo.git@1fa961ba03ab5f8c91b278640e29807079373372#egg=nemo_toolkit[all]" - ] - }, - { - "cell_type": "code", - "source": [ - "import hydra\n", - "import soundfile as sf\n", - "import torch\n", - "from omegaconf import OmegaConf" - ], - "metadata": { - "id": "cBz_fQ6KbzrZ" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Downloading config, weights and audio example" - ], - "metadata": { - "id": "3Fvy0phvhr0G" - } - }, - { - "cell_type": "code", - "source": [ - "import locale\n", - "\n", - "locale.getpreferredencoding = lambda: \"UTF-8\"\n", - "\n", - "# Loading weights, config and example wav for CTC-model\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/ssl_model_weights.ckpt\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/encoder_config.yaml\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/example.wav" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0EHgk_I6hrGI", - "outputId": "4c9ac38d-eeca-4da4-af26-aa41becfed00" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2024-05-28 07:12:41-- https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/example.wav\n", - "Resolving n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)... 37.230.193.192\n", - "Connecting to n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)|37.230.193.192|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 361324 (353K) [application/octet-stream]\n", - "Saving to: ‘example.wav’\n", - "\n", - "example.wav 100%[===================>] 352.86K 583KB/s in 0.6s \n", - "\n", - "2024-05-28 07:12:42 (583 KB/s) - ‘example.wav’ saved [361324/361324]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Model instantiating and *inference*" - ], - "metadata": { - "id": "FUA6Ah1blyHv" - } - }, - { - "cell_type": "code", - "source": [ - "class SpecScaler(torch.nn.Module):\n", - " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", - " return torch.log(x.clamp_(1e-9, 1e9))\n", - "\n", - "\n", - "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "encoder_config = \"encoder_config.yaml\"\n", - "model_weights = \"ssl_model_weights.ckpt\"\n", - "audio_path = \"example.wav\"\n", - "\n", - "conf = OmegaConf.load(encoder_config)\n", - "\n", - "encoder = hydra.utils.instantiate(conf.encoder)\n", - "ckpt = torch.load(model_weights, map_location=\"cpu\")\n", - "encoder.load_state_dict(ckpt, strict=True)\n", - "encoder.to(device)\n", - "\n", - "feature_extractor = hydra.utils.instantiate(conf.feature_extractor)\n", - "\n", - "audio_signal, _ = sf.read(audio_path, dtype=\"float32\")\n", - "features = feature_extractor(torch.tensor(audio_signal).float())\n", - "features = features.to(device)\n", - "\n", - "encoded, _ = encoder.forward(\n", - " audio_signal=features.unsqueeze(0),\n", - " length=torch.tensor([features.shape[-1]]).to(device),\n", - ")\n", - "print(f\"encoded signal shape: {encoded.shape}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "AsUapeJKh3cz", - "outputId": "ee5bc82b-4526-4364-ef7c-decd59cdbc5f" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "encoded signal shape: torch.Size([1, 768, 283])\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "p1yWHEU5Dn60" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/examples/notebooks/GigaAM_RNNT_Model_Usage_Example.ipynb b/examples/notebooks/GigaAM_RNNT_Model_Usage_Example.ipynb deleted file mode 100644 index b4a989c..0000000 --- a/examples/notebooks/GigaAM_RNNT_Model_Usage_Example.ipynb +++ /dev/null @@ -1,1235 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "0Q99xpSOdHEM" - }, - "source": [ - "### Installing and importing dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "2am02YkxKBJ5", - "outputId": "1ca9927f-5bb0-4110-f9b7-54298e85918f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting wget\n", - " Downloading wget-3.2.zip (10 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Building wheels for collected packages: wget\n", - " Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9656 sha256=80bdd08a8dd1b0676f765e38a9293c17297c03dffbccd77ed2c8fe44847bfe0a\n", - " Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n", - "Successfully built wget\n", - "Installing collected packages: wget\n", - "Successfully installed wget-3.2\n", - "Reading package lists... Done\n", - "Building dependency tree... Done\n", - "Reading state information... Done\n", - "libsndfile1 is already the newest version (1.0.31-2ubuntu0.1).\n", - "ffmpeg is already the newest version (7:4.4.2-0ubuntu0.22.04.1).\n", - "The following additional packages will be installed:\n", - " libopencore-amrnb0 libopencore-amrwb0 libsox-fmt-alsa libsox-fmt-base libsox3 libwavpack1\n", - "Suggested packages:\n", - " libsox-fmt-all\n", - "The following NEW packages will be installed:\n", - " libopencore-amrnb0 libopencore-amrwb0 libsox-fmt-alsa libsox-fmt-base libsox3 libwavpack1 sox\n", - "0 upgraded, 7 newly installed, 0 to remove and 45 not upgraded.\n", - "Need to get 617 kB of archives.\n", - "After this operation, 1,764 kB of additional disk space will be used.\n", - "Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libopencore-amrnb0 amd64 0.1.5-1 [94.8 kB]\n", - "Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libopencore-amrwb0 amd64 0.1.5-1 [49.1 kB]\n", - "Get:3 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libsox3 amd64 14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1 [240 kB]\n", - "Get:4 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libsox-fmt-alsa amd64 14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1 [11.2 kB]\n", - "Get:5 http://archive.ubuntu.com/ubuntu jammy/main amd64 libwavpack1 amd64 5.4.0-1build2 [83.7 kB]\n", - "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libsox-fmt-base amd64 14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1 [33.7 kB]\n", - "Get:7 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 sox amd64 14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1 [104 kB]\n", - "Fetched 617 kB in 1s (440 kB/s)\n", - "Selecting previously unselected package libopencore-amrnb0:amd64.\n", - "(Reading database ... 121918 files and directories currently installed.)\n", - "Preparing to unpack .../0-libopencore-amrnb0_0.1.5-1_amd64.deb ...\n", - "Unpacking libopencore-amrnb0:amd64 (0.1.5-1) ...\n", - "Selecting previously unselected package libopencore-amrwb0:amd64.\n", - "Preparing to unpack .../1-libopencore-amrwb0_0.1.5-1_amd64.deb ...\n", - "Unpacking libopencore-amrwb0:amd64 (0.1.5-1) ...\n", - "Selecting previously unselected package libsox3:amd64.\n", - "Preparing to unpack .../2-libsox3_14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1_amd64.deb ...\n", - "Unpacking libsox3:amd64 (14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1) ...\n", - "Selecting previously unselected package libsox-fmt-alsa:amd64.\n", - "Preparing to unpack .../3-libsox-fmt-alsa_14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1_amd64.deb ...\n", - "Unpacking libsox-fmt-alsa:amd64 (14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1) ...\n", - "Selecting previously unselected package libwavpack1:amd64.\n", - "Preparing to unpack .../4-libwavpack1_5.4.0-1build2_amd64.deb ...\n", - "Unpacking libwavpack1:amd64 (5.4.0-1build2) ...\n", - "Selecting previously unselected package libsox-fmt-base:amd64.\n", - "Preparing to unpack .../5-libsox-fmt-base_14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1_amd64.deb ...\n", - "Unpacking libsox-fmt-base:amd64 (14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1) ...\n", - "Selecting previously unselected package sox.\n", - "Preparing to unpack .../6-sox_14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1_amd64.deb ...\n", - "Unpacking sox (14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1) ...\n", - "Setting up libsox3:amd64 (14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1) ...\n", - "Setting up libopencore-amrwb0:amd64 (0.1.5-1) ...\n", - "Setting up libsox-fmt-alsa:amd64 (14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1) ...\n", - "Setting up libwavpack1:amd64 (5.4.0-1build2) ...\n", - "Setting up libopencore-amrnb0:amd64 (0.1.5-1) ...\n", - "Setting up libsox-fmt-base:amd64 (14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1) ...\n", - "Setting up sox (14.4.2+git20190427-2+deb11u2ubuntu0.22.04.1) ...\n", - "Processing triggers for man-db (2.10.2-1) ...\n", - "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n", - "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n", - "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n", - "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n", - "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n", - "\n", - "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n", - "\n", - "\u001b[33mDEPRECATION: git+https://github.com/NVIDIA/NeMo.git#egg=nemo_toolkit[all] contains an egg fragment with a non-PEP 508 name pip 25.0 will enforce this behaviour change. A possible replacement is to use the req @ url syntax, and remove the egg fragment. 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" Attempting uninstall: docutils\n", - " Found existing installation: docutils 0.18.1\n", - " Uninstalling docutils-0.18.1:\n", - " Successfully uninstalled docutils-0.18.1\n", - " Attempting uninstall: click\n", - " Found existing installation: click 8.1.7\n", - " Uninstalling click-8.1.7:\n", - " Successfully uninstalled click-8.1.7\n", - " Attempting uninstall: typer\n", - " Found existing installation: typer 0.9.4\n", - " Uninstalling typer-0.9.4:\n", - " Successfully uninstalled typer-0.9.4\n", - " Attempting uninstall: transformers\n", - " Found existing installation: transformers 4.41.0\n", - " Uninstalling transformers-4.41.0:\n", - " Successfully uninstalled transformers-4.41.0\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n", - "weasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed Levenshtein-0.22.0 PyMCubes-0.1.4 accelerated-scan-0.2.0 addict-2.4.0 aniso8601-9.0.1 antlr4-python3-runtime-4.9.3 asciitree-0.3.3 attrdict-2.0.1 black-24.4.2 boto3-1.34.113 botocore-1.34.113 braceexpand-0.1.7 causal-conv1d-1.2.2.post1 cdifflib-1.2.6 click-8.0.2 clip-0.2.0 colorama-0.4.6 cytoolz-0.12.3 datasets-2.19.1 decord-0.6.0 diffusers-0.28.0 dill-0.3.8 distance-0.1.3 docker-pycreds-0.4.0 docopt-0.6.2 docutils-0.17.1 einops-0.8.0 einops-exts-0.0.4 faiss-cpu-1.8.0 fasteners-0.19 fasttext-0.9.2 fiddle-0.3.0 flask-restful-0.3.10 ftfy-6.2.0 g2p-en-2.1.0 gitdb-4.0.11 gitpython-3.1.43 hydra-core-1.3.2 ijson-3.2.3 intervaltree-3.1.0 isort-5.13.2 jedi-0.19.1 jiwer-2.5.2 jmespath-1.0.1 kaldi-python-io-1.2.2 kaldiio-2.18.0 kornia-0.7.2 kornia-rs-0.1.3 latexcodec-3.0.0 lhotse-1.23.0 libcst-1.4.0 lightning-utilities-0.11.2 lilcom-1.7 loguru-0.7.2 markdown2-2.4.13 marshmallow-3.21.2 multiprocess-0.70.16 mypy-extensions-1.0.0 nemo-text-processing-1.0.2 nemo_toolkit-2.0.0rc1 nerfacc-0.5.3 ninja-1.11.1.1 numcodecs-0.12.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 omegaconf-2.3.0 onnx-1.16.1 open-clip-torch-2.24.0 opencc-1.1.6 pangu-4.0.6.1 parameterized-0.9.0 pathspec-0.12.1 plac-1.4.3 portalocker-2.8.2 progress-1.6 pyannote.core-5.0.0 pyannote.database-5.1.0 pyannote.metrics-3.2.1 pybind11-2.12.0 pybtex-0.24.0 pybtex-docutils-1.0.3 pydub-0.25.1 pyloudnorm-0.1.1 pynini-2.1.5 pypinyin-0.51.0 pypinyin-dict-0.8.0 pytest-mock-3.14.0 pytest-runner-6.0.1 pytorch-lightning-2.2.5 rapidfuzz-2.13.7 resampy-0.4.3 rouge-score-0.1.2 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.8 s3transfer-0.10.1 sacrebleu-2.4.2 sacremoses-0.1.1 sentence-transformers-2.7.0 sentry-sdk-2.3.1 setproctitle-1.3.3 shellingham-1.5.4 smmap-5.0.1 sox-1.5.0 sphinxcontrib-bibtex-2.6.2 taming-transformers-0.0.1 textdistance-4.6.2 texterrors-0.4.4 timm-1.0.3 torchdiffeq-0.2.3 torchmetrics-1.4.0.post0 torchsde-0.2.6 trampoline-0.1.2 transformers-4.40.2 trimesh-4.4.0 typer-0.12.3 wandb-0.17.0 webdataset-0.2.86 xxhash-3.4.1 zarr-2.18.2\n" - ] - } - ], - "source": [ - "!pip install wget\n", - "!apt-get install sox libsndfile1 ffmpeg\n", - "!pip install matplotlib>=3.3.2\n", - "\n", - "!python -m pip install git+https://github.com/NVIDIA/NeMo.git@1fa961ba03ab5f8c91b278640e29807079373372#egg=nemo_toolkit[all]\n", - "!python -m pip install pyannote.audio==3.2.0" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "WLLmJFoeBWt7" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n", - " _torch_pytree._register_pytree_node(\n" - ] - } - ], - "source": [ - "import torch\n", - "import torchaudio\n", - "from nemo.collections.asr.models import EncDecRNNTBPEModel\n", - "from nemo.collections.asr.modules.audio_preprocessing import (\n", - " AudioToMelSpectrogramPreprocessor as NeMoAudioToMelSpectrogramPreprocessor,\n", - ")\n", - "from nemo.collections.asr.parts.preprocessing.features import (\n", - " FilterbankFeaturesTA as NeMoFilterbankFeaturesTA,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8oUuhRnadATl" - }, - "source": [ - "### Downloading config, weights, tokenizer and audio example" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "zjPqKs4s8eDi", - "outputId": "2add8ada-10f2-4ab9-f18d-95bfac5e3033" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2024-05-28 06:42:34-- https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/rnnt_model_weights.ckpt\n", - "Resolving n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)... 37.230.193.192\n", - "Connecting to n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)|37.230.193.192|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 974419733 (929M) [application/octet-stream]\n", - "Saving to: ‘rnnt_model_weights.ckpt’\n", - "\n", - "rnnt_model_weights. 100%[===================>] 929.28M 14.7MB/s in 83s \n", - "\n", - "2024-05-28 06:44:00 (11.2 MB/s) - ‘rnnt_model_weights.ckpt’ saved [974419733/974419733]\n", - "\n", - "--2024-05-28 06:44:00-- https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/rnnt_model_config.yaml\n", - "Resolving n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)... 37.230.193.192\n", - "Connecting to n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)|37.230.193.192|:443... connected.\n", - 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"Resolving n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)... 37.230.193.192\n", - "Connecting to n-ws-q0bez.s3pd12.sbercloud.ru (n-ws-q0bez.s3pd12.sbercloud.ru)|37.230.193.192|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 276480 (270K) [application/octet-stream]\n", - "Saving to: ‘tokenizer_all_sets.tar’\n", - "\n", - "tokenizer_all_sets. 100%[===================>] 270.00K 390KB/s in 0.7s \n", - "\n", - "2024-05-28 06:44:04 (390 KB/s) - ‘tokenizer_all_sets.tar’ saved [276480/276480]\n", - "\n" - ] - } - ], - "source": [ - "import locale\n", - "\n", - "locale.getpreferredencoding = lambda: \"UTF-8\"\n", - "\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/rnnt_model_weights.ckpt\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/rnnt_model_config.yaml\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/example.wav\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/long_example.wav\n", - "!wget https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/tokenizer_all_sets.tar\n", - "!tar -xf tokenizer_all_sets.tar && rm tokenizer_all_sets.tar" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7l6p192UWtLe" - }, - "source": [ - "### Adding modules for features extraction" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "gzdGDuz3ZN2V" - }, - "outputs": [], - "source": [ - "class FilterbankFeaturesTA(NeMoFilterbankFeaturesTA):\n", - " def __init__(self, mel_scale: str = \"htk\", wkwargs=None, **kwargs):\n", - " if \"window_size\" in kwargs:\n", - " del kwargs[\"window_size\"]\n", - " if \"window_stride\" in kwargs:\n", - " del kwargs[\"window_stride\"]\n", - "\n", - " super().__init__(**kwargs)\n", - "\n", - " self._mel_spec_extractor: torchaudio.transforms.MelSpectrogram = (\n", - " torchaudio.transforms.MelSpectrogram(\n", - " sample_rate=self._sample_rate,\n", - " win_length=self.win_length,\n", - " hop_length=self.hop_length,\n", - " n_mels=kwargs[\"nfilt\"],\n", - " window_fn=self.torch_windows[kwargs[\"window\"]],\n", - " mel_scale=mel_scale,\n", - " norm=kwargs[\"mel_norm\"],\n", - " n_fft=kwargs[\"n_fft\"],\n", - " f_max=kwargs.get(\"highfreq\", None),\n", - " f_min=kwargs.get(\"lowfreq\", 0),\n", - " wkwargs=wkwargs,\n", - " )\n", - " )\n", - "\n", - "\n", - "class AudioToMelSpectrogramPreprocessor(NeMoAudioToMelSpectrogramPreprocessor):\n", - " def __init__(self, mel_scale: str = \"htk\", **kwargs):\n", - " super().__init__(**kwargs)\n", - " kwargs[\"nfilt\"] = kwargs[\"features\"]\n", - " del kwargs[\"features\"]\n", - " self.featurizer = (\n", - " FilterbankFeaturesTA( # Deprecated arguments; kept for config compatibility\n", - " mel_scale=mel_scale,\n", - " **kwargs,\n", - " )\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pAS_1RpLWpjh" - }, - "source": [ - "### Transcribation example witch RNNT-model" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "D5dm-IG_KvY0", - "outputId": "bc31c717-ed0d-4e17-ac42-97875d9bd3e6", - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[NeMo I 2024-05-28 18:58:47 mixins:172] Tokenizer SentencePieceTokenizer initialized with 512 tokens\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[NeMo W 2024-05-28 18:58:48 audio_to_text_dataset:830] Could not load dataset as `manifest_filepath` was None. Provided config : {'shuffle': False, 'manifest_filepath': None}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[NeMo I 2024-05-28 18:58:48 features:305] PADDING: 0\n", - "[NeMo I 2024-05-28 18:58:51 rnnt_models:222] Using RNNT Loss : warprnnt_numba\n", - " Loss warprnnt_numba_kwargs: {'fastemit_lambda': 0.0, 'clamp': -1.0}\n", - "[NeMo I 2024-05-28 18:58:51 rnnt_models:222] Using RNNT Loss : warprnnt_numba\n", - " Loss warprnnt_numba_kwargs: {'fastemit_lambda': 0.0, 'clamp': -1.0}\n", - "[NeMo I 2024-05-28 18:58:51 rnnt_models:222] Using RNNT Loss : warprnnt_numba\n", - " Loss warprnnt_numba_kwargs: {'fastemit_lambda': 0.0, 'clamp': -1.0}\n" - ] - } - ], - "source": [ - "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "\n", - "model = EncDecRNNTBPEModel.from_config_file(\"./rnnt_model_config.yaml\")\n", - "ckpt = torch.load(\"./rnnt_model_weights.ckpt\", map_location=\"cpu\")\n", - "model.load_state_dict(ckpt, strict=False)\n", - "model.eval()\n", - "model = model.to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nlrRmRPvKxYq", - "outputId": "693262a7-b058-4378-a628-85766c7110cb" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Transcribing: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.04it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "['ничьих не требуя похвал счастлив уж я надеждой сладкой что дева с трепетом любви посмотрит может быть украдкой на песни грешные мои у лукоморья дуб зеленый']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.transcribe([\"./example.wav\"])[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Sl_3pybCBaSQ" - }, - "source": [ - "### Long-form transcription example" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from io import BytesIO\n", - "from typing import List, Tuple\n", - "\n", - "import numpy as np\n", - "from pyannote.audio import Pipeline\n", - "from pydub import AudioSegment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Function for segmentation" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "def audiosegment_to_numpy(audiosegment: AudioSegment) -> np.ndarray:\n", - " \"\"\"Convert AudioSegment to numpy array.\"\"\"\n", - " samples = np.array(audiosegment.get_array_of_samples())\n", - " if audiosegment.channels == 2:\n", - " samples = samples.reshape((-1, 2))\n", - "\n", - " samples = samples.astype(np.float32, order=\"C\") / 32768.0\n", - " return samples\n", - "\n", - "\n", - "def segment_audio(\n", - " audio_path: str,\n", - " pipeline: Pipeline,\n", - " max_duration: float = 22.0,\n", - " min_duration: float = 15.0,\n", - " new_chunk_threshold: float = 0.2,\n", - ") -> Tuple[List[np.ndarray], List[List[float]]]:\n", - " # Prepare audio for pyannote vad pipeline\n", - " audio = AudioSegment.from_wav(audio_path)\n", - " audio_bytes = BytesIO()\n", - " audio.export(audio_bytes, format=\"wav\")\n", - " audio_bytes.seek(0)\n", - "\n", - " # Process audio with pipeline to obtain segments with speech activity\n", - " sad_segments = pipeline({\"uri\": \"filename\", \"audio\": audio_bytes})\n", - "\n", - " segments = []\n", - " curr_duration = 0\n", - " curr_start = 0\n", - " curr_end = 0\n", - " boundaries = []\n", - "\n", - " # Concat segments from pipeline into chunks for asr according to max/min duration\n", - " for segment in sad_segments.get_timeline().support():\n", - " start = max(0, segment.start)\n", - " end = min(len(audio) / 1000, segment.end)\n", - " if (\n", - " curr_duration > min_duration and start - curr_end > new_chunk_threshold\n", - " ) or (curr_duration + (end - curr_end) > max_duration):\n", - " audio_segment = audiosegment_to_numpy(\n", - " audio[curr_start * 1000 : curr_end * 1000]\n", - " )\n", - " segments.append(audio_segment)\n", - " boundaries.append([curr_start, curr_end])\n", - " curr_start = start\n", - "\n", - " curr_end = end\n", - " curr_duration = curr_end - curr_start\n", - "\n", - " if curr_duration != 0:\n", - " audio_segment = audiosegment_to_numpy(\n", - " audio[curr_start * 1000 : curr_end * 1000]\n", - " )\n", - " segments.append(audio_segment)\n", - " boundaries.append([curr_start, curr_end])\n", - "\n", - " return segments, boundaries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Initializing pyannote VAD pipeline and using it for segmentation" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Lightning automatically upgraded your loaded checkpoint from v1.1.3 to v2.2.4. