forked from mrq/DL-Art-School
50 lines
2.1 KiB
Python
50 lines
2.1 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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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
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from models.tacotron2.text import sequence_to_text
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# Fine-tuned target for w2v-large: 4.487% WER.
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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):
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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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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 val_loader:
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clip = batch[self.clip_key]
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assert clip.shape[0] == 1
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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(only_letters(sequence_to_text(pred_seq[0])))
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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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reals.append(only_letters(sequence_to_text(real_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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