diff --git a/examples/ctc_with_timestamp.py b/examples/ctc_with_timestamp.py
new file mode 100644
index 0000000..0b0765e
--- /dev/null
+++ b/examples/ctc_with_timestamp.py
@@ -0,0 +1,131 @@
+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 get_timestamps(logprobs, blank_id, stride, sample_rate):
+    hypotheses, word_timestamps = [], []
+    timestamp_dict = {}
+    last_char = None
+    current_word = ''
+    word_start_frame = 0
+
+    # Алфавит из конфигурации
+    alphabet = [' ', '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']
+
+    for frame, logprob in enumerate(logprobs[0]):
+        char = logprob.argmax().item()
+        
+        if char != blank_id:
+            if char != last_char:
+                if current_word and char == 0:  # Пробел
+                    end_time = frame * stride / sample_rate
+                    timestamp_dict[current_word] = (word_start_frame * stride / sample_rate, end_time)
+                    word_timestamps.append((current_word, word_start_frame * stride / sample_rate, end_time))
+                    hypotheses.append(current_word)
+                    current_word = ''
+                    word_start_frame = frame
+                else:
+                    current_word += alphabet[char]
+            last_char = char
+
+    if current_word:
+        end_time = len(logprobs[0]) * stride / sample_rate
+        timestamp_dict[current_word] = (word_start_frame * stride / sample_rate, end_time)
+        word_timestamps.append((current_word, word_start_frame * stride / sample_rate, end_time))
+        hypotheses.append(current_word)
+
+    return ' '.join(hypotheses), word_timestamps
+
+def parse_with_timestamp(audio_path: str, model: EncDecCTCModel):
+    device = model.device
+    
+    # Загрузка аудио
+    audio, sample_rate = torchaudio.load(audio_path)
+    audio = audio.to(device)
+
+    # Убедимся, что аудио имеет правильную форму (batch, time)
+    if audio.dim() == 1:
+        audio = audio.unsqueeze(0)
+    elif audio.dim() > 2:
+        audio = audio.squeeze()
+    if audio.shape[0] > 1:
+        audio = audio.mean(dim=0, keepdim=True)
+
+    # Получаем длину аудио
+    audio_length = torch.tensor([audio.shape[1]], device=device)
+
+    # Получаем логарифмические вероятности
+    with torch.no_grad():
+        log_probs, encoded_len, greedy_predictions = model(
+            input_signal=audio, input_signal_length=audio_length
+        )
+
+    # Получаем stride
+    stride = model.cfg.preprocessor['n_window_stride']
+    
+    blank_id = len(model.decoder.vocabulary)
+    transcription, timestamps = get_timestamps(log_probs.cpu().numpy(), blank_id, stride, sample_rate)
+
+    # print(f"Transcription: {transcription}")
+    # print("Word timestamps:")
+    # for word, start, end in timestamps:
+    #     print(f"  {word}: {start:.2f}s - {end:.2f}s")
+    
+    return transcription, timestamps
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(device)
+model = EncDecCTCModel.from_config_file("./ctc_model_config.yaml")
+ckpt = torch.load("./ctc_model_weights.ckpt", map_location="cpu")
+model.load_state_dict(ckpt, strict=False)
+model.eval()
+model = model.to(device)
+
+transcription, timestamps = parse_with_timestamp("example.wav", model)
+print(f"transcription: {transcription}")
+print(f"timestamps: {timestamps}")