Merge pull request #756 from PaddlePaddle/filter

test w/ all example & fix ctc api & add new io

.notebook/u2_confermer_model_wenet.ipynb

@@ -3431,7 +3431,7 @@
     "        convolution_layer_args = (output_size, cnn_module_kernel, activation,\n",
     "                                  cnn_module_norm, causal)\n",
     "\n",
-    "        self.encoders = nn.ModuleList([\n",
+    "        self.encoders = nn.LayerList([\n",
     "            ConformerEncoderLayer(\n",
     "                size=output_size,\n",
     "                self_attn=encoder_selfattn_layer(*encoder_selfattn_layer_args),\n",

deepspeech/__init__.py

@@ -30,24 +30,13 @@ logger = Log(__name__).getlog()
 logger.warn = logger.warning
 
 ########### hcak paddle #############
-paddle.bool = 'bool'
-paddle.float16 = 'float16'
 paddle.half = 'float16'
-paddle.float32 = 'float32'
 paddle.float = 'float32'
-paddle.float64 = 'float64'
 paddle.double = 'float64'
-paddle.int8 = 'int8'
-paddle.int16 = 'int16'
 paddle.short = 'int16'
-paddle.int32 = 'int32'
 paddle.int = 'int32'
-paddle.int64 = 'int64'
 paddle.long = 'int64'
-paddle.uint8 = 'uint8'
 paddle.uint16 = 'uint16'
-paddle.complex64 = 'complex64'
-paddle.complex128 = 'complex128'
 paddle.cdouble = 'complex128'
 
 
@@ -403,45 +392,7 @@ if not hasattr(paddle.nn.functional, 'glu'):
 #         return x * 0.5 * (1.0 + paddle.erf(x / math.sqrt(2.0)))
 
 
-# hack loss
-def ctc_loss(logits,
-             labels,
-             input_lengths,
-             label_lengths,
-             blank=0,
-             reduction='mean',
-             norm_by_times=True):
-    #logger.info("my ctc loss with norm by times")
-    ## https://github.com/PaddlePaddle/Paddle/blob/f5ca2db2cc/paddle/fluid/operators/warpctc_op.h#L403
-    loss_out = paddle.fluid.layers.warpctc(logits, labels, blank, norm_by_times,
-                                           input_lengths, label_lengths)
-
-    loss_out = paddle.fluid.layers.squeeze(loss_out, [-1])
-    assert reduction in ['mean', 'sum', 'none']
-    if reduction == 'mean':
-        loss_out = paddle.mean(loss_out / label_lengths)
-    elif reduction == 'sum':
-        loss_out = paddle.sum(loss_out)
-    return loss_out
-
-
-logger.warn(
-    "override ctc_loss of paddle.nn.functional if exists, remove this when fixed!"
-)
-F.ctc_loss = ctc_loss
-
 ########### hcak paddle.nn #############
-if not hasattr(paddle.nn, 'Module'):
-    logger.warn("register user Module to paddle.nn, remove this when fixed!")
-    setattr(paddle.nn, 'Module', paddle.nn.Layer)
-
-# maybe cause assert isinstance(sublayer, core.Layer)
-if not hasattr(paddle.nn, 'ModuleList'):
-    logger.warn(
-        "register user ModuleList to paddle.nn, remove this when fixed!")
-    setattr(paddle.nn, 'ModuleList', paddle.nn.LayerList)
-
-
 class GLU(nn.Layer):
     """Gated Linear Units (GLU) Layer"""
 

deepspeech/exps/u2/model.py

@@ -264,12 +264,12 @@ class U2Trainer(Trainer):
         config.data.manifest = config.data.test_manifest
         # filter test examples, will cause less examples, but no mismatch with training
         # and can use large batch size , save training time, so filter test egs now.
-        # config.data.min_input_len = 0.0  # second
-        # config.data.max_input_len = float('inf')  # second
-        # config.data.min_output_len = 0.0  # tokens
-        # config.data.max_output_len = float('inf')  # tokens
-        # config.data.min_output_input_ratio = 0.00
-        # config.data.max_output_input_ratio = float('inf')
+        config.data.min_input_len = 0.0  # second
+        config.data.max_input_len = float('inf')  # second
+        config.data.min_output_len = 0.0  # tokens
+        config.data.max_output_len = float('inf')  # tokens
+        config.data.min_output_input_ratio = 0.00
+        config.data.max_output_input_ratio = float('inf')
 
         test_dataset = ManifestDataset.from_config(config)
         # return text ord id

deepspeech/frontend/augmentor/augmentation.py

@@ -13,18 +13,28 @@
 # limitations under the License.
 """Contains the data augmentation pipeline."""
 import json
+from collections.abc import Sequence
+from inspect import signature
 
 import numpy as np
 
-from deepspeech.frontend.augmentor.impulse_response import ImpulseResponseAugmentor
-from deepspeech.frontend.augmentor.noise_perturb import NoisePerturbAugmentor
-from deepspeech.frontend.augmentor.online_bayesian_normalization import \
-    OnlineBayesianNormalizationAugmentor
-from deepspeech.frontend.augmentor.resample import ResampleAugmentor
-from deepspeech.frontend.augmentor.shift_perturb import ShiftPerturbAugmentor
-from deepspeech.frontend.augmentor.spec_augment import SpecAugmentor
-from deepspeech.frontend.augmentor.speed_perturb import SpeedPerturbAugmentor
-from deepspeech.frontend.augmentor.volume_perturb import VolumePerturbAugmentor
+from deepspeech.frontend.augmentor.base import AugmentorBase
+from deepspeech.utils.dynamic_import import dynamic_import
+from deepspeech.utils.log import Log
+
+__all__ = ["AugmentationPipeline"]
+
+logger = Log(__name__).getlog()
+
+import_alias = dict(
+    volume="deepspeech.frontend.augmentor.impulse_response:VolumePerturbAugmentor",
+    shift="deepspeech.frontend.augmentor.shift_perturb:ShiftPerturbAugmentor",
+    speed="deepspeech.frontend.augmentor.speed_perturb:SpeedPerturbAugmentor",
+    resample="deepspeech.frontend.augmentor.resample:ResampleAugmentor",
+    bayesian_normal="deepspeech.frontend.augmentor.online_bayesian_normalization:OnlineBayesianNormalizationAugmentor",
+    noise="deepspeech.frontend.augmentor.noise_perturb:NoisePerturbAugmentor",
+    impulse="deepspeech.frontend.augmentor.impulse_response:ImpulseResponseAugmentor",
+    specaug="deepspeech.frontend.augmentor.spec_augment:SpecAugmentor", )
 
 
 class AugmentationPipeline():
@@ -78,20 +88,74 @@ class AugmentationPipeline():
     augmentor to take effect. If "prob" is zero, the augmentor does not take
     effect.
 
-    :param augmentation_config: Augmentation configuration in json string.
-    :type augmentation_config: str
-    :param random_seed: Random seed.
-    :type random_seed: int
-    :raises ValueError: If the augmentation json config is in incorrect format".
+    Params:
+        augmentation_config(str): Augmentation configuration in json string.
+        random_seed(int): Random seed.
+        train(bool): whether is train mode.
+    
+    Raises:
+        ValueError: If the augmentation json config is in incorrect format".
     """
 
-    def __init__(self, augmentation_config: str, random_seed=0):
+    def __init__(self, augmentation_config: str, random_seed: int=0):
         self._rng = np.random.RandomState(random_seed)
         self._spec_types = ('specaug')
-        self._augmentors, self._rates = self._parse_pipeline_from(
-            augmentation_config, 'audio')
+
+        if augmentation_config is None:
+            self.conf = {}
+        else:
+            self.conf = json.loads(augmentation_config)
+
+        self._augmentors, self._rates = self._parse_pipeline_from('all')
+        self._audio_augmentors, self._audio_rates = self._parse_pipeline_from(
+            'audio')
         self._spec_augmentors, self._spec_rates = self._parse_pipeline_from(
-            augmentation_config, 'feature')
+            'feature')
+
+    def __call__(self, xs, uttid_list=None, **kwargs):
+        if not isinstance(xs, Sequence):
+            is_batch = False
+            xs = [xs]
+        else:
+            is_batch = True
+
+        if isinstance(uttid_list, str):
+            uttid_list = [uttid_list for _ in range(len(xs))]
+
+        if self.conf.get("mode", "sequential") == "sequential":
+            for idx, (func, rate) in enumerate(
+                    zip(self._augmentors, self._rates), 0):
+                if self._rng.uniform(0., 1.) >= rate:
+                    continue
+
+                # Derive only the args which the func has
+                try:
+                    param = signature(func).parameters
+                except ValueError:
+                    # Some function, e.g. built-in function, are failed
+                    param = {}
+                _kwargs = {k: v for k, v in kwargs.items() if k in param}
+
+                try:
+                    if uttid_list is not None and "uttid" in param:
+                        xs = [
+                            func(x, u, **_kwargs)
+                            for x, u in zip(xs, uttid_list)
+                        ]
+                    else:
+                        xs = [func(x, **_kwargs) for x in xs]
+                except Exception:
+                    logger.fatal("Catch a exception from {}th func: {}".format(
+                        idx, func))
+                    raise
+        else:
+            raise NotImplementedError(
+                "Not supporting mode={}".format(self.conf["mode"]))
+
+        if is_batch:
+            return xs
+        else:
+            return xs[0]
 
     def transform_audio(self, audio_segment):
         """Run the pre-processing pipeline for data augmentation.
@@ -101,7 +165,7 @@ class AugmentationPipeline():
         :param audio_segment: Audio segment to process.
         :type audio_segment: AudioSegmenet|SpeechSegment
         """
-        for augmentor, rate in zip(self._augmentors, self._rates):
+        for augmentor, rate in zip(self._audio_augmentors, self._audio_rates):
             if self._rng.uniform(0., 1.) < rate:
                 augmentor.transform_audio(audio_segment)
 
