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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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LANG_TO_ID = { |
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"en": 0, "zh": 1, "de": 2, "es": 3, "ru": 4, "ko": 5, "fr": 6, "ja": 7, |
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"pt": 8, "tr": 9, "pl": 10, "ca": 11, "nl": 12, "ar": 13, "sv": 14, |
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"it": 15, "id": 16, "hi": 17, "fi": 18, "vi": 19, "he": 20, "uk": 21, |
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"el": 22, "ms": 23, "cs": 24, "ro": 25, "da": 26, "hu": 27, "ta": 28, |
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"no": 29, "th": 30, "ur": 31, "hr": 32, "bg": 33, "lt": 34, "la": 35, |
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"mi": 36, "ml": 37, "cy": 38, "sk": 39, "te": 40, "fa": 41, "lv": 42, |
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"bn": 43, "sr": 44, "az": 45, "sl": 46, "kn": 47, "et": 48, "mk": 49, |
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"br": 50, "eu": 51, "is": 52, "hy": 53, "ne": 54, "mn": 55, "bs": 56, |
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"kk": 57, "sq": 58, "sw": 59, "gl": 60, "mr": 61, "pa": 62, "si": 63, |
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"km": 64, "sn": 65, "yo": 66, "so": 67, "af": 68, "oc": 69, "ka": 70, |
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"be": 71, "tg": 72, "sd": 73, "gu": 74, "am": 75, "yi": 76, "lo": 77, |
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"uz": 78, "fo": 79, "ht": 80, "ps": 81, "tk": 82, "nn": 83, "mt": 84, |
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"sa": 85, "lb": 86, "my": 87, "bo": 88, "tl": 89, "mg": 90, "as": 91, |
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"tt": 92, "haw": 93, "ln": 94, "ha": 95, "ba": 96, "jw": 97, "su": 98 |
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} |
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@spaces.GPU |
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def transcribe( |
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inputs, |
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model, |
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language, |
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batch_size, |
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chunk_length_s, |
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stride_length_s |
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): |
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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( |
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inputs, |
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batch_size=batch_size, |
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**generate_kwargs |
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) |
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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 |
