feat_dim, vocab_size

deepspeech/exps/deepspeech2/model.py

@@ -137,7 +137,7 @@ class DeepSpeech2Trainer(Trainer):
     def setup_dataloader(self):
         config = self.config.clone()
         config.defrost()
-        config.data.keep_transcription_text = False
+        config.collator.keep_transcription_text = False
 
         config.data.manifest = config.data.train_manifest
         train_dataset = ManifestDataset.from_config(config)
@@ -165,7 +165,7 @@ class DeepSpeech2Trainer(Trainer):
                 sortagrad=config.data.sortagrad,
                 shuffle_method=config.data.shuffle_method)
 
-        collate_fn = SpeechCollator(config=config, keep_transcription_text=False)
+        collate_fn = SpeechCollator.from_config(config)
         self.train_loader = DataLoader(
             train_dataset,
             batch_sampler=batch_sampler,

deepspeech/frontend/featurizer/speech_featurizer.py

@@ -104,50 +104,7 @@ 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):

deepspeech/frontend/utility.py

@@ -82,7 +82,7 @@ def read_manifest(
         ]
         if all(conditions):
             manifest.append(json_data)
-    return manifest
+    return manifest, json_data["feat_shape"][-1]
 
 
 def rms_to_db(rms: float):

deepspeech/io/collator.py

@@ -22,6 +22,8 @@ from deepspeech.frontend.normalizer import FeatureNormalizer
 from deepspeech.frontend.speech import SpeechSegment
 import io
 import time
+from yacs.config import CfgNode
+from typing import Optional
 
 from collections import namedtuple
 
@@ -33,51 +35,134 @@ logger = Log(__name__).getlog()
 TarLocalData = namedtuple('TarLocalData', ['tar2info', 'tar2object'])
 
 class SpeechCollator():
-    def __init__(self, config, keep_transcription_text=True):
-        """
-        Padding audio features with zeros to make them have the same shape (or
-        a user-defined shape) within one bach.
+    @classmethod
+    def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
+        default = CfgNode(
+            dict(
+                augmentation_config="",
+                random_seed=0,
+                mean_std_filepath="",
+                unit_type="char",
+                vocab_filepath="",
+                spm_model_prefix="",
+                specgram_type='linear',  # 'linear', 'mfcc', 'fbank'
+                feat_dim=0,  # 'mfcc', 'fbank'
+                delta_delta=False,  # 'mfcc', 'fbank'
+                stride_ms=10.0,  # ms
+                window_ms=20.0,  # ms
+                n_fft=None,  # fft points
+                max_freq=None,  # None for samplerate/2
+                target_sample_rate=16000,  # target sample rate
+                use_dB_normalization=True,
+                target_dB=-20,
+                dither=1.0,  # feature dither
+                keep_transcription_text=True
+            ))
 
-        if ``keep_transcription_text`` is False, text is token ids else is raw string.
+        if config is not None:
+            config.merge_from_other_cfg(default)
+        return default
+
+    @classmethod
+    def from_config(cls, config):
+        """Build a SpeechCollator object from a config.
+
+        Args:
+            config (yacs.config.CfgNode): configs object.
+
+        Returns:
+            SpeechCollator: collator object.
         """
-        self._keep_transcription_text = keep_transcription_text
+        assert 'augmentation_config' in config.collator
+        assert 'keep_transcription_text' in config.collator
+        assert 'mean_std_filepath' in config.collator
+        assert 'vocab_filepath' in config.data
+        assert 'specgram_type' in config.collator
+        assert 'n_fft' in config.collator
+        assert config.collator
 
-        if isinstance(config.data.augmentation_config, (str, bytes)):
-            if config.data.augmentation_config:
+        if isinstance(config.collator.augmentation_config, (str, bytes)):
+            if config.collator.augmentation_config:
                 aug_file = io.open(
-                    config.data.augmentation_config, mode='r', encoding='utf8')
+                    config.collator.augmentation_config, mode='r', encoding='utf8')
             else:
                 aug_file = io.StringIO(initial_value='{}', newline='')
         else:
-            aug_file = config.data.augmentation_config
+            aug_file = config.collator.augmentation_config
             assert isinstance(aug_file, io.StringIO)
 
