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minor updates

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This commit is contained in:
JinZr 2023-08-19 21:22:28 +08:00
parent e13b01a313
commit f7fac705d5
5 changed files with 242 additions and 65 deletions
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16
egs/swbd/ASR/conformer_ctc/asr_datamodule.py
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@ -225,8 +225,6 @@ class SwitchBoardAsrDataModule:
else:
logging.info("Disable MUSAN")
cuts_train = cuts_train.trim_to_supervisions(keep_overlapping=False)
if self.args.concatenate_cuts:
logging.info(
f"Using cut concatenation with duration factor "
@ -393,18 +391,16 @@ class SwitchBoardAsrDataModule:
@lru_cache()
def train_all_cuts(self) -> CutSet:
logging.info("switchboard: About to get train cuts")
return (
load_manifest_lazy(self.args.manifest_dir / "swbd" / "swbd_cuts_all.jsonl.gz")
.subset(last=2388)
)
return load_manifest_lazy(
self.args.manifest_dir / "swbd_cuts_all.jsonl.gz"
).subset(last=2388)
@lru_cache()
def dev_cuts(self) -> CutSet:
logging.info("switchboard: About to get dev cuts")
return (
load_manifest_lazy(self.args.manifest_dir / "swbd" / "swbd_cuts_all.jsonl.gz")
.subset(first=50)
)
return load_manifest_lazy(
self.args.manifest_dir / "swbd_cuts_all.jsonl.gz"
).subset(first=50)
@lru_cache()
def test_eval2000_cuts(self) -> CutSet:

2
egs/swbd/ASR/conformer_ctc/train.py
View File

@ -697,7 +697,7 @@ def run(rank, world_size, args):
# You should use ../local/display_manifest_statistics.py to get
# an utterance duration distribution for your dataset to select
# the threshold
return 1.0 <= c.duration <= 600.0
return 1.0 <= c.duration
train_cuts = train_cuts.filter(remove_short_and_long_utt)

138
egs/swbd/ASR/local/compute_fbank_eval2000.py Executable file
View File

@ -0,0 +1,138 @@
#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
#
# Modified 2023 The Chinese University of Hong Kong (author: Zengrui Jin)
#
# 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.
"""
This file computes fbank features of the SwitchBoard dataset.
It looks for manifests in the directory data/manifests.
The generated fbank features are saved in data/fbank.
"""
import argparse
import logging
import os
from pathlib import Path
from typing import Optional
import sentencepiece as spm
import torch
from filter_cuts import filter_cuts
from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
from lhotse.recipes.utils import read_manifests_if_cached
from icefall.utils import get_executor, str2bool
# Torch's multithreaded behavior needs to be disabled or
# it wastes a lot of CPU and slow things down.
# Do this outside of main() in case it needs to take effect
# even when we are not invoking the main (e.g. when spawning subprocesses).
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--bpe-model",
type=str,
help="""Path to the bpe.model. If not None, we will remove short and
long utterances before extracting features""",
)
parser.add_argument(
"--dataset",
type=str,
help="""Dataset parts to compute fbank. If None, we will use all""",
)
parser.add_argument(
"--perturb-speed",
type=str2bool,
default=False,
help="""Perturb speed with factor 0.9 and 1.1 on train subset.""",
)
return parser.parse_args()
def compute_fbank_switchboard(
dir_name: str,
bpe_model: Optional[str] = None,
dataset: Optional[str] = None,
perturb_speed: Optional[bool] = True,
):
src_dir = Path(f"data/manifests/{dir_name}")
output_dir = Path(f"data/fbank/{dir_name}")
num_jobs = min(1, os.cpu_count())
num_mel_bins = 80
if bpe_model:
logging.info(f"Loading {bpe_model}")
sp = spm.SentencePieceProcessor()
sp.load(bpe_model)
if dataset is None:
dataset_parts = ("all",)
else:
dataset_parts = dataset.split(" ", -1)
prefix = dir_name
suffix = "jsonl.gz"
manifests = {
"eval2000": "data/manifests/eval2000/eval2000_cuts_all_trimmed.jsonl.gz",
}
assert manifests is not None
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins, sampling_rate=16000))
with get_executor() as ex: # Initialize the executor only once.
partition = "all"
cuts_filename = f"{prefix}_cuts_{partition}.{suffix}"
print(cuts_filename)
if (output_dir / cuts_filename).is_file():
logging.info(f"{prefix} already exists - skipping.")
return
logging.info(f"Processing {prefix}")
cut_set = CutSet.from_file(manifests[prefix]).resample(16000)
cut_set = cut_set.compute_and_store_features(
extractor=extractor,
storage_path=f"{output_dir}/{prefix}_feats_{partition}",
# when an executor is specified, make more partitions
num_jobs=num_jobs if ex is None else 80,
executor=ex,
storage_type=LilcomChunkyWriter,
)
cut_set.to_file(output_dir / cuts_filename)
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
args = get_args()
logging.info(vars(args))
compute_fbank_switchboard(
dir_name="eval2000",
bpe_model=args.bpe_model,
dataset=args.dataset,
perturb_speed=args.perturb_speed,
)