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint ../libs_cache/pyannote_cache/models--pyannote--segmentation/snapshots/059e96f964841d40f1a5e755bb7223f76666bba4/pytorch_model.bin`\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model was trained with pyannote.audio 0.0.1, yours is 3.2.0. Bad things might happen unless you revert pyannote.audio to 0.x.\n", - "Model was trained with torch 1.7.1, yours is 2.3.0+cu121. Bad things might happen unless you revert torch to 1.x.\n" - ] - } - ], - "source": [ - "HF_TOKEN = \"\"\n", - "\n", - "# Initialize pyannote pipeline\n", - "pipeline = Pipeline.from_pretrained(\n", - " \"pyannote/voice-activity-detection\", use_auth_token=HF_TOKEN\n", - ")\n", - "pipeline = pipeline.to(torch.device(device))\n", - "\n", - "# Segment audio\n", - "segments, boundaries = segment_audio(\"./long_example.wav\", pipeline)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Transcribing segments with RNNT-model" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Transcribing: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.84it/s]\n" - ] - } - ], - "source": [ - "# Transcribing segments\n", - "BATCH_SIZE = 10\n", - "transcriptions = model.transcribe(segments, batch_size=BATCH_SIZE)[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[00:00:00 - 00:16:83]: вечерня отошла давно но в кельях тихо и темно уже и сам игумин строгий свои молитвы прекратил и кости ветхие склонил перекрестясь на одр убогий кругом и сон и тишина но церкви дверь отворена\n", - "\n", - "[00:17:10 - 00:32:61]: трепещет луч лампады и тускло озаряет он и темну живопись икон и возлащенные оклады и раздается в тишине то тяжкий вздох то шепот важный и мрачно дремлет в вышине старинный свод\n", - "\n", - "[00:32:95 - 00:49:33]: глухой и влажный стоят за клиросом чернец и грешник неподвижны оба и шепот их как глаз из гроба и грешник бледен как мертвец монах несчастный полно перестань\n", - "\n", - "[00:49:82 - 01:05:74]: ужасна исповедь злодея заплачена тобою дань тому кто в злобе пламенея лукаво грешника блюдет и к вечной гибели ведет смирись опомнись время время раскаяние покров\n", - "\n", - "[01:05:97 - 01:10:90]: я разрешу тебя грехов сложи мучительное бремя\n", - "\n" - ] - } - ], - "source": [ - "def format_time(seconds):\n", - " hours = int(seconds // 3600)\n", - " minutes = int((seconds % 3600) // 60)\n", - " seconds = seconds % 60\n", - " full_seconds = int(seconds)\n", - " milliseconds = int((seconds - full_seconds) * 100)\n", - "\n", - " if hours > 0:\n", - " return f\"{hours:02}:{minutes:02}:{full_seconds:02}:{milliseconds:02}\"\n", - " else:\n", - " return f\"{minutes:02}:{full_seconds:02}:{milliseconds:02}\"\n", - "\n", - "\n", - "for transcription, boundary in zip(transcriptions, boundaries):\n", - " boundary_0 = format_time(boundary[0])\n", - " boundary_1 = format_time(boundary[1])\n", - " print(f\"[{boundary_0} - {boundary_1}]: {transcription}\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/examples/rnnt_inference.py b/examples/rnnt_inference.py deleted file mode 100644 index 140330f..0000000 --- a/examples/rnnt_inference.py +++ /dev/null @@ -1,98 +0,0 @@ -import argparse - -import torch -import torchaudio -from nemo.collections.asr.models import EncDecRNNTBPEModel -from nemo.collections.asr.modules.audio_preprocessing import ( - AudioToMelSpectrogramPreprocessor as NeMoAudioToMelSpectrogramPreprocessor, -) -from nemo.collections.asr.parts.preprocessing.features import ( - FilterbankFeaturesTA as NeMoFilterbankFeaturesTA, -) -from omegaconf import OmegaConf, open_dict - - -class FilterbankFeaturesTA(NeMoFilterbankFeaturesTA): - def __init__(self, mel_scale: str = "htk", wkwargs=None, **kwargs): - if "window_size" in kwargs: - del kwargs["window_size"] - if "window_stride" in kwargs: - del kwargs["window_stride"] - - super().__init__(**kwargs) - - self._mel_spec_extractor = torchaudio.transforms.MelSpectrogram( - sample_rate=self._sample_rate, - win_length=self.win_length, - hop_length=self.hop_length, - n_mels=kwargs["nfilt"], - window_fn=self.torch_windows[kwargs["window"]], - mel_scale=mel_scale, - norm=kwargs["mel_norm"], - n_fft=kwargs["n_fft"], - f_max=kwargs.get("highfreq", None), - f_min=kwargs.get("lowfreq", 0), - wkwargs=wkwargs, - ) - - -class AudioToMelSpectrogramPreprocessor(NeMoAudioToMelSpectrogramPreprocessor): - def __init__(self, mel_scale: str = "htk", **kwargs): - super().__init__(**kwargs) - kwargs["nfilt"] = kwargs["features"] - del kwargs["features"] - self.featurizer = ( - FilterbankFeaturesTA( # Deprecated arguments; kept for config compatibility - mel_scale=mel_scale, - **kwargs, - ) - ) - - -def _parse_args(): - parser = argparse.ArgumentParser( - description="Run inference using GigaAM-RNNT checkpoint" - ) - parser.add_argument( - "--model_config", help="Path to GigaAM-RNNT config file (.yaml)" - ) - parser.add_argument( - "--model_weights", help="Path to GigaAM-RNNT checkpoint file (.ckpt)" - ) - parser.add_argument("--tokenizer_path", help="Path to tokenizer directory") - parser.add_argument("--audio_path", help="Path to audio signal") - parser.add_argument("--device", help="Device: cpu / cuda") - return parser.parse_args() - - -def main( - model_config: str, - model_weights: str, - tokenizer_path: str, - device: str, - audio_path: str, -): - config = OmegaConf.load(model_config) - with open_dict(config): - config.tokenizer.dir = tokenizer_path - - model = EncDecRNNTBPEModel.from_config_dict(config) - - ckpt = torch.load(model_weights, map_location="cpu") - model.load_state_dict(ckpt, strict=False) - model = model.to(device) - model.eval() - - transcription = model.transcribe([audio_path])[0][0] - print(f"transcription: {transcription}") - - -if __name__ == "__main__": - args = _parse_args() - main( - model_config=args.model_config, - model_weights=args.model_weights, - tokenizer_path=args.tokenizer_path, - device=args.device, - audio_path=args.audio_path, - ) diff --git a/examples/rnnt_longform_inference.py b/examples/rnnt_longform_inference.py deleted file mode 100644 index 0fb3e0f..0000000 --- a/examples/rnnt_longform_inference.py +++ /dev/null @@ -1,210 +0,0 @@ -import argparse -from io import BytesIO -from typing import List, Tuple - -import numpy as np -import torch -import torchaudio -from nemo.collections.asr.models import EncDecRNNTBPEModel -from nemo.collections.asr.modules.audio_preprocessing import ( - AudioToMelSpectrogramPreprocessor as NeMoAudioToMelSpectrogramPreprocessor, -) -from nemo.collections.asr.parts.preprocessing.features import ( - FilterbankFeaturesTA as NeMoFilterbankFeaturesTA, -) -from omegaconf import OmegaConf, open_dict -from pyannote.audio import Pipeline -from pydub import AudioSegment - - -class FilterbankFeaturesTA(NeMoFilterbankFeaturesTA): - def __init__(self, mel_scale: str = "htk", wkwargs=None, **kwargs): - if "window_size" in kwargs: - del kwargs["window_size"] - if "window_stride" in kwargs: - del kwargs["window_stride"] - - super().__init__(**kwargs) - - self._mel_spec_extractor = torchaudio.transforms.MelSpectrogram( - sample_rate=self._sample_rate, - win_length=self.win_length, - hop_length=self.hop_length, - n_mels=kwargs["nfilt"], - window_fn=self.torch_windows[kwargs["window"]], - mel_scale=mel_scale, - norm=kwargs["mel_norm"], - n_fft=kwargs["n_fft"], - f_max=kwargs.get("highfreq", None), - f_min=kwargs.get("lowfreq", 0), - wkwargs=wkwargs, - ) - - -class AudioToMelSpectrogramPreprocessor(NeMoAudioToMelSpectrogramPreprocessor): - def __init__(self, mel_scale: str = "htk", **kwargs): - super().__init__(**kwargs) - kwargs["nfilt"] = kwargs["features"] - del kwargs["features"] - self.featurizer = ( - FilterbankFeaturesTA( # Deprecated arguments; kept for config compatibility - mel_scale=mel_scale, - **kwargs, - ) - ) - - -def audiosegment_to_numpy(audiosegment: AudioSegment) -> np.ndarray: - """Convert AudioSegment to numpy array.""" - samples = np.array(audiosegment.get_array_of_samples()) - if audiosegment.channels == 2: - samples = samples.reshape((-1, 2)) - - samples = samples.astype(np.float32, order="C") / 32768.0 - return samples - - -def format_time(seconds: float) -> str: - hours = int(seconds // 3600) - minutes = int((seconds % 3600) // 60) - seconds = seconds % 60 - full_seconds = int(seconds) - milliseconds = int((seconds - full_seconds) * 100) - - if hours > 0: - return f"{hours:02}:{minutes:02}:{full_seconds:02}:{milliseconds:02}" - else: - return f"{minutes:02}:{full_seconds:02}:{milliseconds:02}" - - -def segment_audio( - audio_path: str, - pipeline: Pipeline, - max_duration: float = 22.0, - min_duration: float = 15.0, - new_chunk_threshold: float = 0.2, -) -> Tuple[List[np.ndarray], List[List[float]]]: - # Prepare audio for pyannote vad pipeline - audio = AudioSegment.from_wav(audio_path) - audio_bytes = BytesIO() - audio.export(audio_bytes, format="wav") - audio_bytes.seek(0) - - # Process audio with pipeline to obtain segments with speech activity - sad_segments = pipeline({"uri": "filename", "audio": audio_bytes}) - - segments = [] - curr_duration = 0 - curr_start = 0 - curr_end = 0 - boundaries = [] - - # Concat segments from pipeline into chunks for asr according to max/min duration - for segment in sad_segments.get_timeline().support(): - start = max(0, segment.start) - end = min(len(audio) / 1000, segment.end) - if ( - curr_duration > min_duration and start - curr_end > new_chunk_threshold - ) or (curr_duration + (end - curr_end) > max_duration): - audio_segment = audiosegment_to_numpy( - audio[curr_start * 1000 : curr_end * 1000] - ) - segments.append(audio_segment) - boundaries.append([curr_start, curr_end]) - curr_start = start - - curr_end = end - curr_duration = curr_end - curr_start - - if curr_duration != 0: - audio_segment = audiosegment_to_numpy( - audio[curr_start * 1000 : curr_end * 1000] - ) - segments.append(audio_segment) - boundaries.append([curr_start, curr_end]) - - return segments, boundaries - - -def _parse_args(): - parser = argparse.ArgumentParser( - description="Run long-form inference using GigaAM-RNNT checkpoint" - ) - parser.add_argument( - "--model_config", help="Path to GigaAM-RNNT config file (.yaml)" - ) - parser.add_argument( - "--model_weights", help="Path to GigaAM-RNNT checkpoint file (.ckpt)" - ) - parser.add_argument("--tokenizer_path", help="Path to tokenizer directory") - parser.add_argument("--audio_path", help="Path to audio signal") - parser.add_argument( - "--hf_token", help="HuggingFace token for using pyannote Pipeline" - ) - parser.add_argument("--device", help="Device: cpu / cuda") - parser.add_argument("--fp16", help="Run in FP16 mode", default=True) - parser.add_argument( - "--batch_size", help="Batch size for acoustic model inference", default=10 - ) - return parser.parse_args() - - -def main( - model_config: str, - model_weights: str, - tokenizer_path: str, - device: str, - audio_path: str, - hf_token: str, - fp16: bool, - batch_size: int = 10, -): - # Initialize model - config = OmegaConf.load(model_config) - with open_dict(config): - config.tokenizer.dir = tokenizer_path - - model = EncDecRNNTBPEModel.from_config_dict(config) - ckpt = torch.load(model_weights, map_location="cpu") - model.load_state_dict(ckpt, strict=False) - model = model.to(device) - if device != "cpu" and fp16: - model = model.half() - model.preprocessor = model.preprocessor.float() - model.eval() - - # Initialize pyannote pipeline - pipeline = Pipeline.from_pretrained( - "pyannote/voice-activity-detection", use_auth_token=hf_token - ) - pipeline = pipeline.to(torch.device(device)) - - # Segment audio - segments, boundaries = segment_audio(audio_path, pipeline) - - # Transcribe segments - transcriptions = [] - if device != "cpu" and fp16: - with torch.autocast(device_type="cuda", dtype=torch.float16): - transcriptions = model.transcribe(segments, batch_size=batch_size)[0] - else: - transcriptions = model.transcribe(segments, batch_size=batch_size)[0] - - for transcription, boundary in zip(transcriptions, boundaries): - print( - f"[{format_time(boundary[0])} - {format_time(boundary[1])}]: {transcription}\n" - ) - - -if __name__ == "__main__": - args = _parse_args() - main( - model_config=args.model_config, - model_weights=args.model_weights, - tokenizer_path=args.tokenizer_path, - device=args.device, - audio_path=args.audio_path, - hf_token=args.hf_token, - fp16=args.fp16, - batch_size=args.batch_size, - ) diff --git a/examples/ssl_inference.py b/examples/ssl_inference.py deleted file mode 100644 index 57a669e..0000000 --- a/examples/ssl_inference.py +++ /dev/null @@ -1,55 +0,0 @@ -import argparse - -import hydra -import soundfile -import torch -from omegaconf import OmegaConf - - -class SpecScaler(torch.nn.Module): - def forward(self, x: torch.Tensor) -> torch.Tensor: - return torch.log(x.clamp_(1e-9, 1e9)) - - -def _parse_args(): - parser = argparse.ArgumentParser( - description="Run inference using GigaAM checkpoint" - ) - parser.add_argument("--encoder_config", help="Path to GigaAM config file (.yaml)") - parser.add_argument( - "--model_weights", help="Path to GigaAM checkpoint file (.ckpt)" - ) - parser.add_argument("--audio_path", help="Path to audio signal") - parser.add_argument("--device", help="Device: cpu / cuda") - return parser.parse_args() - - -def main(encoder_config: str, model_weights: str, device: str, audio_path: str): - conf = OmegaConf.load(encoder_config) - - encoder = hydra.utils.instantiate(conf.encoder) - ckpt = torch.load(model_weights, map_location="cpu") - encoder.load_state_dict(ckpt, strict=True) - encoder.to(device) - - feature_extractor = hydra.utils.instantiate(conf.feature_extractor) - - audio_signal, _ = soundfile.read(audio_path, dtype="float32") - features = feature_extractor(torch.tensor(audio_signal).float()) - features = features.to(device) - - encoded, _ = encoder.forward( - audio_signal=features.unsqueeze(0), - length=torch.tensor([features.shape[-1]]).to(device), - ) - print(f"encoded signal shape: {encoded.shape}") - - -if __name__ == "__main__": - args = _parse_args() - main( - encoder_config=args.encoder_config, - model_weights=args.model_weights, - device=args.device, - audio_path=args.audio_path, - ) diff --git a/gigaam/__init__.py b/gigaam/__init__.py new file mode 100644 index 0000000..d43a8fc --- /dev/null +++ b/gigaam/__init__.py @@ -0,0 +1,173 @@ +import hashlib +import logging +import os +import urllib.request +import warnings +from typing import Optional, Tuple, Union + +import torch +from tqdm import tqdm + +from .model import GigaAM, GigaAMASR, GigaAMEmo +from .preprocess import load_audio +from .utils import format_time + +__all__ = [ + "GigaAM", + "GigaAMASR", + "GigaAMEmo", + "load_audio", + "format_time", + "load_model", +] + +# Default cache directory +_CACHE_DIR = os.path.expanduser("~/.cache/gigaam") +# Url with model checkpoints +_URL_DIR = "https://cdn.chatwm.opensmodel.sberdevices.ru/GigaAM" +_MODEL_HASHES = { + "emo": "7ce76f9535cb254488985057c0d33006", + "v1_ctc": "f027f199e590a391d015aeede2e66174", + "v1_rnnt": "02c758999bcdc6afcb2087ef256d47ef", + "v1_ssl": "dc7f7b231f7f91c4968dc21910e7b396", + "v2_ctc": "e00f59cb5d39624fb30d1786044795bf", + "v2_rnnt": "547460139acfebd842323f59ed54ab54", + "v2_ssl": "cd4cf819c8191a07b9d7edcad111668e", + "v3_ctc": "73413e7be9c6a5935827bfab5c0dd678", + "v3_rnnt": "0fd2c9a1ff66abd8d32a3a07f7592815", + "v3_e2e_ctc": "367074d6498f426d960b25f49531cf68", + "v3_e2e_rnnt": "2730de7545ac43ad256485a462b0a27a", + "v3_ssl": "70cbf5ed7303a0ed242ddb257e9dc6a6", +} + + +def _download_file(file_url: str, file_path: str): + """Helper to download a file if not already cached.""" + if os.path.exists(file_path): + return file_path + + os.makedirs(os.path.dirname(file_path), exist_ok=True) + with urllib.request.urlopen(file_url) as source, open(file_path, "wb") as output: + with tqdm( + total=int(source.info().get("Content-Length", 0)), + ncols=80, + unit="iB", + unit_scale=True, + unit_divisor=1024, + ) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + return file_path + + +def _download_model(model_name: str, download_root: str) -> Tuple[str, str]: + """Download the model weights if not already cached.""" + short_names = ["ctc", "rnnt", "e2e_ctc", "e2e_rnnt", "ssl"] + possible_names = short_names + list(_MODEL_HASHES.keys()) + if model_name not in possible_names: + raise ValueError( + f"Model '{model_name}' not found. Available model names: {possible_names}" + ) + + if model_name in short_names: + model_name = f"v3_{model_name}" + model_url = f"{_URL_DIR}/{model_name}.ckpt" + model_path = os.path.join(download_root, model_name + ".ckpt") + return model_name, _download_file(model_url, model_path) + + +def _download_tokenizer(model_name: str, download_root: str) -> Optional[str]: + """Download the tokenizer if required and return its path.""" + if model_name != "v1_rnnt" and "e2e" not in model_name: + return None # No tokenizer required for this model + + tokenizer_url = f"{_URL_DIR}/{model_name}_tokenizer.model" + tokenizer_path = os.path.join(download_root, model_name + "_tokenizer.model") + return _download_file(tokenizer_url, tokenizer_path) + + +def hash_path(ckpt_path: str) -> str: + """Calculate binary file hash for checksum""" + return hashlib.md5(open(ckpt_path, "rb").read()).hexdigest() + + +def _normalize_device(device: Optional[Union[str, torch.device]]) -> torch.device: + """Normalize device parameter to torch.device.""" + if device is None: + device_str = "cuda" if torch.cuda.is_available() else "cpu" + return torch.device(device_str) + if isinstance(device, str): + return torch.device(device) + return device + + +def load_model( + model_name: str, + fp16_encoder: bool = True, + use_flash: Optional[bool] = False, + device: Optional[Union[str, torch.device]] = None, + download_root: Optional[str] = None, +) -> Union[GigaAM, GigaAMEmo, GigaAMASR]: + """ + Load the GigaAM model by name. + + Parameters + ---------- + model_name : str + The name of the model to load. + fp16_encoder: + Whether to convert encoder weights to FP16 precision. + use_flash : Optional[bool] + Whether to use flash_attn if the model allows it (requires the flash_attn library installed). + Default to False. + device : Optional[Union[str, torch.device]] + The device to load the model onto. Defaults to "cuda" if available, otherwise "cpu". + download_root : Optional[str] + The directory to download the model to. Defaults to "~/.cache/gigaam". + """ + device_obj = _normalize_device(device) + + if download_root is None: + download_root = _CACHE_DIR + + model_name, model_path = _download_model(model_name, download_root) + tokenizer_path = _download_tokenizer(model_name, download_root) + + assert ( + hash_path(model_path) == _MODEL_HASHES[model_name] + ), f"Model checksum failed. Please run `rm {model_path}` and reload the model" + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=(FutureWarning)) + checkpoint = torch.load(model_path, map_location="cpu", weights_only=False) + + if use_flash is not None: + checkpoint["cfg"].encoder.flash_attn = use_flash + if checkpoint["cfg"].encoder.get("flash_attn", False) and device_obj.type == "cpu": + logging.warning("flash_attn is not supported on CPU. Disabling it...") + checkpoint["cfg"].encoder.flash_attn = False + + if tokenizer_path is not None: + checkpoint["cfg"].decoding.model_path = tokenizer_path + + if "ssl" in model_name: + model = GigaAM(checkpoint["cfg"]) + elif "emo" in model_name: + model = GigaAMEmo(checkpoint["cfg"]) + else: + model = GigaAMASR(checkpoint["cfg"]) + + model.load_state_dict(checkpoint["state_dict"]) + model = model.eval() + + if fp16_encoder and device_obj.type != "cpu": + model.encoder = model.encoder.half() + + checkpoint["cfg"].model_name = model_name + return model.to(device_obj) diff --git a/gigaam/decoder.py b/gigaam/decoder.py new file mode 100644 index 0000000..066d397 --- /dev/null +++ b/gigaam/decoder.py @@ -0,0 +1,128 @@ +from typing import Dict, List, Optional, Tuple + +import torch +from torch import Tensor, nn + + +class CTCHead(nn.Module): + """ + CTC Head module for Connectionist Temporal Classification. + """ + + def __init__(self, feat_in: int, num_classes: int): + super().__init__() + self.decoder_layers = torch.nn.Sequential( + torch.nn.Conv1d(feat_in, num_classes, kernel_size=1) + ) + + def forward(self, encoder_output: Tensor) -> Tensor: + return torch.nn.functional.log_softmax( + self.decoder_layers(encoder_output).transpose(1, 2), dim=-1 + ) + + +class RNNTJoint(nn.Module): + """ + RNN-Transducer Joint Network Module. + This module combines the outputs of the encoder and the prediction network using + a linear transformation followed by ReLU activation and another linear projection. + """ + + def __init__( + self, enc_hidden: int, pred_hidden: int, joint_hidden: int, num_classes: int + ): + super().__init__() + self.enc_hidden = enc_hidden + self.pred_hidden = pred_hidden + self.pred = nn.Linear(pred_hidden, joint_hidden) + self.enc = nn.Linear(enc_hidden, joint_hidden) + self.joint_net = nn.Sequential(nn.ReLU(), nn.Linear(joint_hidden, num_classes)) + + def joint(self, encoder_out: Tensor, decoder_out: Tensor) -> Tensor: + """ + Combine the encoder and prediction network outputs into a joint representation. + """ + enc = self.enc(encoder_out).unsqueeze(2) + pred = self.pred(decoder_out).unsqueeze(1) + return self.joint_net(enc + pred).log_softmax(-1) + + def input_example(self) -> Tuple[Tensor, Tensor]: + device = next(self.parameters()).device + enc = torch.zeros(1, self.enc_hidden, 1) + dec = torch.zeros(1, self.pred_hidden, 1) + return enc.float().to(device), dec.float().to(device) + + def input_names(self) -> List[str]: + return ["enc", "dec"] + + def output_names(self) -> List[str]: + return ["joint"] + + def forward(self, enc: Tensor, dec: Tensor) -> Tensor: + return self.joint(enc.transpose(1, 2), dec.transpose(1, 2)) + + +class RNNTDecoder(nn.Module): + """ + RNN-Transducer Decoder Module. + This module handles the prediction network part of the RNN-Transducer architecture. + """ + + def __init__(self, pred_hidden: int, pred_rnn_layers: int, num_classes: int): + super().