Change for asr_datamodule.py (#241)

* change for asr_datamodule.py

* fix style check

* do a fix

egs/aishell/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -1,4 +1,5 @@
 # Copyright      2021  Piotr Żelasko
+# Copyright      2022  Xiaomi Corporation     (Author: Mingshuang Luo)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -16,6 +17,7 @@
 
 
 import argparse
+import inspect
 import logging
 from functools import lru_cache
 from pathlib import Path
@@ -210,10 +212,20 @@ class AishellAsrDataModule:
             logging.info(
                 f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
             )
+            # Set the value of num_frame_masks according to Lhotse's version.
+            # In different Lhotse's versions, the default of num_frame_masks is
+            # different.
+            num_frame_masks = 10
+            num_frame_masks_parameter = inspect.signature(
+                SpecAugment.__init__
+            ).parameters["num_frame_masks"]
+            if num_frame_masks_parameter.default == 1:
+                num_frame_masks = 2
+            logging.info(f"Num frame mask: {num_frame_masks}")
             input_transforms.append(
                 SpecAugment(
                     time_warp_factor=self.args.spec_aug_time_warp_factor,
-                    num_frame_masks=2,
+                    num_frame_masks=num_frame_masks,
                     features_mask_size=27,
                     num_feature_masks=2,
                     frames_mask_size=100,

egs/aishell/ASR/transducer_stateless_modified-2/asr_datamodule.py

@@ -1,5 +1,6 @@
 # Copyright      2021  Piotr Żelasko
-#                2022  Xiaomi Corp.        (authors: Fangjun Kuang)
+#                2022  Xiaomi Corp.        (authors: Fangjun Kuang
+#                                                    Mingshuang Luo)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -16,6 +17,7 @@
 # limitations under the License.
 
 import argparse
+import inspect
 import logging
 from pathlib import Path
 from typing import Optional
@@ -180,10 +182,20 @@ class AsrDataModule:
             logging.info(
                 f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
             )
+            # Set the value of num_frame_masks according to Lhotse's version.
+            # In different Lhotse's versions, the default of num_frame_masks is
+            # different.
+            num_frame_masks = 10
+            num_frame_masks_parameter = inspect.signature(
+                SpecAugment.__init__
+            ).parameters["num_frame_masks"]
+            if num_frame_masks_parameter.default == 1:
+                num_frame_masks = 2
+            logging.info(f"Num frame mask: {num_frame_masks}")
             input_transforms.append(
                 SpecAugment(
                     time_warp_factor=self.args.spec_aug_time_warp_factor,
-                    num_frame_masks=2,
+                    num_frame_masks=num_frame_masks,
                     features_mask_size=27,
                     num_feature_masks=2,
                     frames_mask_size=100,

