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116 lines
3.3 KiB
Python
Executable File
116 lines
3.3 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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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 musan dataset.
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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 logging
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from pathlib import Path
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import torch
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from lhotse import CutSet, LilcomChunkyWriter, combine
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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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# 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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def compute_fbank_musan():
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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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dataset_parts = (
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"music",
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"speech",
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"noise",
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)
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manifests = read_manifests_if_cached(
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prefix="musan", dataset_parts=dataset_parts, output_dir=src_dir
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)
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assert manifests is not None
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assert len(manifests) == len(dataset_parts), (
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len(manifests),
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len(dataset_parts),
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list(manifests.keys()),
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dataset_parts,
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)
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musan_cuts_path = src_dir / "cuts_musan.jsonl.gz"
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if musan_cuts_path.is_file():
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logging.info(f"{musan_cuts_path} already exists - skipping")
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return
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logging.info("Extracting features for Musan")
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# create chunks of Musan with duration 5 - 10 seconds
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musan_cuts = (
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CutSet.from_manifests(
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recordings=combine(
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part["recordings"] for part in manifests.values()
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)
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)
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.cut_into_windows(10.0)
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.filter(lambda c: c.duration > 5)
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.compute_and_store_features_batch(
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extractor=extractor,
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storage_path=output_dir / "feats_musan",
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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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overwrite=True,
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)
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)
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logging.info(f"Saving to {musan_cuts_path}")
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musan_cuts.to_file(musan_cuts_path)
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if __name__ == "__main__":
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formatter = (
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"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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)
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logging.basicConfig(format=formatter, level=logging.INFO)
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compute_fbank_musan()
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