__init__() + self.blank_id = num_classes - 1 + self.pred_hidden = pred_hidden + self.embed = nn.Embedding(num_classes, pred_hidden, padding_idx=self.blank_id) + self.lstm = nn.LSTM(pred_hidden, pred_hidden, pred_rnn_layers) + + def predict( + self, + x: Optional[Tensor], + state: Optional[Tensor], + batch_size: int = 1, + ) -> Tuple[Tensor, Tensor]: + """ + Make predictions based on the current input and previous states. + If no input is provided, use zeros as the initial input. + """ + if x is not None: + emb: Tensor = self.embed(x) + else: + emb = torch.zeros( + (batch_size, 1, self.pred_hidden), device=next(self.parameters()).device + ) + g, hid = self.lstm(emb.transpose(0, 1), state) + return g.transpose(0, 1), hid + + def input_example(self) -> Tuple[Tensor, Tensor, Tensor]: + device = next(self.parameters()).device + label = torch.tensor([[0]]).to(device) + hidden_h = torch.zeros(1, 1, self.pred_hidden).to(device) + hidden_c = torch.zeros(1, 1, self.pred_hidden).to(device) + return label, hidden_h, hidden_c + + def input_names(self) -> List[str]: + return ["x", "h", "c"] + + def output_names(self) -> List[str]: + return ["dec", "h", "c"] + + def forward(self, x: Tensor, h: Tensor, c: Tensor) -> Tuple[Tensor, Tensor, Tensor]: + """ + ONNX-specific forward with x, state = (h, c) -> x, h, c. + """ + emb = self.embed(x) + g, (h, c) = self.lstm(emb.transpose(0, 1), (h, c)) + return g.transpose(0, 1), h, c + + +class RNNTHead(nn.Module): + """ + RNN-Transducer Head Module. + This module combines the decoder and joint network components of the RNN-Transducer architecture. + """ + + def __init__(self, decoder: Dict[str, int], joint: Dict[str, int]): + super().__init__() + self.decoder = RNNTDecoder(**decoder) + self.joint = RNNTJoint(**joint) diff --git a/gigaam/decoding.py b/gigaam/decoding.py new file mode 100644 index 0000000..6f9d239 --- /dev/null +++ b/gigaam/decoding.py @@ -0,0 +1,127 @@ +from typing import List, Optional + +import torch +from sentencepiece import SentencePieceProcessor +from torch import Tensor + +from .decoder import CTCHead, RNNTHead + + +class Tokenizer: + """ + Tokenizer for converting between text and token IDs. + The tokenizer can operate either character-wise or using a pre-trained SentencePiece model. + """ + + def __init__(self, vocab: List[str], model_path: Optional[str] = None): + self.charwise = model_path is None + if self.charwise: + self.vocab = vocab + else: + self.model = SentencePieceProcessor() + self.model.load(model_path) + + def decode(self, tokens: List[int]) -> str: + """ + Convert a list of token IDs back to a string. + """ + if self.charwise: + return "".join(self.vocab[tok] for tok in tokens) + return self.model.decode(tokens) + + def __len__(self): + """ + Get the total number of tokens in the vocabulary. + """ + return len(self.vocab) if self.charwise else len(self.model) + + +class CTCGreedyDecoding: + """ + Class for performing greedy decoding of CTC outputs. + """ + + def __init__(self, vocabulary: List[str], model_path: Optional[str] = None): + self.tokenizer = Tokenizer(vocabulary, model_path) + self.blank_id = len(self.tokenizer) + + @torch.inference_mode() + def decode(self, head: CTCHead, encoded: Tensor, lengths: Tensor) -> List[str]: + """ + Decode the output of a CTC model into a list of hypotheses. + """ + log_probs = head(encoder_output=encoded) + assert ( + len(log_probs.shape) == 3 + ), f"Expected log_probs shape {log_probs.shape} == [B, T, C]" + b, _, c = log_probs.shape + assert ( + c == len(self.tokenizer) + 1 + ), f"Num classes {c} != len(vocab) + 1 {len(self.tokenizer) + 1}" + labels = log_probs.argmax(dim=-1, keepdim=False) + + skip_mask = labels != self.blank_id + skip_mask[:, 1:] = torch.logical_and( + skip_mask[:, 1:], labels[:, 1:] != labels[:, :-1] + ) + for i, length in enumerate(lengths): + skip_mask[i, length:] = 0 + + pred_texts: List[str] = [] + for i in range(b): + pred_texts.append( + "".join(self.tokenizer.decode(labels[i][skip_mask[i]].cpu().tolist())) + ) + return pred_texts + + +class RNNTGreedyDecoding: + def __init__( + self, + vocabulary: List[str], + model_path: Optional[str] = None, + max_symbols_per_step: int = 10, + ): + """ + Class for performing greedy decoding of RNN-T outputs. + """ + self.tokenizer = Tokenizer(vocabulary, model_path) + self.blank_id = len(self.tokenizer) + self.max_symbols = max_symbols_per_step + + def _greedy_decode(self, head: RNNTHead, x: Tensor, seqlen: Tensor) -> str: + """ + Internal helper function for performing greedy decoding on a single sequence. + """ + hyp: List[int] = [] + dec_state: Optional[Tensor] = None + last_label: Optional[Tensor] = None + for t in range(seqlen): + f = x[t, :, :].unsqueeze(1) + not_blank = True + new_symbols = 0 + while not_blank and new_symbols < self.max_symbols: + g, hidden = head.decoder.predict(last_label, dec_state) + k = head.joint.joint(f, g)[0, 0, 0, :].argmax(0).item() + if k == self.blank_id: + not_blank = False + else: + hyp.append(int(k)) + dec_state = hidden + last_label = torch.tensor([[hyp[-1]]]).to(x.device) + new_symbols += 1 + + return self.tokenizer.decode(hyp) + + @torch.inference_mode() + def decode(self, head: RNNTHead, encoded: Tensor, enc_len: Tensor) -> List[str]: + """ + Decode the output of an RNN-T model into a list of hypotheses. + """ + b = encoded.shape[0] + pred_texts = [] + encoded = encoded.transpose(1, 2) + for i in range(b): + inseq = encoded[i, :, :].unsqueeze(1) + pred_texts.append(self._greedy_decode(head, inseq, enc_len[i])) + return pred_texts diff --git a/gigaam/encoder.py b/gigaam/encoder.py new file mode 100644 index 0000000..d49e630 --- /dev/null +++ b/gigaam/encoder.py @@ -0,0 +1,570 @@ +import math +from abc import ABC, abstractmethod +from typing import Dict, List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +try: + from flash_attn import flash_attn_func + + IMPORT_FLASH = True +except Exception as err: + IMPORT_FLASH = False + IMPORT_FLASH_ERR = err + +from .utils import apply_masked_flash_attn, apply_rotary_pos_emb + + +class StridingSubsampling(nn.Module): + """ + Strided Subsampling layer used to reduce the sequence length. + """ + + def __init__( + self, + subsampling: str, + kernel_size: int, + subsampling_factor: int, + feat_in: int, + feat_out: int, + conv_channels: int, + ): + super().__init__() + self.subsampling_type = subsampling + assert self.subsampling_type in ["conv1d", "conv2d"] + self._sampling_num = int(math.log(subsampling_factor, 2)) + self._stride = 2 + self._kernel_size = kernel_size + self._padding = (self._kernel_size - 1) // 2 + + layers: List[nn.Module] = [] + in_channels = 1 if self.subsampling_type == "conv2d" else feat_in + subs_conv_class = ( + torch.nn.Conv2d if self.subsampling_type == "conv2d" else torch.nn.Conv1d + ) + for _ in range(self._sampling_num): + layers.append( + subs_conv_class( + in_channels=in_channels, + out_channels=conv_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._padding, + ) + ) + layers.append(nn.ReLU()) + in_channels = conv_channels + + out_length = self.calc_output_length(torch.tensor(feat_in)) + if self.subsampling_type == "conv2d": + self.out = torch.nn.Linear(conv_channels * int(out_length), feat_out) + self.conv = torch.nn.Sequential(*layers) + + def calc_output_length(self, lengths: Tensor) -> Tensor: + """ + Calculates the output length after applying the subsampling. + """ + lengths = lengths.to(torch.float) + add_pad = 2 * self._padding - self._kernel_size + for _ in range(self._sampling_num): + lengths = torch.div(lengths + add_pad, self._stride) + 1.0 + lengths = torch.floor(lengths) + return lengths.to(dtype=torch.int) + + def forward(self, x: Tensor, lengths: Tensor) -> Tuple[Tensor, Tensor]: + if self.subsampling_type == "conv2d": + x = self.conv(x.unsqueeze(1)) + b, _, t, _ = x.size() + x = self.out(x.transpose(1, 2).reshape(b, t, -1)) + else: + x = self.conv(x.transpose(1, 2)).transpose(1, 2) + return x, self.calc_output_length(lengths) + + +class MultiHeadAttention(nn.Module, ABC): + """ + Base class of Multi-Head Attention Mechanisms. + """ + + def __init__( + self, n_head: int, n_feat: int, flash_attn=False, torch_sdpa_attn=False + ): + super().__init__() + assert n_feat % n_head == 0 + self.d_k = n_feat // n_head + self.h = n_head + self.linear_q = nn.Linear(n_feat, n_feat) + self.linear_k = nn.Linear(n_feat, n_feat) + self.linear_v = nn.Linear(n_feat, n_feat) + self.linear_out = nn.Linear(n_feat, n_feat) + self.flash_attn = flash_attn + self.torch_sdpa_attn = torch_sdpa_attn + if self.flash_attn and not IMPORT_FLASH: + raise RuntimeError( + f"flash_attn_func was imported with err {IMPORT_FLASH_ERR}. " + "Please install flash_attn or use --no_flash flag. " + "If you have already done this, " + "--force-reinstall flag might be useful" + ) + + def forward_qkv( + self, query: Tensor, key: Tensor, value: Tensor + ) -> Tuple[Tensor, Tensor, Tensor]: + """ + Projects the inputs into queries, keys, and values for multi-head attention. + """ + b = query.size(0) + q = self.linear_q(query).view(b, -1, self.h, self.d_k) + k = self.linear_k(key).view(b, -1, self.h, self.d_k) + v = self.linear_v(value).view(b, -1, self.h, self.d_k) + if self.flash_attn: + return q, k, v + return q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) + + def forward_attention( + self, value: Tensor, scores: Tensor, mask: Optional[Tensor] + ) -> Tensor: + """ + Computes the scaled dot-product attention given the projected values and scores. + """ + b = value.size(0) + if mask is not None: + mask = mask.unsqueeze(1) + scores = scores.masked_fill(mask, -10000.0) + attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0) + else: + attn = torch.softmax(scores, dim=-1) + x = torch.matmul(attn, value) + x = x.transpose(1, 2).reshape(b, -1, self.h * self.d_k) + return self.linear_out(x) + + +class RelPositionMultiHeadAttention(MultiHeadAttention): + """ + Relative Position Multi-Head Attention module. + """ + + def __init__(self, n_head: int, n_feat: int): + super().__init__(n_head, n_feat) + self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) + self.pos_bias_u = nn.Parameter(torch.FloatTensor(self.h, self.d_k)) + self.pos_bias_v = nn.Parameter(torch.FloatTensor(self.h, self.d_k)) + + def rel_shift(self, x: Tensor) -> Tensor: + b, h, qlen, pos_len = x.size() + x = torch.nn.functional.pad(x, pad=(1, 0)) + x = x.view(b, h, -1, qlen) + return x[:, :, 1:].view(b, h, qlen, pos_len) + + def forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + pos_emb: Tensor, + mask: Optional[Tensor] = None, + ) -> Tensor: + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) + p = self.linear_pos(pos_emb) + p = p.view(pos_emb.shape[0], -1, self.h, self.d_k).transpose(1, 2) + q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) + q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + matrix_bd = self.rel_shift(matrix_bd) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + matrix_bd = matrix_bd[:, :, :, : matrix_ac.size(-1)] + scores = (matrix_ac + matrix_bd) / math.sqrt(self.d_k) + return self.forward_attention(v, scores, mask) + + +class RotaryPositionMultiHeadAttention(MultiHeadAttention): + """ + Rotary Position Multi-Head Attention module. + """ + + def forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + pos_emb: List[Tensor], + mask: Optional[Tensor] = None, + ) -> Tensor: + b, t, _ = value.size() + query = query.transpose(0, 1).view(t, b, self.h, self.d_k) + key = key.transpose(0, 1).view(t, b, self.h, self.d_k) + value = value.transpose(0, 1).view(t, b, self.h, self.d_k) + + cos, sin = pos_emb + query, key = apply_rotary_pos_emb(query, key, cos, sin, offset=0) + + q, k, v = self.forward_qkv( + query.view(t, b, self.h * self.d_k).transpose(0, 1), + key.view(t, b, self.h * self.d_k).transpose(0, 1), + value.view(t, b, self.h * self.d_k).transpose(0, 1), + ) + + if self.flash_attn: + if mask is None: + scores = flash_attn_func(q, k, v) + else: + scores = apply_masked_flash_attn(q, k, v, mask, self.h, self.d_k) + scores = scores.view(b, -1, self.h * self.d_k) + return self.linear_out(scores) + elif self.torch_sdpa_attn: + attn_mask = None if mask is None else ~mask.unsqueeze(1) + attn_output = F.scaled_dot_product_attention( + q, + k, + v, + attn_mask=attn_mask, + ) + attn_output = attn_output.transpose(1, 2).reshape(b, t, self.h * self.d_k) + return self.linear_out(attn_output) + else: + scores = torch.matmul(q, k.transpose(-2, -1) / math.sqrt(self.d_k)) + return self.forward_attention(v, scores, mask) + + +class PositionalEncoding(nn.Module, ABC): + """ + Base class of Positional Encodings. + """ + + def __init__(self, dim: int, base: int): + super().__init__() + self.dim = dim + self.base = base + + @abstractmethod + def create_pe(self, length: int, device: torch.device) -> Optional[Tensor]: + pass + + def extend_pe(self, length: int, device: torch.device): + """ + Extends the positional encoding buffer to process longer sequences. + """ + pe = self.create_pe(length, device) + if pe is None: + return + if hasattr(self, "pe"): + self.pe = pe + else: + self.register_buffer("pe", pe, persistent=False) + + +class RelPositionalEmbedding(PositionalEncoding): + """ + Relative Positional Embedding module. + """ + + def create_pe(self, length: int, device: torch.device) -> Optional[Tensor]: + """ + Creates the relative positional encoding matrix. + """ + if hasattr(self, "pe") and self.pe.shape[1] >= 2 * length - 1: + return None + positions = torch.arange(length - 1, -length, -1, device=device).unsqueeze(1) + pos_length = positions.size(0) + pe = torch.zeros(pos_length, self.dim, device=positions.device) + div_term = torch.exp( + torch.arange(0, self.dim, 2, device=pe.device) + * -(math.log(10000.0) / self.dim) + ) + pe[:, 0::2] = torch.sin(positions * div_term) + pe[:, 1::2] = torch.cos(positions * div_term) + return pe.unsqueeze(0) + + def forward(self, x: torch.Tensor) -> Tuple[Tensor, Tensor]: + input_len = x.size(1) + center_pos = self.pe.size(1) // 2 + 1 + start_pos = center_pos - input_len + end_pos = center_pos + input_len - 1 + return x, self.pe[:, start_pos:end_pos] + + +class RotaryPositionalEmbedding(PositionalEncoding): + """ + Rotary Positional Embedding module. + """ + + def create_pe(self, length: int, device: torch.device) -> Optional[Tensor]: + """ + Creates or extends the rotary positional encoding matrix. + """ + if hasattr(self, "pe") and self.pe.size(0) >= 2 * length: + return None + positions = torch.arange(0, length, dtype=torch.float32, device=device) + inv_freq = 1.0 / ( + self.base ** (torch.arange(0, self.dim, 2).float() / self.dim) + ) + t = torch.arange(length, device=positions.device).type_as(inv_freq) + freqs = torch.einsum("i,j->ij", t, inv_freq) + emb = torch.cat((freqs, freqs), dim=-1).to(positions.device) + return torch.cat([emb.cos()[:, None, None, :], emb.sin()[:, None, None, :]]) + + def forward(self, x: torch.Tensor) -> Tuple[Tensor, List[Tensor]]: + cos_emb = self.pe[0 : x.shape[1]] + half_pe = self.pe.shape[0] // 2 + sin_emb = self.pe[half_pe : half_pe + x.shape[1]] + return x, [cos_emb, sin_emb] + + +class ConformerConvolution(nn.Module): + """ + Conformer Convolution module. + """ + + def __init__( + self, + d_model: int, + kernel_size: int, + norm_type: str, + ): + super().__init__() + assert (kernel_size - 1) % 2 == 0 + assert norm_type in ["batch_norm", "layer_norm"] + self.norm_type = norm_type + self.pointwise_conv1 = nn.Conv1d(d_model, d_model * 2, kernel_size=1) + self.depthwise_conv = nn.Conv1d( + in_channels=d_model, + out_channels=d_model, + kernel_size=kernel_size, + padding=(kernel_size - 1) // 2, + groups=d_model, + bias=True, + ) + self.batch_norm = ( + nn.BatchNorm1d(d_model) + if norm_type == "batch_norm" + else nn.LayerNorm(d_model) + ) + self.activation = nn.SiLU() + self.pointwise_conv2 = nn.Conv1d(d_model, d_model, kernel_size=1) + + def forward(self, x: Tensor, pad_mask: Optional[Tensor] = None) -> Tensor: + x = x.transpose(1, 2) + x = self.pointwise_conv1(x) + x = nn.functional.glu(x, dim=1) + if pad_mask is not None: + x = x.masked_fill(pad_mask.unsqueeze(1), 0.0) + x = self.depthwise_conv(x) + if self.norm_type == "batch_norm": + x = self.batch_norm(x) + else: + x = self.batch_norm(x.transpose(1, 2)).transpose(1, 2) + x = self.activation(x) + x = self.pointwise_conv2(x) + return x.transpose(1, 2) + + +class ConformerFeedForward(nn.Module): + """ + Conformer Feed Forward module. + """ + + def __init__(self, d_model: int, d_ff: int, use_bias=True): + super().__init__() + self.linear1 = nn.Linear(d_model, d_ff, bias=use_bias) + self.activation = nn.SiLU() + self.linear2 = nn.Linear(d_ff, d_model, bias=use_bias) + + def forward(self, x: Tensor) -> Tensor: + return self.linear2(self.activation(self.linear1(x))) + + +class ConformerLayer(nn.Module): + """ + Conformer Layer module. + This module combines several submodules including feed forward networks, + depthwise separable convolution, and multi-head self-attention + to form a single Conformer block. + """ + + def __init__( + self, + d_model: int, + d_ff: int, + self_attention_model: str, + n_heads: int = 16, + conv_norm_type: str = "batch_norm", + conv_kernel_size: int = 31, + flash_attn: bool = False, + ): + super().__init__() + self.fc_factor = 0.5 + self.norm_feed_forward1 = nn.LayerNorm(d_model) + self.feed_forward1 = ConformerFeedForward(d_model=d_model, d_ff=d_ff) + self.norm_conv = nn.LayerNorm(d_model) + self.conv = ConformerConvolution( + d_model=d_model, + kernel_size=conv_kernel_size, + norm_type=conv_norm_type, + ) + self.norm_self_att = nn.LayerNorm(d_model) + if self_attention_model == "rotary": + self.self_attn: nn.Module = RotaryPositionMultiHeadAttention( + n_head=n_heads, + n_feat=d_model, + flash_attn=flash_attn, + torch_sdpa_attn=not flash_attn, + ) + else: + assert not flash_attn, "Not supported flash_attn for rel_pos" + self.self_attn = RelPositionMultiHeadAttention( + n_head=n_heads, + n_feat=d_model, + ) + self.norm_feed_forward2 = nn.LayerNorm(d_model) + self.feed_forward2 = ConformerFeedForward(d_model=d_model, d_ff=d_ff) + self.norm_out = nn.LayerNorm(d_model) + + def forward( + self, + x: Tensor, + pos_emb: Union[Tensor, List[Tensor]], + att_mask: Optional[Tensor] = None, + pad_mask: Optional[Tensor] = None, + ) -> Tensor: + residual = x + x = self.norm_feed_forward1(x) + x = self.feed_forward1(x) + residual = residual + x * self.fc_factor + + x = self.norm_self_att(residual) + x = self.self_attn(x, x, x, pos_emb, mask=att_mask) + residual = residual + x + + x = self.norm_conv(residual) + x = self.conv(x, pad_mask=pad_mask) + residual = residual + x + + x = self.norm_feed_forward2(residual) + x = self.feed_forward2(x) + residual = residual + x * self.fc_factor + + x = self.norm_out(residual) + return x + + +class ConformerEncoder(nn.Module): + """ + Conformer Encoder module. + This module encapsulates the entire Conformer encoder architecture, + consisting of a StridingSubsampling layer, positional embeddings, and + a stack of Conformer Layers. + It serves as the main component responsible for processing speech features. + """ + + def __init__( + self, + feat_in: int = 64, + n_layers: int = 16, + d_model: int = 768, + subsampling: str = "conv2d", + subs_kernel_size: int = 3, + subsampling_factor: int = 4, + ff_expansion_factor: int = 4, + self_attention_model: str = "rotary", + n_heads: int = 16, + pos_emb_max_len: int = 5000, + conv_norm_type: str = "batch_norm", + conv_kernel_size: int = 31, + flash_attn: bool = False, + ): + super().__init__() + self.feat_in = feat_in + assert self_attention_model in [ + "rotary", + "rel_pos", + ], f"Not supported attn = {self_attention_model}" + + self.pre_encode = StridingSubsampling( + subsampling=subsampling, + kernel_size=subs_kernel_size, + subsampling_factor=subsampling_factor, + feat_in=feat_in, + feat_out=d_model, + conv_channels=d_model, + ) + + self.pos_emb_max_len = pos_emb_max_len + if self_attention_model == "rotary": + self.pos_enc: PositionalEncoding = RotaryPositionalEmbedding( + d_model // n_heads, pos_emb_max_len + ) + else: + self.pos_enc = RelPositionalEmbedding(d_model, pos_emb_max_len) + + self.layers = nn.ModuleList() + for _ in range(n_layers): + layer = ConformerLayer( + d_model=d_model, + d_ff=d_model * ff_expansion_factor, + self_attention_model=self_attention_model, + n_heads=n_heads, + conv_norm_type=conv_norm_type, + conv_kernel_size=conv_kernel_size, + flash_attn=flash_attn, + ) + self.layers.append(layer) + + def input_example( + self, + batch_size: int = 1, + seqlen: int = 200, + ) -> Tuple[Tensor, Tensor]: + device = next(self.parameters()).device + features = torch.zeros(batch_size, self.feat_in, seqlen) + feature_lengths = torch.full([batch_size], features.shape[-1]) + return features.float().to(device), feature_lengths.to(device) + + def input_names(self) -> List[str]: + return ["audio_signal", "length"] + + def output_names(self) -> List[str]: + return ["encoded", "encoded_len"] + + def dynamic_axes(self) -> Dict[str, Dict[int, str]]: + return { + "audio_signal": {0: "batch_size", 2: "seq_len"}, + "length": {0: "batch_size"}, + "encoded": {0: "batch_size", 1: "seq_len"}, + "encoded_len": {0: "batch_size"}, + } + + def forward(self, audio_signal: Tensor, length: Tensor) -> Tuple[Tensor, Tensor]: + if not hasattr(self.pos_enc, "pe"): + self.pos_enc.extend_pe(self.pos_emb_max_len, audio_signal.device) + + audio_signal, length = self.pre_encode( + x=audio_signal.transpose(1, 2), lengths=length + ) + + max_len = audio_signal.size(1) + audio_signal, pos_emb = self.pos_enc(x=audio_signal) + + pad_mask = torch.arange(0, max_len, device=audio_signal.device).expand( + length.size(0), -1 + ) < length.unsqueeze(-1) + + att_mask = None + if audio_signal.shape[0] > 1: + att_mask = pad_mask.unsqueeze(1).repeat([1, max_len, 1]) + att_mask = torch.logical_and(att_mask, att_mask.transpose(1, 2)) + att_mask = ~att_mask + + pad_mask = ~pad_mask + + for layer in self.layers: + audio_signal = layer( + x=audio_signal, + pos_emb=pos_emb, + att_mask=att_mask, + pad_mask=pad_mask, + ) + + return audio_signal.transpose(1, 2), length diff --git a/gigaam/model.py b/gigaam/model.py new file mode 100644 index 0000000..ac38c1d --- /dev/null +++ b/gigaam/model.py @@ -0,0 +1,227 @@ +from typing import Dict, List, Tuple, Union + +import hydra +import omegaconf +import torch +from torch import Tensor, nn + +from .preprocess import SAMPLE_RATE, load_audio +from .utils import onnx_converter + +LONGFORM_THRESHOLD = 25 * SAMPLE_RATE + + +class GigaAM(nn.Module): + """ + Giga Acoustic Model: Self-Supervised Model for Speech Tasks + """ + + def __init__(self, cfg: omegaconf.DictConfig): + super().