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https://github.com/k2-fsa/icefall.git
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74806b744b
* disable speed perturbation by default
* minor fixes
* minor updates
* updated bash scripts to incorporate with the `speed-perturb` arg
* minor fixes
1. changed the naming scheme from `speed-perturb` to `perturb-speed` to align with the librispeech recipe
>> 00256a7669/egs/librispeech/ASR/local/compute_fbank_librispeech.py (L65)
2. changed arg type for `perturb-speed` to str2bool
218 lines
7.1 KiB
Python
Executable File
218 lines
7.1 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2022 Johns Hopkins University (authors: Desh Raj)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This file computes fbank features of the AliMeeting dataset.
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For the training data, we prepare IHM, reverberated IHM, SDM, and GSS-enhanced
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audios. For the test data, we separately prepare IHM, SDM, and GSS-enhanced
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parts (which are the 3 evaluation settings).
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It looks for manifests in the directory data/manifests.
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The generated fbank features are saved in data/fbank.
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"""
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import argparse
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import logging
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from pathlib import Path
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import torch
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import torch.multiprocessing
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from lhotse import CutSet, LilcomChunkyWriter
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from lhotse.features.kaldifeat import (
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KaldifeatFbank,
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KaldifeatFbankConfig,
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KaldifeatFrameOptions,
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KaldifeatMelOptions,
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)
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from lhotse.recipes.utils import read_manifests_if_cached
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from icefall.utils import str2bool
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# Torch's multithreaded behavior needs to be disabled or
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# it wastes a lot of CPU and slow things down.
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# Do this outside of main() in case it needs to take effect
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# even when we are not invoking the main (e.g. when spawning subprocesses).
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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torch.multiprocessing.set_sharing_strategy("file_system")
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def compute_fbank_ami(perturb_speed: bool = False):
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src_dir = Path("data/manifests")
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output_dir = Path("data/fbank")
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sampling_rate = 16000
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num_mel_bins = 80
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extractor = KaldifeatFbank(
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KaldifeatFbankConfig(
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frame_opts=KaldifeatFrameOptions(sampling_rate=sampling_rate),
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mel_opts=KaldifeatMelOptions(num_bins=num_mel_bins),
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device="cuda",
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)
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)
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logging.info("Reading manifests")
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manifests_ihm = read_manifests_if_cached(
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dataset_parts=["train", "eval", "test"],
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output_dir=src_dir,
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prefix="alimeeting-ihm",
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suffix="jsonl.gz",
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)
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manifests_sdm = read_manifests_if_cached(
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dataset_parts=["train", "eval", "test"],
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output_dir=src_dir,
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prefix="alimeeting-sdm",
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suffix="jsonl.gz",
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)
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# For GSS we already have cuts so we read them directly.
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manifests_gss = read_manifests_if_cached(
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dataset_parts=["train", "eval", "test"],
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output_dir=src_dir,
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prefix="alimeeting-gss",
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suffix="jsonl.gz",
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)
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def _extract_feats(
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cuts: CutSet, storage_path: Path, manifest_path: Path, speed_perturb: bool
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) -> None:
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if speed_perturb:
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logging.info(f"Doing speed perturb")
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cuts = cuts + cuts.perturb_speed(0.9) + cuts.perturb_speed(1.1)
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_ = cuts.compute_and_store_features_batch(
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extractor=extractor,
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storage_path=storage_path,
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manifest_path=manifest_path,
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batch_duration=5000,
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num_workers=8,
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storage_type=LilcomChunkyWriter,
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)
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logging.info(
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"Preparing training cuts: IHM + reverberated IHM + SDM + GSS (optional)"
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)
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logging.info("Processing train split IHM")
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cuts_ihm = (
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CutSet.from_manifests(**manifests_ihm["train"])
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.trim_to_supervisions(keep_overlapping=False, keep_all_channels=False)
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.modify_ids(lambda x: x + "-ihm")
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)
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_extract_feats(
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cuts_ihm,
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output_dir / "feats_train_ihm",
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src_dir / "cuts_train_ihm.jsonl.gz",
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perturb_speed,
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)
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logging.info("Processing train split IHM + reverberated IHM")
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cuts_ihm_rvb = cuts_ihm.reverb_rir()
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_extract_feats(
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cuts_ihm_rvb,
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output_dir / "feats_train_ihm_rvb",
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src_dir / "cuts_train_ihm_rvb.jsonl.gz",
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perturb_speed,
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)
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logging.info("Processing train split SDM")
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cuts_sdm = (
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CutSet.from_manifests(**manifests_sdm["train"])
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.trim_to_supervisions(keep_overlapping=False)
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.modify_ids(lambda x: x + "-sdm")
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)
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_extract_feats(
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cuts_sdm,
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output_dir / "feats_train_sdm",
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src_dir / "cuts_train_sdm.jsonl.gz",
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perturb_speed,
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)
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logging.info("Processing train split GSS")
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cuts_gss = (
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CutSet.from_manifests(**manifests_gss["train"])
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.trim_to_supervisions(keep_overlapping=False)
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.modify_ids(lambda x: x + "-gss")
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)
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_extract_feats(
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cuts_gss,
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output_dir / "feats_train_gss",
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src_dir / "cuts_train_gss.jsonl.gz",
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perturb_speed,
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)
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logging.info("Preparing test cuts: IHM, SDM, GSS (optional)")
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for split in ["eval", "test"]:
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logging.info(f"Processing {split} IHM")
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cuts_ihm = (
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CutSet.from_manifests(**manifests_ihm[split])
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.trim_to_supervisions(keep_overlapping=False, keep_all_channels=False)
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.compute_and_store_features_batch(
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extractor=extractor,
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storage_path=output_dir / f"feats_{split}_ihm",
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manifest_path=src_dir / f"cuts_{split}_ihm.jsonl.gz",
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batch_duration=500,
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num_workers=4,
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storage_type=LilcomChunkyWriter,
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)
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)
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logging.info(f"Processing {split} SDM")
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cuts_sdm = (
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CutSet.from_manifests(**manifests_sdm[split])
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.trim_to_supervisions(keep_overlapping=False)
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.compute_and_store_features_batch(
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extractor=extractor,
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storage_path=output_dir / f"feats_{split}_sdm",
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manifest_path=src_dir / f"cuts_{split}_sdm.jsonl.gz",
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batch_duration=500,
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num_workers=4,
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storage_type=LilcomChunkyWriter,
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)
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)
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logging.info(f"Processing {split} GSS")
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cuts_gss = (
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CutSet.from_manifests(**manifests_gss[split])
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.trim_to_supervisions(keep_overlapping=False)
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.compute_and_store_features_batch(
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extractor=extractor,
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storage_path=output_dir / f"feats_{split}_gss",
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manifest_path=src_dir / f"cuts_{split}_gss.jsonl.gz",
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batch_duration=500,
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num_workers=4,
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storage_type=LilcomChunkyWriter,
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)
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)
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--perturb-speed",
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type=str2bool,
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default=False,
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help="Enable 0.9 and 1.1 speed perturbation for data augmentation. Default: False.",
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)
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return parser.parse_args()
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if __name__ == "__main__":
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formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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logging.basicConfig(format=formatter, level=logging.INFO)
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args = get_args()
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compute_fbank_ami(perturb_speed=args.perturb_speed)
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