main files

main.py

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+import torch
+import librosa
+import numpy as np
+import soundfile as sf
+from scipy.io import wavfile
+from IPython.display import Audio
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
+
+
+tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+
+file_name = 'my-audio.wav'
+
+data = wavfile.read(file_name)
+framerate = data[0]
+sounddata = data[1]
+time = np.arange(0,len(sounddata))/framerate
+input_audio, _ = librosa.load(file_name, sr=16000)
+input_values = tokenizer(input_audio, return_tensors="pt").input_values
+logits = model(input_values).logits
+predicted_ids = torch.argmax(logits, dim=-1)
+transcription = tokenizer.batch_decode(predicted_ids)[0]
+print(transcription)