Merge pull request #864 from PaddlePaddle/collator

refactor st and asr collator

deepspeech/exps/u2_st/model.py

@@ -28,10 +28,8 @@ from paddle import distributed as dist
 from paddle.io import DataLoader
 from yacs.config import CfgNode
 
-from deepspeech.io.collator_st import KaldiPrePorocessedCollator
-from deepspeech.io.collator_st import SpeechCollator
-from deepspeech.io.collator_st import TripletKaldiPrePorocessedCollator
-from deepspeech.io.collator_st import TripletSpeechCollator
+from deepspeech.io.collator import SpeechCollator
+from deepspeech.io.collator import TripletSpeechCollator
 from deepspeech.io.dataset import ManifestDataset
 from deepspeech.io.dataset import TripletManifestDataset
 from deepspeech.io.sampler import SortagradBatchSampler
@@ -258,22 +256,13 @@ class U2STTrainer(Trainer):
         config.data.manifest = config.data.dev_manifest
         dev_dataset = Dataset.from_config(config)
 
-        if config.collator.raw_wav:
-            if config.model.model_conf.asr_weight > 0.:
-                Collator = TripletSpeechCollator
-                TestCollator = SpeechCollator
-            else:
-                TestCollator = Collator = SpeechCollator
-            # Not yet implement the mtl loader for raw_wav.
+        if config.model.model_conf.asr_weight > 0.:
+            Collator = TripletSpeechCollator
+            TestCollator = SpeechCollator
         else:
-            if config.model.model_conf.asr_weight > 0.:
-                Collator = TripletKaldiPrePorocessedCollator
-                TestCollator = KaldiPrePorocessedCollator
-            else:
-                TestCollator = Collator = KaldiPrePorocessedCollator
+            TestCollator = Collator = SpeechCollator
 
         collate_fn_train = Collator.from_config(config)
-
         config.collator.augmentation_config = ""
         collate_fn_dev = Collator.from_config(config)
 

deepspeech/frontend/audio.py

@@ -24,8 +24,10 @@ import soundfile
 import soxbindings as sox
 from scipy import signal
 
+from .utility import subfile_from_tar
 
-class AudioSegment(object):
+
+class AudioSegment():
     """Monaural audio segment abstraction.
 
     :param samples: Audio samples [num_samples x num_channels].
@@ -68,16 +70,20 @@ class AudioSegment(object):
                                 self.duration, self.rms_db))
 
     @classmethod
-    def from_file(cls, file):
+    def from_file(cls, file, infos=None):
         """Create audio segment from audio file.
-        
-        :param filepath: Filepath or file object to audio file.
-        :type filepath: str|file
-        :return: Audio segment instance.
-        :rtype: AudioSegment
+
+        Args:
+            filepath (str|file): Filepath or file object to audio file.
+            infos (TarLocalData, optional): tar2obj and tar2infos. Defaults to None.
+
+        Returns:
+            AudioSegment: Audio segment instance.
         """
         if isinstance(file, str) and re.findall(r".seqbin_\d+$", file):
             return cls.from_sequence_file(file)
+        elif isinstance(file, str) and file.startswith('tar:'):
+            return cls.from_file(subfile_from_tar(file, infos))
         else:
             samples, sample_rate = soundfile.read(file, dtype='float32')
             return cls(samples, sample_rate)

deepspeech/frontend/featurizer/speech_featurizer.py

@@ -64,8 +64,12 @@ class SpeechFeaturizer():
                  target_sample_rate=16000,
                  use_dB_normalization=True,
                  target_dB=-20,
-                 dither=1.0):
-        self._audio_featurizer = AudioFeaturizer(
+                 dither=1.0,
+                 maskctc=False):
+        self.stride_ms = stride_ms
+        self.window_ms = window_ms
+
+        self.audio_feature = AudioFeaturizer(
             specgram_type=specgram_type,
             feat_dim=feat_dim,
             delta_delta=delta_delta,
@@ -77,8 +81,12 @@ class SpeechFeaturizer():
             use_dB_normalization=use_dB_normalization,
             target_dB=target_dB,
             dither=dither)
-        self._text_featurizer = TextFeaturizer(unit_type, vocab_filepath,
-                                               spm_model_prefix)
+
+        self.text_feature = TextFeaturizer(
+            unit_type=unit_type,
+            vocab_filepath=vocab_filepath,
+            spm_model_prefix=spm_model_prefix,
+            maskctc=maskctc)
 
     def featurize(self, speech_segment, keep_transcription_text):
         """Extract features for speech segment.
@@ -94,60 +102,33 @@ class SpeechFeaturizer():
         Returns:
             tuple: 1) spectrogram audio feature in 2darray, 2) list oftoken indices.
         """
-        spec_feature = self._audio_featurizer.featurize(speech_segment)
+        spec_feature = self.audio_feature.featurize(speech_segment)
+
         if keep_transcription_text:
             return spec_feature, speech_segment.transcript
+
         if speech_segment.has_token:
             text_ids = speech_segment.token_ids
         else:
-            text_ids = self._text_featurizer.featurize(
-                speech_segment.transcript)
+            text_ids = self.text_feature.featurize(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
+    def text_featurize(self, text, keep_transcription_text):
+        """Extract features for speech segment.
 
