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-100h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-100h")

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)