# import 
import librosa
from transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM

# load the processor
processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h")

# load the audio data (use your own wav file here!)
input_audio, sr = librosa.load('my_wav_file.wav', sr=16000)

# tokenize
input_values = processor(input_audio, return_tensors="pt", padding="longest").input_values

# retrieve logits
logits = model(input_values).logits

# decode using n-gram
transcription = processor.batch_decode(logits.detach().numpy()).text

# print the output
print(transcription)