-    @property
-    def vocab_dict(self):
-        """Return the vocabulary in dict.
-        Returns:
-            Dict[str, int]: 
-        """
-        return self._text_featurizer.vocab_dict
+        1. For audio parts, extract the audio features.
+        2. For transcript parts, keep the original text or convert text string
+           to a list of token indices in char-level.
 
-    @property
-    def feature_size(self):
-        """Return the audio feature size.
-        Returns:
-            int: audio feature size.
-        """
-        return self._audio_featurizer.feature_size
+        Args:
+            text (str): text.
+            keep_transcription_text (bool): True, keep transcript text, False, token ids
 
-    @property
-    def stride_ms(self):
-        """time length in `ms` unit per frame
         Returns:
-            float: time(ms)/frame
+            (str|List[int]): text, or list of token indices.
         """
-        return self._audio_featurizer.stride_ms
+        if keep_transcription_text:
+            return text
 
-    @property
-    def text_feature(self):
-        """Return the text feature object.
-        Returns:
-            TextFeaturizer: object.
-        """
-        return self._text_featurizer
+        text_ids = self.text_feature.featurize(text)
+        return text_ids

deepspeech/frontend/speech.py

@@ -68,7 +68,12 @@ class SpeechSegment(AudioSegment):
         return not self.__eq__(other)
 
     @classmethod
-    def from_file(cls, filepath, transcript, tokens=None, token_ids=None):
+    def from_file(cls,
+                  filepath,
+                  transcript,
+                  tokens=None,
+                  token_ids=None,
+                  infos=None):
         """Create speech segment from audio file and corresponding transcript.
 
         Args:
@@ -76,12 +81,12 @@ class SpeechSegment(AudioSegment):
             transcript (str): Transcript text for the speech.
             tokens (List[str], optional): text tokens. Defaults to None.
             token_ids (List[int], optional): text token ids. Defaults to None.
+            infos (TarLocalData, optional): tar2obj and tar2infos. Defaults to None.
 
         Returns:
             SpeechSegment: Speech segment instance.
         """
-
-        audio = AudioSegment.from_file(filepath)
+        audio = AudioSegment.from_file(filepath, infos)
         return cls(audio.samples, audio.sample_rate, transcript, tokens,
                    token_ids)
 

deepspeech/frontend/utility.py

@@ -14,6 +14,7 @@
 """Contains data helper functions."""
 import json
 import math
+import tarfile
 from typing import List
 from typing import Optional
 from typing import Text
@@ -112,6 +113,51 @@ def read_manifest(
     return manifest
 
 
+# Tar File read
+TarLocalData = namedtuple('TarLocalData', ['tar2info', 'tar2object'])
+
+
+def parse_tar(file):
+    """Parse a tar file to get a tarfile object
+    and a map containing tarinfoes
+    """
+    result = {}
+    f = tarfile.open(file)
+    for tarinfo in f.getmembers():
+        result[tarinfo.name] = tarinfo
+    return f, result
+
+
+def subfile_from_tar(file, local_data=None):
+    """Get subfile object from tar.
+
+    tar:tarpath#filename
+
+    It will return a subfile object from tar file
+    and cached tar file info for next reading request.
+    """
+    tarpath, filename = file.split(':', 1)[1].split('#', 1)
+
+    if local_data is None:
+        local_data = TarLocalData(tar2info={}, tar2object={})
+
+    assert isinstance(local_data, TarLocalData)
+
+    if 'tar2info' not in local_data.__dict__:
+        local_data.tar2info = {}
+    if 'tar2object' not in local_data.__dict__:
+        local_data.tar2object = {}
+
+    if tarpath not in local_data.tar2info:
+        fobj, infos = parse_tar(tarpath)
+        local_data.tar2info[tarpath] = infos
+        local_data.tar2object[tarpath] = fobj
+    else:
+        fobj = local_data.tar2object[tarpath]
+        infos = local_data.tar2info[tarpath]
+    return fobj.extractfile(infos[filename])
+
+
 def rms_to_db(rms: float):
     """Root Mean Square to dB.
 

deepspeech/io/collator.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import io
-from collections import namedtuple
 from typing import Optional
 
 import numpy as np
@@ -23,96 +22,17 @@ from deepspeech.frontend.featurizer.speech_featurizer import SpeechFeaturizer
 from deepspeech.frontend.normalizer import FeatureNormalizer
 from deepspeech.frontend.speech import SpeechSegment
 from deepspeech.frontend.utility import IGNORE_ID
+from deepspeech.frontend.utility import TarLocalData
+from deepspeech.io.reader import LoadInputsAndTargets
 from deepspeech.io.utility import pad_list
 from deepspeech.utils.log import Log
 
-__all__ = ["SpeechCollator"]
+__all__ = ["SpeechCollator", "TripletSpeechCollator"]
 
 logger = Log(__name__).getlog()
 
