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import librosa |
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from transformers import Wav2Vec2ForCTC, AutoProcessor |
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import torch |
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ASR_SAMPLING_RATE = 16_000 |
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MODEL_ID = "facebook/mms-1b-all" |
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processor = AutoProcessor.from_pretrained(MODEL_ID) |
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) |
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def transcribe(audio_source=None, microphone=None, file_upload=None): |
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audio_fp = file_upload if file_upload else microphone |
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if audio_fp is None: |
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return "ERROR: You have to either use the microphone or upload an audio file" |
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audio_samples = librosa.load(audio_fp, sr=ASR_SAMPLING_RATE, mono=True)[0] |
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processor.tokenizer.set_target_lang("fao") |
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model.load_adapter("fao") |
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inputs = processor(audio_samples, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt") |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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inputs = inputs.to(device) |
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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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transcription = processor.decode(ids) |
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return transcription |
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