mirror of
https://github.com/k2-fsa/icefall.git
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140 lines
4.3 KiB
Python
Executable File
140 lines
4.3 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (Author: Liyong Guo)
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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 HI_MIA and HI_MIA_CW 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 argparse
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import logging
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import os
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from pathlib import Path
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import torch
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from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer
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from lhotse.recipes.utils import read_manifests_if_cached
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from icefall.utils import get_executor, 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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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--train-set-channel",
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type=str,
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default="_7_01",
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help="""channel of HI_MIA dataset.
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All channels are used if it is set "all".
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""",
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)
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parser.add_argument(
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"--enable-speed-perturb",
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type=str2bool,
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default=False,
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help="""channel of training set.
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""",
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)
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return parser.parse_args()
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def compute_fbank_himia(
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train_set_channel: str = None,
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enable_speed_perturb: bool = True,
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):
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src_dir = Path("data/manifests")
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output_dir = Path("data/fbank")
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num_jobs = min(40, os.cpu_count())
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num_mel_bins = 80
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if "all" == train_set_channel:
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dataset_parts = (
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"train",
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"dev",
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"test",
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"cw_test",
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)
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else:
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dataset_parts = (
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f"train{train_set_channel}",
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f"dev{train_set_channel}",
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f"test{train_set_channel}",
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"cw_test",
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)
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manifests = read_manifests_if_cached(
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dataset_parts=dataset_parts, prefix="himia", output_dir=src_dir
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)
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assert manifests is not None
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extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
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with get_executor() as ex: # Initialize the executor only once.
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for partition, m in manifests.items():
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if (output_dir / f"cuts_{partition}.jsonl.gz").is_file():
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logging.info(f"{partition} already exists - skipping.")
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continue
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logging.info(f"Processing {partition}")
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cut_set = CutSet.from_manifests(
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recordings=m["recordings"],
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supervisions=m["supervisions"],
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)
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if "train" in partition and enable_speed_perturb:
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cut_set = (
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cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
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)
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cut_set = cut_set.resample(16000)
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cut_set = cut_set.compute_and_store_features(
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extractor=extractor,
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storage_path=f"{output_dir}/feats_{partition}",
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# when an executor is specified, make more partitions
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num_jobs=num_jobs if ex is None else 80,
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executor=ex,
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storage_type=LilcomHdf5Writer,
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)
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output_file_name = f"cuts_{partition}.jsonl.gz"
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if "all" != train_set_channel:
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output_file_name = f"cuts_{partition}{train_set_channel}.jsonl.gz"
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cut_set.to_file(output_dir / f"{output_file_name}")
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def main():
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formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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args = get_args()
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logging.basicConfig(format=formatter, level=logging.INFO)
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compute_fbank_himia(
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train_set_channel=args.train_set_channel,
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enable_speed_perturb=args.enable_speed_perturb,
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)
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if __name__ == "__main__":
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main()
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