@@ -116,52 +180,39 @@ class AugmentationPipeline():
                 spec_segment = augmentor.transform_feature(spec_segment)
         return spec_segment
 
-    def _parse_pipeline_from(self, config_json, aug_type='audio'):
+    def _parse_pipeline_from(self, aug_type='all'):
         """Parse the config json to build a augmentation pipelien."""
-        assert aug_type in ('audio', 'feature'), aug_type
-        try:
-            configs = json.loads(config_json)
-            audio_confs = []
-            feature_confs = []
-            for config in configs:
-                if config["type"] in self._spec_types:
-                    feature_confs.append(config)
-                else:
-                    audio_confs.append(config)
-
-            if aug_type == 'audio':
-                aug_confs = audio_confs
-            elif aug_type == 'feature':
-                aug_confs = feature_confs
-
-            augmentors = [
-                self._get_augmentor(config["type"], config["params"])
-                for config in aug_confs
-            ]
-            rates = [config["prob"] for config in aug_confs]
-
-        except Exception as e:
-            raise ValueError("Failed to parse the augmentation config json: "
-                             "%s" % str(e))
+        assert aug_type in ('audio', 'feature', 'all'), aug_type
+        audio_confs = []
+        feature_confs = []
+        all_confs = []
+        for config in self.conf:
+            all_confs.append(config)
+            if config["type"] in self._spec_types:
+                feature_confs.append(config)
+            else:
+                audio_confs.append(config)
+
+        if aug_type == 'audio':
+            aug_confs = audio_confs
+        elif aug_type == 'feature':
+            aug_confs = feature_confs
+        else:
+            aug_confs = all_confs
+
+        augmentors = [
+            self._get_augmentor(config["type"], config["params"])
+            for config in aug_confs
+        ]
+        rates = [config["prob"] for config in aug_confs]
         return augmentors, rates
 
     def _get_augmentor(self, augmentor_type, params):
         """Return an augmentation model by the type name, and pass in params."""
-        if augmentor_type == "volume":
-            return VolumePerturbAugmentor(self._rng, **params)
-        elif augmentor_type == "shift":
-            return ShiftPerturbAugmentor(self._rng, **params)
-        elif augmentor_type == "speed":
-            return SpeedPerturbAugmentor(self._rng, **params)
-        elif augmentor_type == "resample":
-            return ResampleAugmentor(self._rng, **params)
-        elif augmentor_type == "bayesian_normal":
-            return OnlineBayesianNormalizationAugmentor(self._rng, **params)
-        elif augmentor_type == "noise":
-            return NoisePerturbAugmentor(self._rng, **params)
-        elif augmentor_type == "impulse":
-            return ImpulseResponseAugmentor(self._rng, **params)
-        elif augmentor_type == "specaug":
-            return SpecAugmentor(self._rng, **params)
-        else:
+        class_obj = dynamic_import(augmentor_type, import_alias)
+        assert issubclass(class_obj, AugmentorBase)
+        try:
+            obj = class_obj(self._rng, **params)
+        except Exception:
             raise ValueError("Unknown augmentor type [%s]." % augmentor_type)
+        return obj

deepspeech/frontend/augmentor/base.py

@@ -28,6 +28,10 @@ class AugmentorBase():
     def __init__(self):
         pass
 
+    @abstractmethod
+    def __call__(self, xs):
+        raise NotImplementedError
+
     @abstractmethod
     def transform_audio(self, audio_segment):
         """Adds various effects to the input audio segment. Such effects

deepspeech/frontend/augmentor/impulse_response.py

@@ -30,6 +30,11 @@ class ImpulseResponseAugmentor(AugmentorBase):
         self._rng = rng
         self._impulse_manifest = read_manifest(impulse_manifest_path)
 
+    def __call__(self, x, uttid=None, train=True):
+        if not train:
+            return
+        self.transform_audio(x)
+
     def transform_audio(self, audio_segment):
         """Add impulse response effect.
 

deepspeech/frontend/augmentor/noise_perturb.py

@@ -36,6 +36,11 @@ class NoisePerturbAugmentor(AugmentorBase):
         self._rng = rng
         self._noise_manifest = read_manifest(manifest_path=noise_manifest_path)
 
+    def __call__(self, x, uttid=None, train=True):
+        if not train:
+            return
+        self.transform_audio(x)
+
     def transform_audio(self, audio_segment):
         """Add background noise audio.
 

deepspeech/frontend/augmentor/online_bayesian_normalization.py

@@ -44,6 +44,11 @@ class OnlineBayesianNormalizationAugmentor(AugmentorBase):
         self._rng = rng
         self._startup_delay = startup_delay
 
+    def __call__(self, x, uttid=None, train=True):
+        if not train:
+            return
+        self.transform_audio(x)
+
     def transform_audio(self, audio_segment):
         """Normalizes the input audio using the online Bayesian approach.
 

deepspeech/frontend/augmentor/resample.py

@@ -31,6 +31,11 @@ class ResampleAugmentor(AugmentorBase):
         self._new_sample_rate = new_sample_rate
         self._rng = rng
 
+    def __call__(self, x, uttid=None, train=True):
+        if not train:
+            return
+        self.transform_audio(x)
+
     def transform_audio(self, audio_segment):
         """Resamples the input audio to a target sample rate.
 

deepspeech/frontend/augmentor/shift_perturb.py

@@ -31,6 +31,11 @@ class ShiftPerturbAugmentor(AugmentorBase):
         self._max_shift_ms = max_shift_ms
         self._rng = rng
 
+    def __call__(self, x, uttid=None, train=True):
+        if not train:
+            return
+        self.transform_audio(x)
+
     def transform_audio(self, audio_segment):
         """Shift audio.
 

deepspeech/frontend/augmentor/spec_augment.py

@@ -157,6 +157,11 @@ class SpecAugmentor(AugmentorBase):
             self._time_mask = (t_0, t_0 + t)
         return xs
 
+    def __call__(self, x, train=True):
+        if not train:
+            return
+        self.transform_audio(x)
+
     def transform_feature(self, xs: np.ndarray):
         """
         Args:

deepspeech/frontend/augmentor/speed_perturb.py

@@ -79,6 +79,11 @@ class SpeedPerturbAugmentor(AugmentorBase):
             self._rates = np.linspace(
                 self._min_rate, self._max_rate, self._num_rates, endpoint=True)
 
+    def __call__(self, x, uttid=None, train=True):
+        if not train:
+            return
+        self.transform_audio(x)
+
     def transform_audio(self, audio_segment):
         """Sample a new speed rate from the given range and
         changes the speed of the given audio clip.

deepspeech/frontend/augmentor/volume_perturb.py

@@ -37,6 +37,11 @@ class VolumePerturbAugmentor(AugmentorBase):
         self._max_gain_dBFS = max_gain_dBFS
         self._rng = rng
 
+    def __call__(self, x, uttid=None, train=True):
+        if not train:
+            return
+        self.transform_audio(x)
+
     def transform_audio(self, audio_segment):
         """Change audio loadness.
 