+        speech_collator = cls(
+                aug_file=aug_file,
+                random_seed=0,
+                mean_std_filepath=config.collator.mean_std_filepath,
+                unit_type=config.collator.unit_type,
+                vocab_filepath=config.data.vocab_filepath,
+                spm_model_prefix=config.collator.spm_model_prefix,
+                specgram_type=config.collator.specgram_type, 
+                feat_dim=config.collator.feat_dim, 
+                delta_delta=config.collator.delta_delta, 
+                stride_ms=config.collator.stride_ms, 
+                window_ms=config.collator.window_ms, 
+                n_fft=config.collator.n_fft, 
+                max_freq=config.collator.max_freq, 
+                target_sample_rate=config.collator.target_sample_rate, 
+                use_dB_normalization=config.collator.use_dB_normalization,
+                target_dB=config.collator.target_dB,
+                dither=config.collator.dither, 
+                keep_transcription_text=config.collator.keep_transcription_text
+            )
+        return speech_collator
+
+    def __init__(self, aug_file, mean_std_filepath,  
+                vocab_filepath, spm_model_prefix,
+                random_seed=0,
+                unit_type="char",
+                specgram_type='linear',  # 'linear', 'mfcc', 'fbank'
+                feat_dim=0,  # 'mfcc', 'fbank'
+                delta_delta=False,  # 'mfcc', 'fbank'
+                stride_ms=10.0,  # ms
+                window_ms=20.0,  # ms
+                n_fft=None,  # fft points
+                max_freq=None,  # None for samplerate/2
+                target_sample_rate=16000,  # target sample rate
+                use_dB_normalization=True,
+                target_dB=-20,
+                dither=1.0,
+                keep_transcription_text=True):
+        """
+        Padding audio features with zeros to make them have the same shape (or
+        a user-defined shape) within one bach.
+
+        if ``keep_transcription_text`` is False, text is token ids else is raw string.
+        """
+        self._keep_transcription_text = keep_transcription_text
+
         self._local_data = TarLocalData(tar2info={}, tar2object={})
         self._augmentation_pipeline = AugmentationPipeline(
             augmentation_config=aug_file.read(), 
-            random_seed=config.data.random_seed)
+            random_seed=random_seed)
         
         self._normalizer = FeatureNormalizer(
-            config.data.mean_std_filepath) if config.data.mean_std_filepath else None
+            mean_std_filepath) if mean_std_filepath else None
 
-        self._stride_ms = config.data.stride_ms
-        self._target_sample_rate = config.data.target_sample_rate
+        self._stride_ms = stride_ms
+        self._target_sample_rate = target_sample_rate
 
         self._speech_featurizer = SpeechFeaturizer(
-            unit_type=config.data.unit_type,
-            vocab_filepath=config.data.vocab_filepath,
-            spm_model_prefix=config.data.spm_model_prefix,
-            specgram_type=config.data.specgram_type,
-            feat_dim=config.data.feat_dim,
-            delta_delta=config.data.delta_delta,
-            stride_ms=config.data.stride_ms,
-            window_ms=config.data.window_ms,
-            n_fft=config.data.n_fft,
-            max_freq=config.data.max_freq,
-            target_sample_rate=config.data.target_sample_rate,
-            use_dB_normalization=config.data.use_dB_normalization,
-            target_dB=config.data.target_dB,
-            dither=config.data.dither)
+            unit_type=unit_type,
+            vocab_filepath=vocab_filepath,
+            spm_model_prefix=spm_model_prefix,
+            specgram_type=specgram_type,
+            feat_dim=feat_dim,
+            delta_delta=delta_delta,
+            stride_ms=stride_ms,
+            window_ms=window_ms,
+            n_fft=n_fft,
+            max_freq=max_freq,
+            target_sample_rate=target_sample_rate,
+            use_dB_normalization=use_dB_normalization,
+            target_dB=target_dB,
+            dither=dither)
 
     def _parse_tar(self, file):
         """Parse a tar file to get a tarfile object
@@ -196,3 +281,28 @@ class SpeechCollator():
             texts, padding_value=IGNORE_ID).astype(np.int64)
         text_lens = np.array(text_lens).astype(np.int64)
         return utts, padded_audios, audio_lens, padded_texts, text_lens
+
+    @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._text_featurizer
+        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

deepspeech/io/dataset.py

@@ -55,20 +55,6 @@ class ManifestDataset(Dataset):
                 min_output_len=0.0,
                 max_output_input_ratio=float('inf'),
                 min_output_input_ratio=0.0,
-                stride_ms=10.0,  # ms
-                window_ms=20.0,  # ms
-                n_fft=None,  # fft points
-                max_freq=None,  # None for samplerate/2
-                raw_wav=True,  # use raw_wav or kaldi feature
-                specgram_type='linear',  # 'linear', 'mfcc', 'fbank'
-                feat_dim=0,  # 'mfcc', 'fbank'
-                delta_delta=False,  # 'mfcc', 'fbank'
-                dither=1.0,  # feature dither
-                target_sample_rate=16000,  # target sample rate
-                use_dB_normalization=True,
-                target_dB=-20,
-                random_seed=0,
-                keep_transcription_text=False,
                 batch_size=32,  # batch size
                 num_workers=0,  # data loader workers
                 sortagrad=False,  # sorted in first epoch when True
@@ -116,21 +102,19 @@ class ManifestDataset(Dataset):
             min_output_len=config.data.min_output_len,
             max_output_input_ratio=config.data.max_output_input_ratio,
             min_output_input_ratio=config.data.min_output_input_ratio,
-            stride_ms=config.data.stride_ms,
-            window_ms=config.data.window_ms,
-            n_fft=config.data.n_fft,
-            max_freq=config.data.max_freq,
-            target_sample_rate=config.data.target_sample_rate,
-            specgram_type=config.data.specgram_type,
-            feat_dim=config.data.feat_dim,
-            delta_delta=config.data.delta_delta,
-            dither=config.data.dither,
-            use_dB_normalization=config.data.use_dB_normalization,
-            target_dB=config.data.target_dB,
-            random_seed=config.data.random_seed,
-            keep_transcription_text=config.data.keep_transcription_text)
+            )
         return dataset
 