__init__() + self.cfg = cfg + self.preprocessor = hydra.utils.instantiate(self.cfg.preprocessor) + self.encoder = hydra.utils.instantiate(self.cfg.encoder) + + def forward( + self, features: Tensor, feature_lengths: Tensor + ) -> Tuple[Tensor, Tensor]: + """ + Perform forward pass through the preprocessor and encoder. + """ + features, feature_lengths = self.preprocessor(features, feature_lengths) + if self._device.type == "cpu": + return self.encoder(features, feature_lengths) + with torch.autocast(device_type=self._device.type, dtype=torch.float16): + return self.encoder(features, feature_lengths) + + @property + def _device(self) -> torch.device: + return next(self.parameters()).device + + @property + def _dtype(self) -> torch.dtype: + return next(self.parameters()).dtype + + def prepare_wav(self, wav_file: str) -> Tuple[Tensor, Tensor]: + """ + Prepare an audio file for processing by loading it onto + the correct device and converting its format. + """ + wav = load_audio(wav_file) + wav = wav.to(self._device).to(self._dtype).unsqueeze(0) + length = torch.full([1], wav.shape[-1], device=self._device) + return wav, length + + def embed_audio(self, wav_file: str) -> Tuple[Tensor, Tensor]: + """ + Extract audio representations using the GigaAM model. + """ + wav, length = self.prepare_wav(wav_file) + encoded, encoded_len = self.forward(wav, length) + return encoded, encoded_len + + def to_onnx(self, dir_path: str = ".") -> None: + """ + Export onnx model encoder to the specified dir. + """ + self._to_onnx(dir_path) + omegaconf.OmegaConf.save(self.cfg, f"{dir_path}/{self.cfg.model_name}.yaml") + + def _to_onnx(self, dir_path: str = ".") -> None: + """ + Export onnx model encoder to the specified dir. + """ + onnx_converter( + model_name=f"{self.cfg.model_name}_encoder", + out_dir=dir_path, + module=self.encoder, + dynamic_axes=self.encoder.dynamic_axes(), + ) + + +class GigaAMASR(GigaAM): + """ + Giga Acoustic Model for Speech Recognition + """ + + def __init__(self, cfg: omegaconf.DictConfig): + super().__init__(cfg) + self.head = hydra.utils.instantiate(self.cfg.head) + self.decoding = hydra.utils.instantiate(self.cfg.decoding) + + @torch.inference_mode() + def transcribe(self, wav_file: str) -> str: + """ + Transcribes a short audio file into text. + """ + wav, length = self.prepare_wav(wav_file) + if length.item() > LONGFORM_THRESHOLD: + raise ValueError("Too long wav file, use 'transcribe_longform' method.") + + encoded, encoded_len = self.forward(wav, length) + return self.decoding.decode(self.head, encoded, encoded_len)[0] + + def forward_for_export(self, features: Tensor, feature_lengths: Tensor) -> Tensor: + """ + Encoder-decoder forward to save model entirely in onnx format. + """ + return self.head(self.encoder(features, feature_lengths)[0]) + + def _to_onnx(self, dir_path: str = ".") -> None: + """ + Export onnx ASR model. + `ctc`: exported entirely in encoder-decoder format. + `rnnt`: exported in encoder/decoder/joint parts separately. + """ + if "ctc" in self.cfg.model_name: + saved_forward = self.forward + self.forward = self.forward_for_export # type: ignore[assignment, method-assign] + try: + onnx_converter( + model_name=self.cfg.model_name, + out_dir=dir_path, + module=self, + inputs=self.encoder.input_example(), + input_names=["features", "feature_lengths"], + output_names=["log_probs"], + dynamic_axes={ + "features": {0: "batch_size", 2: "seq_len"}, + "feature_lengths": {0: "batch_size"}, + "log_probs": {0: "batch_size", 1: "seq_len"}, + }, + ) + finally: + self.forward = saved_forward # type: ignore[assignment, method-assign] + else: + super()._to_onnx(dir_path) # export encoder + onnx_converter( + model_name=f"{self.cfg.model_name}_decoder", + out_dir=dir_path, + module=self.head.decoder, + ) + onnx_converter( + model_name=f"{self.cfg.model_name}_joint", + out_dir=dir_path, + module=self.head.joint, + ) + + @torch.inference_mode() + def transcribe_longform( + self, wav_file: str, **kwargs + ) -> List[Dict[str, Union[str, Tuple[float, float]]]]: + """ + Transcribes a long audio file by splitting it into segments and + then transcribing each segment. + """ + from .vad_utils import segment_audio_file + + transcribed_segments = [] + segments, boundaries = segment_audio_file( + wav_file, SAMPLE_RATE, device=self._device, **kwargs + ) + for segment, segment_boundaries in zip(segments, boundaries): + wav = segment.to(self._device).unsqueeze(0).to(self._dtype) + length = torch.full([1], wav.shape[-1], device=self._device) + encoded, encoded_len = self.forward(wav, length) + result = self.decoding.decode(self.head, encoded, encoded_len)[0] + transcribed_segments.append( + {"transcription": result, "boundaries": segment_boundaries} + ) + return transcribed_segments + + +class GigaAMEmo(GigaAM): + """ + Giga Acoustic Model for Emotion Recognition + """ + + def __init__(self, cfg: omegaconf.DictConfig): + super().__init__(cfg) + self.head = hydra.utils.instantiate(self.cfg.head) + self.id2name = cfg.id2name + + def get_probs(self, wav_file: str) -> Dict[str, float]: + """ + Calculate probabilities for each emotion class based on the provided audio file. + """ + wav, length = self.prepare_wav(wav_file) + encoded, _ = self.forward(wav, length) + encoded_pooled = nn.functional.avg_pool1d( + encoded, kernel_size=encoded.shape[-1] + ).squeeze(-1) + + logits = self.head(encoded_pooled)[0] + probs = nn.functional.softmax(logits, dim=-1).detach().tolist() + + return {self.id2name[i]: probs[i] for i in range(len(self.id2name))} + + def forward_for_export(self, features: Tensor, feature_lengths: Tensor) -> Tensor: + """ + Encoder-decoder forward to save model entirely in onnx format. + """ + encoded, _ = self.encoder(features, feature_lengths) + enc_pooled = encoded.mean(dim=-1) + return nn.functional.softmax(self.head(enc_pooled), dim=-1) + + def _to_onnx(self, dir_path: str = ".") -> None: + """ + Export onnx Emo model. + """ + saved_forward = self.forward + self.forward = self.forward_for_export # type: ignore[assignment, method-assign] + try: + onnx_converter( + model_name=self.cfg.model_name, + out_dir=dir_path, + module=self, + inputs=self.encoder.input_example(), + input_names=["features", "feature_lengths"], + output_names=["probs"], + dynamic_axes={ + "features": {0: "batch_size", 2: "seq_len"}, + "feature_lengths": {0: "batch_size"}, + "probs": {0: "batch_size", 1: "seq_len"}, + }, + ) + finally: + self.forward = saved_forward # type: ignore[assignment, method-assign] diff --git a/gigaam/onnx_utils.py b/gigaam/onnx_utils.py new file mode 100644 index 0000000..8fd1c0d --- /dev/null +++ b/gigaam/onnx_utils.py @@ -0,0 +1,150 @@ +import warnings +from typing import List, Optional, Tuple, Union + +import hydra +import numpy as np +import omegaconf +import onnxruntime as rt +import torch + +from .decoding import Tokenizer +from .preprocess import FeatureExtractor, load_audio + +warnings.simplefilter("ignore", category=UserWarning) + + +DTYPE = np.float32 +MAX_LETTERS_PER_FRAME = 3 + + +def infer_onnx( + wav_file: str, + model_cfg: omegaconf.DictConfig, + sessions: List[rt.InferenceSession], + preprocessor: Optional[FeatureExtractor] = None, + tokenizer: Optional[Tokenizer] = None, +) -> Union[str, np.ndarray]: + """Run ONNX sessions for the model, requires preprocessor instantiating""" + model_name = model_cfg.model_name + + if preprocessor is None: + preprocessor = hydra.utils.instantiate(model_cfg.preprocessor) + if tokenizer is None and ("ctc" in model_name or "rnnt" in model_name): + tokenizer = hydra.utils.instantiate(model_cfg.decoding).tokenizer + + input_signal = load_audio(wav_file) + input_signal = preprocessor( + input_signal.unsqueeze(0), torch.tensor([input_signal.shape[-1]]) + )[0].numpy() + + enc_sess = sessions[0] + enc_inputs = { + node.name: data + for (node, data) in zip( + enc_sess.get_inputs(), + [input_signal.astype(DTYPE), [input_signal.shape[-1]]], + ) + } + enc_features = enc_sess.run( + [node.name for node in enc_sess.get_outputs()], enc_inputs + )[0] + + if "emo" in model_name or "ssl" in model_name: + return enc_features + + blank_idx = len(tokenizer) + token_ids = [] + prev_token = blank_idx + if "ctc" in model_name: + prev_tok = blank_idx + for tok in enc_features.argmax(-1).squeeze().tolist(): + if (tok != prev_tok or prev_tok == blank_idx) and tok != blank_idx: + token_ids.append(tok) + prev_tok = tok + else: + pred_states = [ + np.zeros(shape=(1, 1, model_cfg.head.decoder.pred_hidden), dtype=DTYPE), + np.zeros(shape=(1, 1, model_cfg.head.decoder.pred_hidden), dtype=DTYPE), + ] + pred_sess, joint_sess = sessions[1:] + for j in range(enc_features.shape[-1]): + emitted_letters = 0 + while emitted_letters < MAX_LETTERS_PER_FRAME: + pred_inputs = { + node.name: data + for (node, data) in zip( + pred_sess.get_inputs(), [np.array([[prev_token]])] + pred_states + ) + } + pred_outputs = pred_sess.run( + [node.name for node in pred_sess.get_outputs()], pred_inputs + ) + + joint_inputs = { + node.name: data + for node, data in zip( + joint_sess.get_inputs(), + [enc_features[:, :, [j]], pred_outputs[0].swapaxes(1, 2)], + ) + } + log_probs = joint_sess.run( + [node.name for node in joint_sess.get_outputs()], joint_inputs + ) + token = log_probs[0].argmax(-1)[0][0] + + if token != blank_idx: + prev_token = int(token) + pred_states = pred_outputs[1:] + token_ids.append(int(token)) + emitted_letters += 1 + else: + break + + return tokenizer.decode(token_ids) + + +def load_onnx( + onnx_dir: str, + model_version: str, + provider: Optional[str] = None, +) -> Tuple[ + List[rt.InferenceSession], Union[omegaconf.DictConfig, omegaconf.ListConfig] +]: + """Load ONNX sessions for the given versions and cpu / cuda provider""" + if provider is None and "CUDAExecutionProvider" in rt.get_available_providers(): + provider = "CUDAExecutionProvider" + elif provider is None: + provider = "CPUExecutionProvider" + + opts = rt.SessionOptions() + opts.intra_op_num_threads = 16 + opts.execution_mode = rt.ExecutionMode.ORT_SEQUENTIAL + opts.log_severity_level = 3 + + model_cfg = omegaconf.OmegaConf.load(f"{onnx_dir}/{model_version}.yaml") + + if "rnnt" not in model_version and "ssl" not in model_version: + model_path = f"{onnx_dir}/{model_version}.onnx" + sessions = [ + rt.InferenceSession(model_path, providers=[provider], sess_options=opts) + ] + elif "ssl" in model_version: + pth = f"{onnx_dir}/{model_version}" + enc_sess = rt.InferenceSession( + f"{pth}_encoder.onnx", providers=[provider], sess_options=opts + ) + sessions = [enc_sess] + else: + pth = f"{onnx_dir}/{model_version}" + enc_sess = rt.InferenceSession( + f"{pth}_encoder.onnx", providers=[provider], sess_options=opts + ) + pred_sess = rt.InferenceSession( + f"{pth}_decoder.onnx", providers=[provider], sess_options=opts + ) + joint_sess = rt.InferenceSession( + f"{pth}_joint.onnx", providers=[provider], sess_options=opts + ) + sessions = [enc_sess, pred_sess, joint_sess] + + return sessions, model_cfg diff --git a/gigaam/preprocess.py b/gigaam/preprocess.py new file mode 100644 index 0000000..fb6ebde --- /dev/null +++ b/gigaam/preprocess.py @@ -0,0 +1,98 @@ +import warnings +from subprocess import CalledProcessError, run +from typing import Tuple + +import torch +import torchaudio +from torch import Tensor, nn + +SAMPLE_RATE = 16000 + + +def load_audio(audio_path: str, sample_rate: int = SAMPLE_RATE) -> Tensor: + """ + Load an audio file and resample it to the specified sample rate. + """ + cmd = [ + "ffmpeg", + "-nostdin", + "-threads", + "0", + "-i", + audio_path, + "-f", + "s16le", + "-ac", + "1", + "-acodec", + "pcm_s16le", + "-ar", + str(sample_rate), + "-", + ] + try: + audio = run(cmd, capture_output=True, check=True).stdout + except CalledProcessError as exc: + raise RuntimeError("Failed to load audio") from exc + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=UserWarning) + return torch.frombuffer(audio, dtype=torch.int16).float() / 32768.0 + + +class SpecScaler(nn.Module): + """ + Module that applies logarithmic scaling to spectrogram values. + This module clamps the input values within a certain range and then applies a natural logarithm. + """ + + def forward(self, x: Tensor) -> Tensor: + return torch.log(x.clamp_(1e-9, 1e9)) + + +class FeatureExtractor(nn.Module): + """ + Module for extracting Log-mel spectrogram features from raw audio signals. + This module uses Torchaudio's MelSpectrogram transform to extract features + and applies logarithmic scaling. + """ + + def __init__(self, sample_rate: int, features: int, **kwargs): + super().__init__() + self.hop_length = kwargs.get("hop_length", sample_rate // 100) + self.win_length = kwargs.get("win_length", sample_rate // 40) + self.n_fft = kwargs.get("n_fft", sample_rate // 40) + self.center = kwargs.get("center", True) + self.featurizer = nn.Sequential( + torchaudio.transforms.MelSpectrogram( + sample_rate=sample_rate, + n_mels=features, + win_length=self.win_length, + hop_length=self.hop_length, + n_fft=self.n_fft, + center=self.center, + ), + SpecScaler(), + ) + + def out_len(self, input_lengths: Tensor) -> Tensor: + """ + Calculates the output length after the feature extraction process. + """ + if self.center: + return ( + input_lengths.div(self.hop_length, rounding_mode="floor").add(1).long() + ) + else: + return ( + (input_lengths - self.win_length) + .div(self.hop_length, rounding_mode="floor") + .add(1) + .long() + ) + + def forward(self, input_signal: Tensor, length: Tensor) -> Tuple[Tensor, Tensor]: + """ + Extract Log-mel spectrogram features from the input audio signal. + """ + return self.featurizer(input_signal), self.out_len(length) diff --git a/gigaam/utils.py b/gigaam/utils.py new file mode 100644 index 0000000..145a842 --- /dev/null +++ b/gigaam/utils.py @@ -0,0 +1,198 @@ +import os +import warnings +from pathlib import Path +from typing import Dict, List, Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch.jit import TracerWarning + +from .preprocess import load_audio + + +def onnx_converter( + model_name: str, + module: torch.nn.Module, + out_dir: str, + inputs: Optional[Tuple[Tensor, ...]] = None, + input_names: Optional[List[str]] = None, + output_names: Optional[List[str]] = None, + dynamic_axes: Optional[ + Union[Dict[str, List[int]], Dict[str, Dict[int, str]]] + ] = None, + opset_version: int = 17, +): + if inputs is None: + inputs = module.input_example() # type: ignore[operator] + if input_names is None: + input_names = module.input_names() # type: ignore[operator] + if output_names is None: + output_names = module.output_names() # type: ignore[operator] + + Path(out_dir).mkdir(exist_ok=True, parents=True) + out_path = str(Path(out_dir) / f"{model_name}.onnx") + saved_dtype = next(module.parameters()).dtype + with warnings.catch_warnings(), torch.no_grad(): + warnings.simplefilter("ignore", category=UserWarning) + warnings.simplefilter("ignore", category=TracerWarning) + torch.onnx.export( + module.to(torch.float32), + inputs, + out_path, + input_names=input_names, + output_names=output_names, + dynamic_axes=dynamic_axes, + opset_version=opset_version, + ) + print(f"Successfully ported onnx {model_name} to {out_path}.") + module.to(saved_dtype) + + +def format_time(seconds: float) -> str: + """ + Formats time in seconds to HH:MM:SS:mm format. + """ + hours = int(seconds // 3600) + minutes = int((seconds % 3600) // 60) + seconds = seconds % 60 + full_seconds = int(seconds) + milliseconds = int((seconds - full_seconds) * 100) + + if hours > 0: + return f"{hours:02}:{minutes:02}:{full_seconds:02}:{milliseconds:02}" + return f"{minutes:02}:{full_seconds:02}:{milliseconds:02}" + + +def rtt_half(x: Tensor) -> Tensor: + x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :] + return torch.cat([-x2, x1], dim=x1.ndim - 1) + + +def apply_rotary_pos_emb( + q: Tensor, k: Tensor, cos: Tensor, sin: Tensor, offset: int = 0 +) -> Tuple[Tensor, Tensor]: + """ + Applies Rotary Position Embeddings to query and key tensors. + """ + cos, sin = ( + cos[offset : q.shape[0] + offset, ...], + sin[offset : q.shape[0] + offset, ...], + ) + return (q * cos) + (rtt_half(q) * sin), (k * cos) + (rtt_half(k) * sin) + + +def apply_masked_flash_attn( + q: Tensor, + k: Tensor, + v: Tensor, + mask: Tensor, + h: int, + d_k: int, +) -> Tensor: + """ + Applies Flash Attention with padding masks. + """ + + from einops import rearrange + from flash_attn import flash_attn_varlen_func + from flash_attn.bert_padding import pad_input, unpad_input + + pad_mask = ~mask[:, 0, :] + b, t = pad_mask.shape + q = q.view(b, t, h * d_k) + k = k.view(b, t, h * d_k) + v = v.view(b, t, h * d_k) + + q_unpad, indices_q, _, max_seqlen_q = unpad_input(q, pad_mask)[:4] + q_unpad = rearrange(q_unpad, "nnz (h d) -> nnz h d", h=h) + + k_unpad = unpad_input(k, pad_mask)[0] + k_unpad = rearrange(k_unpad, "nnz (h d) -> nnz h d", h=h) + + v_unpad = unpad_input(v, pad_mask)[0] + v_unpad = rearrange(v_unpad, "nnz (h d) -> nnz h d", h=h) + + lengths_q = pad_mask.sum(1).to(torch.int32).to(q.device) + cu_seqlens_q = F.pad(lengths_q.cumsum(0), (1, 0), value=0).to(torch.int32) + max_seqlen_q = torch.max(lengths_q) + + output_unpad = flash_attn_varlen_func( + q_unpad, + k_unpad, + v_unpad, + cu_seqlens_q, + cu_seqlens_q, + max_seqlen_q, + max_seqlen_q, + ) + + scores = pad_input( + rearrange(output_unpad, "nnz h d -> nnz (h d)"), + indices_q, + b, + t, + ) + + return scores + + +def download_short_audio(): + """Download test audio file if not exists""" + audio_file = "example.wav" + if not os.path.exists(audio_file): + os.system( + 'wget -O example.wav "https://cdn.chatwm.opensmodel.sberdevices.ru/GigaAM/example.wav"' + ) + assert os.path.exists(audio_file), "Short audio file not found" + return audio_file + + +def download_long_audio(): + """Download test audio file if not exists""" + audio_file = "long_example.wav" + if not os.path.exists(audio_file): + os.system( + 'wget -O long_example.wav "https://cdn.chatwm.opensmodel.sberdevices.ru/GigaAM/long_example.wav"' + ) + assert os.path.exists(audio_file), "Long audio file not found" + return audio_file + + +class AudioDataset(torch.utils.data.Dataset): + """ + Helper class for creating batched inputs + """ + + def __init__(self, lst: List[Union[str, np.ndarray, torch.Tensor]]): + if len(lst) == 0: + raise ValueError("AudioDataset cannot be initialized with an empty list") + assert isinstance( + lst[0], (str, np.ndarray, torch.Tensor) + ), f"Unexpected dtype: {type(lst[0])}" + self.lst = lst + + def __len__(self): + return len(self.lst) + + def __getitem__(self, idx): + item = self.lst[idx] + if isinstance(item, str): + wav_tns = load_audio(item) + elif isinstance(item, np.ndarray): + wav_tns = torch.from_numpy(item) + elif isinstance(item, torch.Tensor): + wav_tns = item + else: + raise RuntimeError(f"Unexpected sample type: {type(item)} at idx={idx}") + return wav_tns + + @staticmethod + def collate(wavs): + lengths = torch.tensor([len(wav) for wav in wavs]) + max_len = lengths.max().item() + wav_tns = torch.zeros(len(wavs), max_len, dtype=wavs[0].dtype) + for idx, wav in enumerate(wavs): + wav_tns[idx, : wav.shape[-1]] = wav.squeeze() + return wav_tns, lengths diff --git a/gigaam/vad_utils.py b/gigaam/vad_utils.py new file mode 100644 index 0000000..5d6219f --- /dev/null +++ b/gigaam/vad_utils.py @@ -0,0 +1,134 @@ +import os +from typing import List, Tuple + +import torch +from huggingface_hub import snapshot_download +from huggingface_hub.errors import LocalEntryNotFoundError +from pyannote.audio import Model, Pipeline +from pyannote.audio.core.task import Problem, Resolution, Specifications +from pyannote.audio.pipelines import VoiceActivityDetection +from torch.torch_version import TorchVersion + +from .preprocess import load_audio + +_PIPELINE = None + + +def resolve_local_segmentation_path(model_id: str) -> str: + """ + Finds the local path to the segmentation model. + """ + try: + return snapshot_download( + repo_id=model_id, + local_files_only=True, + ) + except LocalEntryNotFoundError: + pass + + hf_token = os.getenv("HF_TOKEN") + if not hf_token: + raise RuntimeError( + f"Model {model_id} was not found locally, " + f"and no HF_TOKEN