-# namedtupe need global for pickle.
-TarLocalData = namedtuple('TarLocalData', ['tar2info', 'tar2object'])
-
-
-class SpeechCollator():
-    @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=False))
-
-        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.
-        """
-        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.collator
-        assert 'specgram_type' in config.collator
-        assert 'n_fft' in config.collator
-        assert config.collator
-
-        if isinstance(config.collator.augmentation_config, (str, bytes)):
-            if config.collator.augmentation_config:
-                aug_file = io.open(
-                    config.collator.augmentation_config,
-                    mode='r',
-                    encoding='utf8')
-            else:
-                aug_file = io.StringIO(initial_value='{}', newline='')
-        else:
-            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.collator.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
 
+class SpeechCollatorBase():
     def __init__(
             self,
             aug_file,
@@ -121,7 +41,7 @@ class SpeechCollator():
             spm_model_prefix,
             random_seed=0,
             unit_type="char",
-            specgram_type='linear',  # 'linear', 'mfcc', 'fbank'
+            spectrum_type='linear',  # 'linear', 'mfcc', 'fbank'
             feat_dim=0,  # 'mfcc', 'fbank'
             delta_delta=False,  # 'mfcc', 'fbank'
             stride_ms=10.0,  # ms
@@ -146,7 +66,7 @@ class SpeechCollator():
             n_fft (int, optional): fft points for rfft. Defaults to None.
             max_freq (int, optional): max cut freq. Defaults to None.
             target_sample_rate (int, optional): target sample rate which used for training. Defaults to 16000.
-            specgram_type (str, optional): 'linear', 'mfcc' or 'fbank'. Defaults to 'linear'.
+            spectrum_type (str, optional): 'linear', 'mfcc' or 'fbank'. Defaults to 'linear'.
             feat_dim (int, optional): audio feature dim, using by 'mfcc' or 'fbank'. Defaults to None.
             delta_delta (bool, optional): audio feature with delta-delta, using by 'fbank' or 'mfcc'. Defaults to False.
             use_dB_normalization (bool, optional): do dB normalization. Defaults to True.
@@ -159,23 +79,27 @@ class SpeechCollator():
         Padding audio features with zeros to make them have the same shape (or
         a user-defined shape) within one batch.
         """
-        self._keep_transcription_text = keep_transcription_text
+        self.keep_transcription_text = keep_transcription_text
+        self.stride_ms = stride_ms
+        self.window_ms = window_ms
+        self.feat_dim = feat_dim
+
+        self.loader = LoadInputsAndTargets()
 
+        # only for tar filetype
         self._local_data = TarLocalData(tar2info={}, tar2object={})
-        self._augmentation_pipeline = AugmentationPipeline(
+
+        self.augmentation = AugmentationPipeline(
             augmentation_config=aug_file.read(), random_seed=random_seed)
 
         self._normalizer = FeatureNormalizer(
             mean_std_filepath) if mean_std_filepath else None
 
-        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,
+            spectrum_type=spectrum_type,
             feat_dim=feat_dim,
             delta_delta=delta_delta,
             stride_ms=stride_ms,
@@ -187,33 +111,11 @@ class SpeechCollator():
             target_dB=target_dB,
             dither=dither)
 
-    def _parse_tar(self, file):
-        """Parse a tar file to get a tarfile object
-        and a map containing tarinfoes
-        """
-        result = {}
-        f = tarfile.open(file)
-        for tarinfo in f.getmembers():
-            result[tarinfo.name] = tarinfo
-        return f, result
-
-    def _subfile_from_tar(self, file):
-        """Get subfile object from tar.
-
-        It will return a subfile object from tar file
-        and cached tar file info for next reading request.
-        """
-        tarpath, filename = file.split(':', 1)[1].split('#', 1)
-        if 'tar2info' not in self._local_data.__dict__:
-            self._local_data.tar2info = {}
-        if 'tar2object' not in self._local_data.__dict__:
-            self._local_data.tar2object = {}
-        if tarpath not in self._local_data.tar2info:
-            object, infoes = self._parse_tar(tarpath)
-            self._local_data.tar2info[tarpath] = infoes
-            self._local_data.tar2object[tarpath] = object
-        return self._local_data.tar2object[tarpath].extractfile(
-            self._local_data.tar2info[tarpath][filename])
+        self.feature_size = self._speech_featurizer.audio_feature.feature_size
+        self.text_feature = self._speech_featurizer.text_feature
+        self.vocab_dict = self.text_feature.vocab_dict
+        self.vocab_list = self.text_feature.vocab_list
+        self.vocab_size = self.text_feature.vocab_size
 
     def process_utterance(self, audio_file, transcript):
         """Load, augment, featurize and normalize for speech data.
@@ -226,23 +128,36 @@ class SpeechCollator():
                  where transcription part could be token ids or text.
         :rtype: tuple of (2darray, list)
         """
-        if isinstance(audio_file, str) and audio_file.startswith('tar:'):
-            speech_segment = SpeechSegment.from_file(
-                self._subfile_from_tar(audio_file), transcript)
+        filetype = self.loader.file_type(audio_file)
+
+        if filetype != 'sound':
+            spectrum = self.loader._get_from_loader(audio_file, filetype)
+            feat_dim = spectrum.shape[1]
+            assert feat_dim == self.feat_dim, f"expect feat dim {self.feat_dim}, but got {feat_dim}"
+
+            if self.keep_transcription_text:
+                transcript_part = transcript
+            else:
+                text_ids = self.text_feature.featurize(transcript)
+                transcript_part = text_ids
         else:
-            speech_segment = SpeechSegment.from_file(audio_file, transcript)
+            # read audio
+            speech_segment = SpeechSegment.from_file(
+                audio_file, transcript, infos=self._local_data)
+            # audio augment
+            self.augmentation.transform_audio(speech_segment)
 
