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102 lines
3.3 KiB
Python
Executable File
102 lines
3.3 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 trimmed sub-segments which will be
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used for simulating the training mixtures.
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The generated fbank features are saved in data/fbank.
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"""
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import logging
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import math
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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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import torchaudio
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from lhotse import CutSet, LilcomChunkyWriter, load_manifest
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from lhotse.audio import set_ffmpeg_torchaudio_info_enabled
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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 tqdm import tqdm
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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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torchaudio.set_audio_backend("soundfile")
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set_ffmpeg_torchaudio_info_enabled(False)
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def compute_fbank_ihm():
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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 = {}
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for data in ["ami", "icsi"]:
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manifests[data] = read_manifests_if_cached(
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dataset_parts=["train"],
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output_dir=src_dir,
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types=["recordings", "supervisions"],
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prefix=f"{data}-ihm",
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suffix="jsonl.gz",
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)
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logging.info("Computing features")
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for data in ["ami", "icsi"]:
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cs = CutSet.from_manifests(**manifests[data]["train"])
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cs = cs.trim_to_supervisions(keep_overlapping=False)
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cs = cs.normalize_loudness(target=-23.0, affix_id=False)
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cs = cs + cs.perturb_speed(0.9) + cs.perturb_speed(1.1)
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_ = cs.compute_and_store_features_batch(
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extractor=extractor,
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storage_path=output_dir / f"{data}-ihm_train_feats",
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manifest_path=src_dir / f"{data}-ihm_cuts_train.jsonl.gz",
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batch_duration=5000,
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num_workers=4,
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storage_type=LilcomChunkyWriter,
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overwrite=True,
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)
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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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compute_fbank_ihm()
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