+    
+    def _read_vocab(self, vocab_filepath):
+        """Load vocabulary from file."""
+        vocab_lines = []
+        with open(vocab_filepath, 'r', encoding='utf-8') as file:
+            vocab_lines.extend(file.readlines())
+        vocab_list = [line[:-1] for line in vocab_lines]
+        return vocab_list
+
+
     def __init__(self,
                  manifest_path,
                  unit_type,
@@ -143,20 +127,7 @@ class ManifestDataset(Dataset):
                  max_output_len=float('inf'),
                  min_output_len=0.0,
                  max_output_input_ratio=float('inf'),
-                 min_output_input_ratio=0.0,
-                 stride_ms=10.0,
-                 window_ms=20.0,
-                 n_fft=None,
-                 max_freq=None,
-                 target_sample_rate=16000,
-                 specgram_type='linear',
-                 feat_dim=None,
-                 delta_delta=False,
-                 dither=1.0,
-                 use_dB_normalization=True,
-                 target_dB=-20,
-                 random_seed=0,
-                 keep_transcription_text=False):
+                 min_output_input_ratio=0.0):
         """Manifest Dataset
 
         Args:
@@ -186,30 +157,11 @@ class ManifestDataset(Dataset):
             keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False.
         """
         super().__init__()
-        self._stride_ms = stride_ms
-        self._target_sample_rate = target_sample_rate
-
-        self._speech_featurizer = SpeechFeaturizer(
-            unit_type=unit_type,
-            vocab_filepath=vocab_filepath,
-            spm_model_prefix=spm_model_prefix,
-            specgram_type=specgram_type,
-            feat_dim=feat_dim,
-            delta_delta=delta_delta,
-            stride_ms=stride_ms,
-            window_ms=window_ms,
-            n_fft=n_fft,
-            max_freq=max_freq,
-            target_sample_rate=target_sample_rate,
-            use_dB_normalization=use_dB_normalization,
-            target_dB=target_dB,
-            dither=dither)
-
-        self._rng = np.random.RandomState(random_seed)
-        self._keep_transcription_text = keep_transcription_text
+
+        # self._rng = np.random.RandomState(random_seed)
 
         # read manifest
-        self._manifest = read_manifest(
+        self._manifest, self._feature_size = read_manifest(
             manifest_path=manifest_path,
             max_input_len=max_input_len,
             min_input_len=min_input_len,
@@ -219,10 +171,60 @@ 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)
+
     @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._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
+    
+
     def __len__(self):
         return len(self._manifest)
 

examples/tiny/s0/conf/deepspeech2.yaml

@@ -4,9 +4,10 @@ data:
   dev_manifest: data/manifest.tiny
   test_manifest: data/manifest.tiny
   mean_std_filepath: data/mean_std.json
+  unit_type: char
   vocab_filepath: data/vocab.txt 
   augmentation_config: conf/augmentation.json
-  batch_size: 2
+  batch_size: 4
   min_input_len: 0.0
   max_input_len: 27.0
   min_output_len: 0.0
@@ -29,6 +30,24 @@ data:
   shuffle_method: batch_shuffle
   num_workers: 0
 
+collator:
+  augmentation_config: conf/augmentation.json
+  random_seed: 0
+  mean_std_filepath: data/mean_std.json
+  spm_model_prefix: 
+  specgram_type: linear
+  feat_dim: 
+  delta_delta: False
+  stride_ms: 10.0
+  window_ms: 20.0
+  n_fft: None
+  max_freq: None
+  target_sample_rate: 16000
+  use_dB_normalization: True
+  target_dB: -20
+  dither: 1.0
+  keep_transcription_text: True
+  
 model:
   num_conv_layers: 2
   num_rnn_layers: 3
@@ -37,7 +56,7 @@ model:
   share_rnn_weights: True 
 
 training:
-  n_epoch: 10
+  n_epoch: 21
   lr: 1e-5 
   lr_decay: 1.0 
   weight_decay: 1e-06