| import librosa | |
| from transformers import Wav2Vec2ForCTC, AutoProcessor | |
| import torch | |
| ASR_SAMPLING_RATE = 16_000 | |
| MODEL_ID = "facebook/wav2vec2-large-960h-lv60-self" | |
| 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 | |