egs/librispeech/ASR/conformer_mmi/asr_datamodule.py

@@ -1,356 +0,0 @@
-# Copyright      2021  Piotr Żelasko
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# 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 argparse
-import logging
-from functools import lru_cache
-from pathlib import Path
-from typing import List, Union
-
-from lhotse import CutSet, Fbank, FbankConfig, load_manifest
-from lhotse.dataset import (
-    BucketingSampler,
-    CutConcatenate,
-    CutMix,
-    K2SpeechRecognitionDataset,
-    PrecomputedFeatures,
-    SingleCutSampler,
-    SpecAugment,
-)
-from lhotse.dataset.input_strategies import OnTheFlyFeatures
-from torch.utils.data import DataLoader
-
-from icefall.dataset.datamodule import DataModule
-from icefall.utils import str2bool
-
-
-class LibriSpeechAsrDataModule(DataModule):
-    """
-    DataModule for k2 ASR experiments.
-    It assumes there is always one train and valid dataloader,
-    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
-    and test-other).
-
-    It contains all the common data pipeline modules used in ASR
-    experiments, e.g.:
-    - dynamic batch size,
-    - bucketing samplers,
-    - cut concatenation,
-    - augmentation,
-    - on-the-fly feature extraction
-
-    This class should be derived for specific corpora used in ASR tasks.
-    """
-
-    @classmethod
-    def add_arguments(cls, parser: argparse.ArgumentParser):
-        super().add_arguments(parser)
-        group = parser.add_argument_group(
-            title="ASR data related options",
-            description="These options are used for the preparation of "
-            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
-            "effective batch sizes, sampling strategies, applied data "
-            "augmentations, etc.",
-        )
-        group.add_argument(
-            "--full-libri",
-            type=str2bool,
-            default=True,
-            help="When enabled, use 960h LibriSpeech. "
-            "Otherwise, use 100h subset.",
-        )
-        group.add_argument(
-            "--feature-dir",
-            type=Path,
-            default=Path("data/fbank"),
-            help="Path to directory with train/valid/test cuts.",
-        )
-        group.add_argument(
-            "--max-duration",
-            type=int,
-            default=200.0,
-            help="Maximum pooled recordings duration (seconds) in a "
-            "single batch. You can reduce it if it causes CUDA OOM.",
-        )
-        group.add_argument(
-            "--bucketing-sampler",
-            type=str2bool,
-            default=True,
-            help="When enabled, the batches will come from buckets of "
-            "similar duration (saves padding frames).",
-        )
-        group.add_argument(
-            "--num-buckets",
-            type=int,
-            default=30,
-            help="The number of buckets for the BucketingSampler"
-            "(you might want to increase it for larger datasets).",
-        )
-        group.add_argument(
-            "--concatenate-cuts",
-            type=str2bool,
-            default=False,
-            help="When enabled, utterances (cuts) will be concatenated "
-            "to minimize the amount of padding.",
-        )
-        group.add_argument(
-            "--duration-factor",
-            type=float,
-            default=1.0,
-            help="Determines the maximum duration of a concatenated cut "
-            "relative to the duration of the longest cut in a batch.",
-        )
-        group.add_argument(
-            "--gap",
-            type=float,
-            default=1.0,
-            help="The amount of padding (in seconds) inserted between "
-            "concatenated cuts. This padding is filled with noise when "
-            "noise augmentation is used.",
-        )
-        group.add_argument(
-            "--on-the-fly-feats",
-            type=str2bool,
-            default=False,
-            help="When enabled, use on-the-fly cut mixing and feature "
-            "extraction. Will drop existing precomputed feature manifests "
-            "if available.",
-        )
-        group.add_argument(
-            "--shuffle",
-            type=str2bool,
-            default=True,
-            help="When enabled (=default), the examples will be "
-            "shuffled for each epoch.",
-        )
-        group.add_argument(
-            "--return-cuts",
-            type=str2bool,
-            default=True,
-            help="When enabled, each batch will have the "
-            "field: batch['supervisions']['cut'] with the cuts that "
-            "were used to construct it.",
-        )
-
-        group.add_argument(
-            "--num-workers",
-            type=int,
-            default=2,
-            help="The number of training dataloader workers that "
-            "collect the batches.",
-        )
-
-    def train_dataloaders(self) -> DataLoader:
-        logging.info("About to get train cuts")
-        cuts_train = self.train_cuts()
-
-        logging.info("About to get Musan cuts")
-        cuts_musan = load_manifest(self.args.feature_dir / "cuts_musan.json.gz")
-
-        logging.info("About to create train dataset")
-        transforms = [
-            CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True)
-        ]
-        if self.args.concatenate_cuts:
-            logging.info(
-                f"Using cut concatenation with duration factor "
-                f"{self.args.duration_factor} and gap {self.args.gap}."
-            )
-            # Cut concatenation should be the first transform in the list,
-            # so that if we e.g. mix noise in, it will fill the gaps between
-            # different utterances.
-            transforms = [
-                CutConcatenate(
-                    duration_factor=self.args.duration_factor, gap=self.args.gap
-                )
-            ] + transforms
-
-        input_transforms = [
-            SpecAugment(
-                num_frame_masks=2,
-                features_mask_size=27,
