finish aishell/s0

deepspeech/exps/deepspeech2/model.py

@@ -102,8 +102,8 @@ class DeepSpeech2Trainer(Trainer):
     def setup_model(self):
         config = self.config
         model = DeepSpeech2Model(
-            feat_size=self.train_loader.dataset.feature_size,
+            feat_size=self.train_loader.collate_fn.feature_size,
-            dict_size=self.train_loader.dataset.vocab_size,
+            dict_size=self.train_loader.collate_fn.vocab_size,
             num_conv_layers=config.model.num_conv_layers,
             num_rnn_layers=config.model.num_rnn_layers,
             rnn_size=config.model.rnn_layer_size,
@@ -199,7 +199,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors
         error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer
 
-        vocab_list = self.test_loader.dataset.vocab_list
+        vocab_list = self.test_loader.collate_fn.vocab_list
 
         target_transcripts = self.ordid2token(texts, texts_len)
         result_transcripts = self.model.decode(
@@ -272,7 +272,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         infer_model = DeepSpeech2InferModel.from_pretrained(
             self.test_loader.dataset, self.config, self.args.checkpoint_path)
         infer_model.eval()
-        feat_dim = self.test_loader.dataset.feature_size
+        feat_dim = self.test_loader.collate_fn.feature_size
         static_model = paddle.jit.to_static(
             infer_model,
             input_spec=[
@@ -308,8 +308,8 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
     def setup_model(self):
         config = self.config
         model = DeepSpeech2Model(
-            feat_size=self.test_loader.dataset.feature_size,
+            feat_size=self.test_loader.collate_fn.feature_size,
-            dict_size=self.test_loader.dataset.vocab_size,
+            dict_size=self.test_loader.collate_fn.vocab_size,
             num_conv_layers=config.model.num_conv_layers,
             num_rnn_layers=config.model.num_rnn_layers,
             rnn_size=config.model.rnn_layer_size,

deepspeech/exps/u2/model.py

@@ -279,8 +279,8 @@ class U2Trainer(Trainer):
         config = self.config
         model_conf = config.model
         model_conf.defrost()
-        model_conf.input_dim = self.train_loader.dataset.feature_size
+        model_conf.input_dim = self.train_loader.collate_fn.feature_size
-        model_conf.output_dim = self.train_loader.dataset.vocab_size
+        model_conf.output_dim = self.train_loader.collate_fn.vocab_size
         model_conf.freeze()
         model = U2Model.from_config(model_conf)
 
@@ -497,7 +497,7 @@ class U2Tester(U2Trainer):
         infer_model = U2InferModel.from_pretrained(self.test_loader.dataset,
                                                    self.config.model.clone(),
                                                    self.args.checkpoint_path)
-        feat_dim = self.test_loader.dataset.feature_size
+        feat_dim = self.test_loader.collate_fn.feature_size
         input_spec = [
             paddle.static.InputSpec(
                 shape=[None, feat_dim, None],

deepspeech/frontend/featurizer/speech_featurizer.py

@@ -104,13 +104,60 @@ class SpeechFeaturizer(object):
                 speech_segment.transcript)
         return spec_feature, text_ids
 
+    @property
+    def vocab_size(self):
+        """Return the vocabulary size.
+        Returns:
+            int: Vocabulary size.
+        """
+        return self._text_featurizer.vocab_size
 
+    @property
+    def vocab_list(self):
+        """Return the vocabulary in list.
+        Returns:
+            List[str]: 
+        """
+        return self._text_featurizer.vocab_list
+
+    @property
+    def vocab_dict(self):
+        """Return the vocabulary in dict.
+        Returns:
+            Dict[str, int]: 
+        """
+        return self._text_featurizer.vocab_dict
+
+    @property
+    def feature_size(self):
+        """Return the audio feature size.
+        Returns:
+            int: audio feature size.
+        """
+        return self._audio_featurizer.feature_size
+
+    @property
+    def stride_ms(self):
+        """time length in `ms` unit per frame
+        Returns:
+            float: time(ms)/frame
+        """
+        return self._audio_featurizer.stride_ms
 
     @property
     def text_feature(self):
         """Return the text feature object.
-
         Returns:
             TextFeaturizer: object.
         """
         return self._text_featurizer
+        
+
+    # @property
+    # def text_feature(self):
+    #     """Return the text feature object.
+
+    #     Returns:
+    #         TextFeaturizer: object.
+    #     """
+    #     return self._text_featurizer

deepspeech/io/collator.py

@@ -283,11 +283,41 @@ class SpeechCollator():
         return utts, padded_audios, audio_lens, padded_texts, text_lens
 
 
+    # @property
+    # def text_feature(self):
+    #     return self._speech_featurizer.text_feature
+
+
+    # @property
+    # def stride_ms(self):
+    #     return self._speech_featurizer.stride_ms
+
+###########
+    
+    @property
+    def manifest(self):
+        return self._manifest
+
+    @property
+    def vocab_size(self):
+        return self._speech_featurizer.vocab_size
+
+    @property
+    def vocab_list(self):
+        return self._speech_featurizer.vocab_list
+
+    @property
+    def vocab_dict(self):
+        return self._speech_featurizer.vocab_dict
+
     @property
     def text_feature(self):
         return self._speech_featurizer.text_feature
 
+    @property
+    def feature_size(self):
+        return self._speech_featurizer.feature_size
 