deepspeech/io/__init__.py

@@ -11,139 +11,3 @@
 # 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 numpy as np
-from paddle.io import DataLoader
-
-from deepspeech.io.collator import SpeechCollator
-from deepspeech.io.dataset import ManifestDataset
-from deepspeech.io.sampler import SortagradBatchSampler
-from deepspeech.io.sampler import SortagradDistributedBatchSampler
-
-
-def create_dataloader(manifest_path,
-                      unit_type,
-                      vocab_filepath,
-                      mean_std_filepath,
-                      spm_model_prefix,
-                      augmentation_config='{}',
-                      max_input_len=float('inf'),
-                      min_input_len=0.0,
-                      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,
-                      max_freq=None,
-                      specgram_type='linear',
-                      feat_dim=None,
-                      delta_delta=False,
-                      use_dB_normalization=True,
-                      random_seed=0,
-                      keep_transcription_text=False,
-                      is_training=False,
-                      batch_size=1,
-                      num_workers=0,
-                      sortagrad=False,
-                      shuffle_method=None,
-                      dist=False):
-
-    dataset = ManifestDataset(
-        manifest_path=manifest_path,
-        unit_type=unit_type,
-        vocab_filepath=vocab_filepath,
-        mean_std_filepath=mean_std_filepath,
-        spm_model_prefix=spm_model_prefix,
-        augmentation_config=augmentation_config,
-        max_input_len=max_input_len,
-        min_input_len=min_input_len,
-        max_output_len=max_output_len,
-        min_output_len=min_output_len,
-        max_output_input_ratio=max_output_input_ratio,
-        min_output_input_ratio=min_output_input_ratio,
-        stride_ms=stride_ms,
-        window_ms=window_ms,
-        max_freq=max_freq,
-        specgram_type=specgram_type,
-        feat_dim=feat_dim,
-        delta_delta=delta_delta,
-        use_dB_normalization=use_dB_normalization,
-        random_seed=random_seed,
-        keep_transcription_text=keep_transcription_text)
-
-    if dist:
-        batch_sampler = SortagradDistributedBatchSampler(
-            dataset,
-            batch_size,
-            num_replicas=None,
-            rank=None,
-            shuffle=is_training,
-            drop_last=is_training,
-            sortagrad=is_training,
-            shuffle_method=shuffle_method)
-    else:
-        batch_sampler = SortagradBatchSampler(
-            dataset,
-            shuffle=is_training,
-            batch_size=batch_size,
-            drop_last=is_training,
-            sortagrad=is_training,
-            shuffle_method=shuffle_method)
-
-    def padding_batch(batch,
-                      padding_to=-1,
-                      flatten=False,
-                      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 ``padding_to`` is -1, the maximun shape in the batch will be used	
-        as the target shape for padding. Otherwise, `padding_to` will be the	
-        target shape (only refers to the second axis).	
-
-        If `flatten` is True, features will be flatten to 1darray.	
-        """
-        new_batch = []
-        # get target shape	
-        max_length = max([audio.shape[1] for audio, text in batch])
-        if padding_to != -1:
-            if padding_to < max_length:
-                raise ValueError("If padding_to is not -1, it should be larger "
-                                 "than any instance's shape in the batch")
-            max_length = padding_to
-        max_text_length = max([len(text) for audio, text in batch])
-        # padding	
-        padded_audios = []
-        audio_lens = []
-        texts, text_lens = [], []
-        for audio, text in batch:
-            padded_audio = np.zeros([audio.shape[0], max_length])
-            padded_audio[:, :audio.shape[1]] = audio
-            if flatten:
-                padded_audio = padded_audio.flatten()
-            padded_audios.append(padded_audio)
-            audio_lens.append(audio.shape[1])
-
-            padded_text = np.zeros([max_text_length])
-            if keep_transcription_text:
-                padded_text[:len(text)] = [ord(t) for t in text]  # string
-            else:
-                padded_text[:len(text)] = text  # ids
-            texts.append(padded_text)
-            text_lens.append(len(text))
-
-        padded_audios = np.array(padded_audios).astype('float32')
-        audio_lens = np.array(audio_lens).astype('int64')
-        texts = np.array(texts).astype('int32')
-        text_lens = np.array(text_lens).astype('int64')
-        return padded_audios, audio_lens, texts, text_lens
-
-    # collate_fn=functools.partial(padding_batch, keep_transcription_text=keep_transcription_text),
-    collate_fn = SpeechCollator(keep_transcription_text=keep_transcription_text)
-    loader = DataLoader(
-        dataset,
-        batch_sampler=batch_sampler,
-        collate_fn=collate_fn,
-        num_workers=num_workers)
-    return loader