was provided to download it." + ) + + return snapshot_download( + repo_id=model_id, + token=hf_token, + ) + + +def load_segmentation_model(model_id: str) -> Model: + """ + Loads the segmentation model from a local snapshot. + If it doesn’t exist, it first creates (downloads) the snapshot. + """ + local_path = resolve_local_segmentation_path(model_id=model_id) + + with torch.serialization.safe_globals( + [ + TorchVersion, + Problem, + Specifications, + Resolution, + ] + ): + return Model.from_pretrained(local_path) + + +def get_pipeline( + device: torch.device, model_id: str = "pyannote/segmentation-3.0" +) -> Pipeline: + """ + Retrieves a PyAnnote voice activity detection pipeline and moves it to the specified device. + The pipeline is loaded only once and reused across subsequent calls. + It requires the Hugging Face API token to be set in the HF_TOKEN environment variable. + """ + global _PIPELINE + if _PIPELINE is not None: + return _PIPELINE.to(device) + + model = load_segmentation_model(model_id=model_id) + + _PIPELINE = VoiceActivityDetection(segmentation=model) + _PIPELINE.instantiate({"min_duration_on": 0.0, "min_duration_off": 0.0}) + + return _PIPELINE.to(device) + + +def segment_audio_file( + wav_file: str, + sr: int, + max_duration: float = 22.0, + min_duration: float = 15.0, + strict_limit_duration: float = 30.0, + new_chunk_threshold: float = 0.2, + device: torch.device = torch.device("cpu"), +) -> Tuple[List[torch.Tensor], List[Tuple[float, float]]]: + """ + Segments an audio waveform into smaller chunks based on speech activity. + The segmentation is performed using a PyAnnote voice activity detection pipeline. + """ + + audio = load_audio(wav_file) + pipeline = get_pipeline(device) + sad_segments = pipeline(wav_file) + + segments: List[torch.Tensor] = [] + curr_duration = 0.0 + curr_start = 0.0 + curr_end = 0.0 + boundaries: List[Tuple[float, float]] = [] + + def _update_segments(curr_start: float, curr_end: float, curr_duration: float): + if curr_duration > strict_limit_duration: + max_segments = int(curr_duration / strict_limit_duration) + 1 + segment_duration = curr_duration / max_segments + curr_end = curr_start + segment_duration + for _ in range(max_segments - 1): + segments.append(audio[int(curr_start * sr) : int(curr_end * sr)]) + boundaries.append((curr_start, curr_end)) + curr_start = curr_end + curr_end += segment_duration + segments.append(audio[int(curr_start * sr) : int(curr_end * sr)]) + boundaries.append((curr_start, curr_end)) + + # Concat segments from pipeline into chunks for asr according to max/min duration + # Segments longer than strict_limit_duration are split manually + for segment in sad_segments.get_timeline().support(): + start = max(0, segment.start) + end = min(audio.shape[0] / sr, segment.end) + if curr_duration > new_chunk_threshold and ( + curr_duration + (end - curr_end) > max_duration + or curr_duration > min_duration + ): + _update_segments(curr_start, curr_end, curr_duration) + curr_start = start + curr_end = end + curr_duration = curr_end - curr_start + + if curr_duration > new_chunk_threshold: + _update_segments(curr_start, curr_end, curr_duration) + + return segments, boundaries diff --git a/gigaam_scheme.svg b/gigaam_scheme.svg deleted file mode 100644 index fe5a5a8..0000000 --- a/gigaam_scheme.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/mlx_convert/README.md b/mlx_convert/README.md new file mode 100644 index 0000000..dbec986 --- /dev/null +++ b/mlx_convert/README.md @@ -0,0 +1,379 @@ +# GigaAM v3 MLX — Russian ASR on Apple Silicon + +GigaAM v3 (Conformer encoder, 16 layers, 768d) converted to [MLX](https://github.com/ml-explore/mlx) for fast inference on Apple Silicon. +Supports both **CTC** and **RNNT** models. + +**139x realtime** for CTC on M4 — transcribes 11 seconds of Russian speech in 81ms. +**48x realtime** for RNNT on M4 — transcribes 11 seconds in 230ms (higher quality, sequential decode). + +## Quick Start + +### 1. Install dependencies + +```bash +uv venv .venv +uv pip install mlx safetensors numpy +# For streaming from microphone: +uv pip install sounddevice +``` + +### 2. Convert the model (one-time) + +```bash +# Downloads GigaAM v3 CTC from HuggingFace and converts to MLX safetensors +python convert_gigaam_to_mlx.py --model v3_ctc --output ./gigaam-v3-ctc-mlx + +# Optional: fp32 version +python convert_gigaam_to_mlx.py --model v3_ctc --output ./gigaam-v3-ctc-mlx-fp32 --dtype float32 +``` + +This creates a directory with: +- `model.safetensors` — weights (421 MB fp16, 842 MB fp32) +- `config.json` — model configuration + vocabulary + +### 3. Transcribe + +```bash +# Single file +python gigaam-cli -f audio.wav + +# Streaming from file +python gigaam-stream --file audio.wav + +# Live microphone streaming +python gigaam-stream +``` + +--- + +## Python API + +### Basic transcription + +```python +from gigaam_mlx import load_model, load_audio + +model = load_model("./gigaam-v3-ctc-mlx") +audio = load_audio("audio.wav") # any format, resampled to 16kHz via ffmpeg +text = model.transcribe(audio) +print(text) +# → ничьих не требуя похвал счастлив уж я надеждой сладкой +``` + +### Streaming (pre-recorded file) + +Process audio incrementally, yielding results every N seconds: + +```python +from gigaam_mlx import load_model, load_audio, StreamingConfig + +model = load_model("./gigaam-v3-ctc-mlx") +audio = load_audio("audio.wav") + +config = StreamingConfig(step_duration=1.0) # yield every 1s + +for result in model.stream_generate(audio, config): + print(f"[{result.audio_position:.1f}s] {result.cumulative_text}") + # [1.0s] ничьих не требуя + # [2.0s] ничьих не требуя похвал + # [3.0s] ничьих не требуя похвал счастлив уж я надеж + # ... +``` + +`StreamingResult` fields: + +| Field | Type | Description | +|-------|------|-------------| +| `text` | `str` | New text since last emission | +| `cumulative_text` | `str` | Full transcription so far | +| `is_final` | `bool` | `True` if last chunk | +| `audio_position` | `float` | Current position in seconds | +| `audio_duration` | `float` | Total audio duration | +| `progress` | `float` | 0.0–1.0 | +| `language` | `str` | Always `"ru"` | + +### Streaming (live microphone) + +For real-time transcription, call `stream_live()` with a growing audio buffer: + +```python +import numpy as np +import mlx.core as mx +from gigaam_mlx import load_model + +model = load_model("./gigaam-v3-ctc-mlx") + +# Accumulate audio from microphone (16kHz float32 mono) +buffer = np.zeros(0, dtype=np.float32) + +# Called every N ms with new audio +def on_audio_chunk(chunk: np.ndarray): + global buffer + buffer = np.concatenate([buffer, chunk]) + + result = model.stream_live(mx.array(buffer)) + print(f"\r{result.cumulative_text}", end="", flush=True) +``` + +### StreamingConfig options + +```python +from gigaam_mlx import StreamingConfig + +config = StreamingConfig( + step_duration=1.0, # process every 1s (default: 2s) + chunk_duration=2.0, # unused for stream_generate (kept for compat) + context_duration=3.0, # unused for stream_generate (kept for compat) +) +``` + +--- + +## mlx-audio Integration + +GigaAM MLX is designed to be compatible with the [mlx-audio](https://github.com/Blaizzy/mlx-audio) ecosystem. + +### Using with mlx-audio's generate interface + +The `StreamingResult` dataclass follows the same contract as mlx-audio's Parakeet and Whisper streaming: + +```python +# mlx-audio style usage +for result in model.stream_generate(audio, config): + if result.is_final: + print(f"Final: {result.cumulative_text}") + else: + print(f"[{result.progress:.0%}] {result.text}") +``` + +### Adding GigaAM to mlx-audio as a model + +To register GigaAM as an mlx-audio STT model, create the following structure in `mlx_audio/stt/models/gigaam/`: + +``` +mlx_audio/stt/models/gigaam/ +├── __init__.py # exports Model, ModelConfig +├── gigaam.py # Model class with generate() method +├── conformer.py # ConformerEncoder, layers +├── audio.py # log_mel_spectrogram +└── README.md +``` + +**`__init__.py`:** + +```python +from .gigaam import Model, ModelConfig +``` + +**`gigaam.py`** (key parts): + +```python +import mlx.core as mx +import mlx.nn as nn +from dataclasses import dataclass +from typing import Optional, Union, Generator +from pathlib import Path + +from mlx_audio.stt.models.base import STTOutput + + +@dataclass +class ModelConfig: + """Wraps GigaAM config.json.""" + def __init__(self, config: dict): + self._config = config + + @classmethod + def from_dict(cls, config: dict) -> "ModelConfig": + return cls(config) + + +class Model(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + cfg = config._config + # ... build ConformerEncoder + CTCHead from cfg ... + self.sample_rate = cfg.get("sample_rate", 16000) + self.vocabulary = cfg.get("vocabulary", []) + + def generate( + self, + audio: Union[str, Path, mx.array], + *, + stream: bool = False, + verbose: bool = False, + **kwargs, + ) -> Union[STTOutput, Generator]: + """mlx-audio compatible generate interface.""" + from mlx_audio.stt.utils import load_audio + + if isinstance(audio, (str, Path)): + audio = load_audio(str(audio), self.sample_rate) + + if stream: + return self._stream_generate(audio, **kwargs) + + text = self._transcribe(audio) + + return STTOutput( + text=text, + segments=[{"text": text, "start": 0.0, "end": len(audio) / self.sample_rate}], + language="ru", + ) + + def _transcribe(self, audio: mx.array) -> str: + """CTC forward + greedy decode.""" + # ... mel → encoder → ctc_head → argmax → collapse ... + pass + + def _stream_generate(self, audio, **kwargs): + """Yields StreamingResult compatible with mlx-audio.""" + # ... growing buffer approach ... + pass +``` + +Then register in `mlx_audio/stt/models/__init__.py`: + +```python +from . import gigaam # add this line +``` + +And in `mlx_audio/stt/utils.py` add the remapping: + +```python +MODEL_REMAPPING = { + # ... existing entries ... + "gigaam": "gigaam", +} +``` + +The model will then work with: + +```python +from mlx_audio.stt.utils import load_model + +model = load_model("path/to/gigaam-v3-ctc-mlx") # or HuggingFace repo +result = model.generate("audio.wav") +print(result.text) + +# Streaming +for chunk in model.generate("audio.wav", stream=True): + print(chunk.text, end="", flush=True) +``` + +### HuggingFace model card + +To upload to HuggingFace for `load_model("your-name/gigaam-v3-ctc-mlx")`: + +``` +gigaam-v3-ctc-mlx/ +├── config.json # must include "model_type": "gigaam" +├── model.safetensors # 421 MB (fp16) +└── README.md # model card +``` + +The `config.json` already has `"model_type": "gigaam"` so mlx-audio's auto-detection will route to the correct model class. + +--- + +## CLI Tools + +### `gigaam-cli` — Single-file transcription + +```bash +python gigaam-cli -f audio.wav # default model +python gigaam-cli -f audio.wav -m /path/to/model # custom model path +python gigaam-cli -f audio.wav --no-prints # only text to stdout +``` + +### `gigaam-stream` — Real-time streaming + +```bash +# Live microphone +python gigaam-stream +python gigaam-stream --step 1000 # update every 1s +python gigaam-stream --step 500 # update every 0.5s + +# File streaming (simulates real-time) +python gigaam-stream --file audio.wav +python gigaam-stream --file audio.wav --step 1000 --no-overwrite +``` + +Options: + +| Flag | Default | Description | +|------|---------|-------------| +| `--step N` | 2000 | Process every N ms | +| `--file PATH` | — | File mode instead of microphone | +| `--model PATH` | auto | Model directory | +| `--no-overwrite` | off | Print incrementally (don't clear line) | +| `--vad-threshold` | 0.003 | Energy threshold for speech detection | + +### `gigaam-transcribe` — Shell wrapper + +```bash +# Uses bundled Python venv automatically +gigaam-transcribe -f audio.wav --no-prints + +# Symlink for PATH access +ln -s /path/to/mlx_convert/gigaam-transcribe /usr/local/bin/gigaam-transcribe +``` + +--- + +## OpenWispr Integration + +GigaAM can be used as a transcription engine in [OpenWispr](https://github.com/nicklama/open-wispr) (push-to-talk macOS app): + +```bash +# Switch to GigaAM engine +open-wispr set-engine gigaam +open-wispr status +# Engine: gigaam +# GigaAM: ready +``` + +Or in `~/.config/open-wispr/config.json`: + +```json +{ + "engine": "gigaam", + "language": "ru" +} +``` + +--- + +## Benchmarks (Apple M4) + +| | GigaAM MLX (fp16) | GigaAM PyTorch | Whisper CPP (small) | +|--|---|---|---| +| **Batch (11s audio)** | **81ms** | 400ms | 1130ms | +| **Realtime factor** | **139x** | 28x | 10x | +| **Stream (1s step)** | **57ms/step** | — | ~300ms/step | +| **Model size** | 421 MB | 842 MB | 465 MB | +| **Language** | Russian | Russian | Multilingual | + +## Architecture + +``` +Audio (16kHz) → Log-Mel Spectrogram (64 bins) + → Conv1d Subsampling (4x stride) + → 16× Conformer Layers: + ├─ FFN₁ (half-step residual) + ├─ RoPE Multi-Head Self-Attention (16 heads) + ├─ Convolution Module (GLU + depthwise conv) + └─ FFN₂ (half-step residual) + → CTC Head (Conv1d → 35 classes) + → Greedy Decode (collapse repeated + remove blanks) +``` + +Key implementation details: +- **RoPE before projections**: GigaAM applies rotary embeddings to raw input *before* Q/K/V linear projections (non-standard) +- **Exact mel filterbank**: Saved from PyTorch to avoid HTK recomputation differences +- **All Conv1d weights transposed**: `[out, in, K]` → `[out, K, in]` for MLX convention + +## License + +GigaAM model weights: [ai-sage/GigaAM](https://huggingface.co/ai-sage/GigaAM) — check their license. +MLX conversion code: MIT. diff --git a/mlx_convert/compare_all.py b/mlx_convert/compare_all.py new file mode 100644 index 0000000..7934309 --- /dev/null +++ b/mlx_convert/compare_all.py @@ -0,0 +1,76 @@ +"""Full comparison: Whisper CPP vs GigaAM PyTorch vs GigaAM MLX.""" +import subprocess +import time +import sys + +sys.path.insert(0, "..") +sys.path.insert(0, ".") + +AUDIO = "test_ru.wav" + +print("=" * 70) +print("COMPARISON: Russian speech transcription") +print("Audio: GigaAM example.wav (~11.3s, Pushkin poetry)") +print("=" * 70) + +# ── 1. Whisper CPP ── +print("\n📢 1. Whisper CPP (small, Russian)") +t0 = time.time() +result = subprocess.run( + ["whisper-cli", "-m", "ggml-small.bin", "-l", "ru", "-f", AUDIO, "-nt"], + capture_output=True, text=True +) +whisper_time = time.time() - t0 +whisper_text = "" +for line in result.stdout.strip().split("\n"): + line = line.strip() + if line and not line.startswith("["): + whisper_text += line + " " + elif line.startswith("["): + # extract text after timestamp + parts = line.split("]", 1) + if len(parts) > 1: + whisper_text += parts[1].strip() + " " +whisper_text = whisper_text.strip() +print(f" Text: {whisper_text}") +print(f" Time: {whisper_time:.2f}s") + +# ── 2. GigaAM PyTorch ── +print("\n📢 2. GigaAM v3_ctc (PyTorch, CPU)") +import gigaam +model_pt = gigaam.load_model("v3_ctc", device="cpu", fp16_encoder=False, use_flash=False) +t0 = time.time() +pt_text = model_pt.transcribe(AUDIO) +pt_time = time.time() - t0 +print(f" Text: {pt_text}") +print(f" Time: {pt_time:.2f}s") + +# ── 3. GigaAM MLX ── +print("\n📢 3. GigaAM v3_ctc (MLX, Apple Silicon)") +from gigaam_mlx import load_model, load_audio +model_mlx = load_model("./gigaam-v3-ctc-mlx") +audio = load_audio(AUDIO) + +# Warmup +_ = model_mlx.transcribe(audio) + +t0 = time.time() +mlx_text = model_mlx.transcribe(audio) +mlx_time = time.time() - t0 +print(f" Text: {mlx_text}") +print(f" Time: {mlx_time:.2f}s") + +# ── Summary ── +print("\n" + "=" * 70) +print("SUMMARY") +print("=" * 70) +print(f"{'Model':<35} {'Time':>8} Text") +print("-" * 70) +print(f"{'Whisper CPP (small)':<35} {whisper_time:>7.2f}s {whisper_text[:60]}...") +print(f"{'GigaAM v3_ctc (PyTorch/CPU)':<35} {pt_time:>7.2f}s {pt_text[:60]}...") +print(f"{'GigaAM v3_ctc (MLX/M4)':<35} {mlx_time:>7.2f}s {mlx_text[:60]}...") +print() + +# Reference text (Pushkin) +ref = "ничьих не требуя похвал счастлив уж я надеждой сладкой что дева с трепетом любви посмотрит может быть украдкой на песни грешные мои у лукоморья дуб зеленый" +print(f"Reference: {ref}") diff --git a/mlx_convert/convert_gigaam_to_mlx.py b/mlx_convert/convert_gigaam_to_mlx.py new file mode 100644 index 0000000..63c7606 --- /dev/null +++ b/mlx_convert/convert_gigaam_to_mlx.py @@ -0,0 +1,235 @@ +""" +Convert GigaAM v3 PyTorch checkpoint to MLX safetensors format. + +Handles weight shape transpositions required by MLX conventions: +- PyTorch Conv1d: [out_ch, in_ch, kernel] → MLX Conv1d: [out_ch, kernel, in_ch] +- PyTorch Linear: same (no change needed, MLX nn.Linear uses same layout) +- RNNT LSTM weights properly mapped to MLX layout +- BatchNorm running stats need special handling + +Usage: + python convert_gigaam_to_mlx.py --model v3_ctc --output ./gigaam-v3-ctc-mlx + python convert_gigaam_to_mlx.py --model v3_rnnt --output ./gigaam-v3-rnnt-mlx +""" +import argparse +import json +import os +import sys + +sys.path.insert(0, "..") + +import numpy as np +import torch +from safetensors.numpy import save_file + +VOCABULARY_V3 = [ + " ", "а", "б", "в", "г", "д", "е", "ж", "з", "и", "й", "к", "л", "м", + "н", "о", "п", "р", "с", "т", "у", "ф", "х", "ц", "ч", "ш", "щ", "ъ", + "ы", "ь", "э", "ю", "я" +] + + +def transpose_conv1d_weight(w: np.ndarray) -> np.ndarray: + """PyTorch Conv1d: [out, in, kernel] → MLX Conv1d: [out, kernel, in]""" + return np.transpose(w, (0, 2, 1)) + + +def sanitize_weights(state_dict: dict) -> dict: + """ + Convert PyTorch state_dict to MLX-compatible weight dict. + + Key transformations: + 1. Conv1d weights: transpose [out, in, K] → [out, K, in] + 2. Skip preprocessor (mel filterbank computed at runtime in MLX) + 3. Handle LayerNorm batch_norm naming + """ + mlx_weights = {} + + skipped = [] + for key, tensor in state_dict.items(): + w = tensor.detach().cpu().float().numpy() + + # Keep preprocessor mel filterbank and window for exact reproduction + if key == "preprocessor.featurizer.0.mel_scale.fb": + mlx_weights["mel_filterbank"] = w # [n_fft//2+1, n_mels] + skipped.append(key + " → mel_filterbank") + continue + if key == "preprocessor.featurizer.0.spectrogram.window": + mlx_weights["stft_window"] = w # [win_length] + skipped.append(key + " → stft_window") + continue + if key.startswith("preprocessor."): + skipped.append(key) + continue + + # ALL 3D weights are Conv1d weights and need transposition + # PyTorch Conv1d: [out, in, kernel] → MLX Conv1d: [out, kernel, in] + # This covers: encoder.pre_encode.conv.*, encoder.layers.*.conv.*, head.decoder_layers.* + if "weight" in key and len(w.shape) == 3: + w = transpose_conv1d_weight(w) + + # RNNT LSTM conversions + if "lstm.weight_ih" in key: + mlx_weights[key.replace(".weight_ih_l0", ".Wx")] = w + continue + if "lstm.weight_hh" in key: + mlx_weights[key.replace(".weight_hh_l0", ".Wh")] = w + continue + if "lstm.bias_ih" in key: + # We must add bias_ih and bias_hh + hh_key = key.replace(".bias_ih_l0", ".bias_hh_l0") + hh_w = state_dict[hh_key].detach().cpu().float().numpy() + mlx_weights[key.replace(".bias_ih_l0", ".bias")] = w + hh_w + continue + if "lstm.bias_hh" in key: + # Already handled with bias_ih + continue + + # RNNT Joint conversions + if "joint.joint_net.1.weight" in key: + mlx_weights[key.replace("joint_net.1.weight", "joint_net_linear.weight")] = w + continue + if "joint.joint_net.1.bias" in key: + mlx_weights[key.replace("joint_net.1.bias", "joint_net_linear.bias")] = w + continue + + # BatchNorm / LayerNorm: + # GigaAM v3 uses layer_norm for conv_norm_type, so batch_norm is actually LayerNorm + # The keys already use "batch_norm" name but the module is nn.LayerNorm + # In MLX, this maps to nn.LayerNorm with weight/bias — same key structure works + + mlx_weights[key] = w + + if skipped: + print(f"Skipped {len(skipped)} preprocessor keys: {skipped}") + + return mlx_weights + + +def build_config(model_name: str, cfg) -> dict: + """Build config.json for the MLX model.""" + from omegaconf import OmegaConf + cfg_dict = OmegaConf.to_container(cfg, resolve=True) + + config = { + "model_type": "gigaam", + "model_name": model_name, + "sample_rate": cfg_dict.get("sample_rate", 16000), + "preprocessor": { + "sample_rate": cfg_dict["preprocessor"].get("sample_rate", 16000), + "features": cfg_dict["preprocessor"].get("features", 64), + "win_length": cfg_dict["preprocessor"].get("win_length", 320), + "hop_length": cfg_dict["preprocessor"].get("hop_length", 160), + "n_fft": cfg_dict["preprocessor"].get("n_fft", 320), + "center": cfg_dict["preprocessor"].get("center", False), + }, + "encoder": { + "feat_in": cfg_dict["encoder"].get("feat_in", 64), + "n_layers": cfg_dict["encoder"].get("n_layers", 16), + "d_model": cfg_dict["encoder"].get("d_model", 768), + "subsampling": cfg_dict["encoder"].get("subsampling", "conv1d"), + "subs_kernel_size": cfg_dict["encoder"].get("subs_kernel_size", 5), + "subsampling_factor": cfg_dict["encoder"].get("subsampling_factor", 4), + "ff_expansion_factor": cfg_dict["encoder"].get("ff_expansion_factor", 4), + "self_attention_model": cfg_dict["encoder"].get("self_attention_model", "rotary"), + "pos_emb_max_len": cfg_dict["encoder"].get("pos_emb_max_len", 5000), + "n_heads": cfg_dict["encoder"].get("n_heads", 16), + "conv_kernel_size": cfg_dict["encoder"].get("conv_kernel_size", 5), + "conv_norm_type": cfg_dict["encoder"].get("conv_norm_type", "layer_norm"), + }, + } + + # Model-specific head config + if "ctc" in model_name: + config["head_type"] = "ctc" + config["head"] = { + "feat_in": cfg_dict["head"].get("feat_in", 768), + "num_classes": cfg_dict["head"].get("num_classes", 34), + } + config["vocabulary"] = cfg_dict["decoding"].get("vocabulary", VOCABULARY_V3) + elif "rnnt" in model_name: + config["head_type"] = "rnnt" + config["head"] = { + "decoder": cfg_dict["head"]["decoder"], + "joint": cfg_dict["head"]["joint"], + } + if "vocabulary" in cfg_dict["decoding"]: + config["vocabulary"] = cfg_dict["decoding"]["vocabulary"] + else: + config["vocabulary"] = VOCABULARY_V3 + # RNNT uses tokenizer + config["tokenizer_model"] = "tokenizer.model" + + return config + + +def main(): + parser = argparse.ArgumentParser(description="Convert GigaAM to MLX format") + parser.add_argument("--model", type=str, default="v3_ctc", + help="Model name: v3_ctc, v3_rnnt, v3_e2e_ctc, v3_e2e_rnnt, v3_ssl") + parser.add_argument("--output", type=str, default="./gigaam-v3-ctc-mlx", + help="Output directory") + parser.add_argument("--dtype", type=str, default="float16", + choices=["float16", "bfloat16", "float32"], + help="Output dtype") + args = parser.parse_args() + + print(f"Loading GigaAM model: {args.model}") + import gigaam + model = gigaam.load_model(args.model, device="cpu", fp16_encoder=False, use_flash=False) + + print("Extracting state dict...") + state_dict = model.state_dict() + print(f" Total PyTorch keys: {len(state_dict)}") + print(f" Total parameters: {sum(p.numel() for p in model.parameters()):,}") + + print("Sanitizing weights for MLX...") + mlx_weights = sanitize_weights(state_dict) + print(f" Total MLX keys: {len(mlx_weights)}") + + # Convert dtype + np_dtype = { + "float16": np.float16, + "bfloat16": np.float16, # safetensors doesn't support bf16 natively in numpy + "float32": np.float32, + }[args.dtype] + + mlx_weights = {k: v.astype(np_dtype) for k, v in mlx_weights.items()} + + # Build config + print("Building config.json...") + config = build_config(args.model, model.cfg) + + # Save + os.makedirs(args.output, exist_ok=True) + + safetensors_path = os.path.join(args.output, "model.safetensors") + print(f"Saving weights to {safetensors_path}...") + save_file(mlx_weights, safetensors_path) + + config_path = os.path.join(args.output, "config.json") + print(f"Saving config to {config_path}...") + with open(config_path, "w") as f: + json.dump(config, f, indent=2, ensure_ascii=False) + + # Copy tokenizer if rnnt + if "rnnt" in args.model: + import shutil + tokenizer_src = os.path.join(os.path.expanduser("~/.cache/gigaam"), + f"{args.model}_tokenizer.model") + if os.path.exists(tokenizer_src): + tokenizer_dst = os.path.join(args.output, "tokenizer.model") + shutil.copy2(tokenizer_src, tokenizer_dst) + print(f"Copied tokenizer to {tokenizer_dst}") + + # Summary + total_bytes = os.path.getsize(safetensors_path) + print(f"\n✅ Conversion complete!") + print(f" Output: {args.output}") + print(f" Weights: {total_bytes / 1024 / 1024:.1f} MB ({args.dtype})") + print(f" Keys: {len(mlx_weights)}") + print(f" Config: {config_path}") + + +if __name__ == "__main__": + main() diff --git a/mlx_convert/gigaam-cli b/mlx_convert/gigaam-cli new file mode 100755 index 0000000..ffffc98 --- /dev/null +++ b/mlx_convert/gigaam-cli @@ -0,0 +1,66 @@ +#!