-                num_feature_masks=2,
-                frames_mask_size=100,
-            )
-        ]
-
-        train = K2SpeechRecognitionDataset(
-            cut_transforms=transforms,
-            input_transforms=input_transforms,
-            return_cuts=self.args.return_cuts,
-        )
-
-        if self.args.on_the_fly_feats:
-            # NOTE: the PerturbSpeed transform should be added only if we
-            # remove it from data prep stage.
-            # Add on-the-fly speed perturbation; since originally it would
-            # have increased epoch size by 3, we will apply prob 2/3 and use
-            # 3x more epochs.
-            # Speed perturbation probably should come first before
-            # concatenation, but in principle the transforms order doesn't have
-            # to be strict (e.g. could be randomized)
-            # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms   # noqa
-            # Drop feats to be on the safe side.
-            train = K2SpeechRecognitionDataset(
-                cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(
-                    Fbank(FbankConfig(num_mel_bins=80))
-                ),
-                input_transforms=input_transforms,
-                return_cuts=self.args.return_cuts,
-            )
-
-        if self.args.bucketing_sampler:
-            logging.info("Using BucketingSampler.")
-            train_sampler = BucketingSampler(
-                cuts_train,
-                max_duration=self.args.max_duration,
-                shuffle=self.args.shuffle,
-                num_buckets=self.args.num_buckets,
-                bucket_method="equal_duration",
-                drop_last=True,
-            )
-        else:
-            logging.info("Using SingleCutSampler.")
-            train_sampler = SingleCutSampler(
-                cuts_train,
-                max_duration=self.args.max_duration,
-                shuffle=self.args.shuffle,
-            )
-        logging.info("About to create train dataloader")
-
-        train_dl = DataLoader(
-            train,
-            sampler=train_sampler,
-            batch_size=None,
-            num_workers=self.args.num_workers,
-            persistent_workers=False,
-        )
-
-        return train_dl
-
-    def valid_dataloaders(self) -> DataLoader:
-        logging.info("About to get dev cuts")
-        cuts_valid = self.valid_cuts()
-
-        transforms = []
-        if self.args.concatenate_cuts:
-            transforms = [
-                CutConcatenate(
-                    duration_factor=self.args.duration_factor, gap=self.args.gap
-                )
-            ] + transforms
-
-        logging.info("About to create dev dataset")
-        if self.args.on_the_fly_feats:
-            validate = K2SpeechRecognitionDataset(
-                cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(
-                    Fbank(FbankConfig(num_mel_bins=80))
-                ),
-                return_cuts=self.args.return_cuts,
-            )
-        else:
-            validate = K2SpeechRecognitionDataset(
-                cut_transforms=transforms,
-                return_cuts=self.args.return_cuts,
-            )
-        valid_sampler = SingleCutSampler(
-            cuts_valid,
-            max_duration=self.args.max_duration,
-            shuffle=False,
-        )
-        logging.info("About to create dev dataloader")
-        valid_dl = DataLoader(
-            validate,
-            sampler=valid_sampler,
-            batch_size=None,
-            num_workers=2,
-            persistent_workers=False,
-        )
-
-        return valid_dl
-
-    def test_dataloaders(self) -> Union[DataLoader, List[DataLoader]]:
-        cuts = self.test_cuts()
-        is_list = isinstance(cuts, list)
-        test_loaders = []
-        if not is_list:
-            cuts = [cuts]
-
-        for cuts_test in cuts:
-            logging.debug("About to create test dataset")
-            test = K2SpeechRecognitionDataset(
-                input_strategy=OnTheFlyFeatures(
-                    Fbank(FbankConfig(num_mel_bins=80))
-                )
-                if self.args.on_the_fly_feats
-                else PrecomputedFeatures(),
-                return_cuts=self.args.return_cuts,
-            )
-            sampler = SingleCutSampler(
-                cuts_test, max_duration=self.args.max_duration
-            )
-            logging.debug("About to create test dataloader")
-            test_dl = DataLoader(
-                test, batch_size=None, sampler=sampler, num_workers=1
-            )
-            test_loaders.append(test_dl)
-
-        if is_list:
-            return test_loaders
-        else:
-            return test_loaders[0]
-
-    @lru_cache()
-    def train_cuts(self) -> CutSet:
-        logging.info("About to get train cuts")
-        cuts_train = load_manifest(
-            self.args.feature_dir / "cuts_train-clean-100.json.gz"
-        )
-        if self.args.full_libri:
-            cuts_train = (
-                cuts_train
-                + load_manifest(
-                    self.args.feature_dir / "cuts_train-clean-360.json.gz"
-                )
-                + load_manifest(
-                    self.args.feature_dir / "cuts_train-other-500.json.gz"
-                )
-            )
-        return cuts_train
-
-    @lru_cache()
-    def valid_cuts(self) -> CutSet:
-        logging.info("About to get dev cuts")
-        cuts_valid = load_manifest(
-            self.args.feature_dir / "cuts_dev-clean.json.gz"
-        ) + load_manifest(self.args.feature_dir / "cuts_dev-other.json.gz")
-        return cuts_valid
-
-    @lru_cache()
-    def test_cuts(self) -> List[CutSet]:
-        test_sets = ["test-clean", "test-other"]
-        cuts = []
-        for test_set in test_sets:
-            logging.debug("About to get test cuts")
-            cuts.append(
-                load_manifest(
-                    self.args.feature_dir / f"cuts_{test_set}.json.gz"
-                )
-            )
-        return cuts