-        # audio augment
-        self._augmentation_pipeline.transform_audio(speech_segment)
+            # extract speech feature
+            spectrum, transcript_part = self._speech_featurizer.featurize(
+                speech_segment, self.keep_transcription_text)
 
-        specgram, transcript_part = self._speech_featurizer.featurize(
-            speech_segment, self._keep_transcription_text)
-        if self._normalizer:
-            specgram = self._normalizer.apply(specgram)
+            # CMVN spectrum
+            if self._normalizer:
+                spectrum = self._normalizer.apply(spectrum)
 
-        # specgram augment
-        specgram = self._augmentation_pipeline.transform_feature(specgram)
-        return specgram, transcript_part
+        # spectrum augment
+        spectrum = self.augmentation.transform_feature(spectrum)
+        return spectrum, transcript_part
 
     def __call__(self, batch):
         """batch examples
@@ -272,16 +187,14 @@ class SpeechCollator():
             audios.append(audio)  # [T, D]
             audio_lens.append(audio.shape[0])
             # text
-            # for training, text is token ids
-            # else text is string, convert to unicode ord
+            # for training, text is token ids, else text is string, convert to unicode ord
             tokens = []
-            if self._keep_transcription_text:
+            if self.keep_transcription_text:
                 assert isinstance(text, str), (type(text), text)
                 tokens = [ord(t) for t in text]
             else:
                 tokens = text  # token ids
-            tokens = tokens if isinstance(tokens, np.ndarray) else np.array(
-                tokens, dtype=np.int64)
+            tokens = np.array(tokens, dtype=np.int64)
             texts.append(tokens)
             text_lens.append(tokens.shape[0])
 
@@ -292,26 +205,162 @@ class SpeechCollator():
         olens = np.array(text_lens).astype(np.int64)
         return utts, xs_pad, ilens, ys_pad, olens
 
-    @property
-    def vocab_size(self):
-        return self._speech_featurizer.vocab_size
 
-    @property
-    def vocab_list(self):
-        return self._speech_featurizer.vocab_list
+class SpeechCollator(SpeechCollatorBase):
+    @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="",
+                spectrum_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=False))
+
+        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.
+        """
+        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.collator
+        assert 'spectrum_type' in config.collator
+        assert 'n_fft' in config.collator
+        assert config.collator
+
+        if isinstance(config.collator.augmentation_config, (str, bytes)):
+            if config.collator.augmentation_config:
+                aug_file = io.open(
+                    config.collator.augmentation_config,
+                    mode='r',
+                    encoding='utf8')
+            else:
+                aug_file = io.StringIO(initial_value='{}', newline='')
+        else:
+            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.collator.vocab_filepath,
+            spm_model_prefix=config.collator.spm_model_prefix,
+            spectrum_type=config.collator.spectrum_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
+
+
+class TripletSpeechCollator(SpeechCollator):
+    def process_utterance(self, audio_file, translation, transcript):
+        """Load, augment, featurize and normalize for speech data.
 
-    @property
-    def vocab_dict(self):
-        return self._speech_featurizer.vocab_dict
+        :param audio_file: Filepath or file object of audio file.
+        :type audio_file: str | file
+        :param translation: translation text.
+        :type translation: str
+        :return: Tuple of audio feature tensor and data of translation part,
+                    where translation part could be token ids or text.
+        :rtype: tuple of (2darray, list)
+        """
+        spectrum, translation_part = super().process_utterance(audio_file,
+                                                               translation)
+        transcript_part = self._speech_featurizer.text_featurize(
+            transcript, self.keep_transcription_text)
+        return spectrum, translation_part, transcript_part
 
-    @property
-    def text_feature(self):
-        return self._speech_featurizer.text_feature
+    def __call__(self, batch):
+        """batch examples
+
+        Args:
+            batch ([List]): batch is (audio, text)
+                audio (np.ndarray) shape (T, D)
+                text (List[int] or str): shape (U,)
 
-    @property
-    def feature_size(self):
-        return self._speech_featurizer.feature_size
+        Returns:
+            tuple(audio, text, audio_lens, text_lens): batched data.
+                audio : (B, Tmax, D)
+                audio_lens: (B)
+                text : (B, Umax)
+                text_lens: (B)
+        """
+        audios = []
+        audio_lens = []
+        translation_text = []
+        translation_text_lens = []
+        transcription_text = []
+        transcription_text_lens = []
 