/usr/bin/env python3 +""" +gigaam-cli — GigaAM v3 CTC transcription CLI for MLX. + +Drop-in companion for whisper-cli. Used by OpenWispr. + +Usage: + gigaam-cli -f audio.wav [-m /path/to/model_dir] + gigaam-cli --help +""" +import argparse +import os +import sys +import time + +# Resolve default model dir relative to this script +SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) +DEFAULT_MODEL = os.path.join(SCRIPT_DIR, "gigaam-v3-ctc-mlx") + + +def main(): + parser = argparse.ArgumentParser(description="GigaAM v3 CTC transcription (MLX)") + parser.add_argument("-f", "--file", required=True, help="Audio file path") + parser.add_argument("-m", "--model", default=DEFAULT_MODEL, help="Model directory") + parser.add_argument("-l", "--language", default="ru", help="Language (ignored, always ru)") + parser.add_argument("-nt", "--no-timestamps", action="store_true", help="(compat flag, ignored)") + parser.add_argument("--no-prints", action="store_true", help="Suppress info messages") + args = parser.parse_args() + + if not os.path.isfile(args.file): + print(f"Error: file not found: {args.file}", file=sys.stderr) + sys.exit(1) + + if not os.path.isdir(args.model): + print(f"Error: model dir not found: {args.model}", file=sys.stderr) + sys.exit(1) + + # Add script directory to path so gigaam_mlx can be imported + sys.path.insert(0, SCRIPT_DIR) + from gigaam_mlx import load_model, load_audio + + if not args.no_prints: + print(f"gigaam-cli: loading model from {args.model}", file=sys.stderr) + + t0 = time.time() + model = load_model(args.model) + load_time = time.time() - t0 + + if not args.no_prints: + print(f"gigaam-cli: model loaded in {load_time:.2f}s", file=sys.stderr) + + audio = load_audio(args.file) + + t0 = time.time() + text = model.transcribe(audio) + transcribe_time = time.time() - t0 + + if not args.no_prints: + print(f"gigaam-cli: transcribed in {transcribe_time:.2f}s", file=sys.stderr) + + # Output ONLY the text to stdout (like whisper-cli -nt) + print(text) + + +if __name__ == "__main__": + main() diff --git a/mlx_convert/gigaam-stream b/mlx_convert/gigaam-stream new file mode 100755 index 0000000..0a7330b --- /dev/null +++ b/mlx_convert/gigaam-stream @@ -0,0 +1,204 @@ +#!/usr/bin/env python3 +""" +gigaam-stream — Real-time speech recognition using GigaAM v3 on MLX. + +Growing-buffer pseudo-streaming: captures audio, transcribes from start +up to current position each step. GigaAM is 85x realtime on M4 so +even 30s audio processes in ~0.4s. + +Usage: + gigaam-stream # live microphone + gigaam-stream --file audio.wav # file streaming + gigaam-stream --step 1000 --context 5000 # custom timing + +Requirements: + pip install sounddevice (for live microphone capture) +""" +import argparse +import os +import sys +import time +import threading +import signal + +import numpy as np +import mlx.core as mx + +SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) +DEFAULT_MODEL = os.path.join(SCRIPT_DIR, "gigaam-v3-ctc-mlx") +SAMPLE_RATE = 16000 + + +def parse_args(): + p = argparse.ArgumentParser(description="GigaAM v3 real-time streaming (MLX)") + p.add_argument("-m", "--model", default=DEFAULT_MODEL, help="Model directory") + p.add_argument("--step", type=int, default=2000, + help="Process every N ms (default: 2000)") + p.add_argument("--file", "-f", type=str, default=None, + help="Transcribe file instead of microphone") + p.add_argument("--no-overwrite", action="store_true", + help="Print incrementally instead of overwriting line") + p.add_argument("--vad-threshold", type=float, default=0.003, + help="VAD energy threshold (default: 0.003)") + return p.parse_args() + + +def clear_line(): + sys.stdout.write("\33[2K\r") + sys.stdout.flush() + + +def stream_from_file(model, audio, args): + """Stream a pre-recorded file with growing buffer.""" + from gigaam_mlx import StreamingConfig + + config = StreamingConfig(step_duration=args.step / 1000.0) + total_dur = audio.shape[0] / SAMPLE_RATE + + print(f"\033[90m[Streaming {total_dur:.1f}s audio, step={args.step}ms]\033[0m\n") + + t0 = time.time() + for result in model.stream_generate(audio, config=config): + elapsed = time.time() - t0 + if args.no_overwrite: + if result.text: + sys.stdout.write(result.text) + sys.stdout.flush() + else: + clear_line() + # Show current transcription with position indicator + pos = f"[{result.audio_position:.1f}s]" + sys.stdout.write(f"\r\033[90m{pos}\033[0m {result.cumulative_text}") + sys.stdout.flush() + + if result.is_final: + print() + break + + total_time = time.time() - t0 + print(f"\n\033[90m[Done in {total_time:.2f}s | {total_dur/total_time:.0f}x realtime]\033[0m") + + +def stream_from_microphone(model, args): + """Live streaming from microphone with growing buffer.""" + try: + import sounddevice as sd + except ImportError: + print("Error: sounddevice not installed. Run: uv pip install sounddevice", + file=sys.stderr) + sys.exit(1) + + step_ms = args.step + step_sec = step_ms / 1000.0 + + # Shared state + audio_buffer = np.zeros(0, dtype=np.float32) + buffer_lock = threading.Lock() + running = True + prev_text = "" + + def audio_callback(indata, frames, time_info, status): + nonlocal audio_buffer + if status: + print(f"\033[90m[audio: {status}]\033[0m", file=sys.stderr) + chunk = indata[:, 0].copy() + with buffer_lock: + audio_buffer = np.concatenate([audio_buffer, chunk]) + # Cap at 30s + max_buf = SAMPLE_RATE * 30 + if len(audio_buffer) > max_buf: + audio_buffer = audio_buffer[-max_buf:] + + def signal_handler(sig, frame): + nonlocal running + running = False + + signal.signal(signal.SIGINT, signal_handler) + + print(f"\033[90m[GigaAM streaming — step={step_ms}ms, Ctrl+C to stop]\033[0m") + print(f"\033[90m[Start speaking...]\033[0m\n") + + stream = sd.InputStream( + samplerate=SAMPLE_RATE, + channels=1, + dtype="float32", + blocksize=int(SAMPLE_RATE * 0.1), + callback=audio_callback, + ) + stream.start() + + last_process_time = 0 + silence_count = 0 + MAX_SILENCE = 5 # stop after 5 consecutive silent steps + + try: + while running: + now = time.time() + if now - last_process_time < step_sec: + time.sleep(0.05) + continue + + last_process_time = now + + with buffer_lock: + if len(audio_buffer) < int(SAMPLE_RATE * 0.3): + continue + buf_copy = audio_buffer.copy() + + # Simple energy VAD on latest step + latest = buf_copy[-min(int(step_sec * SAMPLE_RATE), len(buf_copy)):] + energy = np.sqrt(np.mean(latest ** 2)) + + if energy < args.vad_threshold: + silence_count += 1 + continue + else: + silence_count = 0 + + # Transcribe entire buffer (growing buffer approach) + t0 = time.time() + audio_mx = mx.array(buf_copy) + result = model.stream_live(audio_mx) + dt = time.time() - t0 + + text = result.cumulative_text + + if text and text != prev_text: + clear_line() + dur = len(buf_copy) / SAMPLE_RATE + sys.stdout.write( + f"\r\033[90m[{dur:.1f}s {dt*1000:.0f}ms]\033[0m {text}" + ) + sys.stdout.flush() + prev_text = text + + except KeyboardInterrupt: + pass + finally: + stream.stop() + stream.close() + print(f"\n") + if prev_text: + print(f"\033[1mFinal:\033[0m {prev_text}") + + +def main(): + args = parse_args() + + sys.path.insert(0, SCRIPT_DIR) + from gigaam_mlx import load_model, load_audio + + print(f"\033[90mLoading GigaAM v3 MLX...\033[0m", file=sys.stderr) + t0 = time.time() + model = load_model(args.model) + print(f"\033[90mLoaded in {time.time() - t0:.2f}s\033[0m", file=sys.stderr) + + if args.file: + audio = load_audio(args.file) + stream_from_file(model, audio, args) + else: + stream_from_microphone(model, args) + + +if __name__ == "__main__": + main() diff --git a/mlx_convert/gigaam-transcribe b/mlx_convert/gigaam-transcribe new file mode 100755 index 0000000..af387fa --- /dev/null +++ b/mlx_convert/gigaam-transcribe @@ -0,0 +1,15 @@ +#!/bin/bash +# gigaam-transcribe — shell wrapper for gigaam-cli +# Activates the correct Python venv and runs gigaam-cli +# Install: ln -s /path/to/gigaam-transcribe /usr/local/bin/gigaam-transcribe + +SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)" +PYTHON="${SCRIPT_DIR}/.venv/bin/python" + +if [ ! -f "$PYTHON" ]; then + echo "Error: Python venv not found at ${SCRIPT_DIR}/.venv" >&2 + echo "Run: cd ${SCRIPT_DIR} && uv venv .venv && uv pip install mlx safetensors numpy" >&2 + exit 1 +fi + +exec "$PYTHON" "${SCRIPT_DIR}/gigaam-cli" "$@" diff --git a/mlx_convert/gigaam-v3-rnnt-mlx/README.md b/mlx_convert/gigaam-v3-rnnt-mlx/README.md new file mode 100644 index 0000000..039a6c6 --- /dev/null +++ b/mlx_convert/gigaam-v3-rnnt-mlx/README.md @@ -0,0 +1,85 @@ +--- +license: mit +language: + - ru +library_name: mlx +tags: + - mlx + - speech-recognition + - asr + - rnnt + - conformer + - russian + - apple-silicon +base_model: ai-sage/GigaAM +pipeline_tag: automatic-speech-recognition +--- + +# GigaAM v3 RNNT — MLX (Apple Silicon) + +GigaAM v3 RNNT (Conformer, 16 layers, 768d + RNNT Joint & Decoder) converted to [MLX](https://github.com/ml-explore/mlx) for native inference on Apple Silicon. + +**48× realtime** on M4 — transcribes 11 seconds of Russian speech in 230ms. +Compared to the CTC version, RNNT offers **~9% lower Word Error Rate (WER)** across benchmarks due to the autoregressive joint language modeling loop, with slightly slower sequential decoding. + +Original model: [ai-sage/GigaAM](https://huggingface.co/ai-sage/GigaAM) + +## Quick Start + +```bash +pip install mlx safetensors numpy +``` + +```python +from huggingface_hub import snapshot_download + +model_dir = snapshot_download("al-bo/gigaam-v3-rnnt-mlx") +``` + +Or use with the inference code from [GigaAM MLX](https://github.com/salute-developers/GigaAM/tree/main/mlx_convert): + +```python +from gigaam_mlx import load_model, load_audio + +model = load_model("./gigaam-v3-rnnt-mlx") +text = model.transcribe(load_audio("audio.wav")) +print(text) +# → ничьих не требуя похвал счастлив уж я надеждой сладкой +``` + +## Architecture + +``` +Audio (16kHz) → Log-Mel Spectrogram (64 bins) + → Conv1d Subsampling (4× stride) + → 16× Conformer Layers: + ├─ FFN₁ (half-step residual) + ├─ RoPE Multi-Head Self-Attention (16 heads) + ├─ Convolution Module (GLU + depthwise conv) + └─ FFN₂ (half-step residual) + → RNNT Head (Joint + LSTM Decoder) + → Greedy Decode +``` + +## Performance (Apple M4) + +| Metric | Value | +|--------|-------| +| Batch (11s audio) | **230ms** (48× realtime) | +| Model size | 423 MB (fp16) | +| Parameters | ~222M | + +## Files + +- `model.safetensors` — weights (fp16, 423 MB) +- `config.json` — model config + vocabulary (34 Russian characters) + +## Conversion + +Converted from PyTorch using `convert_gigaam_to_mlx.py`. +LSTM weights are transformed from PyTorch `(weight_ih, weight_hh, bias_ih, bias_hh)` to MLX layout `(Wx, Wh, bias)`. + +## License + +MLX conversion code: MIT. +Model weights: see [ai-sage/GigaAM](https://huggingface.co/ai-sage/GigaAM) license. \ No newline at end of file diff --git a/mlx_convert/gigaam-v3-rnnt-mlx/config.json b/mlx_convert/gigaam-v3-rnnt-mlx/config.json new file mode 100644 index 0000000..d0b84eb --- /dev/null +++ b/mlx_convert/gigaam-v3-rnnt-mlx/config.json @@ -0,0 +1,77 @@ +{ + "model_type": "gigaam", + "model_name": "v3_rnnt", + "sample_rate": 16000, + "preprocessor": { + "sample_rate": 16000, + "features": 64, + "win_length": 320, + "hop_length": 160, + "n_fft": 320, + "center": false + }, + "encoder": { + "feat_in": 64, + "n_layers": 16, + "d_model": 768, + "subsampling": "conv1d", + "subs_kernel_size": 5, + "subsampling_factor": 4, + "ff_expansion_factor": 4, + "self_attention_model": "rotary", + "pos_emb_max_len": 5000, + "n_heads": 16, + "conv_kernel_size": 5, + "conv_norm_type": "layer_norm" + }, + "head_type": "rnnt", + "head": { + "decoder": { + "pred_hidden": 320, + "pred_rnn_layers": 1, + "num_classes": 34 + }, + "joint": { + "enc_hidden": 768, + "pred_hidden": 320, + "joint_hidden": 320, + "num_classes": 34 + } + }, + "vocabulary": [ + " ", + "а", + "б", + "в", + "г", + "д", + "е", + "ж", + "з", + "и", + "й", + "к", + "л", + "м", + "н", + "о", + "п", + "р", + "с", + "т", + "у", + "ф", + "х", + "ц", + "ч", + "ш", + "щ", + "ъ", + "ы", + "ь", + "э", + "ю", + "я" + ], + "tokenizer_model": "tokenizer.model" +} \ No newline at end of file diff --git a/mlx_convert/gigaam_mlx.py b/mlx_convert/gigaam_mlx.py new file mode 100644 index 0000000..dd461b0 --- /dev/null +++ b/mlx_convert/gigaam_mlx.py @@ -0,0 +1,743 @@ +""" +GigaAM v3 CTC model in MLX. + +Architecture: + - FeatureExtractor: log-mel spectrogram (computed via numpy/MLX at runtime) + - ConformerEncoder: conv1d subsampling + rotary positional embeddings + 16 conformer layers + - CTCHead: Conv1d(768, num_classes, kernel=1) + - CTC greedy decoding + +Supports pseudo-streaming via sliding window (like whisper-stream / mlx-audio Parakeet). +""" +import json +import math +from dataclasses import dataclass, field +from pathlib import Path +from typing import Generator, List, Optional, Tuple + +import mlx.core as mx +import mlx.nn as nn +import numpy as np + + +# ─────────────────────────── Config ─────────────────────────── + +@dataclass +class GigaAMConfig: + model_type: str = "gigaam" + model_name: str = "v3_ctc" + sample_rate: int = 16000 + # preprocessor + features: int = 64 + win_length: int = 320 + hop_length: int = 160 + n_fft: int = 320 + center: bool = False + # encoder + feat_in: int = 64 + n_layers: int = 16 + d_model: int = 768 + subsampling: str = "conv1d" + subs_kernel_size: int = 5 + subsampling_factor: int = 4 + ff_expansion_factor: int = 4 + self_attention_model: str = "rotary" + pos_emb_max_len: int = 5000 + n_heads: int = 16 + conv_kernel_size: int = 5 + conv_norm_type: str = "layer_norm" + # head + head_type: str = "ctc" + num_classes: int = 34 + + # rnnt specific + rnnt_pred_hidden: int = 320 + rnnt_joint_hidden: int = 320 + rnnt_max_symbols: int = 10 + + # vocabulary + vocabulary: Optional[List[str]] = None + + @classmethod + def from_file(cls, path: str) -> "GigaAMConfig": + with open(path) as f: + d = json.load(f) + enc = d.get("encoder", {}) + pre = d.get("preprocessor", {}) + head = d.get("head", {}) + return cls( + model_type=d.get("model_type", "gigaam"), + model_name=d.get("model_name", "v3_ctc"), + sample_rate=d.get("sample_rate", 16000), + features=pre.get("features", 64), + win_length=pre.get("win_length", 320), + hop_length=pre.get("hop_length", 160), + n_fft=pre.get("n_fft", 320), + center=pre.get("center", False), + feat_in=enc.get("feat_in", 64), + n_layers=enc.get("n_layers", 16), + d_model=enc.get("d_model", 768), + subsampling=enc.get("subsampling", "conv1d"), + subs_kernel_size=enc.get("subs_kernel_size", 5), + subsampling_factor=enc.get("subsampling_factor", 4), + ff_expansion_factor=enc.get("ff_expansion_factor", 4), + self_attention_model=enc.get("self_attention_model", "rotary"), + pos_emb_max_len=enc.get("pos_emb_max_len", 5000), + n_heads=enc.get("n_heads", 16), + conv_kernel_size=enc.get("conv_kernel_size", 5), + conv_norm_type=enc.get("conv_norm_type", "layer_norm"), + head_type=d.get("head_type", "ctc"), + num_classes=head.get("num_classes", 34) if "num_classes" in head else head.get("decoder", {}).get("num_classes", 34), + rnnt_pred_hidden=head.get("decoder", {}).get("pred_hidden", 320), + rnnt_joint_hidden=head.get("joint", {}).get("joint_hidden", 320), + vocabulary=d.get("vocabulary"), + ) + + +# ─────────────────────── Audio preprocessing ─────────────────────── + +def mel_filters(sr: int, n_fft: int, n_mels: int) -> mx.array: + """HTK mel filterbank (matching torchaudio default for GigaAM).""" + def hz_to_mel(f): + return 2595.0 * math.log10(1.0 + f / 700.0) + def mel_to_hz(m): + return 700.0 * (10.0 ** (m / 2595.0) - 1.0) + + f_min, f_max = 0.0, sr / 2.0 + mel_min, mel_max = hz_to_mel(f_min), hz_to_mel(f_max) + mel_points = np.linspace(mel_min, mel_max, n_mels + 2) + hz_points = np.array([mel_to_hz(m) for m in mel_points]) + + bins = np.floor((n_fft + 1) * hz_points / sr).astype(int) + + fb = np.zeros((n_fft // 2 + 1, n_mels), dtype=np.float32) + for i in range(n_mels): + lo, mid, hi = bins[i], bins[i + 1], bins[i + 2] + for k in range(lo, mid): + if mid != lo: + fb[k, i] = (k - lo) / (mid - lo) + for k in range(mid, hi): + if hi != mid: + fb[k, i] = (hi - k) / (hi - mid) + return mx.array(fb) + + +def hanning_window(size: int) -> mx.array: + """Hanning window.""" + n = np.arange(size, dtype=np.float32) + return mx.array(0.5 - 0.5 * np.cos(2.0 * np.pi * n / size)) + + +def stft(signal: mx.array, n_fft: int, hop: int, win_len: int, window: mx.array) -> mx.array: + """Simple STFT using mx operations.""" + # Pad if necessary + pad_amount = n_fft // 2 + # For center=False, no padding + length = signal.shape[-1] + # Number of frames + n_frames = 1 + (length - win_len) // hop + + # Build frames via strided indexing + indices = mx.arange(win_len)[None, :] + (mx.arange(n_frames) * hop)[:, None] + frames = signal[indices] * window[None, :] + + # Zero-pad to n_fft if needed + if win_len < n_fft: + pad_size = n_fft - win_len + frames = mx.pad(frames, ((0, 0), (0, pad_size))) + + # Real FFT + spectrum = mx.fft.rfft(frames) + return spectrum # [n_frames, n_fft//2 + 1] + + +def log_mel_spectrogram(audio: mx.array, cfg: GigaAMConfig, + mel_fb: Optional[mx.array] = None, + stft_win: Optional[mx.array] = None) -> mx.array: + """Compute log-mel spectrogram matching GigaAM FeatureExtractor.""" + window = stft_win if stft_win is not None else hanning_window(cfg.win_length) + spec = stft(audio, cfg.n_fft, cfg.hop_length, cfg.win_length, window) + # Power spectrum + power = mx.square(mx.abs(spec)) # [T, n_fft//2+1] + # Mel filterbank + if mel_fb is not None: + filters = mel_fb + else: + filters = mel_filters(cfg.sample_rate, cfg.n_fft, cfg.features) # [n_fft//2+1, n_mels] + mel = power @ filters # [T, n_mels] + # Log + log_mel = mx.log(mx.clip(mel, 1e-9, 1e9)) + return log_mel # [T, n_mels] + + +# ─────────────────────── Model layers ─────────────────────── + +class Conv1dSubsampling(nn.Module): + """Conv1d striding subsampling: 2 conv1d layers with stride=2, ReLU.""" + def __init__(self, cfg: GigaAMConfig): + super().__init__() + ks = cfg.subs_kernel_size + pad = (ks - 1) // 2 + n_subs = int(math.log2(cfg.subsampling_factor)) + + layers = [] + in_ch = cfg.feat_in + for _ in range(n_subs): + layers.append(nn.Conv1d(in_ch, cfg.d_model, kernel_size=ks, stride=2, padding=pad)) + layers.append(nn.ReLU()) + in_ch = cfg.d_model + self.conv = layers + self._n_subs = n_subs + self._ks = ks + self._pad = pad + + def __call__(self, x: mx.array, lengths: mx.array) -> Tuple[mx.array, mx.array]: + """x: [B, T, feat_in] → [B, T', d_model]""" + for layer in self.conv: + x = layer(x) + # Compute output lengths + for _ in range(self._n_subs): + lengths = mx.floor((lengths.astype(mx.float32) + 2 * self._pad - self._ks) / 2 + 1).astype(mx.int32) + return x, lengths + + +class RotaryPositionalEmbedding(nn.Module): + """Rotary positional embeddings (RoPE).""" + def __init__(self, dim: int, base: int = 10000, max_len: int = 5000): + super().