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| import torch | |
| import spaces | |
| from transformers import pipeline | |
| DEVICE = 0 if torch.cuda.is_available() else "cpu" | |
| # Whisper's full language ID mapping | |
| LANG_TO_ID = { | |
| "en": 0, "zh": 1, "de": 2, "es": 3, "ru": 4, "ko": 5, "fr": 6, "ja": 7, | |
| "pt": 8, "tr": 9, "pl": 10, "ca": 11, "nl": 12, "ar": 13, "sv": 14, | |
| "it": 15, "id": 16, "hi": 17, "fi": 18, "vi": 19, "he": 20, "uk": 21, | |
| "el": 22, "ms": 23, "cs": 24, "ro": 25, "da": 26, "hu": 27, "ta": 28, | |
| "no": 29, "th": 30, "ur": 31, "hr": 32, "bg": 33, "lt": 34, "la": 35, | |
| "mi": 36, "ml": 37, "cy": 38, "sk": 39, "te": 40, "fa": 41, "lv": 42, | |
| "bn": 43, "sr": 44, "az": 45, "sl": 46, "kn": 47, "et": 48, "mk": 49, | |
| "br": 50, "eu": 51, "is": 52, "hy": 53, "ne": 54, "mn": 55, "bs": 56, | |
| "kk": 57, "sq": 58, "sw": 59, "gl": 60, "mr": 61, "pa": 62, "si": 63, | |
| "km": 64, "sn": 65, "yo": 66, "so": 67, "af": 68, "oc": 69, "ka": 70, | |
| "be": 71, "tg": 72, "sd": 73, "gu": 74, "am": 75, "yi": 76, "lo": 77, | |
| "uz": 78, "fo": 79, "ht": 80, "ps": 81, "tk": 82, "nn": 83, "mt": 84, | |
| "sa": 85, "lb": 86, "my": 87, "bo": 88, "tl": 89, "mg": 90, "as": 91, | |
| "tt": 92, "haw": 93, "ln": 94, "ha": 95, "ba": 96, "jw": 97, "su": 98 | |
| } | |
| def transcribe( | |
| inputs, | |
| model, | |
| language, | |
| batch_size, | |
| chunk_length_s, | |
| stride_length_s | |
| ): | |
| if inputs is None: | |
| raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.") | |
| pipe = pipeline( | |
| task="automatic-speech-recognition", | |
| model=model, | |
| chunk_length_s=chunk_length_s, | |
| stride_length_s=stride_length_s, | |
| device=DEVICE, | |
| ) | |
| forced_decoder_ids = None | |
| if model.endswith(".en") == False and language in LANG_TO_ID: | |
| forced_decoder_ids = [[2, LANG_TO_ID[language]]] # Setting forced decoder for language | |
| generate_kwargs = {} | |
| if forced_decoder_ids: | |
| generate_kwargs["forced_decoder_ids"] = forced_decoder_ids | |
| output = pipe(inputs, batch_size=batch_size, **generate_kwargs) | |
| transcription_text = output['text'] | |
| transcription_file_path = "transcription.txt" | |
| with open(transcription_file_path, "w") as f: | |
| f.write(transcription_text) | |
| return transcription_text, transcription_file_path |