-    @property
-    def stride_ms(self):
-        return self._speech_featurizer.stride_ms
+        utts = []
+        for utt, audio, translation, transcription in batch:
+            audio, translation, transcription = self.process_utterance(
+                audio, translation, transcription)
+            #utt
+            utts.append(utt)
+            # audio
+            audios.append(audio)  # [T, D]
+            audio_lens.append(audio.shape[0])
+            # text
+            # for training, text is token ids
+            # else text is string, convert to unicode ord
+            tokens = [[], []]
+            for idx, text in enumerate([translation, transcription]):
+                if self.keep_transcription_text:
+                    assert isinstance(text, str), (type(text), text)
+                    tokens[idx] = [ord(t) for t in text]
+                else:
+                    tokens[idx] = text  # token ids
+                tokens[idx] = np.array(tokens[idx], dtype=np.int64)
+
+            translation_text.append(tokens[0])
+            translation_text_lens.append(tokens[0].shape[0])
+            transcription_text.append(tokens[1])
+            transcription_text_lens.append(tokens[1].shape[0])
+
+        padded_audios = pad_sequence(
+            audios, padding_value=0.0).astype(np.float32)  #[B, T, D]
+        audio_lens = np.array(audio_lens).astype(np.int64)
+        padded_translation = pad_sequence(
+            translation_text, padding_value=IGNORE_ID).astype(np.int64)
+        translation_lens = np.array(translation_text_lens).astype(np.int64)
+        padded_transcription = pad_sequence(
+            transcription_text, padding_value=IGNORE_ID).astype(np.int64)
+        transcription_lens = np.array(transcription_text_lens).astype(np.int64)
+        return utts, padded_audios, audio_lens, (
+            padded_translation, padded_transcription), (translation_lens,
+                                                        transcription_lens)