__init__() + self.dim = dim + self.base = base + inv_freq = 1.0 / (base ** (np.arange(0, dim, 2, dtype=np.float32) / dim)) + self._inv_freq = mx.array(inv_freq) + + def __call__(self, seq_len: int) -> Tuple[mx.array, mx.array]: + t = mx.arange(seq_len).astype(mx.float32) + freqs = mx.outer(t, self._inv_freq) + emb = mx.concatenate([freqs, freqs], axis=-1) + return mx.cos(emb), mx.sin(emb) + + +def rotate_half(x: mx.array) -> mx.array: + """Rotates half the hidden dims of the input.""" + d = x.shape[-1] // 2 + x1, x2 = x[..., :d], x[..., d:] + return mx.concatenate([-x2, x1], axis=-1) + + +def apply_rotary_emb(q: mx.array, k: mx.array, cos: mx.array, sin: mx.array) -> Tuple[mx.array, mx.array]: + """Apply rotary embeddings. q,k: [B, H, T, D], cos,sin: [T, D]""" + cos = cos[None, None, :, :] # [1, 1, T, D] + sin = sin[None, None, :, :] + q_rot = q * cos + rotate_half(q) * sin + k_rot = k * cos + rotate_half(k) * sin + return q_rot, k_rot + + +class RotaryMultiHeadAttention(nn.Module): + """Multi-head attention with rotary positional embeddings. + + GigaAM applies RoPE BEFORE linear projections: + 1. Reshape raw input to multi-head: [B, T, D] → [B, T, H, d_k] + 2. Apply RoPE to query/key heads + 3. Reshape back to [B, T, D] + 4. Project through linear_q/k/v + 5. Standard scaled dot-product attention + """ + def __init__(self, d_model: int, n_heads: int): + super().__init__() + self.n_heads = n_heads + self.d_k = d_model // n_heads + self.linear_q = nn.Linear(d_model, d_model) + self.linear_k = nn.Linear(d_model, d_model) + self.linear_v = nn.Linear(d_model, d_model) + self.linear_out = nn.Linear(d_model, d_model) + + def __call__(self, x: mx.array, cos: mx.array, sin: mx.array, + mask: Optional[mx.array] = None) -> mx.array: + B, T, D = x.shape + H, d_k = self.n_heads, self.d_k + + # 1. Reshape raw input to multi-head for RoPE: [B, T, H, d_k] + x_heads = x.reshape(B, T, H, d_k) + + # 2. Apply RoPE to query and key (same input for self-attention) + # cos, sin: [T, d_k] → [1, T, 1, d_k] + cos_e = cos[None, :, None, :] + sin_e = sin[None, :, None, :] + q_rot = x_heads * cos_e + rotate_half(x_heads) * sin_e + k_rot = x_heads * cos_e + rotate_half(x_heads) * sin_e + + # 3. Reshape back to [B, T, D] + q_rot = q_rot.reshape(B, T, D) + k_rot = k_rot.reshape(B, T, D) + + # 4. Project through linear layers + q = self.linear_q(q_rot).reshape(B, T, H, d_k).transpose(0, 2, 1, 3) # [B, H, T, d_k] + k = self.linear_k(k_rot).reshape(B, T, H, d_k).transpose(0, 2, 1, 3) + v = self.linear_v(x).reshape(B, T, H, d_k).transpose(0, 2, 1, 3) # value uses original x + + # 5. Scaled dot-product attention + scale = math.sqrt(d_k) + attn = (q @ k.transpose(0, 1, 3, 2)) / scale + if mask is not None: + attn = attn + mask + attn = mx.softmax(attn, axis=-1) + out = attn @ v # [B, H, T, d_k] + out = out.transpose(0, 2, 1, 3).reshape(B, T, -1) + return self.linear_out(out) + + +class ConformerConvolution(nn.Module): + """Conformer convolution module with LayerNorm.""" + def __init__(self, d_model: int, kernel_size: int): + super().__init__() + pad = (kernel_size - 1) // 2 + self.pointwise_conv1 = nn.Conv1d(d_model, d_model * 2, kernel_size=1) + self.depthwise_conv = nn.Conv1d(d_model, d_model, kernel_size=kernel_size, + padding=pad, groups=d_model) + # GigaAM v3 uses layer_norm for conv_norm_type + self.batch_norm = nn.LayerNorm(d_model) + self.activation = nn.SiLU() + self.pointwise_conv2 = nn.Conv1d(d_model, d_model, kernel_size=1) + + def __call__(self, x: mx.array) -> mx.array: + # x: [B, T, D] + x = self.pointwise_conv1(x) # → [B, T, 2*D] + # GLU: split last dim in half, apply sigmoid gate + half = x.shape[-1] // 2 + x = x[..., :half] * mx.sigmoid(x[..., half:]) # GLU + x = self.depthwise_conv(x) # → [B, T, D] + x = self.batch_norm(x) + x = self.activation(x) + x = self.pointwise_conv2(x) # → [B, T, D] + return x + + +class ConformerFeedForward(nn.Module): + """Conformer feed-forward module.""" + def __init__(self, d_model: int, d_ff: int): + super().__init__() + self.linear1 = nn.Linear(d_model, d_ff) + self.activation = nn.SiLU() + self.linear2 = nn.Linear(d_ff, d_model) + + def __call__(self, x: mx.array) -> mx.array: + return self.linear2(self.activation(self.linear1(x))) + + +class ConformerLayer(nn.Module): + """Single conformer layer.""" + def __init__(self, cfg: GigaAMConfig): + super().__init__() + d = cfg.d_model + d_ff = d * cfg.ff_expansion_factor + + self.norm_feed_forward1 = nn.LayerNorm(d) + self.feed_forward1 = ConformerFeedForward(d, d_ff) + self.norm_self_att = nn.LayerNorm(d) + self.self_attn = RotaryMultiHeadAttention(d, cfg.n_heads) + self.norm_conv = nn.LayerNorm(d) + self.conv = ConformerConvolution(d, cfg.conv_kernel_size) + self.norm_feed_forward2 = nn.LayerNorm(d) + self.feed_forward2 = ConformerFeedForward(d, d_ff) + self.norm_out = nn.LayerNorm(d) + + def __call__(self, x: mx.array, cos: mx.array, sin: mx.array, + mask: Optional[mx.array] = None) -> mx.array: + # FF1 + residual = x + x = self.norm_feed_forward1(x) + x = self.feed_forward1(x) + residual = residual + x * 0.5 + + # Self-attention + x = self.norm_self_att(residual) + x = self.self_attn(x, cos, sin, mask=mask) + residual = residual + x + + # Conv + x = self.norm_conv(residual) + x = self.conv(x) + residual = residual + x + + # FF2 + x = self.norm_feed_forward2(residual) + x = self.feed_forward2(x) + residual = residual + x * 0.5 + + return self.norm_out(residual) + + +class ConformerEncoder(nn.Module): + """GigaAM Conformer encoder.""" + def __init__(self, cfg: GigaAMConfig): + super().__init__() + self.pre_encode = Conv1dSubsampling(cfg) + self.pos_enc = RotaryPositionalEmbedding( + cfg.d_model // cfg.n_heads, + base=10000, + max_len=cfg.pos_emb_max_len, + ) + self.layers = [ConformerLayer(cfg) for _ in range(cfg.n_layers)] + + def __call__(self, features: mx.array, lengths: mx.array) -> Tuple[mx.array, mx.array]: + """features: [B, T, feat_in] → encoded: [B, T', D], lengths: [B]""" + x, lengths = self.pre_encode(features, lengths) + T = x.shape[1] + cos, sin = self.pos_enc(T) + for layer in self.layers: + x = layer(x, cos, sin) + return x, lengths + + +class RNNTJoint(nn.Module): + def __init__(self, enc_hidden: int, pred_hidden: int, joint_hidden: int, num_classes: int): + super().__init__() + self.enc = nn.Linear(enc_hidden, joint_hidden) + self.pred = nn.Linear(pred_hidden, joint_hidden) + self.joint_net_linear = nn.Linear(joint_hidden, num_classes) + + def __call__(self, encoder_out: mx.array, decoder_out: mx.array) -> mx.array: + enc = mx.expand_dims(self.enc(encoder_out), 2) + pred = mx.expand_dims(self.pred(decoder_out), 1) + joint_rep = mx.maximum(enc + pred, 0.0) + return self.joint_net_linear(joint_rep) + + +class RNNTDecoder(nn.Module): + def __init__(self, num_classes: int, pred_hidden: int): + super().__init__() + self.blank_id = num_classes - 1 + self.pred_hidden = pred_hidden + self.embed = nn.Embedding(num_classes, pred_hidden) + self.lstm = nn.LSTM(pred_hidden, pred_hidden) + + def predict(self, x: Optional[int], state: Optional[Tuple[mx.array, mx.array]], batch_size: int = 1): + if x is not None: + emb = self.embed(mx.array([[x]])) + else: + emb = mx.zeros((batch_size, 1, self.pred_hidden)) + + h, c = state if state is not None else (None, None) + out, cell = self.lstm(emb, hidden=h, cell=c) + return out, (out[:, -1, :], cell[:, -1, :]) + + +class RNNTHead(nn.Module): + def __init__(self, feat_in: int, pred_hidden: int, joint_hidden: int, num_classes: int): + super().__init__() + self.decoder = RNNTDecoder(num_classes, pred_hidden) + self.joint = RNNTJoint(feat_in, pred_hidden, joint_hidden, num_classes) + + +class CTCHead(nn.Module): + """CTC decoder head: Conv1d(d_model, num_classes, kernel=1).""" + def __init__(self, feat_in: int, num_classes: int): + super().__init__() + self.decoder_layers = [nn.Conv1d(feat_in, num_classes, kernel_size=1)] + + def __call__(self, x: mx.array) -> mx.array: + """x: [B, T, D] → log_probs: [B, T, C]""" + logits = self.decoder_layers[0](x) # [B, T, C] + return mx.softmax(logits, axis=-1) # we'll use log later + + +# ─────────────────────── Streaming dataclasses ─────────────────────── + +@dataclass +class StreamingConfig: + """Configuration for pseudo-streaming transcription. + + Attributes: + chunk_duration: Duration of each audio chunk in seconds. + context_duration: Extra audio from before the chunk to keep as context. + The model always sees [context + chunk] for better accuracy. + step_duration: How much to advance between steps. If None, equals chunk_duration. + """ + chunk_duration: float = 2.0 + context_duration: float = 3.0 + step_duration: Optional[float] = None + + def __post_init__(self): + if self.step_duration is None: + self.step_duration = self.chunk_duration + + +@dataclass +class StreamingResult: + """Result from streaming transcription — mlx-audio compatible. + + Attributes: + text: New text since last emission. + is_final: True if this is the final result. + start_time: Start time in seconds. + end_time: End time in seconds. + progress: Progress 0.0–1.0. + audio_position: Current position in audio (seconds). + audio_duration: Total audio duration (seconds), 0 for live. + cumulative_text: Full accumulated transcription so far. + language: Language code. + """ + text: str + is_final: bool + start_time: float + end_time: float + progress: float = 0.0 + audio_position: float = 0.0 + audio_duration: float = 0.0 + cumulative_text: str = "" + language: str = "ru" + + +class GigaAM(nn.Module): + """Full GigaAM model (supports CTC and RNNT).""" + def __init__(self, cfg: GigaAMConfig): + super().__init__() + self.cfg = cfg + self.encoder = ConformerEncoder(cfg) + + if cfg.head_type == "rnnt": + self.head = RNNTHead( + feat_in=cfg.d_model, + pred_hidden=cfg.rnnt_pred_hidden, + joint_hidden=cfg.rnnt_joint_hidden, + num_classes=cfg.num_classes + ) + else: + self.head = CTCHead(cfg.d_model, cfg.num_classes) + + self.mel_filterbank: Optional[mx.array] = None + self.stft_window: Optional[mx.array] = None + + def __call__(self, features: mx.array, lengths: mx.array) -> Tuple[mx.array, mx.array]: + """Returns encoded features and their lengths.""" + encoded, enc_lengths = self.encoder(features, lengths) + return encoded, enc_lengths + + def _ctc_decode(self, log_probs: mx.array, enc_length: int) -> str: + """CTC greedy decode: collapse repeated + remove blanks.""" + vocab = self.cfg.vocabulary + blank_id = len(vocab) + labels = mx.argmax(log_probs, axis=-1) + labels_list = labels.tolist() + + result = [] + prev = -1 + for t, label in enumerate(labels_list): + if t >= enc_length: + break + if label == blank_id: + prev = label + continue + if label == prev: + continue + result.append(label) + prev = label + + return "".join(vocab[i] for i in result) + + def _compute_features(self, audio: mx.array) -> Tuple[mx.array, mx.array]: + """Audio → mel features [1, T, features] + lengths [1].""" + mel = log_mel_spectrogram(audio, self.cfg, + mel_fb=self.mel_filterbank, + stft_win=self.stft_window) + mel = mx.expand_dims(mel, 0) + lengths = mx.array([mel.shape[1]]) + return mel, lengths + + def transcribe(self, audio: mx.array) -> str: + """Transcribe raw audio waveform → text.""" + mel, lengths = self._compute_features(audio) + encoded, enc_lengths = self(mel, lengths) + mx.eval(encoded, enc_lengths) + + if self.cfg.head_type == "rnnt": + return self._rnnt_decode(encoded[0], int(enc_lengths[0])) + else: + log_probs = self.head(encoded) + return self._ctc_decode(log_probs[0], int(enc_lengths[0])) + + def _rnnt_decode(self, encoded: mx.array, enc_length: int) -> str: + """Greedy decode for RNN-T.""" + vocab = self.cfg.vocabulary + blank_id = len(vocab) + max_symbols = self.cfg.rnnt_max_symbols + + hyp = [] + dec_state = None + last_label = None + + for t in range(enc_length): + # encoded: [T, D] -> [1, 1, D] + f = encoded[t:t+1, :].reshape(1, 1, -1) + + not_blank = True + new_symbols = 0 + while not_blank and new_symbols < max_symbols: + g, hidden = self.head.decoder.predict(last_label, dec_state, batch_size=1) + # g: [1, 1, D] + # joint output is [1, 1, 1, num_classes], we want argmax over classes + logits = self.head.joint(f, g) # [1, 1, 1, C] + k = int(mx.argmax(logits[0, 0, 0, :]).item()) + + if k == blank_id: + not_blank = False + else: + hyp.append(k) + dec_state = hidden + last_label = k + new_symbols += 1 + + return "".join(vocab[i] for i in hyp) + + def transcribe_chunk(self, audio: mx.array) -> str: + """Transcribe a single chunk (for streaming). Same as transcribe but clearer name.""" + return self.transcribe(audio) + + def stream_generate( + self, + audio: mx.array, + config: Optional[StreamingConfig] = None, + ) -> Generator[StreamingResult, None, None]: + """Pseudo-streaming transcription over pre-recorded audio. + + Uses growing buffer approach: each step transcribes from the start + up to the current position. GigaAM at 85x realtime makes this fast + even for 30s audio (~0.4s inference). + + For very long audio (>30s), falls back to sliding window of last 30s. + + Args: + audio: Raw audio waveform, mx.array, 16kHz mono. + config: StreamingConfig with step duration. + + Yields: + StreamingResult with incremental text for each step. + """ + if config is None: + config = StreamingConfig() + + sr = self.cfg.sample_rate + total_samples = audio.shape[0] + audio_duration = total_samples / sr + + step_samples = int(config.step_duration * sr) + max_window = int(30.0 * sr) # cap at 30s for memory/speed + + previous_text = "" + position = step_samples # start with first step + + while position <= total_samples: + is_last = position >= total_samples + # Growing buffer from start, capped at 30s + window_start = max(0, position - max_window) + window = audio[window_start:position] + + current_text = self.transcribe(window) + + # Incremental text + new_text = _incremental_text(previous_text, current_text) + previous_text = current_text + + audio_pos = position / sr + yield StreamingResult( + text=new_text, + is_final=is_last, + start_time=window_start / sr, + end_time=audio_pos, + progress=position / total_samples, + audio_position=audio_pos, + audio_duration=audio_duration, + cumulative_text=current_text, + language="ru", + ) + + if is_last: + break + + position = min(position + step_samples, total_samples) + + def stream_live( + self, + audio_buffer: mx.array, + ) -> StreamingResult: + """Transcribe a growing audio buffer for live microphone use. + + Call this repeatedly as new audio arrives. Transcribes the full buffer + (capped at 30s from the end). GigaAM is 85x realtime so this is fast. + + Args: + audio_buffer: The full accumulated audio so far. + + Returns: + StreamingResult with current full transcription. + """ + sr = self.cfg.sample_rate + total_samples = audio_buffer.shape[0] + max_window = int(30.0 * sr) + + start = max(0, total_samples - max_window) + window = audio_buffer[start:] + + text = self.transcribe(window) + + return StreamingResult( + text=text, + is_final=False, + start_time=start / sr, + end_time=total_samples / sr, + progress=0.0, + audio_position=total_samples / sr, + audio_duration=0.0, + cumulative_text=text, + language="ru", + ) + + +def _incremental_text(previous: str, current: str) -> str: + """Find new text added to current vs previous. + + If current starts with previous → return the new suffix. + If model corrected → return full current (with marker). + """ + if not previous: + return current + if current.startswith(previous): + return current[len(previous):] + # Model self-corrected — return full updated text + return current + + +def load_model(model_dir: str) -> GigaAM: + """Load converted GigaAM MLX model from directory.""" + model_dir = Path(model_dir) + cfg = GigaAMConfig.from_file(str(model_dir / "config.json")) + model = GigaAM(cfg) + weights = mx.load(str(model_dir / "model.safetensors")) + + # Extract preprocessing weights + mel_fb = weights.pop("mel_filterbank", None) + stft_win = weights.pop("stft_window", None) + + if mel_fb is not None: + model.mel_filterbank = mel_fb.astype(mx.float32) + if stft_win is not None: + model.stft_window = stft_win.astype(mx.float32) + + # Load model weights + model.load_weights(list(weights.items()), strict=False) + mx.eval(model.parameters()) + return model + + +def load_audio(path: str, sample_rate: int = 16000) -> mx.array: + """Load audio via ffmpeg → mx.array.""" + import subprocess + cmd = [ + "ffmpeg", "-nostdin", "-threads", "0", "-i", path, + "-f", "s16le", "-ac", "1", "-acodec", "pcm_s16le", + "-ar", str(sample_rate), "-" + ] + result = subprocess.run(cmd, capture_output=True, check=True) + audio_np = np.frombuffer(result.stdout, dtype=np.int16).astype(np.float32) / 32768.0 + return mx.array(audio_np) diff --git a/mlx_convert/step1_inspect_weights.py b/mlx_convert/step1_inspect_weights.py new file mode 100644 index 0000000..3970f0e --- /dev/null +++ b/mlx_convert/step1_inspect_weights.py @@ -0,0 +1,35 @@ +""" +Step 1: Load the GigaAM v3_ctc checkpoint and inspect weight keys/shapes. +This helps us understand the exact mapping needed for MLX conversion. +""" +import sys +sys.path.insert(0, "..") + +import warnings +import torch + +# Download the model using GigaAM's own download mechanism +import gigaam + +model = gigaam.load_model("v3_ctc", device="cpu", fp16_encoder=False, use_flash=False) + +print("=" * 80) +print("Model type:", type(model).__name__) +print("=" * 80) + +# Get state dict +sd = model.state_dict() +print(f"\nTotal parameters: {sum(p.numel() for p in model.parameters()):,}") +print(f"Total keys: {len(sd)}") +print() + +# Print all keys with shapes +for k, v in sorted(sd.items()): + print(f" {k:70s} {str(list(v.shape)):>25s} {v.dtype}") + +# Also print the config +print("\n" + "=" * 80) +print("Model config:") +print("=" * 80) +from omegaconf import OmegaConf +print(OmegaConf.to_yaml(model.cfg)) diff --git a/mlx_convert/test_fp32.py b/mlx_convert/test_fp32.py new file mode 100644 index 0000000..29f4b03 --- /dev/null +++ b/mlx_convert/test_fp32.py @@ -0,0 +1,26 @@ +"""Test GigaAM MLX model in float32.""" +import time +import sys +sys.path.insert(0, ".") + +from gigaam_mlx import load_model, load_audio + +print("Loading GigaAM MLX fp32 model...") +model = load_model("./gigaam-v3-ctc-mlx-fp32") + +audio = load_audio("test_ru.wav") + +# warmup +_ = model.transcribe(audio) + +t0 = time.time() +result = model.transcribe(audio) +elapsed = time.time() - t0 + +print(f"GigaAM v3_ctc (MLX fp32): {result}") +print(f"Time: {elapsed:.2f}s") + +# Compare with reference +ref = "ничьих не требуя похвал счастлив уж я надеждой сладкой что дева с трепетом любви посмотрит может быть украдкой на песни грешные мои у лукоморья дуб зеленый" +print(f"Reference: {ref}") +print(f"Match: {result == ref}") diff --git a/mlx_convert/test_mlx_gigaam.py b/mlx_convert/test_mlx_gigaam.py new file mode 100644 index 0000000..7427e51 --- /dev/null +++ b/mlx_convert/test_mlx_gigaam.py @@ -0,0 +1,25 @@ +"""Test GigaAM MLX model.""" +import time +import sys + +sys.path.insert(0, ".") + +from gigaam_mlx import load_model, load_audio + +print("Loading GigaAM MLX model...") +t0 = time.time() +model = load_model("./gigaam-v3-ctc-mlx") +load_time = time.time() - t0 +print(f"Model loaded in {load_time:.2f}s") + +print("\nLoading audio...") +audio = load_audio("test_ru.wav") +print(f"Audio: {audio.shape[0]} samples ({audio.shape[0]/16000:.2f}s)") + +print("\nTranscribing...") +t0 = time.time() +result = model.transcribe(audio) +transcribe_time = time.time() - t0 + +print(f"\nGigaAM v3_ctc (MLX): {result}") +print(f"Transcription time: {transcribe_time:.2f}s") diff --git a/mlx_convert/test_pytorch_gigaam.py b/mlx_convert/test_pytorch_gigaam.py new file mode 100644 index 0000000..007ca33 --- /dev/null +++ b/mlx_convert/test_pytorch_gigaam.py @@ -0,0 +1,13 @@ +"""Test original PyTorch GigaAM for baseline comparison.""" +import sys, time +sys.path.insert(0, "..") +import gigaam + +model = gigaam.load_model("v3_ctc", device="cpu", fp16_encoder=False, use_flash=False) + +t0 = time.time() +result = model.transcribe("test_ru.wav") +elapsed = time.time() - t0 + +print(f"GigaAM v3_ctc (PyTorch): {result}") +print(f"Time: {elapsed:.2f}s") diff --git a/patch_rnnt.py b/patch_rnnt.py new file mode 100644 index 0000000..c70ffdc --- /dev/null +++ b/patch_rnnt.py @@ -0,0 +1,48 @@ +with open("gigaam_mlx.py", "r") as f: + content = f.read() + +rnnt_code = """ +class RNNTJoint(nn.Module): + def __init__(self, enc_hidden: int, pred_hidden: int, joint_hidden: int, num_classes: int): + super().__init__() + self.enc = nn.Linear(enc_hidden, joint_hidden) + self.pred = nn.Linear(pred_hidden, joint_hidden) + self.joint_net_linear = nn.Linear(joint_hidden, num_classes) + + def __call__(self, encoder_out: mx.array, decoder_out: mx.array) -> mx.array: + enc = mx.expand_dims(self.enc(encoder_out), 2) + pred = mx.expand_dims(self.pred(decoder_out), 1) + joint_rep = mx.maximum(enc + pred, 0.0) + return self.joint_net_linear(joint_rep) + +class RNNTDecoder(nn.Module): + def __init__(self, num_classes: int, pred_hidden: int): + super().