106
egs/swbd/ASR/local/compute_fbank_swbd.py
View File

@ -70,18 +70,25 @@ def get_args():
help="""Perturb speed with factor 0.9 and 1.1 on train subset.""",
)
parser.add_argument(
"--split-index",
type=int,
required=True,
)
return parser.parse_args()
def compute_fbank_switchboard(
dir_name: str,
split_index: int,
bpe_model: Optional[str] = None,
dataset: Optional[str] = None,
perturb_speed: Optional[bool] = True,
):
src_dir = Path(f"data/manifests/{dir_name}")
output_dir = Path(f"data/fbank/{dir_name}")
num_jobs = min(15, os.cpu_count())
output_dir = Path(f"data/fbank/{dir_name}_split16")
num_jobs = min(1, os.cpu_count())
num_mel_bins = 80
if bpe_model:
@ -96,54 +103,48 @@ def compute_fbank_switchboard(
prefix = dir_name
suffix = "jsonl.gz"
manifests = read_manifests_if_cached(
dataset_parts=dataset_parts,
output_dir=src_dir,
prefix=prefix,
suffix=suffix,
)
assert manifests is not None
split_dir = Path("data/manifests/swbd_split16/")
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins, sampling_rate=8000))
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins, sampling_rate=16000))
with get_executor() as ex: # Initialize the executor only once.
for partition, m in manifests.items():
cuts_filename = f"{prefix}_cuts_{partition}.{suffix}"
if (output_dir / cuts_filename).is_file():
logging.info(f"{partition} already exists - skipping.")
continue
logging.info(f"Processing {partition}")
cut_set = CutSet.from_manifests(
recordings=m["recordings"],
supervisions=m["supervisions"],
partition = "all"
cuts_filename = (
f"{prefix}_cuts_{partition}.{str(split_index).zfill(2)}.{suffix}"
)
print(cuts_filename)
if (output_dir / cuts_filename).is_file():
logging.info(f"{prefix} already exists - skipping.")
return
logging.info(f"Processing {prefix}")
cut_set = (
CutSet.from_file(
split_dir
/ f"swbd_train_all_trimmed.{str(split_index).zfill(2)}.jsonl.gz"
)
.resample(16000)
.to_eager()
.filter(lambda c: c.duration > 2.0)
)
if "train" in partition:
if bpe_model:
cut_set = filter_cuts(cut_set, sp)
if perturb_speed:
logging.info(f"Doing speed perturb")
cut_set = (
cut_set
+ cut_set.perturb_speed(0.9)
+ cut_set.perturb_speed(1.1)
)
cut_set = cut_set.compute_and_store_features(
extractor=extractor,
storage_path=f"{output_dir}/{prefix}_feats_{partition}",
# when an executor is specified, make more partitions
num_jobs=num_jobs if ex is None else 80,
executor=ex,
storage_type=LilcomChunkyWriter,
)
cut_set.to_file(output_dir / cuts_filename)
if bpe_model:
cut_set = filter_cuts(cut_set, sp)
if perturb_speed:
logging.info(f"Doing speed perturb")
cut_set = cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
cut_set = cut_set.compute_and_store_features(
extractor=extractor,
storage_path=f"{output_dir}/{prefix}_feats_{partition}_{str(split_index).zfill(2)}",
# when an executor is specified, make more partitions
num_jobs=num_jobs if ex is None else 80,
executor=ex,
storage_type=LilcomChunkyWriter,
)
cut_set = cut_set.trim_to_supervisions(
keep_overlapping=False,
min_duration=None,
)
cut_set.to_file(output_dir / cuts_filename)
if __name__ == "__main__":
@ -152,10 +153,11 @@ if __name__ == "__main__":
logging.basicConfig(format=formatter, level=logging.INFO)
args = get_args()
logging.info(vars(args))
for dir_name in ["swbd", "eval2000"]:
compute_fbank_switchboard(
dir_name=dir_name,
bpe_model=args.bpe_model,
dataset=args.dataset,
perturb_speed=args.perturb_speed,
)
compute_fbank_switchboard(
dir_name="swbd",
split_index=args.split_index,
bpe_model=args.bpe_model,
dataset=args.dataset,
perturb_speed=args.perturb_speed,
)

