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--- |
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license: apache-2.0 |
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datasets: |
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- mozilla-foundation/common_voice_17_0 |
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language: |
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- uz |
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base_model: |
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- facebook/wav2vec2-large-xlsr-53 |
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pipeline_tag: automatic-speech-recognition |
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--- |
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# Fine-tuned Wav2Vec2-Large-XLSR-53 large model for speech recognition on Uzbek Language |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) |
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on Uzbek using the `train` splits of [Common Voice](https://huggingface.co/datasets/common_voice_17_0). |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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## Usage |
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```python |
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC |
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import torch |
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import torchaudio |
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from typing import Optional, Tuple |
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class Wav2Vec2STTModel: |
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def __init__(self, model_name: str): |
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"""Initialize the Wav2Vec2 model and processor""" |
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self.model_name = model_name |
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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self._load_model() |
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def _load_model(self) -> None: |
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"""Load model and processor from HuggingFace""" |
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try: |
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self.processor = Wav2Vec2Processor.from_pretrained(self.model_name) |
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self.model = Wav2Vec2ForCTC.from_pretrained(self.model_name).to(self.device) |
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except Exception as e: |
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raise RuntimeError(f"Failed to load model: {str(e)}") |
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def preprocess_audio(self, file_path: str) -> Tuple[torch.Tensor, int]: |
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"""Load and preprocess audio file""" |
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try: |
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speech_array, sampling_rate = torchaudio.load(file_path) |
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# Resample if needed |
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if sampling_rate != 16000: |
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resampler = torchaudio.transforms.Resample( |
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orig_freq=sampling_rate, |
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new_freq=16000 |
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) |
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speech_array = resampler(speech_array) |
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return speech_array.squeeze().numpy(), 16000 |
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except FileNotFoundError: |
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raise FileNotFoundError(f"Audio file not found: {file_path}") |
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except Exception as e: |
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raise RuntimeError(f"Audio processing error: {str(e)}") |
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def _replace_unk(self, transcription: str) -> str: |
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"""Replace unknown tokens with apostrophe""" |
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return transcription.replace("[UNK]", "ʼ") |
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def transcribe(self, file_path: str) -> str: |
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"""Transcribe audio file to text""" |
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try: |
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# Preprocess audio |
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speech_array, sampling_rate = self.preprocess_audio(file_path) |
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# Process input |
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inputs = self.processor( |
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speech_array, |
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sampling_rate=sampling_rate, |
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return_tensors="pt" |
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).to(self.device) |
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# Model inference |
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with torch.no_grad(): |
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logits = self.model(inputs.input_values).logits |
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# Decode prediction |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = self.processor.batch_decode(predicted_ids)[0] |
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# Clean up result |
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return self._replace_unk(transcription) |
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except Exception as e: |
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raise RuntimeError(f"Transcription error: {str(e)}") |
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# Example usage |
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if __name__ == "__main__": |
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try: |
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# Initialize model |
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stt_model = Wav2Vec2STTModel("ipilot7/uzbek_speach_to_text") |
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# Transcribe audio |
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result = stt_model.transcribe("1.mp3") |
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print("Transcription:", result) |
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except Exception as e: |
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print(f"Error occurred: {str(e)}") |
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``` |
