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https://github.com/k2-fsa/icefall.git
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c1ce7ca9e3
This recipe is mostly based on egs/csj, but tweaked to the point that can be run with ReazonSpeech corpus. That being said, there are some big caveats: * Currently the model quality is not very good. Actually, it is very bad. I trained a model with 1000h corpus, and it resulted in >80% CER on JSUT. * The core issue seems that Zipformer is prone to ignore untterances as sielent segments. It often produces an empty hypothesis despite that the audio actually contains human voice. * This issue is already reported in the upstream and not fully resolved yet as of Dec 2023. Signed-off-by: Fujimoto Seiji <fujimoto@ceptord.net>
132 lines
4.1 KiB
Python
132 lines
4.1 KiB
Python
#!/usr/bin/env python3
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# Copyright 2023 The University of Electro-Communications (Author: Teo Wen Shen) # noqa
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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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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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from typing import List, Tuple
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import torch
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# fmt: off
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from lhotse import ( # See the following for why LilcomChunkyWriter is preferred; https://github.com/k2-fsa/icefall/pull/404; https://github.com/lhotse-speech/lhotse/pull/527
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CutSet,
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Fbank,
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FbankConfig,
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LilcomChunkyWriter,
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RecordingSet,
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SupervisionSet,
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)
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# fmt: on
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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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RNG_SEED = 42
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concat_params = {"gap": 1.0, "maxlen": 10.0}
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def make_cutset_blueprints(
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manifest_dir: Path,
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) -> List[Tuple[str, CutSet]]:
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cut_sets = []
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# Create test dataset
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logging.info("Creating test cuts.")
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cut_sets.append(("test", CutSet.from_manifests(
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recordings=RecordingSet.from_file(
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manifest_dir / "reazonspeech_recordings_test.jsonl.gz"
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),
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supervisions=SupervisionSet.from_file(
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manifest_dir / "reazonspeech_supervisions_test.jsonl.gz"
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),
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)))
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# Create valid dataset
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logging.info("Creating valid cuts.")
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cut_sets.append(("valid", CutSet.from_manifests(
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recordings=RecordingSet.from_file(
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manifest_dir / "reazonspeech_recordings_valid.jsonl.gz"
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),
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supervisions=SupervisionSet.from_file(
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manifest_dir / "reazonspeech_supervisions_valid.jsonl.gz"
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),
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)))
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# Create train dataset
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logging.info("Creating train cuts.")
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cut_sets.append(("train", CutSet.from_manifests(
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recordings=RecordingSet.from_file(
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manifest_dir / "reazonspeech_recordings_train.jsonl.gz"
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),
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supervisions=SupervisionSet.from_file(
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manifest_dir / "reazonspeech_supervisions_train.jsonl.gz"
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),
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)))
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return cut_sets
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def get_args():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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parser.add_argument("-m", "--manifest-dir", type=Path)
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return parser.parse_args()
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def main():
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args = get_args()
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extractor = Fbank(FbankConfig(num_mel_bins=80))
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num_jobs = min(16, os.cpu_count())
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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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if (args.manifest_dir / ".reazonspeech-fbank.done").exists():
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logging.info(
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"Previous fbank computed for ReazonSpeech found. "
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f"Delete {args.manifest_dir / '.reazonspeech-fbank.done'} to allow recomputing fbank."
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)
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return
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else:
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cut_sets = make_cutset_blueprints(args.manifest_dir)
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for part, cut_set in cut_sets:
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logging.info(f"Processing {part}")
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cut_set = cut_set.compute_and_store_features(
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extractor=extractor,
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num_jobs=num_jobs,
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storage_path=(args.manifest_dir / f"feats_{part}").as_posix(),
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storage_type=LilcomChunkyWriter,
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)
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cut_set.to_file(args.manifest_dir / f"reazonspeech_cuts_{part}.jsonl.gz")
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logging.info("All fbank computed for ReazonSpeech.")
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(args.manifest_dir / ".reazonspeech-fbank.done").touch()
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if __name__ == "__main__":
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main()
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