45
egs/swbd/ASR/prepare.sh
View File

@ -76,11 +76,38 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
mv data/manifests/swbd/swbd_supervisions_all.jsonl.gz data/manifests/swbd/swbd_supervisions_orig.jsonl.gz
mv data/manifests/swbd/swbd_supervisions_all_norm.jsonl.gz data/manifests/swbd/swbd_supervisions_all.jsonl.gz
lhotse cut simple \
-r data/manifests/swbd/swbd_recordings_all.jsonl.gz \
-s data/manifests/swbd/swbd_supervisions_all.jsonl.gz \
data/manifests/swbd/swbd_train_all.jsonl.gz
lhotse cut trim-to-supervisions \
--discard-overlapping \
--discard-extra-channels \
data/manifests/swbd/swbd_train_all.jsonl.gz \
data/manifests/swbd/swbd_train_all_trimmed.jsonl.gz
num_splits=16
mkdir -p data/manifests/swbd_split${num_splits}
lhotse split ${num_splits} \
data/manifests/swbd/swbd_train_all_trimmed.jsonl.gz \
data/manifests/swbd_split${num_splits}
lhotse prepare eval2000 --absolute-paths 1 $eval2000_dir data/manifests/eval2000
./local/normalize_eval2000.py \
data/manifests/eval2000/eval2000_supervisions_unnorm.jsonl.gz \
data/manifests/eval2000/eval2000_supervisions_all.jsonl.gz
lhotse cut simple \
-r data/manifests/eval2000/eval2000_recordings_all.jsonl.gz \
-s data/manifests/eval2000/eval2000_supervisions_all.jsonl.gz \
data/manifests/eval2000/eval2000_cuts_all.jsonl.gz
lhotse cut trim-to-supervisions \
--discard-overlapping \
--discard-extra-channels \
data/manifests/eval2000/eval2000_cuts_all.jsonl.gz \
data/manifests/eval2000/eval2000_cuts_all_trimmed.jsonl.gz
# ./local/rt03_data_prep.sh $rt03_dir
# normalize eval2000 and rt03 texts by
@ -116,13 +143,27 @@ fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Compute fbank for SwitchBoard"
mkdir -p data/fbank
if [ ! -e data/fbank/.swbd.done ]; then
./local/compute_fbank_swbd.py
mkdir -p data/fbank/swbd_split${num_splits}/
for index in $(seq 1 16); do
./local/compute_fbank_swbd.py --split-index ${index} &
done
wait
pieces=$(find data/fbank/swbd_split${num_splits} -name "swbd_cuts_all.*.jsonl.gz")
lhotse combine $pieces data/fbank/swbd_cuts_all.jsonl.gz
touch data/fbank/.swbd.done
fi
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Compute fbank for eval2000"
if [ ! -e data/fbank/.eval2000.done ]; then
mkdir -p data/fbank/eval2000/
./local/compute_fbank_eval2000.py
touch data/fbank/.eval2000.done
fi
fi
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 4: Compute fbank for musan"
mkdir -p data/fbank