deepspeech/io/batchfy.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 itertools
+
+import numpy as np
+
+from deepspeech.utils.log import Log
+
+__all__ = ["make_batchset"]
+
+logger = Log(__name__).getlog()
+
+
+def batchfy_by_seq(
+        sorted_data,
+        batch_size,
+        max_length_in,
+        max_length_out,
+        min_batch_size=1,
+        shortest_first=False,
+        ikey="input",
+        iaxis=0,
+        okey="output",
+        oaxis=0, ):
+    """Make batch set from json dictionary
+
+    :param List[(str, Dict[str, Any])] sorted_data: dictionary loaded from data.json
+    :param int batch_size: batch size
+    :param int max_length_in: maximum length of input to decide adaptive batch size
+    :param int max_length_out: maximum length of output to decide adaptive batch size
+    :param int min_batch_size: mininum batch size (for multi-gpu)
+    :param bool shortest_first: Sort from batch with shortest samples
+        to longest if true, otherwise reverse
+    :param str ikey: key to access input
+        (for ASR ikey="input", for TTS, MT ikey="output".)
+    :param int iaxis: dimension to access input
+        (for ASR, TTS iaxis=0, for MT iaxis="1".)
+    :param str okey: key to access output
+        (for ASR, MT okey="output". for TTS okey="input".)
+    :param int oaxis: dimension to access output
+        (for ASR, TTS, MT oaxis=0, reserved for future research, -1 means all axis.)
+    :return: List[List[Tuple[str, dict]]] list of batches
+    """
+    if batch_size <= 0:
+        raise ValueError(f"Invalid batch_size={batch_size}")
+
+    # check #utts is more than min_batch_size
+    if len(sorted_data) < min_batch_size:
+        raise ValueError(
+            f"#utts({len(sorted_data)}) is less than min_batch_size({min_batch_size})."
+        )
+
+    # make list of minibatches
+    minibatches = []
+    start = 0
+    while True:
+        _, info = sorted_data[start]
+        ilen = int(info[ikey][iaxis]["shape"][0])
+        olen = (int(info[okey][oaxis]["shape"][0]) if oaxis >= 0 else
+                max(map(lambda x: int(x["shape"][0]), info[okey])))
+        factor = max(int(ilen / max_length_in), int(olen / max_length_out))
+        # change batchsize depending on the input and output length
+        # if ilen = 1000 and max_length_in = 800
+        # then b = batchsize / 2
+        # and max(min_batches, .) avoids batchsize = 0
+        bs = max(min_batch_size, int(batch_size / (1 + factor)))
+        end = min(len(sorted_data), start + bs)
+        minibatch = sorted_data[start:end]
+        if shortest_first:
+            minibatch.reverse()
+
+        # check each batch is more than minimum batchsize
+        if len(minibatch) < min_batch_size:
+            mod = min_batch_size - len(minibatch) % min_batch_size
+            additional_minibatch = [
+                sorted_data[i] for i in np.random.randint(0, start, mod)
+            ]
+            if shortest_first:
+                additional_minibatch.reverse()
+            minibatch.extend(additional_minibatch)
+        minibatches.append(minibatch)
+
+        if end == len(sorted_data):
+            break
+        start = end
+
+    # batch: List[List[Tuple[str, dict]]]
+    return minibatches
+
+
+def batchfy_by_bin(
+        sorted_data,
+        batch_bins,
+        num_batches=0,
+        min_batch_size=1,
+        shortest_first=False,
+        ikey="input",
+        okey="output", ):
+    """Make variably sized batch set, which maximizes
+
+    the number of bins up to `batch_bins`.
+
+    :param List[(str, Dict[str, Any])] sorted_data: dictionary loaded from data.json
+    :param int batch_bins: Maximum frames of a batch
+    :param int num_batches: # number of batches to use (for debug)
+    :param int min_batch_size: minimum batch size (for multi-gpu)
+    :param int test: Return only every `test` batches
+    :param bool shortest_first: Sort from batch with shortest samples
+        to longest if true, otherwise reverse
+
+    :param str ikey: key to access input (for ASR ikey="input", for TTS ikey="output".)
+    :param str okey: key to access output (for ASR okey="output". for TTS okey="input".)
+
+    :return: List[Tuple[str, Dict[str, List[Dict[str, Any]]]] list of batches
+    """
+    if batch_bins <= 0:
+        raise ValueError(f"invalid batch_bins={batch_bins}")
+    length = len(sorted_data)
+    idim = int(sorted_data[0][1][ikey][0]["shape"][1])
+    odim = int(sorted_data[0][1][okey][0]["shape"][1])
+    logger.info("# utts: " + str(len(sorted_data)))
+    minibatches = []
+    start = 0
+    n = 0
+    while True:
+        # Dynamic batch size depending on size of samples
+        b = 0
+        next_size = 0
+        max_olen = 0
+        while next_size < batch_bins and (start + b) < length:
+            ilen = int(sorted_data[start + b][1][ikey][0]["shape"][0]) * idim
+            olen = int(sorted_data[start + b][1][okey][0]["shape"][0]) * odim
+            if olen > max_olen:
+                max_olen = olen
+            next_size = (max_olen + ilen) * (b + 1)
+            if next_size <= batch_bins:
+                b += 1
+            elif next_size == 0:
+                raise ValueError(
+                    f"Can't fit one sample in batch_bins ({batch_bins}): "
+                    f"Please increase the value")
+        end = min(length, start + max(min_batch_size, b))
+        batch = sorted_data[start:end]
+        if shortest_first:
+            batch.reverse()
+        minibatches.append(batch)
+        # Check for min_batch_size and fixes the batches if needed
+        i = -1
+        while len(minibatches[i]) < min_batch_size:
+            missing = min_batch_size - len(minibatches[i])
+            if -i == len(minibatches):
+                minibatches[i + 1].extend(minibatches[i])
+                minibatches = minibatches[1:]
+                break
+            else:
+                minibatches[i].extend(minibatches[i - 1][:missing])
+                minibatches[i - 1] = minibatches[i - 1][missing:]
+                i -= 1
+        if end == length:
+            break
+        start = end
+        n += 1
+    if num_batches > 0:
+        minibatches = minibatches[:num_batches]
+    lengths = [len(x) for x in minibatches]
+    logger.info(
+        str(len(minibatches)) + " batches containing from " + str(min(lengths))
+        + " to " + str(max(lengths)) + " samples " + "(avg " + str(
+            int(np.mean(lengths))) + " samples).")
+    return minibatches
+
+
+def batchfy_by_frame(
+        sorted_data,
+        max_frames_in,
+        max_frames_out,
+        max_frames_inout,
+        num_batches=0,
+        min_batch_size=1,
+        shortest_first=False,
+        ikey="input",
+        okey="output", ):
+    """Make variable batch set, which maximizes the number of frames to max_batch_frame.
+
+    :param List[(str, Dict[str, Any])] sorteddata: dictionary loaded from data.json
+    :param int max_frames_in: Maximum input frames of a batch
+    :param int max_frames_out: Maximum output frames of a batch
+    :param int max_frames_inout: Maximum input+output frames of a batch
+    :param int num_batches: # number of batches to use (for debug)
+    :param int min_batch_size: minimum batch size (for multi-gpu)
+    :param int test: Return only every `test` batches
+    :param bool shortest_first: Sort from batch with shortest samples
+        to longest if true, otherwise reverse
+
+    :param str ikey: key to access input (for ASR ikey="input", for TTS ikey="output".)
+    :param str okey: key to access output (for ASR okey="output". for TTS okey="input".)
+
+    :return: List[Tuple[str, Dict[str, List[Dict[str, Any]]]] list of batches
+    """
+    if max_frames_in <= 0 and max_frames_out <= 0 and max_frames_inout <= 0:
+        raise ValueError(
+            "At least, one of `--batch-frames-in`, `--batch-frames-out` or "
+            "`--batch-frames-inout` should be > 0")
+    length = len(sorted_data)
+    minibatches = []
+    start = 0
+    end = 0
+    while end != length:
+        # Dynamic batch size depending on size of samples
+        b = 0
+        max_olen = 0
+        max_ilen = 0
+        while (start + b) < length:
+            ilen = int(sorted_data[start + b][1][ikey][0]["shape"][0])
+            if ilen > max_frames_in and max_frames_in != 0:
+                raise ValueError(
+                    f"Can't fit one sample in --batch-frames-in ({max_frames_in}): "
+                    f"Please increase the value")
+            olen = int(sorted_data[start + b][1][okey][0]["shape"][0])
+            if olen > max_frames_out and max_frames_out != 0:
+                raise ValueError(
+                    f"Can't fit one sample in --batch-frames-out ({max_frames_out}): "
+                    f"Please increase the value")
+            if ilen + olen > max_frames_inout and max_frames_inout != 0:
+                raise ValueError(
+                    f"Can't fit one sample in --batch-frames-out ({max_frames_inout}): "
+                    f"Please increase the value")
+            max_olen = max(max_olen, olen)
+            max_ilen = max(max_ilen, ilen)
+            in_ok = max_ilen * (b + 1) <= max_frames_in or max_frames_in == 0
+            out_ok = max_olen * (b + 1) <= max_frames_out or max_frames_out == 0
+            inout_ok = (max_ilen + max_olen) * (
+                b + 1) <= max_frames_inout or max_frames_inout == 0
+            if in_ok and out_ok and inout_ok:
+                # add more seq in the minibatch
+                b += 1
+            else:
+                # no more seq in the minibatch
+                break
+        end = min(length, start + b)
+        batch = sorted_data[start:end]
+        if shortest_first:
+            batch.reverse()
+        minibatches.append(batch)
+        # Check for min_batch_size and fixes the batches if needed
+        i = -1
+        while len(minibatches[i]) < min_batch_size:
+            missing = min_batch_size - len(minibatches[i])
+            if -i == len(minibatches):
+                minibatches[i + 1].extend(minibatches[i])
+                minibatches = minibatches[1:]
+                break
+            else:
+                minibatches[i].extend(minibatches[i - 1][:missing])
+                minibatches[i - 1] = minibatches[i - 1][missing:]
+                i -= 1
+        start = end
+    if num_batches > 0:
+        minibatches = minibatches[:num_batches]
+    lengths = [len(x) for x in minibatches]
+    logger.info(
+        str(len(minibatches)) + " batches containing from " + str(min(lengths))
+        + " to " + str(max(lengths)) + " samples" + "(avg " + str(
+            int(np.mean(lengths))) + " samples).")
+
+    return minibatches
+
+
+def batchfy_shuffle(data, batch_size, min_batch_size, num_batches,
+                    shortest_first):
+    import random
+
+    logger.info("use shuffled batch.")
+    sorted_data = random.sample(data.items(), len(data.items()))
+    logger.info("# utts: " + str(len(sorted_data)))
+    # make list of minibatches
+    minibatches = []
+    start = 0
+    while True:
+        end = min(len(sorted_data), start + batch_size)
+        # check each batch is more than minimum batchsize
+        minibatch = sorted_data[start:end]
+        if shortest_first:
+            minibatch.reverse()
+        if len(minibatch) < min_batch_size:
+            mod = min_batch_size - len(minibatch) % min_batch_size
+            additional_minibatch = [
+                sorted_data[i] for i in np.random.randint(0, start, mod)
+            ]
+            if shortest_first:
+                additional_minibatch.reverse()
+            minibatch.extend(additional_minibatch)
+        minibatches.append(minibatch)
+        if end == len(sorted_data):
+            break
+        start = end
+
+    # for debugging
+    if num_batches > 0:
+        minibatches = minibatches[:num_batches]
+        logger.info("# minibatches: " + str(len(minibatches)))
+    return minibatches
+
+
+BATCH_COUNT_CHOICES = ["auto", "seq", "bin", "frame"]
+BATCH_SORT_KEY_CHOICES = ["input", "output", "shuffle"]
+
+
+def make_batchset(
+        data,
+        batch_size=0,
+        max_length_in=float("inf"),
+        max_length_out=float("inf"),
+        num_batches=0,
+        min_batch_size=1,
+        shortest_first=False,
+        batch_sort_key="input",
+        count="auto",
+        batch_bins=0,
+        batch_frames_in=0,
+        batch_frames_out=0,
+        batch_frames_inout=0,
+        iaxis=0,
+        oaxis=0, ):
+    """Make batch set from json dictionary
+
+    if utts have "category" value,
+
+        >>> data = [{'category': 'A', 'input': ..., 'utt':'utt1'},
+        ...         {'category': 'B', 'input': ..., 'utt':'utt2'},
+        ...         {'category': 'B', 'input': ..., 'utt':'utt3'},
+        ...         {'category': 'A', 'input': ..., 'utt':'utt4'}]
+        >>> make_batchset(data, batchsize=2, ...)
+        [[('utt1', ...), ('utt4', ...)], [('utt2', ...), ('utt3': ...)]]
+
+    Note that if any utts doesn't have "category",
+    perform as same as batchfy_by_{count}
+
+    :param List[Dict[str, Any]] data: dictionary loaded from data.json
+    :param int batch_size: maximum number of sequences in a minibatch.
+    :param int batch_bins: maximum number of bins (frames x dim) in a minibatch.
+    :param int batch_frames_in:  maximum number of input frames in a minibatch.
+    :param int batch_frames_out: maximum number of output frames in a minibatch.
+    :param int batch_frames_out: maximum number of input+output frames in a minibatch.
+    :param str count: strategy to count maximum size of batch.
+        For choices, see espnet.asr.batchfy.BATCH_COUNT_CHOICES
+
+    :param int max_length_in: maximum length of input to decide adaptive batch size
+    :param int max_length_out: maximum length of output to decide adaptive batch size
+    :param int num_batches: # number of batches to use (for debug)
+    :param int min_batch_size: minimum batch size (for multi-gpu)
+    :param bool shortest_first: Sort from batch with shortest samples
+        to longest if true, otherwise reverse
+    :param str batch_sort_key: how to sort data before creating minibatches
+        ["input", "output", "shuffle"]
+    :param bool swap_io: if True, use "input" as output and "output"
+        as input in `data` dict
+    :param bool mt: if True, use 0-axis of "output" as output and 1-axis of "output"
+        as input in `data` dict
+    :param int iaxis: dimension to access input
+        (for ASR, TTS iaxis=0, for MT iaxis="1".)
+    :param int oaxis: dimension to access output (for ASR, TTS, MT oaxis=0,
+        reserved for future research, -1 means all axis.)
+    :return: List[List[Tuple[str, dict]]] list of batches
+    """
+    # check args
+    if count not in BATCH_COUNT_CHOICES:
+        raise ValueError(
+            f"arg 'count' ({count}) should be one of {BATCH_COUNT_CHOICES}")
+    if batch_sort_key not in BATCH_SORT_KEY_CHOICES:
+        raise ValueError(f"arg 'batch_sort_key' ({batch_sort_key}) should be "
+                         f"one of {BATCH_SORT_KEY_CHOICES}")
+
+    ikey = "input"
+    okey = "output"
+    batch_sort_axis = 0  # index of list 
+    if count == "auto":
+        if batch_size != 0:
+            count = "seq"
+        elif batch_bins != 0:
+            count = "bin"
+        elif batch_frames_in != 0 or batch_frames_out != 0 or batch_frames_inout != 0:
+            count = "frame"
+        else:
+            raise ValueError(
+                f"cannot detect `count` manually set one of {BATCH_COUNT_CHOICES}"
+            )
+        logger.info(f"count is auto detected as {count}")
+
+    if count != "seq" and batch_sort_key == "shuffle":
+        raise ValueError(
+            "batch_sort_key=shuffle is only available if batch_count=seq")
+
+    category2data = {}  # Dict[str, dict]
+    for v in data:
+        k = v['utt']
+        category2data.setdefault(v.get("category"), {})[k] = v
+
+    batches_list = []  # List[List[List[Tuple[str, dict]]]]
+    for d in category2data.values():
+        if batch_sort_key == "shuffle":
+            batches = batchfy_shuffle(d, batch_size, min_batch_size,
+                                      num_batches, shortest_first)
+            batches_list.append(batches)
+            continue
+
+        # sort it by input lengths (long to short)
+        sorted_data = sorted(
+            d.items(),
+            key=lambda data: int(data[1][batch_sort_key][batch_sort_axis]["shape"][0]),
+            reverse=not shortest_first, )
+        logger.info("# utts: " + str(len(sorted_data)))
+
+        if count == "seq":
+            batches = batchfy_by_seq(
+                sorted_data,
+                batch_size=batch_size,
+                max_length_in=max_length_in,
+                max_length_out=max_length_out,
+                min_batch_size=min_batch_size,
+                shortest_first=shortest_first,
+                ikey=ikey,
+                iaxis=iaxis,
+                okey=okey,
+                oaxis=oaxis, )
+        if count == "bin":
+            batches = batchfy_by_bin(
+                sorted_data,
+                batch_bins=batch_bins,
+                min_batch_size=min_batch_size,
+                shortest_first=shortest_first,
+                ikey=ikey,
+                okey=okey, )
+        if count == "frame":
+            batches = batchfy_by_frame(
+                sorted_data,
+                max_frames_in=batch_frames_in,
+                max_frames_out=batch_frames_out,
+                max_frames_inout=batch_frames_inout,
+                min_batch_size=min_batch_size,
+                shortest_first=shortest_first,
+                ikey=ikey,
+                okey=okey, )
+        batches_list.append(batches)
+
+    if len(batches_list) == 1:
+        batches = batches_list[0]
+    else:
+        # Concat list. This way is faster than "sum(batch_list, [])"
+        batches = list(itertools.chain(*batches_list))
+
+    # for debugging
+    if num_batches > 0:
+        batches = batches[:num_batches]
+    logger.info("# minibatches: " + str(len(batches)))
+
+    # batch: List[List[Tuple[str, dict]]]
+    return batches

