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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(audio):
if audio is None:
return "ERROR: You have to either use the microphone or upload an audio file"
audio_samples = librosa.load(audio, sr=ASR_SAMPLING_RATE, mono=True)[0]
inputs = processor(audio_samples, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
inputs = inputs.to(device)
with torch.no_grad():
outputs = model(**inputs).logits
ids = torch.argmax(outputs, dim=-1)[0]
transcription = processor.decode(ids)
return transcription
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