icefall/egs/libricss/SURT/local/compute_fbank_librimix.py
2023-03-09 16:52:34 -05:00

116 lines
3.8 KiB
Python
Executable File

#!/usr/bin/env python3
# Copyright 2022 Johns Hopkins University (authors: Desh Raj)
#
# 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 synthetically mixed LibriSpeech
train and dev sets.
It looks for manifests in the directory data/manifests.
The generated fbank features are saved in data/fbank.
"""
import logging
from pathlib import Path
import torch
import torch.multiprocessing
from lhotse import LilcomChunkyWriter
from lhotse.features.kaldifeat import (
KaldifeatFbank,
KaldifeatFbankConfig,
KaldifeatFrameOptions,
KaldifeatMelOptions,
)
from lhotse.recipes.utils import read_manifests_if_cached
# 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)
torch.multiprocessing.set_sharing_strategy("file_system")
def compute_fbank_librimix():
src_dir = Path("data/manifests")
output_dir = Path("data/fbank")
sampling_rate = 16000
num_mel_bins = 80
extractor = KaldifeatFbank(
KaldifeatFbankConfig(
frame_opts=KaldifeatFrameOptions(sampling_rate=sampling_rate),
mel_opts=KaldifeatMelOptions(num_bins=num_mel_bins),
device="cuda",
)
)
logging.info("Reading manifests")
manifests = read_manifests_if_cached(
dataset_parts=["train_norvb_v1", "dev_norvb_v1"],
types=["cuts"],
output_dir=src_dir,
prefix="libri-mix",
suffix="jsonl.gz",
lazy=True,
)
train_cuts = manifests["train_norvb_v1"]["cuts"]
dev_cuts = manifests["dev_norvb_v1"]["cuts"]
# train_2spk_cuts = manifests["train_2spk_norvb"]["cuts"]
logging.info("Extracting fbank features for training cuts")
_ = train_cuts.compute_and_store_features_batch(
extractor=extractor,
storage_path=output_dir / "librimix_feats_train_norvb_v1",
manifest_path=src_dir / "cuts_train_norvb_v1.jsonl.gz",
batch_duration=5000,
num_workers=4,
storage_type=LilcomChunkyWriter,
overwrite=True,
)
logging.info("Extracting fbank features for dev cuts")
_ = dev_cuts.compute_and_store_features_batch(
extractor=extractor,
storage_path=output_dir / "librimix_feats_dev_norvb_v1",
manifest_path=src_dir / "cuts_dev_norvb_v1.jsonl.gz",
batch_duration=5000,
num_workers=4,
storage_type=LilcomChunkyWriter,
overwrite=True,
)
# logging.info("Extracting fbank features for 2-spk train cuts")
# _ = train_2spk_cuts.compute_and_store_features_batch(
# extractor=extractor,
# storage_path=output_dir / "librimix_feats_train_2spk_norvb",
# manifest_path=src_dir / "cuts_train_2spk_norvb.jsonl.gz",
# batch_duration=5000,
# num_workers=4,
# storage_type=LilcomChunkyWriter,
# overwrite=True,
# )
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
compute_fbank_librimix()