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import torch |
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from transformers import pipeline |
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import sys |
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import os |
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MODEL_NAME = "openai/whisper-large-v3-turbo" |
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BATCH_SIZE = 8 |
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device = 0 if torch.cuda.is_available() else "cpu" |
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pipe = pipeline( |
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task="automatic-speech-recognition", |
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model=MODEL_NAME, |
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chunk_length_s=30, |
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device=device, |
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) |
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def transcribe(audio_file_path, task="transcribe"): |
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if not os.path.exists(audio_file_path): |
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print(f"Error: The file '{audio_file_path}' does not exist.") |
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return |
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try: |
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text = pipe(audio_file_path, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"] |
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return text |
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except Exception as e: |
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print(f"Error during transcription: {str(e)}") |
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return None |
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if __name__ == "__main__": |
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if len(sys.argv) < 2: |
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print("Usage: python script.py <audio_file_path> [task]") |
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print("task can be 'transcribe' or 'translate' (default is 'transcribe')") |
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sys.exit(1) |
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audio_file_path = sys.argv[1] |
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task = sys.argv[2] if len(sys.argv) > 2 else "transcribe" |
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if task not in ["transcribe", "translate"]: |
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print("Error: task must be either 'transcribe' or 'translate'") |
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sys.exit(1) |
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result = transcribe(audio_file_path, task) |
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if result: |
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print("Transcription result:") |
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print(result) |
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