from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC import librosa as lb import torch

tokenizer = Wav2Vec2Tokenizer.from_pretrained('facebook/wav2vec2-base-960h') # Initialize the model model = Wav2Vec2ForCTC.from_pretrained('facebook/wav2vec2-base-960h') model.save_pretrained('./') # Read the sound file waveform, rate = lb.load('./004ae714_nohash_1.wav', sr = 16000) # Tokenize the waveform input_values = tokenizer(waveform, return_tensors='pt').input_values # Retrieve logits from the model logits = model(input_values).logit # Take argmax value and decode into transcription predicted_ids = torch.argmax(logits, dim=-1) transcription = tokenizer.batch_decode(predicted_ids) # Print the output print(transcription)