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egs/librispeech/ASR/pruned2_knowledge/asr_datamodule.py
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../transducer/asr_datamodule.py

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egs/librispeech/ASR/pruned2_knowledge/asr_datamodule.py Normal file
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# Copyright 2021 Piotr Żelasko
# Copyright 2022 Xiaomi Corporation (Author: 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 inspect
import logging
from functools import lru_cache
from pathlib import Path
from typing import Any, Dict, Optional
import torch
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 lhotse.utils import fix_random_seed
from torch.utils.data import DataLoader
from icefall.utils import str2bool
class _SeedWorkers:
def __init__(self, seed: int):
self.seed = seed
def __call__(self, worker_id: int):
fix_random_seed(self.seed + worker_id)
class LibriSpeechAsrDataModule:
"""
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.
"""
def __init__(self, args: argparse.Namespace):
self.args = args
@classmethod
def add_arguments(cls, parser: argparse.ArgumentParser):
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(
"--manifest-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.",
)
group.add_argument(
"--enable-spec-aug",
type=str2bool,
default=True,
help="When enabled, use SpecAugment for training dataset.",
)
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. ",
)
def train_dataloaders(
self,
cuts_train: CutSet,
sampler_state_dict: Optional[Dict[str, Any]] = None,
) -> DataLoader:
"""
Args:
cuts_train:
CutSet for training.
sampler_state_dict:
The state dict for the training sampler.
"""
transforms = []
if self.args.enable_musan:
logging.info("Enable MUSAN")
logging.info("About to get Musan cuts")
cuts_musan = load_manifest(
self.args.manifest_dir / "cuts_musan.json.gz"
)
transforms.append(
CutMix(
cuts=cuts_musan, prob=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(
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")
if sampler_state_dict is not None:
logging.info("Loading sampler state dict")
train_sampler.load_state_dict(sampler_state_dict)
# 'seed' is derived from the current random state, which will have
# previously been set in the main process.
seed = torch.randint(0, 100000, ()).item()
worker_init_fn = _SeedWorkers(seed)
train_dl = DataLoader(
train,
sampler=train_sampler,
batch_size=None,
num_workers=self.args.num_workers,
persistent_workers=False,
worker_init_fn=worker_init_fn,
)
return train_dl
def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
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 = BucketingSampler(
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, cuts: CutSet) -> DataLoader:
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 = BucketingSampler(
cuts, max_duration=self.args.max_duration, shuffle=False
)
logging.debug("About to create test dataloader")
test_dl = DataLoader(
test,
batch_size=None,
sampler=sampler,
num_workers=self.args.num_workers,
)
return test_dl
@lru_cache()
def train_clean_100_cuts(self) -> CutSet:
logging.info("About to get train-clean-100 cuts")
return load_manifest(
self.args.manifest_dir / "cuts_train-clean-100.json.gz"
)
@lru_cache()
def train_clean_360_cuts(self) -> CutSet:
logging.info("About to get train-clean-360 cuts")
return load_manifest(
self.args.manifest_dir / "cuts_train-clean-360.json.gz"
)
@lru_cache()
def train_other_500_cuts(self) -> CutSet:
logging.info("About to get train-other-500 cuts")
return load_manifest(
self.args.manifest_dir / "cuts_train-other-500.json.gz"
)
@lru_cache()
def dev_clean_cuts(self) -> CutSet:
logging.info("About to get dev-clean cuts")
return load_manifest(self.args.manifest_dir / "cuts_dev-clean.json.gz")
@lru_cache()
def dev_other_cuts(self) -> CutSet:
logging.info("About to get dev-other cuts")
return load_manifest(self.args.manifest_dir / "cuts_dev-other.json.gz")
@lru_cache()
def test_clean_cuts(self) -> CutSet:
logging.info("About to get test-clean cuts")
return load_manifest(self.args.manifest_dir / "cuts_test-clean.json.gz")
@lru_cache()
def test_other_cuts(self) -> CutSet:
logging.info("About to get test-other cuts")
return load_manifest(self.args.manifest_dir / "cuts_test-other.json.gz")