     @property
     def stride_ms(self):
-        return self._speech_featurizer.stride_ms
+        return self._speech_featurizer.stride_ms

deepspeech/io/dataset.py

@@ -55,10 +55,6 @@ class ManifestDataset(Dataset):
                 min_output_len=0.0,
                 max_output_input_ratio=float('inf'),
                 min_output_input_ratio=0.0,
-                batch_size=32,  # batch size
-                num_workers=0,  # data loader workers
-                sortagrad=False,  # sorted in first epoch when True
-                shuffle_method="batch_shuffle",  # 'batch_shuffle', 'instance_shuffle'
             ))
 
         if config is not None:
@@ -77,7 +73,7 @@ class ManifestDataset(Dataset):
         """
         assert 'manifest' in config.data
         assert config.data.manifest
-        assert 'keep_transcription_text' in config.data
+        assert 'keep_transcription_text' in config.collator
 
         if isinstance(config.data.augmentation_config, (str, bytes)):
             if config.data.augmentation_config:
@@ -171,51 +167,51 @@ class ManifestDataset(Dataset):
             min_output_input_ratio=min_output_input_ratio)
         self._manifest.sort(key=lambda x: x["feat_shape"][0])
 
-        self._vocab_list = self._read_vocab(vocab_filepath)
+        # self._vocab_list = self._read_vocab(vocab_filepath)
 
-    @property
-    def manifest(self):
-        return self._manifest
-    
-    @property
-    def vocab_size(self):
-        """Return the vocabulary size.
-
-        Returns:
-            int: Vocabulary size.
-        """
-        return len(self._vocab_list)
-
-    @property
-    def vocab_list(self):
-        """Return the vocabulary in list.
-
-        Returns:
-            List[str]: 
-        """
-        return self._vocab_list
-
-    @property
-    def vocab_dict(self):
-        """Return the vocabulary in dict.
-
-        Returns:
-            Dict[str, int]: 
-        """
-        vocab_dict = dict(
-            [(token, idx) for (idx, token) in enumerate(self._vocab_list)])
-        return vocab_dict
-
-    @property
-    def feature_size(self):
-        """Return the audio feature size.
-
-        Returns:
-            int: audio feature size.
-        """
-        return self._manifest[0]["feat_shape"][-1]
 
+    # @property
+    # def manifest(self):
+    #     return self._manifest
     
+    # @property
+    # def vocab_size(self):
+    #     """Return the vocabulary size.
+
+    #     Returns:
+    #         int: Vocabulary size.
+    #     """
+    #     return len(self._vocab_list)
+
+    # @property
+    # def vocab_list(self):
+    #     """Return the vocabulary in list.
+
+    #     Returns:
+    #         List[str]: 
+    #     """
+    #     return self._vocab_list
+
+    # @property
+    # def vocab_dict(self):
+    #     """Return the vocabulary in dict.
+
+    #     Returns:
+    #         Dict[str, int]: 
+    #     """
+    #     vocab_dict = dict(
+    #         [(token, idx) for (idx, token) in enumerate(self._vocab_list)])
+    #     return vocab_dict
+
+    # @property
+    # def feature_size(self):
+    #     """Return the audio feature size.
+
+    #     Returns:
+    #         int: audio feature size.
+    #     """
+    #     return self._manifest[0]["feat_shape"][-1]
+
 
     def __len__(self):
         return len(self._manifest)

examples/aishell/s0/conf/deepspeech2.yaml

@@ -5,7 +5,6 @@ data:
   test_manifest: data/manifest.test
   mean_std_filepath: data/mean_std.json
   vocab_filepath: data/vocab.txt 
-  augmentation_config: conf/augmentation.json
   batch_size: 64 # one gpu
   min_input_len: 0.0
   max_input_len: 27.0 # second
@@ -13,21 +12,26 @@ data:
   max_output_len: .inf
   min_output_input_ratio: 0.00
   max_output_input_ratio: .inf
+  sortagrad: True
+  shuffle_method: batch_shuffle
+  num_workers: 0
+
+collator:
+  augmentation_config: conf/augmentation.json
+  random_seed: 0
+  spm_model_prefix: 
   specgram_type: linear
-  target_sample_rate: 16000
+  feat_dim: 
-  max_freq: None
+  delta_delta: False
-  n_fft: None
   stride_ms: 10.0
   window_ms: 20.0
-  delta_delta: False
+  n_fft: None
-  dither: 1.0
+  max_freq: None
+  target_sample_rate: 16000
   use_dB_normalization: True
   target_dB: -20
-  random_seed: 0
+  dither: 1.0
   keep_transcription_text: False
-  sortagrad: True
-  shuffle_method: batch_shuffle
-  num_workers: 0
 
 model:
   num_conv_layers: 2

examples/tiny/s0/conf/deepspeech2.yaml

@@ -42,7 +42,7 @@ model:
   share_rnn_weights: True 
 
 training:
-  n_epoch: 21
+  n_epoch: 23
   lr: 1e-5 
   lr_decay: 1.0 
   weight_decay: 1e-06

examples/tiny/s1/conf/transformer.yaml

@@ -72,7 +72,7 @@ model:
 
 
 training:
-  n_epoch: 3
+  n_epoch: 21
   accum_grad: 1
   global_grad_clip: 5.0
   optim: adam