forked from mrq/DL-Art-School
108 lines
4.0 KiB
Python
108 lines
4.0 KiB
Python
from copy import deepcopy
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from datasets import load_metric
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import torch
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from tqdm import tqdm
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
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import trainer.eval.evaluator as evaluator
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from data import create_dataset, create_dataloader
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from models.asr.w2v_wrapper import only_letters, Wav2VecWrapper
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from models.tacotron2.text import sequence_to_text
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# Librispeech:
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# baseline: .045% WER.
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# fine-tuned new head (0): .054% WER
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#
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# baseline: .328
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# 0: .342
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# 24000: .346
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def tacotron_detokenize(seq):
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return only_letters(sequence_to_text(seq))
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fb_processor = None
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def fb_detokenize(seq):
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global fb_processor
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if fb_processor is None:
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fb_processor = Wav2Vec2Processor.from_pretrained(f"facebook/wav2vec2-large-960h")
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return fb_processor.decode(seq)
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class WerEvaluator(evaluator.Evaluator):
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"""
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Evaluator that produces the WER for a speech recognition model on a test set.
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"""
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def __init__(self, model, opt_eval, env, detokenizer_fn=tacotron_detokenize):
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super().__init__(model, opt_eval, env, uses_all_ddp=False)
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self.clip_key = opt_eval['clip_key']
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self.clip_lengths_key = opt_eval['clip_lengths_key']
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self.text_seq_key = opt_eval['text_seq_key']
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self.text_seq_lengths_key = opt_eval['text_seq_lengths_key']
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self.wer_metric = load_metric('wer')
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self.detokenizer_fn = detokenizer_fn
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def perform_eval(self):
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val_opt = deepcopy(self.env['opt']['datasets']['val'])
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val_opt['batch_size'] = 1 # This is important to ensure no padding.
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val_dataset, collate_fn = create_dataset(val_opt, return_collate=True)
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val_loader = create_dataloader(val_dataset, val_opt, self.env['opt'], None, collate_fn=collate_fn)
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model = self.model.module if hasattr(self.model, 'module') else self.model # Unwrap DDP models
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model.eval()
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with torch.no_grad():
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preds = []
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reals = []
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for batch in tqdm(val_loader):
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clip = batch[self.clip_key]
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assert clip.shape[0] == 1
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real_seq = batch[self.text_seq_key]
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real_seq_len = batch[self.text_seq_lengths_key][0]
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real_seq = real_seq[:, :real_seq_len]
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real_str = only_letters(sequence_to_text(real_seq[0]))
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if len(real_str) > 0:
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reals.append(real_str)
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else:
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continue # The WER computer doesn't like this scenario.
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clip_len = batch[self.clip_lengths_key][0]
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clip = clip[:, :, :clip_len].cuda()
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pred_seq = model.inference(clip)
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preds.append(self.detokenizer_fn(pred_seq[0]))
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wer = self.wer_metric.compute(predictions=preds, references=reals)
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model.train()
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return {'eval_wer': wer}
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if __name__ == '__main__':
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env = { 'opt': {
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'datasets': {
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'val': {
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'name': 'mass_test',
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'n_workers': 1,
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'batch_size': 1,
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'mode': 'paired_voice_audio',
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'sample_rate': 16000,
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'path': ['y:/bigasr_dataset/mozcv/en/test.tsv'],
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'fetcher_mode': ['mozilla_cv'],
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'max_wav_length': 200000,
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'use_bpe_tokenizer': False,
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'max_text_length': 400,
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'load_conditioning': False,
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'phase': 'eval',
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}
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}
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}}
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opt_eval = {
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'clip_key': 'wav',
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'clip_lengths_key': 'wav_lengths',
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'text_seq_key': 'padded_text',
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'text_seq_lengths_key': 'text_lengths',
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}
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model = Wav2VecWrapper(vocab_size=148, basis_model='facebook/wav2vec2-large-robust-ft-libri-960h', freeze_transformer=True, checkpointing_enabled=False)
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weights = torch.load('X:\\dlas\\experiments/train_wav2vec_mass_diverse_initial_annealing_large_pt/models/7000_wav2vec.pth')
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model.load_state_dict(weights)
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model = model.cuda()
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eval = WerEvaluator(model, opt_eval, env)
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print(eval.perform_eval()) |