inference_code/run_inference.py

"""
Inference main class.

Author: Marcely Zanon Boito, 2024
"""

from .CTC_model import mHubertForCTC

import torch
from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor
from transformers import HubertConfig

from datasets import load_dataset

fbk_test_id = 'FBK-MT/Speech-MASSIVE-test'
mhubert_id = 'utter-project/mHuBERT-147'

def load_asr_model():
	# Load the ASR model
	tokenizer = Wav2Vec2CTCTokenizer('vocab.json', unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|")
	feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(mhubert_id)
	processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)

	config = HubertConfig.from_pretrained('config.json')
	model = mHubertForCTC.from_pretrained("naver/mHuBERT-147-ASR-fr", config=config)
	model.eval()
	return model, processor

def run_asr_inference(model, processor, example):
	audio = processor(example["array"], sampling_rate=example["sampling_rate"]).input_values[0]
	input_values = torch.tensor(audio).unsqueeze(0) 

	with torch.no_grad():
		logits = model(input_values).logits
	
	pred_ids = torch.argmax(logits, dim=-1)

	prediction = processor.batch_decode(pred_ids)[0].replace('[CTC]', "")
	return prediction

if __name__ == '__main__':

	# Load the dataset in streaming mode
	dataset = load_dataset(fbk_test_id, 'fr-FR', streaming=True)
	dataset = dataset['test']
	generator = iter(dataset)
	
	# load model
	model, processor = load_asr_model()
	print(model)

	# decode 10 examples from speech-MASSIVE
	num_examples= 10
	while num_examples >= 0:
		example = next(generator)
		
		prediction = run_inference(model, processor, example['audio'])
		
		gold_standard = example['utt']

		print("Gold standard:", gold_standard)
		print("Prediction:", prediction)
		print()
		num_examples-=1