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egs/librispeech/ASR/pruned2_knowledge/decode.py
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@ -18,36 +18,36 @@
"""
Usage:
(1) greedy search
./pruned_transducer_stateless2/decode.py \
./pruned2_knowledge/decode.py \
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless2/exp \
--exp-dir ./pruned2_knowledge/exp \
--max-duration 100 \
--decoding-method greedy_search
(2) beam search
./pruned_transducer_stateless2/decode.py \
./pruned2_knowledge/decode.py \
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless2/exp \
--exp-dir ./pruned2_knowledge/exp \
--max-duration 100 \
--decoding-method beam_search \
--beam-size 4
(3) modified beam search
./pruned_transducer_stateless2/decode.py \
./pruned2_knowledge/decode.py \
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless2/exp \
--exp-dir ./pruned2_knowledge/exp \
--max-duration 100 \
--decoding-method modified_beam_search \
--beam-size 4
(4) fast beam search
./pruned_transducer_stateless2/decode.py \
./pruned2_knowledge/decode.py \
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless2/exp \
--exp-dir ./pruned2_knowledge/exp \
--max-duration 1500 \
--decoding-method fast_beam_search \
--beam 4 \
@ -124,7 +124,7 @@ def get_parser():
parser.add_argument(
"--exp-dir",
type=str,
default="pruned_transducer_stateless2/exp",
default="pruned2_knowledge/exp",
help="The experiment dir",
)

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egs/librispeech/ASR/pruned2_knowledge/encoder_interface.py
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../transducer_stateless/encoder_interface.py

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egs/librispeech/ASR/pruned2_knowledge/encoder_interface.py Normal file
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
#
# 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.
from typing import Tuple
import torch
import torch.nn as nn
class EncoderInterface(nn.Module):
def forward(
self, x: torch.Tensor, x_lens: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
x:
A tensor of shape (batch_size, input_seq_len, num_features)
containing the input features.
x_lens:
A tensor of shape (batch_size,) containing the number of frames
in `x` before padding.
Returns:
Return a tuple containing two tensors:
- encoder_out, a tensor of (batch_size, out_seq_len, output_dim)
containing unnormalized probabilities, i.e., the output of a
linear layer.
- encoder_out_lens, a tensor of shape (batch_size,) containing
the number of frames in `encoder_out` before padding.
"""
raise NotImplementedError("Please implement it in a subclass")

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egs/librispeech/ASR/pruned2_knowledge/export.py
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@ -20,23 +20,23 @@
# to a single one using model averaging.
"""
Usage:
./pruned_transducer_stateless2/export.py \
--exp-dir ./pruned_transducer_stateless2/exp \
./pruned2_knowledge/export.py \
--exp-dir ./pruned2_knowledge/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--epoch 20 \
--avg 10
It will generate a file exp_dir/pretrained.pt
To use the generated file with `pruned_transducer_stateless2/decode.py`,
To use the generated file with `pruned2_knowledge/decode.py`,
you can do:
cd /path/to/exp_dir
ln -s pretrained.pt epoch-9999.pt
cd /path/to/egs/librispeech/ASR
./pruned_transducer_stateless2/decode.py \
--exp-dir ./pruned_transducer_stateless2/exp \
./pruned2_knowledge/decode.py \
--exp-dir ./pruned2_knowledge/exp \
--epoch 9999 \
--avg 1 \
--max-duration 100 \
@ -80,7 +80,7 @@ def get_parser():
parser.add_argument(
"--exp-dir",
type=str,
default="pruned_transducer_stateless2/exp",
default="pruned2_knowledge/exp",
help="""It specifies the directory where all training related
files, e.g., checkpoints, log, etc, are saved
""",

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egs/librispeech/ASR/pruned2_knowledge/train.py
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@ -21,22 +21,22 @@ Usage:
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./pruned_transducer_stateless2/train.py \
./pruned2_knowledge/train.py \
--world-size 4 \
--num-epochs 30 \
--start-epoch 0 \
--exp-dir pruned_transducer_stateless2/exp \
--exp-dir pruned2_knowledge/exp \
--full-libri 1 \
--max-duration 300
# For mix precision training:
./pruned_transducer_stateless2/train.py \
./pruned2_knowledge/train.py \
--world-size 4 \
--num-epochs 30 \
--start-epoch 0 \
--use_fp16 1 \
--exp-dir pruned_transducer_stateless2/exp \
--exp-dir pruned2_knowledge/exp \
--full-libri 1 \
--max-duration 550
@ -138,7 +138,7 @@ def get_parser():
parser.add_argument(
"--exp-dir",
type=str,
default="pruned_transducer_stateless2/exp",
default="pruned2_knowledge/exp",
help="""The experiment dir.
It specifies the directory where all training related
files, e.g., checkpoints, log, etc, are saved