deepspeech/io/collator_st.py

-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import io
-from collections import namedtuple
-from typing import Optional
-
-import kaldiio
-import numpy as np
-from yacs.config import CfgNode
-
-from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline
-from deepspeech.frontend.featurizer.speech_featurizer import SpeechFeaturizer
-from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer
-from deepspeech.frontend.normalizer import FeatureNormalizer
-from deepspeech.frontend.speech import SpeechSegment
-from deepspeech.frontend.utility import IGNORE_ID
-from deepspeech.io.utility import pad_sequence
-from deepspeech.utils.log import Log
-
-__all__ = ["SpeechCollator", "KaldiPrePorocessedCollator"]
-
-logger = Log(__name__).getlog()
-
-# namedtupe need global for pickle.
-TarLocalData = namedtuple('TarLocalData', ['tar2info', 'tar2object'])
-
-
-class SpeechCollator():
-    @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=False))
-
-        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.
-        """
-        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.collator
-        assert 'specgram_type' in config.collator
-        assert 'n_fft' in config.collator
-        assert config.collator
-
-        if isinstance(config.collator.augmentation_config, (str, bytes)):
-            if config.collator.augmentation_config:
-                aug_file = io.open(
-                    config.collator.augmentation_config,
-                    mode='r',
-                    encoding='utf8')
-            else:
-                aug_file = io.StringIO(initial_value='{}', newline='')
-        else:
-            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.collator.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):
-        """SpeechCollator Collator
-
-        Args:
-            unit_type(str): token unit type, e.g. char, word, spm
-            vocab_filepath (str): vocab file path.
-            mean_std_filepath (str): mean and std file path, which suffix is *.npy
-            spm_model_prefix (str): spm model prefix, need if `unit_type` is spm.
-            augmentation_config (str, optional): augmentation json str. Defaults to '{}'.
-            stride_ms (float, optional): stride size in ms. Defaults to 10.0.
-            window_ms (float, optional): window size in ms. Defaults to 20.0.
-            n_fft (int, optional): fft points for rfft. Defaults to None.
-            max_freq (int, optional): max cut freq. Defaults to None.
-            target_sample_rate (int, optional): target sample rate which used for training. Defaults to 16000.
-            specgram_type (str, optional): 'linear', 'mfcc' or 'fbank'. Defaults to 'linear'.
-            feat_dim (int, optional): audio feature dim, using by 'mfcc' or 'fbank'. Defaults to None.
-            delta_delta (bool, optional): audio feature with delta-delta, using by 'fbank' or 'mfcc'. Defaults to False.
-            use_dB_normalization (bool, optional): do dB normalization. Defaults to True.
-            target_dB (int, optional): target dB. Defaults to -20.
-            random_seed (int, optional): for random generator. Defaults to 0.
-            keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False.
-            if ``keep_transcription_text`` is False, text is token ids else is raw string.
-
-        Do augmentations
-        Padding audio features with zeros to make them have the same shape (or
-        a user-defined shape) within one batch.
-        """
-        self._keep_transcription_text = keep_transcription_text
-
-        self._local_data = TarLocalData(tar2info={}, tar2object={})
-        self._augmentation_pipeline = AugmentationPipeline(
-            augmentation_config=aug_file.read(), random_seed=random_seed)
-
-        self._normalizer = FeatureNormalizer(
-            mean_std_filepath) if mean_std_filepath else None
-
-        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)
-
-    def _parse_tar(self, file):
-        """Parse a tar file to get a tarfile object
-        and a map containing tarinfoes
-        """
-        result = {}
-        f = tarfile.open(file)
-        for tarinfo in f.getmembers():
-            result[tarinfo.name] = tarinfo
-        return f, result
-
-    def _subfile_from_tar(self, file):
-        """Get subfile object from tar.
-
-        It will return a subfile object from tar file
-        and cached tar file info for next reading request.
-        """
-        tarpath, filename = file.split(':', 1)[1].split('#', 1)
-        if 'tar2info' not in self._local_data.__dict__:
-            self._local_data.tar2info = {}
-        if 'tar2object' not in self._local_data.__dict__:
-            self._local_data.tar2object = {}
-        if tarpath not in self._local_data.tar2info:
-            object, infoes = self._parse_tar(tarpath)
-            self._local_data.tar2info[tarpath] = infoes
-            self._local_data.tar2object[tarpath] = object
-        return self._local_data.tar2object[tarpath].extractfile(
-            self._local_data.tar2info[tarpath][filename])
-
-    @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
-
-    def process_utterance(self, audio_file, translation):
-        """Load, augment, featurize and normalize for speech data.
-
-        :param audio_file: Filepath or file object of audio file.
-        :type audio_file: str | file
-        :param translation: translation text.
-        :type translation: str
-        :return: Tuple of audio feature tensor and data of translation part,
-                 where translation part could be token ids or text.
-        :rtype: tuple of (2darray, list)
-        """
-        if isinstance(audio_file, str) and audio_file.startswith('tar:'):
-            speech_segment = SpeechSegment.from_file(
-                self._subfile_from_tar(audio_file), translation)
-        else:
-            speech_segment = SpeechSegment.from_file(audio_file, translation)
-
-        # audio augment
-        self._augmentation_pipeline.transform_audio(speech_segment)
-
-        specgram, translation_part = self._speech_featurizer.featurize(
-            speech_segment, self._keep_transcription_text)
-        if self._normalizer:
-            specgram = self._normalizer.apply(specgram)
-
-        # specgram augment
-        specgram = self._augmentation_pipeline.transform_feature(specgram)
-        return specgram, translation_part
-
-    def __call__(self, batch):
-        """batch examples
-
-        Args:
-            batch ([List]): batch is (audio, text)
-                audio (np.ndarray) shape (T, D)
-                text (List[int] or str): shape (U,)
-
-        Returns:
-            tuple(audio, text, audio_lens, text_lens): batched data.
-                audio : (B, Tmax, D)
-                audio_lens: (B)
-                text : (B, Umax)
-                text_lens: (B)
-        """
-        audios = []
-        audio_lens = []
-        texts = []
-        text_lens = []
-        utts = []
-        for utt, audio, text in batch:
-            audio, text = self.process_utterance(audio, text)
-            #utt
-            utts.append(utt)
-            # audio
