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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
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