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import librosa |
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import torch |
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from transformers import Wav2Vec2ForCTC, AutoProcessor |
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from transformers import set_seed |
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import time |
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def transcribe(fp:str, target_lang:str) -> str: |
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''' |
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For given audio file, transcribe it. |
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Parameters |
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---------- |
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fp: str |
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The file path to the audio file. |
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target_lang:str |
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The ISO-3 code of the target language. |
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Returns |
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---------- |
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transcript:str |
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The transcribed text. |
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''' |
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set_seed(555) |
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start_time = time.time() |
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model_id = "facebook/mms-1b-all" |
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processor = AutoProcessor.from_pretrained(model_id, target_lang=target_lang) |
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model = Wav2Vec2ForCTC.from_pretrained(model_id, target_lang=target_lang, ignore_mismatched_sizes=True) |
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signal, sampling_rate = librosa.load(fp, sr=16000) |
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inputs = processor(signal, sampling_rate=16_000, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs).logits |
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ids = torch.argmax(outputs, dim=-1)[0] |
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transcript = processor.decode(ids) |
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print("Time elapsed: ", int(time.time() - start_time), " seconds") |
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return transcript |
