diff --git a/codes/trainer/eval/eval_wer.py b/codes/trainer/eval/eval_wer.py
index b1b6ee2b..b38ff8ae 100644
--- a/codes/trainer/eval/eval_wer.py
+++ b/codes/trainer/eval/eval_wer.py
@@ -3,24 +3,47 @@ from copy import deepcopy
 from datasets import load_metric
 
 import torch
+from tqdm import tqdm
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+
 import trainer.eval.evaluator as evaluator
 from data import create_dataset, create_dataloader
-from models.asr.w2v_wrapper import only_letters
+from models.asr.w2v_wrapper import only_letters, Wav2VecWrapper
 from models.tacotron2.text import sequence_to_text
 
-# Fine-tuned target for w2v-large: 4.487% WER.
+# Librispeech:
+# baseline: .045% WER.
+# fine-tuned new head (0):  .054% WER
+#
+# baseline: .328
+# 0: .342
+# 24000: .346
+
+
+def tacotron_detokenize(seq):
+    return only_letters(sequence_to_text(seq))
+
+
+fb_processor = None
+def fb_detokenize(seq):
+    global fb_processor
+    if fb_processor is None:
+        fb_processor = Wav2Vec2Processor.from_pretrained(f"facebook/wav2vec2-large-960h")
+    return fb_processor.decode(seq)
+
 
 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):
+    def __init__(self, model, opt_eval, env, detokenizer_fn=tacotron_detokenize):
         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')
+        self.detokenizer_fn = detokenizer_fn
 
     def perform_eval(self):
         val_opt = deepcopy(self.env['opt']['datasets']['val'])
@@ -32,18 +55,55 @@ class WerEvaluator(evaluator.Evaluator):
         with torch.no_grad():
             preds = []
             reals = []
-            for batch in val_loader:
+            for batch in tqdm(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])))
+                real_str = only_letters(sequence_to_text(real_seq[0]))
+                if len(real_str) > 0:
+                    reals.append(real_str)
+                else:
+                    continue  # The WER computer doesn't like this scenario.
+                clip_len = batch[self.clip_lengths_key][0]
+                clip = clip[:, :, :clip_len].cuda()
+                pred_seq = model.inference(clip)
+                preds.append(self.detokenizer_fn(pred_seq[0]))
         wer = self.wer_metric.compute(predictions=preds, references=reals)
         model.train()
         return {'eval_wer': wer}
 
+
+if __name__ == '__main__':
+    env = { 'opt': {
+        'datasets': {
+            'val': {
+                'name': 'mass_test',
+                'n_workers': 1,
+                'batch_size': 1,
+                'mode': 'paired_voice_audio',
+                'sample_rate': 16000,
+                'path': ['y:/bigasr_dataset/mozcv/en/test.tsv'],
+                'fetcher_mode': ['mozilla_cv'],
+                'max_wav_length': 200000,
+                'use_bpe_tokenizer': False,
+                'max_text_length': 400,
+                'load_conditioning': False,
+                'phase': 'eval',
+            }
+        }
+    }}
+    opt_eval = {
+        'clip_key': 'wav',
+        'clip_lengths_key': 'wav_lengths',
+        'text_seq_key': 'padded_text',
+        'text_seq_lengths_key': 'text_lengths',
+    }
+    model = Wav2VecWrapper(vocab_size=148, basis_model='facebook/wav2vec2-large-960h', freeze_transformer=True, checkpointing_enabled=False)
+    model.w2v = Wav2Vec2ForCTC.from_pretrained('facebook/wav2vec2-large-960h')
+    weights = torch.load('X:\\dlas\\experiments\\train_wav2vec_mass_large\\models\\0_wav2vec.pth')
+    #model.load_state_dict(weights)
+    model = model.cuda()
+    eval = WerEvaluator(model, opt_eval, env, detokenizer_fn=fb_detokenize)
+    print(eval.perform_eval())
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