deepspeech/io/collator.py

@@ -23,7 +23,7 @@ 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.io.utility import pad_sequence
+from deepspeech.io.utility import pad_list
 from deepspeech.utils.log import Log
 
 __all__ = ["SpeechCollator"]
@@ -286,13 +286,12 @@ class SpeechCollator():
             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
+        #[B, T, D]
+        xs_pad = pad_list(audios, 0.0).astype(np.float32)
+        ilens = np.array(audio_lens).astype(np.int64)
+        ys_pad = pad_list(texts, IGNORE_ID).astype(np.int64)
+        olens = np.array(text_lens).astype(np.int64)
+        return utts, xs_pad, ilens, ys_pad, olens
 
     @property
     def manifest(self):

deepspeech/io/converter.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 numpy as np
+
+from deepspeech.io.utility import pad_list
+from deepspeech.utils.log import Log
+
+__all__ = ["CustomConverter"]
+
+logger = Log(__name__).getlog()
+
+
+class CustomConverter():
+    """Custom batch converter.
+
+    Args:
+        subsampling_factor (int): The subsampling factor.
+        dtype (np.dtype): Data type to convert.
+        
+    """
+
+    def __init__(self, subsampling_factor=1, dtype=np.float32):
+        """Construct a CustomConverter object."""
+        self.subsampling_factor = subsampling_factor
+        self.ignore_id = -1
+        self.dtype = dtype
+
+    def __call__(self, batch):
+        """Transform a batch and send it to a device.
+
+        Args:
+            batch (list): The batch to transform.
+
+        Returns:
+            tuple(paddle.Tensor, paddle.Tensor, paddle.Tensor)
+
+        """
+        # batch should be located in list
+        assert len(batch) == 1
+        (xs, ys), utts = batch[0]
+
+        # perform subsampling
+        if self.subsampling_factor > 1:
+            xs = [x[::self.subsampling_factor, :] for x in xs]
+
+        # get batch of lengths of input sequences
+        ilens = np.array([x.shape[0] for x in xs])
+
+        # perform padding and convert to tensor
+        # currently only support real number
+        if xs[0].dtype.kind == "c":
+            xs_pad_real = pad_list([x.real for x in xs], 0).astype(self.dtype)
+            xs_pad_imag = pad_list([x.imag for x in xs], 0).astype(self.dtype)
+            # Note(kamo):
+            # {'real': ..., 'imag': ...} will be changed to ComplexTensor in E2E.
+            # Don't create ComplexTensor and give it E2E here
+            # because torch.nn.DataParellel can't handle it.
+            xs_pad = {"real": xs_pad_real, "imag": xs_pad_imag}
+        else:
+            xs_pad = pad_list(xs, 0).astype(self.dtype)
+
+        # NOTE: this is for multi-output (e.g., speech translation)
+        ys_pad = pad_list(
+            [np.array(y[0][:]) if isinstance(y, tuple) else y for y in ys],
+            self.ignore_id)
+
+        olens = np.array(
+            [y[0].shape[0] if isinstance(y, tuple) else y.shape[0] for y in ys])
+        return utts, xs_pad, ilens, ys_pad, olens

deepspeech/io/dataloader.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.
+from paddle.io import DataLoader
+
+from deepspeech.frontend.utility import read_manifest
+from deepspeech.io.batchfy import make_batchset
+from deepspeech.io.converter import CustomConverter
+from deepspeech.io.dataset import TransformDataset
+from deepspeech.io.reader import LoadInputsAndTargets
+from deepspeech.utils.log import Log
+
+__all__ = ["BatchDataLoader"]
+
+logger = Log(__name__).getlog()
+
+
+class BatchDataLoader():
+    def __init__(self,
+                 json_file: str,
+                 train_mode: bool,
+                 sortagrad: bool=False,
+                 batch_size: int=0,
+                 maxlen_in: float=float('inf'),
+                 maxlen_out: float=float('inf'),
+                 minibatches: int=0,
+                 mini_batch_size: int=1,
+                 batch_count: str='auto',
+                 batch_bins: int=0,
+                 batch_frames_in: int=0,
+                 batch_frames_out: int=0,
+                 batch_frames_inout: int=0,
+                 preprocess_conf=None,
+                 n_iter_processes: int=1,
+                 subsampling_factor: int=1,
+                 num_encs: int=1):
+        self.json_file = json_file
+        self.train_mode = train_mode
+        self.use_sortagrad = sortagrad == -1 or sortagrad > 0
+        self.batch_size = batch_size
+        self.maxlen_in = maxlen_in
+        self.maxlen_out = maxlen_out
+        self.batch_count = batch_count
+        self.batch_bins = batch_bins
+        self.batch_frames_in = batch_frames_in
+        self.batch_frames_out = batch_frames_out
+        self.batch_frames_inout = batch_frames_inout
+        self.subsampling_factor = subsampling_factor
+        self.num_encs = num_encs
+        self.preprocess_conf = preprocess_conf
+        self.n_iter_processes = n_iter_processes
+
+        # read json data
+        self.data_json = read_manifest(json_file)
+
+        # make minibatch list (variable length)
+        self.minibaches = make_batchset(
+            self.data_json,
+            batch_size,
+            maxlen_in,
+            maxlen_out,
+            minibatches,  # for debug
+            min_batch_size=mini_batch_size,
+            shortest_first=self.use_sortagrad,
+            count=batch_count,
+            batch_bins=batch_bins,
+            batch_frames_in=batch_frames_in,
+            batch_frames_out=batch_frames_out,
+            batch_frames_inout=batch_frames_inout,
+            iaxis=0,
+            oaxis=0, )
+
+        # data reader
+        self.reader = LoadInputsAndTargets(
+            mode="asr",
+            load_output=True,
+            preprocess_conf=preprocess_conf,
+            preprocess_args={"train":
+                             train_mode},  # Switch the mode of preprocessing
+        )
+
+        # Setup a converter
+        if num_encs == 1:
+            self.converter = CustomConverter(
+                subsampling_factor=subsampling_factor, dtype=np.float32)
+        else:
+            assert NotImplementedError("not impl CustomConverterMulEnc.")
+
+        # hack to make batchsize argument as 1
+        # actual bathsize is included in a list
+        # default collate function converts numpy array to pytorch tensor
+        # we used an empty collate function instead which returns list
+        self.dataset = TransformDataset(
+            self.minibaches,
+            lambda data: self.converter([self.reader(data, return_uttid=True)]))
+        self.dataloader = DataLoader(
+            dataset=self.dataset,
+            batch_size=1,
+            shuffle=not use_sortagrad if train_mode else False,
+            collate_fn=lambda x: x[0],
+            num_workers=n_iter_processes, )
+
+    def __repr__(self):
+        echo = f"<{self.__class__.__module__}.{self.__class__.__name__} object at {hex(id(self))}> "
+        echo += f"train_mode: {self.train_mode}, "
+        echo += f"sortagrad: {self.use_sortagrad}, "
+        echo += f"batch_size: {self.batch_size}, "
+        echo += f"maxlen_in: {self.maxlen_in}, "
+        echo += f"maxlen_out: {self.maxlen_out}, "
+        echo += f"batch_count: {self.batch_count}, "
+        echo += f"batch_bins: {self.batch_bins}, "
+        echo += f"batch_frames_in: {self.batch_frames_in}, "
+        echo += f"batch_frames_out: {self.batch_frames_out}, "
+        echo += f"batch_frames_inout: {self.batch_frames_inout}, "
+        echo += f"subsampling_factor: {self.subsampling_factor}, "
+        echo += f"num_encs: {self.num_encs}, "
+        echo += f"num_workers: {self.n_iter_processes}, "
+        echo += f"file: {self.json_file}"
+        return echo
+
+    def __len__(self):
+        return len(self.dataloader)
+
+    def __iter__(self):
+        return self.dataloader.__iter__()
+
+    def __call__(self):
+        return self.__iter__()