egs/librispeech/ASR/conformer_mmi/asr_datamodule.py

@@ -0,0 +1 @@
+../conformer_ctc/asr_datamodule.py

egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -1,4 +1,5 @@
 # Copyright      2021  Piotr Żelasko
+# Copyright      2022  Xiaomi Corporation     (Author: Mingshuang Luo)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -16,6 +17,7 @@
 
 
 import argparse
+import inspect
 import logging
 from functools import lru_cache
 from pathlib import Path
@@ -216,10 +218,20 @@ class LibriSpeechAsrDataModule:
             logging.info(
                 f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
             )
+            # Set the value of num_frame_masks according to Lhotse's version.
+            # In different Lhotse's versions, the default of num_frame_masks is
+            # different.
+            num_frame_masks = 10
+            num_frame_masks_parameter = inspect.signature(
+                SpecAugment.__init__
+            ).parameters["num_frame_masks"]
+            if num_frame_masks_parameter.default == 1:
+                num_frame_masks = 2
+            logging.info(f"Num frame mask: {num_frame_masks}")
             input_transforms.append(
                 SpecAugment(
                     time_warp_factor=self.args.spec_aug_time_warp_factor,
-                    num_frame_masks=2,
+                    num_frame_masks=num_frame_masks,
                     features_mask_size=27,
                     num_feature_masks=2,
                     frames_mask_size=100,

egs/librispeech/ASR/transducer_stateless_multi_datasets/asr_datamodule.py

@@ -1,5 +1,6 @@
 # Copyright      2021  Piotr Żelasko
-#                2022  Xiaomi Corp.        (authors: Fangjun Kuang)
+#                2022  Xiaomi Corp.        (authors: Fangjun Kuang
+# 						     Mingshuang Luo)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -16,6 +17,7 @@
 # limitations under the License.
 