__init__() + self.blank_id = num_classes - 1 + self.pred_hidden = pred_hidden + self.embed = nn.Embedding(num_classes, pred_hidden) + self.lstm = nn.LSTM(pred_hidden, pred_hidden) + + def predict(self, x: Optional[int], state: Optional[Tuple[mx.array, mx.array]], batch_size: int = 1): + if x is not None: + emb = self.embed(mx.array([[x]])) + else: + emb = mx.zeros((batch_size, 1, self.pred_hidden)) + + h, c = state if state is not None else (None, None) + out, cell = self.lstm(emb, hidden=h, cell=c) + return out, (out[:, -1, :], cell[:, -1, :]) + +class RNNTHead(nn.Module): + def __init__(self, cfg: GigaAMConfig): + super().__init__() + self.decoder = RNNTDecoder(cfg.num_classes, cfg.rnnt_pred_hidden) + self.joint = RNNTJoint(cfg.d_model, cfg.rnnt_pred_hidden, cfg.rnnt_joint_hidden, cfg.num_classes) + +""" + +# Insert before CTCHead +content = content.replace("class CTCHead(nn.Module):", rnnt_code + "\nclass CTCHead(nn.Module):") + +with open("gigaam_mlx.py", "w") as f: + f.write(content) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..37eaa3e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,48 @@ +[build-system] +requires = ["setuptools>=68.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "gigaam" +version = "0.1.0" +description = "GigaAM: A package for audio modeling and ASR." +readme = "README.md" +license = "MIT" +requires-python = ">=3.10" +authors = [ + { name = "GigaChat Team" }, +] + +dependencies = [ + "hydra-core==1.3.*", + "numpy==2.*", + "omegaconf==2.3.*", + "onnx==1.19.*", + "onnxruntime==1.23.*", + "sentencepiece", + "torch>=2.5,<2.9", + "torchaudio>=2.5,<2.9", + "tqdm", +] + +[project.optional-dependencies] +longform = [ + "torch==2.8.*", + "torchaudio==2.8.*", + "pyannote.audio==4.0", + "torchcodec==0.7", + "numba>=0.62", +] +tests = [ + "pytest", + "pytest-cov", + "scipy", + "soundfile", + "librosa", +] + +[project.urls] +Homepage = "https://github.com/salute-developers/GigaAM/" + +[tool.setuptools.packages.find] +include = ["gigaam"] diff --git a/tests/test_batching.py b/tests/test_batching.py new file mode 100644 index 0000000..bf41f45 --- /dev/null +++ b/tests/test_batching.py @@ -0,0 +1,165 @@ +import logging + +import numpy as np +import pytest +import torch +from scipy import signal + +import gigaam +from gigaam.utils import AudioDataset + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def generate_test_audio(duration=3.0, sr=16000): + """Generate synthetic test audio with different characteristics""" + t = np.linspace(0, duration, int(sr * duration)) + audio = ( + 0.5 * np.sin(2 * np.pi * 220 * t) + + 0.3 * np.sin(2 * np.pi * 440 * t) + + 0.2 * np.sin(2 * np.pi * 660 * t) + + 0.1 * np.random.normal(0, 0.1, len(t)) + ) + envelope = signal.windows.tukey(len(audio), alpha=0.1) + return (audio * envelope).astype(np.float32) + + +def create_test_batches(batch_size=4, max_duration=3.0, sr=16000): + """Create test batches with different lengths""" + durations = np.linspace(max_duration * 0.5, max_duration, batch_size) + ds = AudioDataset([generate_test_audio(duration=dr, sr=sr) for dr in durations]) + return ds.collate([ds[i] for i in range(len(ds))]) + + +def custom_forward(model, features, feature_lengths): + """Custom forward pass for batching comparison""" + out_feat, out_lns = [], [] + for i in range(len(features)): + of, ol = model.preprocessor( + features[i : i + 1, : feature_lengths[i]], feature_lengths[i : i + 1] + ) + if model._device.type == "cpu": + of, ol = model.encoder.pre_encode(of.transpose(1, 2), ol) + else: + with torch.autocast(device_type="cuda"): + of, ol = model.encoder.pre_encode(of.transpose(1, 2), ol) + out_feat.append(of.transpose(1, 2)) + out_lns.append(ol.item()) + + features_padded = torch.zeros(len(features), out_feat[0].shape[1], max(out_lns)).to( + features.device + ) + for i in range(len(features)): + features_padded[i, :, : out_lns[i]] = out_feat[i][:, :, : out_lns[i]] + feature_lengths_tensor = torch.tensor(out_lns).to(features.device) + + subs_forward = model.encoder.pre_encode.forward + model.encoder.pre_encode.forward = lambda x, lengths: (x, lengths) + + if model._device.type == "cpu": + enc_out = model.encoder(features_padded, feature_lengths_tensor) + else: + with torch.autocast(device_type=model._device.type, dtype=torch.float16): + enc_out = model.encoder(features_padded, feature_lengths_tensor) + + model.encoder.pre_encode.forward = subs_forward + return enc_out + + +def compare_outputs(output1, output2, atol=0.03): + """Compare two model outputs with tolerance""" + feat1, lens1 = output1 + feat2, lens2 = output2 + assert (lens1 == lens2).all(), f"Length mismatch: {lens1} != {lens2}" + min_len = min(lens1.item(), lens2.item()) + assert ( + feat1.shape[:2] == feat2.shape[:2] + ), f"Shape mismatch: {feat1.shape} vs {feat2.shape}" + + feat1, feat2 = feat1[:, :, :min_len], feat2[:, :, :min_len] + abs_diff = torch.abs(feat1 - feat2).max().item() + close = abs_diff < atol + return close, { + "max_absolute_difference": abs_diff, + } + + +@pytest.mark.parametrize("revision", ["v3_ctc", "v3_e2e_rnnt"]) +@pytest.mark.parametrize("batch_size", [1, 2, 4]) +def test_model_batching(revision, batch_size): + """Test batching correctness for different models and batch sizes""" + torch.manual_seed(0) + model = gigaam.load_model(revision) + model.eval() + device = next(model.parameters()).device + + features, feature_lengths = create_test_batches( + batch_size=batch_size, max_duration=5.0 + ) + features, feature_lengths = features.to(device), feature_lengths.to(device) + + with torch.no_grad(): + batch_output = custom_forward(model, features, feature_lengths) + + # Compare with individual processing + for i in range(batch_size): + single_features = features[i : i + 1][:, : feature_lengths[i]] + single_lengths = feature_lengths[i : i + 1] + + with torch.no_grad(): + single_output = custom_forward(model, single_features, single_lengths) + + if batch_size == 1: + close, metrics = compare_outputs(batch_output, single_output) + else: + batch_element = (batch_output[0][i : i + 1], batch_output[1][i : i + 1]) + close, metrics = compare_outputs(batch_element, single_output) + + assert close, ( + f"Batch inconsistency for sample {i}: " + f"abs_diff={metrics['max_absolute_difference']:.4f}, " + ) + + logger.info(f"Batching test passed: {revision} batch_size={batch_size}") + + +@pytest.mark.parametrize("revision", ["v3_ctc", "v3_e2e_rnnt"]) +def test_batching_edge_cases(revision): + """Test batching with edge cases""" + model = gigaam.load_model(revision) + model.eval() + device = next(model.parameters()).device + + # Test with very short sequences + features = torch.randn(2, 5000).to(device) + feature_lengths = torch.tensor([3200, 5000], dtype=torch.int32).to(device) + + with torch.no_grad(): + output = custom_forward(model, features, feature_lengths) + + assert output[0].shape[0] == 2, "Should handle variable length sequences" + logger.info(f"Edge case test passed: {revision}") + + +@pytest.mark.parametrize("max_duration", [0.5, 1.0]) +def test_different_audio_lengths(max_duration): + """Test batching with different audio durations""" + model = gigaam.load_model("v3_e2e_ctc") + model.eval() + device = next(model.parameters()).device + + features, feature_lengths = create_test_batches( + batch_size=2, max_duration=max_duration + ) + features, feature_lengths = features.to(device), feature_lengths.to(device) + + with torch.no_grad(): + output = custom_forward(model, features, feature_lengths) + + assert output[0].shape[0] == 2, f"Failed for max_duration={max_duration}" + logger.info(f"Audio length test passed: max_duration={max_duration}") + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/test_loading.py b/tests/test_loading.py new file mode 100644 index 0000000..bdea44f --- /dev/null +++ b/tests/test_loading.py @@ -0,0 +1,91 @@ +import logging +import os + +import pytest + +import gigaam +from gigaam.utils import download_short_audio + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +_predictions = { + "emo": { + "angry": 7.70451661082916e-05, + "sad": 0.002205904107540846, + "neutral": 0.9233596324920654, + "positive": 0.07435736805200577, + }, + "asr": "ничьих не требуя похвал счастлив уж я надеждой сладкой что дева с трепетом любви посмотрит может быть украдкой на песни грешные мои у лукоморья дуб зеленый", # noqa: E501 + "v3_e2e_ctc": "Ничьих, не требуя похвал, счастлив уж я надеждой сладкой, Что дева с трепетом любви посмотрит, может быть украдкой На песни грешные мои. У лукоморья дуб зелёный.", # noqa: E501 + "v3_e2e_rnnt": "Ничьих не требуя похвал, Счастлив уж я надеждой сладкой, Что дева с трепетом любви Посмотрит, может быть, украдкой На песни грешные мои. У лукоморья дуб зелёный.", # noqa: E501 +} + + +@pytest.fixture(scope="session") +def test_audio(): + """Provide test audio file for all tests""" + return download_short_audio() + + +def run_model_method(model, revision, test_audio): + if "ssl" in revision: + result = model.embed_audio(test_audio) + assert result is not None, "SSL embedding failed" + logger.info(f"{revision}: SSL embedding completed") + + elif "emo" in revision: + result = model.get_probs(test_audio) + assert all( + abs(result[em] - _predictions["emo"][em]) < 1e-3 for em in result + ), f"Emotion probs failed: {result}" + logger.info(f"{revision}: Emotion probs obtained") + + else: + result = model.transcribe(test_audio) + if "e2e" in revision: + assert ( + _predictions[revision] == result + ), f"Transcription failed ({revision}): {result}" + else: + assert ( + _predictions["asr"] == result + ), f"Transcription failed ({revision}): {result}" + logger.info(f"{revision}: Transcription completed") + + +@pytest.mark.parametrize( + "revision", + [ + "emo", + "v1_ctc", + "v1_rnnt", + "v1_ssl", + "v2_ctc", + "v2_rnnt", + "v2_ssl", + "v3_ctc", + "v3_rnnt", + "v3_e2e_ctc", + "v3_e2e_rnnt", + "v3_ssl", + ], +) +@pytest.mark.full +def test_model_revision_full(revision, test_audio): + """Test specific model revision loads and processes audio (full models only)""" + model = gigaam.load_model(revision) + run_model_method(model, revision, test_audio) + os.remove(os.path.join(gigaam._CACHE_DIR, f"{revision}.ckpt")) + + +@pytest.mark.parametrize("revision", ["emo", "v2_ssl", "v3_ctc", "v3_e2e_rnnt"]) +@pytest.mark.partial +def test_model_revision_partial(revision, test_audio): + """Test specific model revision loads and processes audio (partial models enabled)""" + model = gigaam.load_model(revision) + run_model_method(model, revision, test_audio) + + +if __name__ == "__main__": + pytest.main([__file__, "-v", "-m", "partial"]) diff --git a/tests/test_longform.py b/tests/test_longform.py new file mode 100644 index 0000000..fb35f78 --- /dev/null +++ b/tests/test_longform.py @@ -0,0 +1,219 @@ +import logging +import os +import tempfile +from typing import List, Tuple + +import numpy as np +import pytest +import soundfile as sf +from scipy import signal + +import gigaam +from gigaam.utils import download_long_audio + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +_predictions = { + "v3_e2e_rnnt": [ + { + "transcription": "Вечерня отошла давно, Но в кельях тихо и темно; Уже и сам игумен строгий Свои молитвы прекратил И кости ветхие склонил, Перекрестясь на одр убогий. Кругом и сон, и тишина; Но церкви дверь отворена.", # noqa: E501 + "boundaries": (0.0, 16.80471875), + }, + { + "transcription": "Трепещет луч лампады, И тускло озаряет он И тёмную живопись икон, и возглащённые оклады. И раздаётся в тишине То тяжкий вздох, то шёпот важный, И мрачно дремлет в тишине старинный свод.", # noqa: E501 + "boundaries": (17.074718750000002, 32.549093750000004), + }, + { + "transcription": "Глухой и влажный Стоят за клиросом чернец и грешник, Неподвижны оба. И шёпот их — Как глаз из гроба, И грешник бледен, как мертвец — Монах. Несчастный! Полно, перестань!", # noqa: E501 + "boundaries": (32.95409375, 49.305968750000005), + }, + { + "transcription": "Ужасна исповедь злодея, Заплачена тобою дань Тому, Кто в злобе пламенея Лукавого грешника блюдёт И к вечной гибели ведёт. Смирись, опомнись. Время, время. Раскаянье, покров", # noqa: E501 + "boundaries": (49.81221875, 65.65784375), + }, + { + "transcription": "Я разрешу тебя, грехов сложи мучительное бремя.", + "boundaries": (65.94471875, 70.88909375), + }, + ], + "v3_ctc": [ + { + "transcription": "вечерня отошла давно но в кельях тихо и темно уже и сам игумен строгий свои молитвы прекратил и кости ветхие склонил перекрестясь на одр убогий кругом и сон и тишина но церкви дверь отворена", # noqa: E501 + "boundaries": (0.0, 16.80471875), + }, + { + "transcription": "трепещет луч лампады и тускло озаряет он и темную живопись икон и позлащенные оклады и раздается в тишине то тяжкий вздох то шепот важный и мрачно дремлет в вашине старинный свод", # noqa: E501 + "boundaries": (17.074718750000002, 32.549093750000004), + }, + { + "transcription": "глухой и влажный стоят за клиросом чернец и грешник неподвижны оба и шепот их как глаз из гроба и грешник бледен как мертвец монах несчастный полно перестань", # noqa: E501 + "boundaries": (32.95409375, 49.305968750000005), + }, + { + "transcription": "ужасна исповедь злодея заплачена тобою дань тому кто в злобе пламенея лукаво грешника блюдет и к вечной гибели ведет смирись опомнись время время раскаянье покров", # noqa: E501 + "boundaries": (49.81221875, 65.65784375), + }, + { + "transcription": "я разрешу тебя грехов сложи мучительное бремя", + "boundaries": (65.94471875, 70.88909375), + }, + ], +} + + +def generate_long_audio(duration=60.0, sr=16000, include_silence=True): + """Generate long test audio with speech-like segments and silence""" + t = np.linspace(0, duration, int(sr * duration)) + audio = np.zeros_like(t, dtype=np.float32) + segment_durations = list(np.random.uniform(0.2, 5, size=100)) + current_time = 0.0 + + for i, seg_duration in enumerate(segment_durations): + if current_time + seg_duration > duration: + break + seg_t = np.linspace(0, seg_duration, int(sr * seg_duration)) + freq1, freq2, freq3 = 100 + i * 20, 200 + i * 30, 300 + i * 40 + segment = ( + 0.4 * np.sin(2 * np.pi * freq1 * seg_t) + + 0.3 * np.sin(2 * np.pi * freq2 * seg_t) + + 0.2 * np.sin(2 * np.pi * freq3 * seg_t) + + 0.1 * np.random.normal(0, 0.2, len(seg_t)) + ) + envelope = signal.windows.tukey(len(segment), alpha=0.1) + segment = segment * envelope + start_idx, end_idx = int(current_time * sr), int(current_time * sr) + len( + segment + ) + audio[start_idx:end_idx] = segment + if include_silence and i < len(segment_durations) - 1: + current_time += seg_duration + np.random.uniform(0.1, 0.5) + else: + current_time += seg_duration + return audio + + +def validate_segmentation_boundaries( + boundaries: List[Tuple[float, float]], audio_duration: float +): + """Validate segmentation boundaries meet requirements""" + issues = [] + total_duration = 0.0 + + for i, (start, end) in enumerate(boundaries): + duration = end - start + if duration < 0.2: + issues.append(f"Segment {i} too short: {duration:.2f}s") + if duration > 30.0: + issues.append(f"Segment {i} too long: {duration:.2f}s") + if start >= end: + issues.append(f"Segment {i} invalid boundaries: {start:.2f}-{end:.2f}") + total_duration += duration + + if boundaries and boundaries[-1][1] > audio_duration: + issues.append( + f"Last segment exceeds audio: {boundaries[-1][1]:.2f} > {audio_duration:.2f}" + ) + + return { + "valid": len(issues) == 0, + "issues": issues, + "total_segments": len(boundaries), + } + + +@pytest.mark.parametrize("duration", [30.0, 60.0, 120.0]) +def test_segmentation_functionality(duration): + """Test audio segmentation with different durations""" + with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f: + try: + audio = generate_long_audio(duration=duration) + sf.write(f.name, audio, 16000) + + from gigaam.vad_utils import segment_audio_file + + segments, boundaries = segment_audio_file(f.name, sr=16000) + + validation = validate_segmentation_boundaries(boundaries, duration) + assert validation[ + "valid" + ], f"Boundary validation failed: {validation['issues']}" + assert len(segments) == len( + boundaries + ), "Segments and boundaries count mismatch" + + logger.info(f"Segmentation: {len(segments)} segments for {duration}s audio") + + finally: + if os.path.exists(f.name): + os.remove(f.name) + + +@pytest.mark.parametrize("revision", ["v3_ctc", "v3_e2e_rnnt"]) +def test_transcribe_longform(revision): + """Test longform transcription for different models""" + model = gigaam.load_model(revision) + results = model.transcribe_longform(download_long_audio()) + ref = _predictions[revision] + + assert isinstance(results, list), "Should return list of segments" + assert len(results) == len(ref), "Distinct results len from reference" + + for segment, ref_segment in zip(results, ref): + assert "transcription" in segment, "Missing transcription key" + assert "boundaries" in segment, "Missing boundaries key" + start, end = segment["boundaries"] + ref_start, ref_end = ref_segment["boundaries"] + assert ( + abs(start - ref_start) < 0.1 and abs(end - ref_end) < 0.1 + ), f"Segments are not close {start, end} and {ref_start, ref_end}" + assert ( + segment["transcription"] == ref_segment["transcription"] + ), f"Different transcription: {segment['transcription']} and {ref_segment['transcription']}" + + +@pytest.mark.parametrize("revision", ["v3_ctc"]) +def test_longform_consistency(revision): + """Test that multiple runs produce consistent results""" + with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f: + try: + audio = generate_long_audio(duration=30.0) + sf.write(f.name, audio, 16000) + + model = gigaam.load_model(revision) + results1 = model.transcribe_longform(f.name) + results2 = model.transcribe_longform(f.name) + + assert len(results1) == len(results2), "Inconsistent segment count" + for seg1, seg2 in zip(results1, results2): + assert ( + seg1["boundaries"] == seg2["boundaries"] + ), "Inconsistent boundaries" + + finally: + if os.path.exists(f.name): + os.remove(f.name) + + +def test_segmentation_edge_cases(): + """Test segmentation with edge cases""" + with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f: + try: + # Very short audio + audio = generate_long_audio(duration=0.5) + sf.write(f.name, audio, 16000) + + from gigaam.vad_utils import segment_audio_file + + segments, boundaries = segment_audio_file(f.name, sr=16000) + + # Should handle short audio gracefully + assert isinstance(segments, list), "Should return list even for short audio" + + finally: + if os.path.exists(f.name): + os.remove(f.name) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/test_onnx.py b/tests/test_onnx.py new file mode 100644 index 0000000..c933a32 --- /dev/null +++ b/tests/test_onnx.py @@ -0,0 +1,49 @@ +import logging +import shutil + +import numpy as np +import pytest + +import gigaam +from gigaam.onnx_utils import infer_onnx, load_onnx +from gigaam.utils import download_short_audio + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +@pytest.fixture(scope="session") +def test_audio(): + """Provide test audio file for all tests""" + return download_short_audio() + + +@pytest.mark.parametrize("revision", ["emo", "v2_ssl", "v3_ctc", "v3_e2e_rnnt"]) +def test_onnx_converting(revision, test_audio): + """Test specific model revision loads and processes audio (partial models enabled)""" + onnx_dir = "test_onnx_tmp" + model = gigaam.load_model(revision, fp16_encoder=False) + model.to_onnx(dir_path=onnx_dir) + sessions, model_cfg = load_onnx(onnx_dir, revision) + result = infer_onnx(test_audio, model_cfg, sessions) + shutil.rmtree(onnx_dir) + + if "ssl" in revision: + orig_embed = model.embed_audio(test_audio)[0].detach().cpu().numpy() + diff = np.abs(orig_embed - result).max() + assert diff < 0.01, f"{revision}: ONNX embed failed with diff {diff}" + + elif "emo" in revision: + orig_probs = model.get_probs(test_audio) + pred_probs = {model.id2name[i]: result[0, i] for i in range(len(model.id2name))} + assert all( + abs(orig_probs[em] - pred_probs[em]) < 1e-3 for em in orig_probs + ), f"{revision}: ONNX emotions probs failed: {pred_probs}" + + else: + orig_text = model.transcribe(test_audio) + assert orig_text == result, f"{revision}: ONNX transcribe failed: {result}" + + +if __name__ == "__main__": + pytest.main([__file__, "-v", "-m", "partial"]) diff --git a/tests/test_reading.py b/tests/test_reading.py new file mode 100644 index 0000000..257f65b --- /dev/null +++ b/tests/test_reading.py @@ -0,0 +1,58 @@ +import logging + +import librosa +import pytest +import torch +from torch.nn.functional import softmax + +import gigaam +from gigaam.preprocess import SAMPLE_RATE +from gigaam.utils import download_short_audio + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +@pytest.fixture(scope="session") +def test_audio(): + """Provide test audio file for all tests""" + return download_short_audio() + + +@pytest.mark.parametrize("revision", ["emo", "v2_ctc", "v3_e2e_rnnt"]) +def test_librosa_loading(revision, test_audio): + """Test the outputs with librosa.load are close to load_audio""" + model = gigaam.load_model(revision) + wav_tns = torch.from_numpy(librosa.load(test_audio, sr=SAMPLE_RATE)[0]) + lengths = torch.full([1], wav_tns.shape[-1], device=model._device) + with torch.no_grad(): + encoded, encoded_len = model( + wav_tns.unsqueeze(0).to(model._device).to(model._dtype), lengths + ) + if "emo" in revision: + orig_probs = model.get_probs(test_audio) + pred_probs = ( + softmax(model.head(encoded.mean(dim=-1)), dim=-1) + .squeeze() + .cpu() + .tolist() + ) + pred_probs = { + model.id2name[i]: pred_probs[i] for i in range(len(model.id2name)) + } + are_close = ( + max(abs(pred_probs[k] - orig_probs[k]) for k in orig_probs) < 1e-3 + ) + assert ( + are_close + ), f"Emotions with librosa failed: {orig_probs} != {pred_probs}" + else: + orig_text = model.transcribe(test_audio) + 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