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") # Set language ID for Faroese language_id = 'fao' # ISO 639-3 code for Faroese processor.tokenizer.set_lang(language_id) 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