| import librosa | |
| from transformers import Wav2Vec2ForCTC, AutoProcessor | |
| import torch | |
| ASR_SAMPLING_RATE = 16_000 | |
| MODEL_ID = "facebook/mms-1b-all" | |
| processor = AutoProcessor.from_pretrained(MODEL_ID) | |
| model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) | |
| def transcribe(microphone, file_upload, lang): | |
| warn_output = "" | |
| if (microphone is not None) and (file_upload is not None): | |
| warn_output = ( | |
| "WARNING: You've uploaded an audio file and used the microphone. " | |
| "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" | |
| ) | |
| elif (microphone is None) and (file_upload is None): | |
| return "ERROR: You have to either use the microphone or upload an audio file" | |
| audio_fp = microphone if microphone is not None else file_upload | |
| audio_samples = librosa.load(audio_fp, sr=ASR_SAMPLING_RATE, mono=True)[0] | |
| lang_code = lang.split(":")[0] | |
| processor.tokenizer.set_target_lang(lang_code) | |
| model.load_adapter(lang_code) | |
| inputs = processor( | |
| audio_samples, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt" | |
| ) | |
| with torch.no_grad(): | |
| outputs = model(**inputs).logits | |
| ids = torch.argmax(outputs, dim=-1)[0] | |
| transcription = processor.decode(ids) | |
| return warn_output + transcription | |