fix bugs

deepspeech/exps/u2/model.py

@@ -368,7 +368,7 @@ class U2Tester(U2Trainer):
             trans.append(''.join([chr(i) for i in ids]))
         return trans
 
-    def compute_metrics(self, utts, audio, audio_len, texts, texts_len, fout=None, fref=None):
+    def compute_metrics(self, utts, audio, audio_len, texts, texts_len, fout=None):
         cfg = self.config.decoding
         errors_sum, len_refs, num_ins = 0.0, 0, 0
         errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors
@@ -402,8 +402,6 @@ class U2Tester(U2Trainer):
             num_ins += 1
             if fout:
                 fout.write(utt + " " + result + "\n")
-            if fref:
-                fref.write(utt + " " + target + "\n")
             logger.info("\nTarget Transcription: %s\nOutput Transcription: %s" %
                         (target, result))
             logger.info("One example error rate [%s] = %f" %
@@ -432,7 +430,6 @@ class U2Tester(U2Trainer):
         num_time = 0.0
         with open(self.args.result_file, 'w') as fout:
             for i, batch in enumerate(self.test_loader):
-                # utt, audio, audio_len, text, text_len = batch
                 metrics = self.compute_metrics(*batch, fout=fout)
                 num_frames += metrics['num_frames']
                 num_time += metrics["decode_time"]

deepspeech/io/dataset.py

@@ -284,7 +284,7 @@ class ManifestDataset(Dataset):
         return self._local_data.tar2object[tarpath].extractfile(
             self._local_data.tar2info[tarpath][filename])
 
-    def process_utterance(self, utt, audio_file, transcript):
+    def process_utterance(self, audio_file, transcript):
         """Load, augment, featurize and normalize for speech data.
 
         :param audio_file: Filepath or file object of audio file.
@@ -323,7 +323,7 @@ class ManifestDataset(Dataset):
         specgram = self._augmentation_pipeline.transform_feature(specgram)
         feature_aug_time = time.time() - start_time
         #logger.debug(f"audio feature augmentation time: {feature_aug_time}")
-        return utt, specgram, transcript_part
+        return specgram, transcript_part
 
     def _instance_reader_creator(self, manifest):
         """
@@ -336,9 +336,7 @@ class ManifestDataset(Dataset):
 
         def reader():
             for instance in manifest:
-                # inst = self.process_utterance(instance["feat"],
-                #                               instance["text"])
-                inst = self.process_utterance(instance["utt"], instance["feat"],
+                inst = self.process_utterance(instance["feat"],
                                               instance["text"])
                 yield inst
 
@@ -349,6 +347,6 @@ class ManifestDataset(Dataset):
 
     def __getitem__(self, idx):
         instance = self._manifest[idx]
-        return self.process_utterance(instance["utt"], instance["feat"],
+        feat, text =self.process_utterance(instance["feat"],
                                       instance["text"])
-        # return self.process_utterance(instance["feat"], instance["text"])
+        return instance["utt"], feat, text

examples/tiny/s1/conf/transformer.yaml

@@ -8,7 +8,7 @@ data:
   spm_model_prefix: 'data/bpe_unigram_200'
   mean_std_filepath: ""
   augmentation_config: conf/augmentation.json
-  batch_size: 2 #4
+  batch_size: 4
   min_input_len: 0.5  # second
   max_input_len: 20.0 # second
   min_output_len: 0.0 # tokens
@@ -31,7 +31,7 @@ data:
   keep_transcription_text: False
   sortagrad: True 
   shuffle_method: batch_shuffle
-  num_workers: 0 #2
+  num_workers: 2
 
 
 # network architecture

examples/tiny/s1/run.sh

@@ -30,12 +30,10 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    # CUDA_VISIBLE_DEVICES=7 
-    ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=7 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     # export ckpt avg_n
-    # CUDA_VISIBLE_DEVICES= 
-    ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
+    CUDA_VISIBLE_DEVICES=  ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
 fi