 import argparse
+import inspect
 import logging
 from pathlib import Path
 from typing import Optional
@@ -180,10 +182,20 @@ class AsrDataModule:
             logging.info(
                 f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
             )
+            # Set the value of num_frame_masks according to Lhotse's version.
+            # In different Lhotse's versions, the default of num_frame_masks is
+            # different.
+            num_frame_masks = 10
+            num_frame_masks_parameter = inspect.signature(
+                SpecAugment.__init__
+            ).parameters["num_frame_masks"]
+            if num_frame_masks_parameter.default == 1:
+                num_frame_masks = 2
+            logging.info(f"Num frame mask: {num_frame_masks}")
             input_transforms.append(
                 SpecAugment(
                     time_warp_factor=self.args.spec_aug_time_warp_factor,
-                    num_frame_masks=2,
+                    num_frame_masks=num_frame_masks,
                     features_mask_size=27,
                     num_feature_masks=2,
                     frames_mask_size=100,

egs/timit/ASR/tdnn_ligru_ctc/asr_datamodule.py

@@ -1,330 +0,0 @@
-# Copyright      2021     Piotr Żelasko
-#                2021     Xiaomi Corp.    (authors: Mingshuang Luo)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# 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 argparse
-import logging
-from functools import lru_cache
-from pathlib import Path
-from typing import List, Union
-
-from lhotse import CutSet, Fbank, FbankConfig, load_manifest
-from lhotse.dataset import (
-    BucketingSampler,
-    CutConcatenate,
-    CutMix,
-    K2SpeechRecognitionDataset,
-    PrecomputedFeatures,
-    SingleCutSampler,
-    SpecAugment,
-)
-from lhotse.dataset.input_strategies import OnTheFlyFeatures
-from torch.utils.data import DataLoader
-
-from icefall.dataset.datamodule import DataModule
-from icefall.utils import str2bool
-
-
-class TimitAsrDataModule(DataModule):
-    """
-    DataModule for k2 ASR experiments.
-    It assumes there is always one train and valid dataloader,
-    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
-    and test-other).
-
-    It contains all the common data pipeline modules used in ASR
-    experiments, e.g.:
-    - dynamic batch size,
-    - bucketing samplers,
-    - cut concatenation,
-    - augmentation,
-    - on-the-fly feature extraction
-
-    This class should be derived for specific corpora used in ASR tasks.
-    """
-
-    @classmethod
-    def add_arguments(cls, parser: argparse.ArgumentParser):
-        super().add_arguments(parser)
-        group = parser.add_argument_group(
-            title="ASR data related options",
-            description="These options are used for the preparation of "
-            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
-            "effective batch sizes, sampling strategies, applied data "
-            "augmentations, etc.",
-        )
-        group.add_argument(
-            "--feature-dir",
-            type=Path,
-            default=Path("data/fbank"),
-            help="Path to directory with train/valid/test cuts.",
-        )
-        group.add_argument(
-            "--max-duration",
-            type=int,
-            default=200.0,
-            help="Maximum pooled recordings duration (seconds) in a "
-            "single batch. You can reduce it if it causes CUDA OOM.",
-        )
-        group.add_argument(
-            "--bucketing-sampler",
-            type=str2bool,
-            default=True,
-            help="When enabled, the batches will come from buckets of "
-            "similar duration (saves padding frames).",
-        )
-        group.add_argument(
-            "--num-buckets",
-            type=int,
-            default=30,
-            help="The number of buckets for the BucketingSampler"
-            "(you might want to increase it for larger datasets).",
-        )
-        group.add_argument(
-            "--concatenate-cuts",
-            type=str2bool,
-            default=False,
-            help="When enabled, utterances (cuts) will be concatenated "
-            "to minimize the amount of padding.",
-        )
-        group.add_argument(
-            "--duration-factor",
-            type=float,
-            default=1.0,
-            help="Determines the maximum duration of a concatenated cut "
-            "relative to the duration of the longest cut in a batch.",
-        )
-        group.add_argument(
-            "--gap",
-            type=float,
-            default=1.0,
-            help="The amount of padding (in seconds) inserted between "
-            "concatenated cuts. This padding is filled with noise when "
-            "noise augmentation is used.",
-        )
-        group.add_argument(
-            "--on-the-fly-feats",
-            type=str2bool,
-            default=False,
-            help="When enabled, use on-the-fly cut mixing and feature "
-            "extraction. Will drop existing precomputed feature manifests "
-            "if available.",
-        )
-        group.add_argument(
-            "--shuffle",
-            type=str2bool,
-            default=True,
-            help="When enabled (=default), the examples will be "
-            "shuffled for each epoch.",
-        )
-        group.add_argument(
-            "--return-cuts",
-            type=str2bool,
-            default=True,
-            help="When enabled, each batch will have the "
-            "field: batch['supervisions']['cut'] with the cuts that "
-            "were used to construct it.",
-        )
-
-        group.add_argument(
-            "--num-workers",
-            type=int,
-            default=2,
-            help="The number of training dataloader workers that "
-            "collect the batches.",
-        )
-
-    def train_dataloaders(self) -> DataLoader:
-        logging.info("About to get train cuts")
-        cuts_train = self.train_cuts()
-
-        logging.info("About to get Musan cuts")
-        cuts_musan = load_manifest(self.args.feature_dir / "cuts_musan.json.gz")
-
-        logging.info("About to create train dataset")
