add dataset example for librispeech

egs/librispeech/ASR/zipformer/asr_datamodule_with_parallel_aug.py

@@ -17,14 +17,16 @@
 
 
 import argparse
-import inspect
 import logging
+import random
 from functools import lru_cache
 from pathlib import Path
 from typing import Any, Dict, Optional
 
+import lhotse
 import torch
-from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
+from lhotse import CutSet, Fbank, FbankConfig, load_manifest_lazy
+from lhotse.cut import Cut
 from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
     CutConcatenate,
     CutMix,
@@ -41,6 +43,9 @@ from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
 from lhotse.utils import fix_random_seed
 from torch.utils.data import DataLoader
 
+from icefall.speech_recognition_dataset import (
+    ConsistencyRegularizationSpeechRecognitionDataset,
+)
 from icefall.utils import str2bool
 
 
@@ -52,7 +57,25 @@ class _SeedWorkers:
         fix_random_seed(self.seed + worker_id)
 
 
-class LibriSpeechAsrDataModule:
+def perturb_speed(c: Cut):
+    factor = random.uniform(0.9, 1.1)
+    print("perturb_speed factor", factor)
+    return lhotse.MonoCut.perturb_speed(c, factor)
+
+
+def perturb_volume(c: Cut):
+    factor = random.uniform(0.9, 1.1)
+    print("perturb_volume factor", factor)
+    return lhotse.MonoCut.perturb_volume(c, factor)
+
+
+def perturb_tempo(c: Cut):
+    factor = random.uniform(0.9, 1.1)
+    print("perturb_tempo factor", factor)
+    return lhotse.MonoCut.perturb_tempo(c, factor)
+
+
+class LibriSpeechAsrDataModuleWithParallelAug:
     """
     DataModule for k2 ASR experiments.
     It assumes there is always one train and valid dataloader,
@@ -123,36 +146,6 @@ class LibriSpeechAsrDataModule:
             help="The number of buckets for the DynamicBucketingSampler"
             "(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,
@@ -184,28 +177,12 @@ class LibriSpeechAsrDataModule:
         )
 
         group.add_argument(
-            "--enable-spec-aug",
+            "--on-the-fly-feats",
             type=str2bool,
-            default=True,
+            default=False,
-            help="When enabled, use SpecAugment for training dataset.",
+            help="When enabled, use on-the-fly cut mixing and feature "
-        )
+            "extraction. Will drop existing precomputed feature manifests "
-
+            "if available. For training dataset, it always uses on_the_fly_feats",
-        group.add_argument(
-            "--spec-aug-time-warp-factor",
-            type=int,
-            default=80,
-            help="Used only when --enable-spec-aug is True. "
-            "It specifies the factor for time warping in SpecAugment. "
-            "Larger values mean more warping. "
-            "A value less than 1 means to disable time warp.",
-        )
-
-        group.add_argument(
-            "--enable-musan",
-            type=str2bool,
-            default=True,
-            help="When enabled, select noise from MUSAN and mix it"
-            "with training dataset. ",
         )
 
         group.add_argument(
@@ -227,83 +204,14 @@ class LibriSpeechAsrDataModule:
           sampler_state_dict:
             The state dict for the training sampler.
         """
-        transforms = []
+        transforms = [perturb_speed, perturb_volume, perturb_tempo]
-        if self.args.enable_musan:
-            logging.info("Enable MUSAN")
-            logging.info("About to get Musan cuts")
-            cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
-            transforms.append(
-                CutMix(cuts=cuts_musan, p=0.5, snr=(10, 20), preserve_id=True)
-            )
-        else:
-            logging.info("Disable MUSAN")
-
-        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 = []
-        if self.args.enable_spec_aug:
-            logging.info("Enable SpecAugment")
-            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=num_frame_masks,
-                    features_mask_size=27,
-                    num_feature_masks=2,
-                    frames_mask_size=100,
-                )
-            )
-        else:
-            logging.info("Disable SpecAugment")
 
         logging.info("About to create train dataset")
-        train = K2SpeechRecognitionDataset(
+        train = ConsistencyRegularizationSpeechRecognitionDataset(
-            input_strategy=eval(self.args.input_strategy)(),
+            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
             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 DynamicBucketingSampler.")
             train_sampler = DynamicBucketingSampler(