wav2vec_aligen.py

import torch
import librosa
import os
from model import Wav2Vec2ForWav2Vec2ForCTCAndUttranceRegression
from transformers import Wav2Vec2Processor
from optimum.bettertransformer import BetterTransformer

device = 'cuda' if torch.cuda.is_available() else 'cpu'
os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python'
os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = '1'
os.environ['TRANSFORMERS_VERBOSITY'] = 'error'
torch.random.manual_seed(0); 
# protobuf==3.20.0

model_name = "seba3y/wav2vec-base-en-pronunciation-assesment"
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2ForWav2Vec2ForCTCAndUttranceRegression.from_pretrained(model_name).to(device)
model = BetterTransformer.transform(model)

def load_audio(audio_path, processor):
    audio, sr = librosa.load(audio_path, sr=16000)

    input_values = processor(audio, sampling_rate=16000, return_tensors="pt").input_values
    return input_values
        
@torch.inference_mode()
def get_emissions(input_values, model):
    results = model(input_values,).logits
    results.pop('logits')
    return results


def speaker_pronunciation_assesment(audio_path):
    input_values = load_audio(audio_path, processor)
    result_scores = get_emissions(input_values, model)

    content_scores         = round(result_scores['content'].cpu().item())
    pronunciation_score = round(result_scores['accuracy'].cpu().item())
    fluency_score           = round(result_scores['fluency'].cpu().item())
    total_score              = round(result_scores['total score'].cpu().item())
     

    result = {'pronunciation_accuracy': pronunciation_score,
              'content_scores': content_scores,
              'total_score': total_score,
              'fluency_score': fluency_score}
    return result

if __name__ == '__main__':
    print(__naem__)