-        transforms = [CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20))]
-        if self.args.concatenate_cuts:
-            logging.info(
-                f"Using cut concatenation with duration factor "
-                f"{self.args.duration_factor} and gap {self.args.gap}."
-            )
-            # Cut concatenation should be the first transform in the list,
-            # so that if we e.g. mix noise in, it will fill the gaps between
-            # different utterances.
-            transforms = [
-                CutConcatenate(
-                    duration_factor=self.args.duration_factor, gap=self.args.gap
-                )
-            ] + transforms
-
-        input_transforms = [
-            SpecAugment(
-                num_frame_masks=2,
-                features_mask_size=27,
-                num_feature_masks=2,
-                frames_mask_size=100,
-            )
-        ]
-
-        train = K2SpeechRecognitionDataset(
-            cut_transforms=transforms,
-            input_transforms=input_transforms,
-            return_cuts=self.args.return_cuts,
-        )
-
-        if self.args.on_the_fly_feats:
-            # NOTE: the PerturbSpeed transform should be added only if we
-            # remove it from data prep stage.
-            # Add on-the-fly speed perturbation; since originally it would
-            # have increased epoch size by 3, we will apply prob 2/3 and use
-            # 3x more epochs.
-            # Speed perturbation probably should come first before
-            # concatenation, but in principle the transforms order doesn't have
-            # to be strict (e.g. could be randomized)
-            # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms   # noqa
-            # Drop feats to be on the safe side.
-            train = K2SpeechRecognitionDataset(
-                cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(
-                    Fbank(FbankConfig(num_mel_bins=80))
-                ),
-                input_transforms=input_transforms,
-                return_cuts=self.args.return_cuts,
-            )
-
-        if self.args.bucketing_sampler:
-            logging.info("Using BucketingSampler.")
-            train_sampler = BucketingSampler(
-                cuts_train,
-                max_duration=self.args.max_duration,
-                shuffle=self.args.shuffle,
-                num_buckets=self.args.num_buckets,
-                bucket_method="equal_duration",
-                drop_last=True,
-            )
-        else:
-            logging.info("Using SingleCutSampler.")
-            train_sampler = SingleCutSampler(
-                cuts_train,
-                max_duration=self.args.max_duration,
-                shuffle=self.args.shuffle,
-            )
-        logging.info("About to create train dataloader")
-
-        train_dl = DataLoader(
-            train,
-            sampler=train_sampler,
-            batch_size=None,
-            num_workers=self.args.num_workers,
-            persistent_workers=False,
-        )
-
-        return train_dl
-
-    def valid_dataloaders(self) -> DataLoader:
-        logging.info("About to get dev cuts")
-        cuts_valid = self.valid_cuts()
-
-        transforms = []
-        if self.args.concatenate_cuts:
-            transforms = [
-                CutConcatenate(
-                    duration_factor=self.args.duration_factor, gap=self.args.gap
-                )
-            ] + transforms
-
-        logging.info("About to create dev dataset")
-        if self.args.on_the_fly_feats:
-            validate = K2SpeechRecognitionDataset(
-                cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(
-                    Fbank(FbankConfig(num_mel_bins=80))
-                ),
-                return_cuts=self.args.return_cuts,
-            )
-        else:
-            validate = K2SpeechRecognitionDataset(
-                cut_transforms=transforms,
-                return_cuts=self.args.return_cuts,
-            )
-        valid_sampler = SingleCutSampler(
-            cuts_valid,
-            max_duration=self.args.max_duration,
-            shuffle=False,
-        )
-        logging.info("About to create dev dataloader")
-        valid_dl = DataLoader(
-            validate,
-            sampler=valid_sampler,
-            batch_size=None,
-            num_workers=2,
-            persistent_workers=False,
-        )
-
-        return valid_dl
-
-    def test_dataloaders(self) -> Union[DataLoader, List[DataLoader]]:
-        cuts = self.test_cuts()
-        is_list = isinstance(cuts, list)
-        test_loaders = []
-        if not is_list:
-            cuts = [cuts]
-
-        for cuts_test in cuts:
-            logging.debug("About to create test dataset")
-            test = K2SpeechRecognitionDataset(
-                input_strategy=OnTheFlyFeatures(
-                    Fbank(FbankConfig(num_mel_bins=80))
-                )
-                if self.args.on_the_fly_feats
-                else PrecomputedFeatures(),
-                return_cuts=self.args.return_cuts,
-            )
-            sampler = SingleCutSampler(
-                cuts_test, max_duration=self.args.max_duration
-            )
-            logging.debug("About to create test dataloader")
-            test_dl = DataLoader(
-                test, batch_size=None, sampler=sampler, num_workers=1
-            )
-            test_loaders.append(test_dl)
-
-        if is_list:
-            return test_loaders
-        else:
-            return test_loaders[0]
-
-    @lru_cache()
-    def train_cuts(self) -> CutSet:
-        logging.info("About to get train cuts")
-        cuts_train = load_manifest(self.args.feature_dir / "cuts_TRAIN.json.gz")
-
-        return cuts_train
-
-    @lru_cache()
-    def valid_cuts(self) -> CutSet:
-        logging.info("About to get dev cuts")
-        cuts_valid = load_manifest(self.args.feature_dir / "cuts_DEV.json.gz")
-
-        return cuts_valid
-
-    @lru_cache()
-    def test_cuts(self) -> CutSet:
-        logging.debug("About to get test cuts")
-        cuts_test = load_manifest(self.args.feature_dir / "cuts_TEST.json.gz")
-
-        return cuts_test