deepspeech/io/dataset.py

@@ -19,7 +19,7 @@ from yacs.config import CfgNode
 from deepspeech.frontend.utility import read_manifest
 from deepspeech.utils.log import Log
 
-__all__ = ["ManifestDataset", "TripletManifestDataset"]
+__all__ = ["ManifestDataset", "TripletManifestDataset", "TransformDataset"]
 
 logger = Log(__name__).getlog()
 
@@ -76,12 +76,18 @@ class ManifestDataset(Dataset):
 
         Args:
             manifest_path (str): manifest josn file path
-            max_input_len ([type], optional): maximum output seq length, in seconds for raw wav, in frame numbers for feature data. Defaults to float('inf').
-            min_input_len (float, optional): minimum input seq length, in seconds for raw wav, in frame numbers for feature data. Defaults to 0.0.
-            max_output_len (float, optional): maximum input seq length, in modeling units. Defaults to 500.0.
-            min_output_len (float, optional): minimum input seq length, in modeling units. Defaults to 0.0.
-            max_output_input_ratio (float, optional): maximum output seq length/output seq length ratio. Defaults to 10.0.
-            min_output_input_ratio (float, optional): minimum output seq length/output seq length ratio. Defaults to 0.05.
+            max_input_len ([type], optional): maximum output seq length, 
+                in seconds for raw wav, in frame numbers for feature data. Defaults to float('inf').
+            min_input_len (float, optional): minimum input seq length, 
+                in seconds for raw wav, in frame numbers for feature data. Defaults to 0.0.
+            max_output_len (float, optional): maximum input seq length, 
+                in modeling units. Defaults to 500.0.
+            min_output_len (float, optional): minimum input seq length, 
+                in modeling units. Defaults to 0.0.
+            max_output_input_ratio (float, optional): maximum output seq length/output seq length ratio. 
+                Defaults to 10.0.
+            min_output_input_ratio (float, optional): minimum output seq length/output seq length ratio.
+                Defaults to 0.05.
         
         """
         super().__init__()
@@ -116,3 +122,27 @@ class TripletManifestDataset(ManifestDataset):
         instance = self._manifest[idx]
         return instance["utt"], instance["feat"], instance["text"], instance[
             "text1"]
+
+
+class TransformDataset(Dataset):
+    """Transform Dataset.
+
+    Args:
+        data: list object from make_batchset
+        transfrom: transform function
+
+    """
+
+    def __init__(self, data, transform):
+        """Init function."""
+        super().__init__()
+        self.data = data
+        self.transform = transform
+
+    def __len__(self):
+        """Len function."""
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        """[] operator."""
+        return self.transform(self.data[idx])

