Add LaughterSegmentation to contrib

contrib/models/LaughterSegmentation/README.md

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+# Contrib Model: LaughterSegmentation
+
+Laughter detection on AWS Inferentia2 using `torch_neuronx.trace()`.
+
+## Model Information
+
+- **HuggingFace ID:** `omine-me/LaughterSegmentation`
+- **Model Type:** Wav2Vec2-based audio frame classifier
+- **Parameters:** ~315M (FP32)
+- **Base Model:** `jonatasgrosman/wav2vec2-large-xlsr-53-english`
+- **License:** Check HuggingFace model card
+
+## Architecture Details
+
+The model uses `Wav2Vec2ForAudioFrameClassification` to classify each audio frame as laughter or not-laughter. It takes 7-second audio windows at 16 kHz (112,000 samples) and outputs 349 per-frame binary predictions.
+
+This model uses `torch_neuronx.trace()` (not NxD Inference) since it is an encoder-only classification model rather than an autoregressive LLM.
+
+## Validation Results
+
+**Validated:** 2026-03-04
+**Instance:** inf2.xlarge (1 Inferentia2 chip, 2 NeuronCores)
+**SDK:** Neuron SDK 2.28, PyTorch 2.9
+
+### Benchmark Results (Single Core)
+
+| Batch Size | Mean Latency | Throughput | Real-Time Factor |
+|-----------|-------------|-----------|-----------------|
+| 1 | 18.44 ms | 54.2 W/s | 380x |
+| 2 | 21.27 ms | 94.0 W/s | 658x |
+| 4 | 42.05 ms | 95.1 W/s | 666x |
+| 8 | 83.93 ms | 95.3 W/s | 667x |
+
+### DataParallel Results (Full Instance, 2 Cores)
+
+| Configuration | Throughput | Real-Time Factor | Speedup |
+|--------------|-----------|-----------------|---------|
+| Single core (BS=2) | 94.0 W/s | 658x | 1.0x |
+| DataParallel (2 cores) | 175.2 W/s | 1226x | 1.86x |
+
+### Accuracy Validation
+
+| Input | Cosine Similarity | Frame Agreement |
+|-------|------------------|----------------|
+| Random normal | 1.000000 | 100.00% |
+| Quiet noise | 1.000000 | 100.00% |
+| Loud signal | 1.000000 | 100.00% |
+| Sine 440 Hz | 1.000000 | 100.00% |
+| Silence | 0.999999 | 100.00% |
+
+**Status:** VALIDATED
+
+## Usage
+
+The included notebook (`laughter_neuron_inf2.ipynb`) contains the complete workflow:
+
+1. Download model weights from HuggingFace
+2. Remove `weight_norm` parametrizations (required for SDK 2.28+)
+3. Compile with `torch_neuronx.trace()` across multiple batch sizes
+4. Benchmark single-core and DataParallel throughput
+5. Validate accuracy against CPU reference
+6. Run end-to-end inference on sample audio
+
+```bash
+# On an inf2 or trn2 instance with DLAMI
+source /opt/aws_neuronx_venv_pytorch_inference_vllm_0_13/bin/activate
+pip install jupyter safetensors librosa scipy pydub
+jupyter notebook laughter_neuron_inf2.ipynb
+```
+
+### Known Issues
+
+**weight_norm crash on SDK 2.28+**: Wav2Vec2 uses `weight_norm` on `pos_conv_embed.conv`, which crashes `torch_neuronx.trace()`. The notebook strips parametrizations before tracing:
+
+```python
+import torch.nn.utils.parametrize as parametrize
+for name, module in model.named_modules():
+    if hasattr(module, "parametrizations"):
+        for param_name in list(module.parametrizations.keys()):
+            parametrize.remove_parametrizations(module, param_name)
+```
+
+### trn2 Usage
+
+To run on trn2.3xlarge, uncomment the `--lnc` compiler arg in the compilation cell.
+
+## Compatibility Matrix
+
+| Instance/Version | SDK 2.28 | SDK 2.27 and earlier |
+|------------------|----------|---------------------|
+| inf2.xlarge | VALIDATED | Not tested |
+| trn2.3xlarge | Tested (see project notes) | Not tested |
+
+## Example Checkpoints
+
+* [omine-me/LaughterSegmentation](https://huggingface.co/omine-me/LaughterSegmentation)
+
+## Maintainer
+
+Jim Burtoft
+Community contribution
+
+**Last Updated:** 2026-03-04