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Revert "add fbank"
This reverts commit ba603e0a0a514056ec6d32677053c41743a1a5dd.
This commit is contained in:
Bailey Hirota
parent
82bd37cacd
commit
b2df5bbb83
@ -1,8 +1,20 @@
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#!/usr/bin/env python3
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# Copyright 2023 The University of Electro-Communications
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# (Author: Teo Wen Shen)
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# Copyright 2023 The University of Electro-Communications (Author: Teo Wen Shen) # noqa
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#
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# Apache-2.0
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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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@ -11,106 +23,115 @@ from pathlib import Path
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from typing import List, Tuple
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import torch
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from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
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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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from lhotse.utils import is_module_available
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# Disable PyTorch intra/inter op threading overhead
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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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mls_eng_hf_dataset_path: str = "parler-tts/mls_eng",
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) -> List[Tuple[str, CutSet]]:
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cut_sets = []
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if not is_module_available("datasets"):
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raise ImportError(
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"To process the MLS English HF corpus, please install datasets: pip install datasets"
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"To process the MLS English HF corpus, please install optional dependency: pip install datasets"
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)
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from datasets import load_dataset
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print(f"{mls_eng_hf_dataset_path=}")
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dataset = load_dataset(str(mls_eng_hf_dataset_path))
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return [
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("test", CutSet.from_huggingface_dataset(dataset["test"], text_key="transcript")),
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("dev", CutSet.from_huggingface_dataset(dataset["dev"], text_key="transcript")),
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("train", CutSet.from_huggingface_dataset(dataset["train"], text_key="transcript")),
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]
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# Create test dataset
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logging.info("Creating test cuts.")
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cut_sets.append(
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(
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"test",
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CutSet.from_huggingface_dataset(dataset["test"], text_key="transcript"),
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)
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)
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# Create dev dataset
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logging.info("Creating dev cuts.")
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cut_sets.append(
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("dev", CutSet.from_huggingface_dataset(dataset["dev"], text_key="transcript"))
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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(
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(
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"train",
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CutSet.from_huggingface_dataset(dataset["train"], text_key="transcript"),
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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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p = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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p.add_argument("-m", "--manifest-dir",
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type=Path,
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default=Path("data/manifests"),
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help="Where to write JSONL cuts")
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p.add_argument("-a", "--audio-dir",
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type=Path,
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default=Path("data/audio"),
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help="Where to copy raw audio")
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p.add_argument("-d", "--dl-dir",
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type=Path,
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required=True,
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help="Where the HF dataset was cloned")
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p.add_argument("--fbank-dir",
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type=Path,
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default=Path("data/fbank"),
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help="Where to write FBANK features")
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return p.parse_args()
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parser.add_argument("-m", "--manifest-dir", type=Path)
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parser.add_argument("-a", "--audio-dir", type=Path)
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parser.add_argument("-d", "--dl-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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# Make sure our directories exist
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for d in (args.manifest_dir, args.audio_dir, args.fbank_dir):
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d.mkdir(parents=True, exist_ok=True)
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# If we've already computed FBANK, skip.
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done_marker = args.fbank_dir / ".mls_eng-fbank.done"
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if done_marker.exists():
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logging.info(
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"Found done-marker at %s. Skipping FBANK computation.",
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done_marker
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)
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return
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# Set up logging
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logging.basicConfig(
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format="%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s",
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level=logging.INFO,
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)
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# Prepare Lhotse cut blueprints from HF dataset
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cut_sets = make_cutset_blueprints(str(args.dl_dir))
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# Feature extractor
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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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for part, cut_set in cut_sets:
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logging.info("===== Processing split: %s =====", part)
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formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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# 1) compute & store FBANK features into fbank-dir
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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.fbank_dir / f"mls_eng_feats_{part}").as_posix(),
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storage_type=LilcomChunkyWriter,
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logging.basicConfig(format=formatter, level=logging.INFO)
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if (args.manifest_dir / ".mls-eng-fbank.done").exists():
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logging.info(
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"Previous fbank computed for MLS English found. "
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f"Delete {args.manifest_dir / '.mls-eng-fbank.done'} to allow recomputing fbank."
