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import torch |
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import spaces |
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from transformers import pipeline |
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DEVICE = 0 if torch.cuda.is_available() else "cpu" |
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@spaces.GPU |
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def transcribe(inputs, model, language, batch_size, chunk_length_s, stride_length_s): |
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if inputs is None: |
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raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.") |
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pipe = pipeline( |
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task="automatic-speech-recognition", |
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model=model, |
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chunk_length_s=chunk_length_s, |
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stride_length_s=stride_length_s, |
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device=DEVICE, |
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) |
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forced_decoder_ids = None |
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if model.endswith(".en") == False and language in LANG_TO_ID: |
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forced_decoder_ids = [[2, LANG_TO_ID[language]]] |
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generate_kwargs = {} |
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if forced_decoder_ids: |
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generate_kwargs["forced_decoder_ids"] = forced_decoder_ids |
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output = pipe(inputs, batch_size=batch_size, **generate_kwargs) |
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transcription_text = output['text'] |
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transcription_file_path = "transcription.txt" |
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with open(transcription_file_path, "w") as f: |
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f.write(transcription_text) |
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return transcription_text, transcription_file_path |