-            audios.append(audio)  # [T, D]
-            audio_lens.append(audio.shape[0])
-            # text
-            # for training, text is token ids
-            # else text is string, convert to unicode ord
-            tokens = []
-            if self._keep_transcription_text:
-                assert isinstance(text, str), (type(text), text)
-                tokens = [ord(t) for t in text]
-            else:
-                tokens = text  # token ids
-            tokens = tokens if isinstance(tokens, np.ndarray) else np.array(
-                tokens, dtype=np.int64)
-            texts.append(tokens)
-            text_lens.append(tokens.shape[0])
-
-        padded_audios = pad_sequence(
-            audios, padding_value=0.0).astype(np.float32)  #[B, T, D]
-        audio_lens = np.array(audio_lens).astype(np.int64)
-        padded_texts = pad_sequence(
-            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
-
-
-class TripletSpeechCollator(SpeechCollator):
-    def process_utterance(self, audio_file, translation, transcript):
-        """Load, augment, featurize and normalize for speech data.
-
-        :param audio_file: Filepath or file object of audio file.
-        :type audio_file: str | file
-        :param translation: translation text.
-        :type translation: str
-        :return: Tuple of audio feature tensor and data of translation part,
-                    where translation part could be token ids or text.
-        :rtype: tuple of (2darray, list)
-        """
-        if isinstance(audio_file, str) and audio_file.startswith('tar:'):
-            speech_segment = SpeechSegment.from_file(
-                self._subfile_from_tar(audio_file), translation)
-        else:
-            speech_segment = SpeechSegment.from_file(audio_file, translation)
-
-        # audio augment
-        self._augmentation_pipeline.transform_audio(speech_segment)
-
-        specgram, translation_part = self._speech_featurizer.featurize(
-            speech_segment, self._keep_transcription_text)
-        transcript_part = self._speech_featurizer._text_featurizer.featurize(
-            transcript)
-        if self._normalizer:
-            specgram = self._normalizer.apply(specgram)
-
-        # specgram augment
-        specgram = self._augmentation_pipeline.transform_feature(specgram)
-        return specgram, translation_part, transcript_part
-
-    def __call__(self, batch):
-        """batch examples
-
-        Args:
-            batch ([List]): batch is (audio, text)
-                audio (np.ndarray) shape (T, D)
-                text (List[int] or str): shape (U,)
-
-        Returns:
-            tuple(audio, text, audio_lens, text_lens): batched data.
-                audio : (B, Tmax, D)
-                audio_lens: (B)
-                text : (B, Umax)
-                text_lens: (B)
-        """
-        audios = []
-        audio_lens = []
-        translation_text = []
-        translation_text_lens = []
-        transcription_text = []
-        transcription_text_lens = []
-
-        utts = []
-        for utt, audio, translation, transcription in batch:
-            audio, translation, transcription = self.process_utterance(
-                audio, translation, transcription)
-            #utt
-            utts.append(utt)
-            # audio
-            audios.append(audio)  # [T, D]
-            audio_lens.append(audio.shape[0])
-            # text
-            # for training, text is token ids
-            # else text is string, convert to unicode ord
-            tokens = [[], []]
-            for idx, text in enumerate([translation, transcription]):
-                if self._keep_transcription_text:
-                    assert isinstance(text, str), (type(text), text)
-                    tokens[idx] = [ord(t) for t in text]
-                else:
-                    tokens[idx] = text  # token ids
-                tokens[idx] = tokens[idx] if isinstance(
-                    tokens[idx], np.ndarray) else np.array(
-                        tokens[idx], dtype=np.int64)
-            translation_text.append(tokens[0])
-            translation_text_lens.append(tokens[0].shape[0])
-            transcription_text.append(tokens[1])
-            transcription_text_lens.append(tokens[1].shape[0])
-
-        padded_audios = pad_sequence(
-            audios, padding_value=0.0).astype(np.float32)  #[B, T, D]
-        audio_lens = np.array(audio_lens).astype(np.int64)
-        padded_translation = pad_sequence(
-            translation_text, padding_value=IGNORE_ID).astype(np.int64)
-        translation_lens = np.array(translation_text_lens).astype(np.int64)
-        padded_transcription = pad_sequence(
-            transcription_text, padding_value=IGNORE_ID).astype(np.int64)
-        transcription_lens = np.array(transcription_text_lens).astype(np.int64)
-        return utts, padded_audios, audio_lens, (
-            padded_translation, padded_transcription), (translation_lens,
-                                                        transcription_lens)
-
-
-class KaldiPrePorocessedCollator(SpeechCollator):
-    @classmethod
-    def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
-        default = CfgNode(
-            dict(
-                augmentation_config="",
-                random_seed=0,
-                unit_type="char",
-                vocab_filepath="",
-                spm_model_prefix="",
-                feat_dim=0,
-                stride_ms=10.0,
-                keep_transcription_text=False))
-
-        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.
-        """
-        assert 'augmentation_config' in config.collator
-        assert 'keep_transcription_text' in config.collator
-        assert 'vocab_filepath' in config.collator
-        assert config.collator
-
-        if isinstance(config.collator.augmentation_config, (str, bytes)):
-            if config.collator.augmentation_config:
-                aug_file = io.open(
-                    config.collator.augmentation_config,
-                    mode='r',
-                    encoding='utf8')
-            else:
-                aug_file = io.StringIO(initial_value='{}', newline='')
-        else:
-            aug_file = config.collator.augmentation_config
-            assert isinstance(aug_file, io.StringIO)
-
-        speech_collator = cls(
-            aug_file=aug_file,
-            random_seed=0,
-            unit_type=config.collator.unit_type,
-            vocab_filepath=config.collator.vocab_filepath,
-            spm_model_prefix=config.collator.spm_model_prefix,
-            feat_dim=config.collator.feat_dim,
-            stride_ms=config.collator.stride_ms,
-            keep_transcription_text=config.collator.keep_transcription_text)
-        return speech_collator
-
-    def __init__(self,
-                 aug_file,
-                 vocab_filepath,
-                 spm_model_prefix,
-                 random_seed=0,
-                 unit_type="char",
-                 feat_dim=0,
-                 stride_ms=10.0,
-                 keep_transcription_text=True):
-        """SpeechCollator Collator
-
-        Args:
-            unit_type(str): token unit type, e.g. char, word, spm