egs/timit/ASR/tdnn_ligru_ctc/asr_datamodule.py

@@ -0,0 +1 @@
+../tdnn_lstm_ctc/asr_datamodule.py

egs/timit/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -1,5 +1,5 @@
 # Copyright      2021     Piotr Żelasko
-#                2021     Xiaomi Corp.     (authors: Mingshuang Luo)
+#                2022     Xiaomi Corporation     (Author: Mingshuang Luo)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -17,6 +17,7 @@
 
 
 import argparse
+import inspect
 import logging
 from functools import lru_cache
 from pathlib import Path
@@ -171,9 +172,19 @@ class TimitAsrDataModule(DataModule):
                 )
             ] + transforms
 
+        # Set the value of num_frame_masks according to Lhotse's version.
+        # In different Lhotse's versions, the default of num_frame_masks is
+        # different.
+        num_frame_masks = 10
+        num_frame_masks_parameter = inspect.signature(
+            SpecAugment.__init__
+        ).parameters["num_frame_masks"]
+        if num_frame_masks_parameter.default == 1:
+            num_frame_masks = 2
+        logging.info(f"Num frame mask: {num_frame_masks}")
         input_transforms = [
             SpecAugment(
-                num_frame_masks=2,
+                num_frame_masks=num_frame_masks,
                 features_mask_size=27,
                 num_feature_masks=2,
                 frames_mask_size=100,