deepspeech/io/reader.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.
+from collections import OrderedDict
+
+import kaldiio
+import numpy as np
+import soundfile
+
+from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline
+from deepspeech.utils.log import Log
+
+__all__ = ["LoadInputsAndTargets"]
+
+logger = Log(__name__).getlog()
+
+
+class LoadInputsAndTargets():
+    """Create a mini-batch from a list of dicts
+
+    >>> batch = [('utt1',
+    ...           dict(input=[dict(feat='some.ark:123',
+    ...                            filetype='mat',
+    ...                            name='input1',
+    ...                            shape=[100, 80])],
+    ...                output=[dict(tokenid='1 2 3 4',
+    ...                             name='target1',
+    ...                             shape=[4, 31])]]))
+    >>> l = LoadInputsAndTargets()
+    >>> feat, target = l(batch)
+
+    :param: str mode: Specify the task mode, "asr" or "tts"
+    :param: str preprocess_conf: The path of a json file for pre-processing
+    :param: bool load_input: If False, not to load the input data
+    :param: bool load_output: If False, not to load the output data
+    :param: bool sort_in_input_length: Sort the mini-batch in descending order
+        of the input length
+    :param: bool use_speaker_embedding: Used for tts mode only
+    :param: bool use_second_target: Used for tts mode only
+    :param: dict preprocess_args: Set some optional arguments for preprocessing
+    :param: Optional[dict] preprocess_args: Used for tts mode only
+    """
+
+    def __init__(
+            self,
+            mode="asr",
+            preprocess_conf=None,
+            load_input=True,
+            load_output=True,
+            sort_in_input_length=True,
+            preprocess_args=None,
+            keep_all_data_on_mem=False, ):
+        self._loaders = {}
+
+        if mode not in ["asr"]:
+            raise ValueError("Only asr are allowed: mode={}".format(mode))
+
+        if preprocess_conf is not None:
+            self.preprocessing = AugmentationPipeline(preprocess_conf)
+            logging.warning(
+                "[Experimental feature] Some preprocessing will be done "
+                "for the mini-batch creation using {}".format(
+                    self.preprocessing))
+        else:
+            # If conf doesn't exist, this function don't touch anything.
+            self.preprocessing = None
+
+        self.mode = mode
+        self.load_output = load_output
+        self.load_input = load_input
+        self.sort_in_input_length = sort_in_input_length
+        if preprocess_args is None:
+            self.preprocess_args = {}
+        else:
+            assert isinstance(preprocess_args, dict), type(preprocess_args)
+            self.preprocess_args = dict(preprocess_args)
+
+        self.keep_all_data_on_mem = keep_all_data_on_mem
+
+    def __call__(self, batch, return_uttid=False):
+        """Function to load inputs and targets from list of dicts
+
+        :param List[Tuple[str, dict]] batch: list of dict which is subset of
+            loaded data.json
+        :param bool return_uttid: return utterance ID information for visualization
+        :return: list of input token id sequences [(L_1), (L_2), ..., (L_B)]
+        :return: list of input feature sequences
+            [(T_1, D), (T_2, D), ..., (T_B, D)]
+        :rtype: list of float ndarray
+        :return: list of target token id sequences [(L_1), (L_2), ..., (L_B)]
+        :rtype: list of int ndarray
+
+        """
+        x_feats_dict = OrderedDict()  # OrderedDict[str, List[np.ndarray]]
+        y_feats_dict = OrderedDict()  # OrderedDict[str, List[np.ndarray]]
+        uttid_list = []  # List[str]
+
+        for uttid, info in batch:
+            uttid_list.append(uttid)
+
+            if self.load_input:
+                # Note(kamo): This for-loop is for multiple inputs
+                for idx, inp in enumerate(info["input"]):
+                    # {"input":
+                    #  [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
+                    #    "filetype": "hdf5",
+                    #    "name": "input1", ...}], ...}
+                    x = self._get_from_loader(
+                        filepath=inp["feat"],
+                        filetype=inp.get("filetype", "mat"))
+                    x_feats_dict.setdefault(inp["name"], []).append(x)
+
+            if self.load_output:
+                for idx, inp in enumerate(info["output"]):
+                    if "tokenid" in inp:
+                        # ======= Legacy format for output =======
+                        # {"output": [{"tokenid": "1 2 3 4"}])
+                        x = np.fromiter(
+                            map(int, inp["tokenid"].split()), dtype=np.int64)
+                    else:
+                        # ======= New format =======
+                        # {"input":
+                        #  [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
+                        #    "filetype": "hdf5",
+                        #    "name": "target1", ...}], ...}
+                        x = self._get_from_loader(
+                            filepath=inp["feat"],
+                            filetype=inp.get("filetype", "mat"))
+
+                    y_feats_dict.setdefault(inp["name"], []).append(x)
+
+        if self.mode == "asr":
+            return_batch, uttid_list = self._create_batch_asr(
+                x_feats_dict, y_feats_dict, uttid_list)
+        else:
+            raise NotImplementedError(self.mode)
+
+        if self.preprocessing is not None:
+            # Apply pre-processing all input features
+            for x_name in return_batch.keys():
+                if x_name.startswith("input"):
+                    return_batch[x_name] = self.preprocessing(
+                        return_batch[x_name], uttid_list,
+                        **self.preprocess_args)
+
+        if return_uttid:
+            return tuple(return_batch.values()), uttid_list
+
+        # Doesn't return the names now.
+        return tuple(return_batch.values())
+
+    def _create_batch_asr(self, x_feats_dict, y_feats_dict, uttid_list):
+        """Create a OrderedDict for the mini-batch
+
+        :param OrderedDict x_feats_dict:
+            e.g. {"input1": [ndarray, ndarray, ...],
+                  "input2": [ndarray, ndarray, ...]}
+        :param OrderedDict y_feats_dict:
+            e.g. {"target1": [ndarray, ndarray, ...],
+                  "target2": [ndarray, ndarray, ...]}
+        :param: List[str] uttid_list:
+            Give uttid_list to sort in the same order as the mini-batch
+        :return: batch, uttid_list
+        :rtype: Tuple[OrderedDict, List[str]]
+        """
+        # handle single-input and multi-input (paralell) asr mode
+        xs = list(x_feats_dict.values())
+
+        if self.load_output:
+            ys = list(y_feats_dict.values())
+            assert len(xs[0]) == len(ys[0]), (len(xs[0]), len(ys[0]))
+
+            # get index of non-zero length samples
+            nonzero_idx = list(
+                filter(lambda i: len(ys[0][i]) > 0, range(len(ys[0]))))
+            for n in range(1, len(y_feats_dict)):
+                nonzero_idx = filter(lambda i: len(ys[n][i]) > 0, nonzero_idx)
+        else:
+            # Note(kamo): Be careful not to make nonzero_idx to a generator
+            nonzero_idx = list(range(len(xs[0])))
+
+        if self.sort_in_input_length:
+            # sort in input lengths based on the first input
+            nonzero_sorted_idx = sorted(
+                nonzero_idx, key=lambda i: -len(xs[0][i]))
+        else:
+            nonzero_sorted_idx = nonzero_idx
+
+        if len(nonzero_sorted_idx) != len(xs[0]):
+            logging.warning(
+                "Target sequences include empty tokenid (batch {} -> {}).".
+                format(len(xs[0]), len(nonzero_sorted_idx)))
+
+        # remove zero-length samples
+        xs = [[x[i] for i in nonzero_sorted_idx] for x in xs]
+        uttid_list = [uttid_list[i] for i in nonzero_sorted_idx]
+
+        x_names = list(x_feats_dict.keys())
+        if self.load_output:
+            ys = [[y[i] for i in nonzero_sorted_idx] for y in ys]
+            y_names = list(y_feats_dict.keys())
+
+            # Keeping x_name and y_name, e.g. input1, for future extension
+            return_batch = OrderedDict([
+                * [(x_name, x) for x_name, x in zip(x_names, xs)],
+                * [(y_name, y) for y_name, y in zip(y_names, ys)],
+            ])
+        else:
+            return_batch = OrderedDict(
+                [(x_name, x) for x_name, x in zip(x_names, xs)])
+        return return_batch, uttid_list
+
+    def _get_from_loader(self, filepath, filetype):
+        """Return ndarray
+
+        In order to make the fds to be opened only at the first referring,
+        the loader are stored in self._loaders
+
+        >>> ndarray = loader.get_from_loader(
+        ...     'some/path.h5:F01_050C0101_PED_REAL', filetype='hdf5')
+
+        :param: str filepath:
+        :param: str filetype:
+        :return:
+        :rtype: np.ndarray
+        """
+        if filetype == "hdf5":
+            # e.g.
+            #    {"input": [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
+            #                "filetype": "hdf5",
+            # -> filepath = "some/path.h5", key = "F01_050C0101_PED_REAL"
+            filepath, key = filepath.split(":", 1)
+
+            loader = self._loaders.get(filepath)
+            if loader is None:
+                # To avoid disk access, create loader only for the first time
+                loader = h5py.File(filepath, "r")
+                self._loaders[filepath] = loader
+            return loader[key][()]
+        elif filetype == "sound.hdf5":
+            # e.g.
+            #    {"input": [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
+            #                "filetype": "sound.hdf5",
+            # -> filepath = "some/path.h5", key = "F01_050C0101_PED_REAL"
+            filepath, key = filepath.split(":", 1)
+
+            loader = self._loaders.get(filepath)
+            if loader is None:
+                # To avoid disk access, create loader only for the first time
+                loader = SoundHDF5File(filepath, "r", dtype="int16")
+                self._loaders[filepath] = loader
+            array, rate = loader[key]
+            return array
+        elif filetype == "sound":
+            # e.g.
+            #    {"input": [{"feat": "some/path.wav",
+            #                "filetype": "sound"},
+            # Assume PCM16
+            if not self.keep_all_data_on_mem:
+                array, _ = soundfile.read(filepath, dtype="int16")
+                return array
+            if filepath not in self._loaders:
+                array, _ = soundfile.read(filepath, dtype="int16")
+                self._loaders[filepath] = array
+            return self._loaders[filepath]
+        elif filetype == "npz":
+            # e.g.
+            #    {"input": [{"feat": "some/path.npz:F01_050C0101_PED_REAL",
+            #                "filetype": "npz",
+            filepath, key = filepath.split(":", 1)
+
+            loader = self._loaders.get(filepath)
+            if loader is None:
+                # To avoid disk access, create loader only for the first time
+                loader = np.load(filepath)
+                self._loaders[filepath] = loader
+            return loader[key]
+        elif filetype == "npy":
+            # e.g.
+            #    {"input": [{"feat": "some/path.npy",
+            #                "filetype": "npy"},
+            if not self.keep_all_data_on_mem:
+                return np.load(filepath)
+            if filepath not in self._loaders:
+                self._loaders[filepath] = np.load(filepath)
+            return self._loaders[filepath]
+        elif filetype in ["mat", "vec"]:
+            # e.g.
+            #    {"input": [{"feat": "some/path.ark:123",
+            #                "filetype": "mat"}]},
+            # In this case, "123" indicates the starting points of the matrix
+            # load_mat can load both matrix and vector
+            if not self.keep_all_data_on_mem:
+                return kaldiio.load_mat(filepath)
+            if filepath not in self._loaders:
+                self._loaders[filepath] = kaldiio.load_mat(filepath)
+            return self._loaders[filepath]
+        elif filetype == "scp":
+            # e.g.
+            #    {"input": [{"feat": "some/path.scp:F01_050C0101_PED_REAL",
+            #                "filetype": "scp",
+            filepath, key = filepath.split(":", 1)
+            loader = self._loaders.get(filepath)
+            if loader is None:
+                # To avoid disk access, create loader only for the first time
+                loader = kaldiio.load_scp(filepath)
+                self._loaders[filepath] = loader
+            return loader[key]
+        else:
+            raise NotImplementedError(
+                "Not supported: loader_type={}".format(filetype))
+
+
+class SoundHDF5File():
+    """Collecting sound files to a HDF5 file
+
+    >>> f = SoundHDF5File('a.flac.h5', mode='a')
+    >>> array = np.random.randint(0, 100, 100, dtype=np.int16)
+    >>> f['id'] = (array, 16000)
+    >>> array, rate = f['id']
+
+
+    :param: str filepath:
+    :param: str mode:
+    :param: str format: The type used when saving wav. flac, nist, htk, etc.
+    :param: str dtype:
+
+    """
+
+    def __init__(self,
+                 filepath,
+                 mode="r+",
+                 format=None,
+                 dtype="int16",
+                 **kwargs):
+        self.filepath = filepath
+        self.mode = mode
+        self.dtype = dtype
+
+        self.file = h5py.File(filepath, mode, **kwargs)
+        if format is None:
+            # filepath = a.flac.h5 -> format = flac
+            second_ext = os.path.splitext(os.path.splitext(filepath)[0])[1]
+            format = second_ext[1:]
+            if format.upper() not in soundfile.available_formats():
+                # If not found, flac is selected
+                format = "flac"
+
+        # This format affects only saving
+        self.format = format
+
+    def __repr__(self):
+        return '<SoundHDF5 file "{}" (mode {}, format {}, type {})>'.format(
+            self.filepath, self.mode, self.format, self.dtype)
+
+    def create_dataset(self, name, shape=None, data=None, **kwds):
+        f = io.BytesIO()
+        array, rate = data
+        soundfile.write(f, array, rate, format=self.format)
+        self.file.create_dataset(
+            name, shape=shape, data=np.void(f.getvalue()), **kwds)
+
+    def __setitem__(self, name, data):
+        self.create_dataset(name, data=data)
+
+    def __getitem__(self, key):
+        data = self.file[key][()]
+        f = io.BytesIO(data.tobytes())
+        array, rate = soundfile.read(f, dtype=self.dtype)
+        return array, rate
+
+    def keys(self):
+        return self.file.keys()
+
+    def values(self):
+        for k in self.file:
+            yield self[k]
+
+    def items(self):
+        for k in self.file:
+            yield k, self[k]
+
+    def __iter__(self):
+        return iter(self.file)
+
+    def __contains__(self, item):
+        return item in self.file
+
+    def __len__(self, item):
+        return len(self.file)
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.file.close()
+
+    def close(self):
+        self.file.close()

deepspeech/io/utility.py

@@ -17,11 +17,16 @@ import numpy as np
 
 from deepspeech.utils.log import Log
 
-__all__ = ["pad_sequence"]
+__all__ = ["pad_list", "pad_sequence"]
 
 logger = Log(__name__).getlog()
 
