DL-Art-School/codes/trainer/eval/eval_wer.py

49 lines
2.0 KiB
Python
Raw Normal View History

2022-02-14 03:47:29 +00:00
from copy import deepcopy
from datasets import load_metric
import torch
import trainer.eval.evaluator as evaluator
from data import create_dataset, create_dataloader
from models.asr.w2v_wrapper import only_letters
from models.tacotron2.text import sequence_to_text
class WerEvaluator(evaluator.Evaluator):
"""
Evaluator that produces the WER for a speech recognition model on a test set.
"""
def __init__(self, model, opt_eval, env):
super().__init__(model, opt_eval, env, uses_all_ddp=False)
self.clip_key = opt_eval['clip_key']
self.clip_lengths_key = opt_eval['clip_lengths_key']
self.text_seq_key = opt_eval['text_seq_key']
self.text_seq_lengths_key = opt_eval['text_seq_lengths_key']
self.wer_metric = load_metric('wer')
def perform_eval(self):
val_opt = deepcopy(self.env['opt']['datasets']['val'])
val_opt['batch_size'] = 1 # This is important to ensure no padding.
val_dataset, collate_fn = create_dataset(val_opt, return_collate=True)
val_loader = create_dataloader(val_dataset, val_opt, self.env['opt'], None, collate_fn=collate_fn)
model = self.model.module if hasattr(self.model, 'module') else self.model # Unwrap DDP models
model.eval()
with torch.no_grad():
preds = []
reals = []
for batch in val_loader:
clip = batch[self.clip_key]
assert clip.shape[0] == 1
clip_len = batch[self.clip_lengths_key][0]
clip = clip[:, :, :clip_len].cuda()
pred_seq = model.inference(clip)
preds.append(only_letters(sequence_to_text(pred_seq[0])))
real_seq = batch[self.text_seq_key]
real_seq_len = batch[self.text_seq_lengths_key][0]
real_seq = real_seq[:, :real_seq_len]
reals.append(only_letters(sequence_to_text(real_seq[0])))
wer = self.wer_metric.compute(predictions=preds, references=reals)
model.train()
return {'eval_wer': wer}