-            vocab_filepath (str): vocab file path.
-            spm_model_prefix (str): spm model prefix, need if `unit_type` is spm.
-            augmentation_config (str, optional): augmentation json str. Defaults to '{}'.
-            random_seed (int, optional): for random generator. Defaults to 0.
-            keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False.
-            if ``keep_transcription_text`` is False, text is token ids else is raw string.
-
-        Do augmentations
-        Padding audio features with zeros to make them have the same shape (or
-        a user-defined shape) within one batch.
-        """
-        self._keep_transcription_text = keep_transcription_text
-        self._feat_dim = feat_dim
-        self._stride_ms = stride_ms
-
-        self._local_data = TarLocalData(tar2info={}, tar2object={})
-        self._augmentation_pipeline = AugmentationPipeline(
-            augmentation_config=aug_file.read(), random_seed=random_seed)
-
-        self._text_featurizer = TextFeaturizer(unit_type, vocab_filepath,
-                                               spm_model_prefix)
-
-    def process_utterance(self, audio_file, translation):
-        """Load, augment, featurize and normalize for speech data.
-
-        :param audio_file: Filepath or file object of kaldi processed feature.
-        :type audio_file: str | file
-        :param translation: Translation text.
-        :type translation: str
-        :return: Tuple of audio feature tensor and data of translation part,
-                 where translation part could be token ids or text.
-        :rtype: tuple of (2darray, list)
-        """
-        specgram = kaldiio.load_mat(audio_file)
-        assert specgram.shape[
-            1] == self._feat_dim, 'expect feat dim {}, but got {}'.format(
-                self._feat_dim, specgram.shape[1])
-
-        # specgram augment
-        specgram = self._augmentation_pipeline.transform_feature(specgram)
-
-        if self._keep_transcription_text:
-            return specgram, translation
-        else:
-            text_ids = self._text_featurizer.featurize(translation)
-            return specgram, text_ids
-
-
-class TripletKaldiPrePorocessedCollator(KaldiPrePorocessedCollator):
-    def process_utterance(self, audio_file, translation, transcript):
-        """Load, augment, featurize and normalize for speech data.
-
-        :param audio_file: Filepath or file object of kali processed feature.
-        :type audio_file: str | file
-        :param translation: Translation text.
-        :type translation: str
-        :param transcript: Transcription text.
-        :type transcript: str
-        :return: Tuple of audio feature tensor and data of translation and transcription parts,
-                 where translation and transcription parts could be token ids or text.
-        :rtype: tuple of (2darray, (list, list))
-        """
-        specgram = kaldiio.load_mat(audio_file)
-        assert specgram.shape[
-            1] == self._feat_dim, 'expect feat dim {}, but got {}'.format(
-                self._feat_dim, specgram.shape[1])
-
-        # specgram augment
-        specgram = self._augmentation_pipeline.transform_feature(specgram)
-
-        if self._keep_transcription_text:
-            return specgram, translation, transcript
-        else:
-            translation_text_ids = self._text_featurizer.featurize(translation)
-            transcript_text_ids = self._text_featurizer.featurize(transcript)
-            return specgram, translation_text_ids, transcript_text_ids
-
-    def __call__(self, batch):
-        """batch examples
-
-        Args:
-            batch ([List]): batch is (audio, text)
-                audio (np.ndarray) shape (T, D)
-                translation (List[int] or str): shape (U,)
-                transcription (List[int] or str): shape (V,)
-
-        Returns:
-            tuple(audio, text, audio_lens, text_lens): batched data.
-                audio : (B, Tmax, D)
-                audio_lens: (B)
-                translation_text : (B, Umax)
-                translation_text_lens: (B)
-                transcription_text : (B, Vmax)
-                transcription_text_lens: (B)
-        """
-        audios = []
-        audio_lens = []
-        translation_text = []
-        translation_text_lens = []
-        transcription_text = []
-        transcription_text_lens = []
-
-        utts = []
-        for utt, audio, translation, transcription in batch:
-            audio, translation, transcription = self.process_utterance(
-                audio, translation, transcription)
-            #utt
-            utts.append(utt)
-            # audio
-            audios.append(audio)  # [T, D]
-            audio_lens.append(audio.shape[0])
-            # text
-            # for training, text is token ids
-            # else text is string, convert to unicode ord
-            tokens = [[], []]
-            for idx, text in enumerate([translation, transcription]):
-                if self._keep_transcription_text:
-                    assert isinstance(text, str), (type(text), text)
-                    tokens[idx] = [ord(t) for t in text]
-                else:
-                    tokens[idx] = text  # token ids
-                tokens[idx] = tokens[idx] if isinstance(
-                    tokens[idx], np.ndarray) else np.array(
-                        tokens[idx], dtype=np.int64)
-            translation_text.append(tokens[0])
-            translation_text_lens.append(tokens[0].shape[0])
-            transcription_text.append(tokens[1])
-            transcription_text_lens.append(tokens[1].shape[0])
-
-        padded_audios = pad_sequence(
-            audios, padding_value=0.0).astype(np.float32)  #[B, T, D]
-        audio_lens = np.array(audio_lens).astype(np.int64)
-        padded_translation = pad_sequence(
-            translation_text, padding_value=IGNORE_ID).astype(np.int64)
-        translation_lens = np.array(translation_text_lens).astype(np.int64)
-        padded_transcription = pad_sequence(
-            transcription_text, padding_value=IGNORE_ID).astype(np.int64)
-        transcription_lens = np.array(transcription_text_lens).astype(np.int64)
-        return utts, padded_audios, audio_lens, (
-            padded_translation, padded_transcription), (translation_lens,
-                                                        transcription_lens)

deepspeech/io/reader.py

@@ -321,6 +321,22 @@ class LoadInputsAndTargets():
             raise NotImplementedError(
                 "Not supported: loader_type={}".format(filetype))
 
+    def file_type(self, filepath):
+        suffix = filepath.split(":")[0].split('.')[1]
+        if suffix == 'ark':
+            return 'mat'
+        elif suffix == 'scp':
+            return 'scp'
+        elif suffix == 'npy':
+            return 'npy'
+        elif suffix == 'npz':
+            return 'npz'
+        elif suffix in ['wav', 'flac']:
+            # PCM16
+            return 'sound'
+        else:
+            raise ValueError(f"Not support filetype: {suffix}")
+
 
 class SoundHDF5File():
     """Collecting sound files to a HDF5 file