 
+def pad_list(sequences: List[np.ndarray],
+             padding_value: float=0.0) -> np.ndarray:
+    return pad_sequence(sequences, True, padding_value)
+
+
 def pad_sequence(sequences: List[np.ndarray],
                  batch_first: bool=True,
                  padding_value: float=0.0) -> np.ndarray:

deepspeech/models/ds2/rnn.py

@@ -297,7 +297,7 @@ class RNNStack(nn.Layer):
                         share_weights=share_rnn_weights))
             i_size = h_size * 2
 
-        self.rnn_stacks = nn.ModuleList(rnn_stacks)
+        self.rnn_stacks = nn.LayerList(rnn_stacks)
 
     def forward(self, x: paddle.Tensor, x_len: paddle.Tensor):
         """

deepspeech/models/u2.py

@@ -54,7 +54,7 @@ __all__ = ["U2Model", "U2InferModel"]
 logger = Log(__name__).getlog()
 
 
-class U2BaseModel(nn.Module):
+class U2BaseModel(nn.Layer):
     """CTC-Attention hybrid Encoder-Decoder model"""
 
     @classmethod

deepspeech/models/u2_st.py

@@ -48,7 +48,7 @@ __all__ = ["U2STModel", "U2STInferModel"]
 logger = Log(__name__).getlog()
 
 
-class U2STBaseModel(nn.Module):
+class U2STBaseModel(nn.Layer):
     """CTC-Attention hybrid Encoder-Decoder model"""
 
     @classmethod

deepspeech/modules/decoder.py

@@ -33,7 +33,7 @@ logger = Log(__name__).getlog()
 __all__ = ["TransformerDecoder"]
 
 
-class TransformerDecoder(nn.Module):
+class TransformerDecoder(nn.Layer):
     """Base class of Transfomer decoder module.
     Args:
         vocab_size: output dim
@@ -86,7 +86,7 @@ class TransformerDecoder(nn.Module):
         self.use_output_layer = use_output_layer
         self.output_layer = nn.Linear(attention_dim, vocab_size)
 
-        self.decoders = nn.ModuleList([
+        self.decoders = nn.LayerList([
             DecoderLayer(
                 size=attention_dim,
                 self_attn=MultiHeadedAttention(attention_heads, attention_dim,

deepspeech/modules/decoder_layer.py

@@ -25,15 +25,15 @@ logger = Log(__name__).getlog()
 __all__ = ["DecoderLayer"]
 
 
-class DecoderLayer(nn.Module):
+class DecoderLayer(nn.Layer):
     """Single decoder layer module.
     Args:
         size (int): Input dimension.
-        self_attn (nn.Module): Self-attention module instance.
+        self_attn (nn.Layer): Self-attention module instance.
             `MultiHeadedAttention` instance can be used as the argument.
-        src_attn (nn.Module): Self-attention module instance.
+        src_attn (nn.Layer): Self-attention module instance.
             `MultiHeadedAttention` instance can be used as the argument.
-        feed_forward (nn.Module): Feed-forward module instance.
+        feed_forward (nn.Layer): Feed-forward module instance.
             `PositionwiseFeedForward` instance can be used as the argument.
         dropout_rate (float): Dropout rate.
         normalize_before (bool):
@@ -48,9 +48,9 @@ class DecoderLayer(nn.Module):
     def __init__(
             self,
             size: int,
-            self_attn: nn.Module,
-            src_attn: nn.Module,
-            feed_forward: nn.Module,
+            self_attn: nn.Layer,
+            src_attn: nn.Layer,
+            feed_forward: nn.Layer,
             dropout_rate: float,
             normalize_before: bool=True,
             concat_after: bool=False, ):

deepspeech/modules/encoder.py

@@ -358,7 +358,7 @@ class TransformerEncoder(BaseEncoder):
                          pos_enc_layer_type, normalize_before, concat_after,
                          static_chunk_size, use_dynamic_chunk, global_cmvn,
                          use_dynamic_left_chunk)
-        self.encoders = nn.ModuleList([
+        self.encoders = nn.LayerList([
             TransformerEncoderLayer(
                 size=output_size,
                 self_attn=MultiHeadedAttention(attention_heads, output_size,
@@ -438,7 +438,7 @@ class ConformerEncoder(BaseEncoder):
         convolution_layer_args = (output_size, cnn_module_kernel, activation,
                                   cnn_module_norm, causal)
 
-        self.encoders = nn.ModuleList([
+        self.encoders = nn.LayerList([
             ConformerEncoderLayer(
                 size=output_size,
                 self_attn=encoder_selfattn_layer(*encoder_selfattn_layer_args),

deepspeech/modules/loss.py

@@ -48,7 +48,8 @@ class CTCLoss(nn.Layer):
         logits = logits.transpose([1, 0, 2])
         # (TODO:Hui Zhang) ctc loss does not support int64 labels
         ys_pad = ys_pad.astype(paddle.int32)
-        loss = self.loss(logits, ys_pad, hlens, ys_lens)
+        loss = self.loss(
+            logits, ys_pad, hlens, ys_lens, norm_by_times=self.batch_average)
         if self.batch_average:
             # Batch-size average
             loss = loss / B

deepspeech/modules/rnn.py

@@ -297,7 +297,7 @@ class RNNStack(nn.Layer):
                         share_weights=share_rnn_weights))
             i_size = h_size * 2
 
-        self.rnn_stacks = nn.ModuleList(rnn_stacks)
+        self.rnn_stacks = nn.LayerList(rnn_stacks)
 
     def forward(self, x: paddle.Tensor, x_len: paddle.Tensor):
         """

examples/librispeech/s1/README.md

@@ -21,7 +21,6 @@
 | --- | --- | --- | --- | --- | --- | --- | --- |
 | conformer | 47.63 M | conf/conformer.yaml | spec_aug + shift | test-clean-all | attention | 6.35 | 0.057117 |  
 
-
 ## Chunk Conformer
 | Model | Params | Config | Augmentation| Test set | Decode method | Chunk Size & Left Chunks | Loss | WER |  
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |  
@@ -39,4 +38,7 @@
 ### Test w/o length filter
 | Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |  
 | --- | --- | --- | --- | --- | --- | --- | --- |
-| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean-all | attention | 6.98 | 0.066500 |  
+| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean-all | attention | 7.63 | 0.056832 |  
+| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean-all | ctc_greedy_search | 7.63 | 0.059742 |  
+| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean-all | ctc_prefix_beam_search | 7.63 | 0.059057 |  
+| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean-all | attention_rescoring | 7.63 | 0.047417 |  

examples/librispeech/s1/conf/transformer.yaml

@@ -4,7 +4,7 @@ data:
   dev_manifest: data/manifest.dev
   test_manifest: data/manifest.test-clean
   min_input_len: 0.5  # second
-  max_input_len: 20.0 # second
+  max_input_len: 30.0 # second
   min_output_len: 0.0 # tokens
   max_output_len: 400.0 # tokens
   min_output_input_ratio: 0.05

examples/librispeech/s1/run.sh

@@ -5,7 +5,7 @@ source path.sh
 stage=0
 stop_stage=100
 conf_path=conf/transformer.yaml
-avg_num=30
+avg_num=5
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
 
 avg_ckpt=avg_${avg_num}

examples/librispeech/s2/local/espnet_json_to_manifest.py

+#!/usr/bin/env python
+import argparse
+import json
+
+
+def main(args):
+    with open(args.json_file, 'r') as fin:
+        data_json = json.load(fin)
+
+    # manifest format:
+    # {"input": [
+    #       {"feat": "dev/deltafalse/feats.1.ark:842920", "name": "input1", "shape": [349, 83]}
+    #  ], 
+    #  "output": [
+    #       {"name": "target1", "shape": [12, 5002], "text": "NO APOLLO", "token": "▁NO ▁A PO LL O", "tokenid": "3144 482 352 269 317"}
+    #  ], 
+    #  "utt2spk": "116-288045", 
+    #  "utt": "116-288045-0019"}
+    with open(args.manifest_file, 'w') as fout:
+        for key, value in data_json['utts'].items():
+            value['utt'] = key
+            fout.write(json.dumps(value, ensure_ascii=False))
+            fout.write("\n")
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        '--json-file', type=str, default=None, help="espnet data json file.")
+    parser.add_argument(
+        '--manifest-file',
+        type=str,
+        default='maniefst.train',
+        help='manifest data json line file.')
+    args = parser.parse_args()
+    main(args)

examples/librispeech/s2/run.sh

@@ -5,7 +5,7 @@ source path.sh
 stage=0
 stop_stage=100
 conf_path=conf/transformer.yaml
-avg_num=30
+avg_num=5
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
 
 avg_ckpt=avg_${avg_num}

requirements.txt

 coverage
 gpustat
+kaldiio
 pre-commit
 pybind11
 resampy==0.2.2