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)
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return
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else:
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mls_eng_hf_dataset_path = args.dl_dir # "/root/datasets/parler-tts--mls_eng"
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cut_sets = make_cutset_blueprints(mls_eng_hf_dataset_path)
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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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# 2) copy raw audio into audio-dir/<split>/
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cut_set = cut_set.save_audios(args.audio_dir / part)
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cut_set = cut_set.save_audios(args.audio_dir / part) # makes new cutset that uses paths to actual audio files
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cut_set.to_file(args.manifest_dir / f"mls_eng_cuts_{part}.jsonl.gz")
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# 3) write final cuts JSONL into manifest-dir
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out_manifest = args.manifest_dir / f"mls_eng_cuts_{part}.jsonl.gz"
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cut_set.to_file(out_manifest)
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logging.info("Wrote cuts manifest to %s", out_manifest)
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# Touch the done marker so next runs skip
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done_marker.touch()
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logging.info("All FBANK computed. Done marker created at %s", done_marker)
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logging.info("All fbank computed for MLS English.")
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(args.manifest_dir / ".mls-eng-fbank.done").touch()
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if __name__ == "__main__":
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@ -1,87 +1,94 @@
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#!/usr/bin/env bash
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# Prepare script for MLS English ASR recipe in icefall
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# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
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export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
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set -eou pipefail
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stage=-1
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stop_stage=100
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# Configuration for BPE tokenizer
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vocab_sizes=(500)
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vocab_sizes=(2000) # You can add more sizes like (500 1000 2000) for comparison
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# Directory where dataset will be downloaded
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dl_dir=$PWD/download
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. shared/parse_options.sh || exit 1
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# All files generated by this script are saved in "data/".
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mkdir -p data/manifests data/fbank data/audio data/lang
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# All files generated by this script are saved in "data".
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mkdir -p data
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mkdir -p data/audio # Add this line
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mkdir -p data/manifests
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mkdir -p data/lang
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log() {
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local fname=${BASH_SOURCE[1]##*/}
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echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${LINENO}:${FUNCNAME[1]}) $*"
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echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
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}
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log "Starting MLS English data preparation"
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# Stage 0: Download corpus
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Download MLS English dataset"
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if [ ! -d $dl_dir/mls_english ]; then
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git clone https://huggingface.co/datasets/parler-tts/mls_eng \
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$dl_dir/mls_english || {
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log "Failed to download MLS English dataset"; exit 1; }
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if ! git clone https://huggingface.co/datasets/parler-tts/mls_eng $dl_dir/mls_english; then
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log "Failed to download MLS English dataset"
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exit 1
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fi
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fi
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fi
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# Stage 1: Compute fbank & emit manifests
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# if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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# log "Stage 1: Prepare MLS English manifest"
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# # We assume that you have downloaded the MLS English corpus
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# # to $dl_dir/mls_english
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# if [ ! -e data/manifests/.mls_english.done ]; then
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# # lhotse prepare mls_english -j $nj $dl_dir/mls_english data/manifests
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# python local/utils/save_audios.py --num-jobs 8 --dataset-dir $dl_dir/mls_english --audio-dir ./data/audio --manifest-dir ./data/manifests
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# touch data/manifests/.mls_english.done
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# fi
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# fi
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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Compute & validate MLS English fbank"
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# we already did `mkdir -p data/manifests data/fbank data/audio` above
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if [ ! -e data/fbank/.mls_eng-fbank.done ]; then
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python local/compute_fbank_mls_english.py \
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--manifest-dir data/manifests \
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--audio-dir data/audio \
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--dl-dir $dl_dir/mls_english \
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--fbank-dir data/fbank
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# Validate each split’s manifest
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for split in train dev test; do
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python local/validate_manifest.py \
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--manifest data/manifests/mls_eng_cuts_${split}.jsonl.gz
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done
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touch data/fbank/.mls_eng-fbank.done
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log "fbank + manifest generation complete."
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else
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log "Skipping: fbank already done (data/fbank/.mls_eng-fbank.done exists)."
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fi
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log "Stage 1: Compute MLS English fbank"
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if [ ! -e data/manifests/.mls_english-validated.done ]; then
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python local/compute_fbank_mls_english.py \
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--manifest-dir data/manifests \
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--audio-dir data/audio \
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--dl-dir $dl_dir/mls_english
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# --dl-dir /root/datasets/parler-tts--mls_eng
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python local/validate_manifest.py --manifest data/manifests/mls_english_cuts_train.jsonl.gz
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python local/validate_manifest.py --manifest data/manifests/mls_english_cuts_dev.jsonl.gz
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python local/validate_manifest.py --manifest data/manifests/mls_english_cuts_test.jsonl.gz
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touch data/manifests/.mls_english-validated.done
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fi
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fi
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# Stage 2: Prepare transcript for BPE
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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log "Stage 2: Generate transcript for BPE"
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log "Stage 2: Prepare transcript for BPE training"
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if [ ! -f data/lang/transcript.txt ]; then
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log "Generating transcripts for BPE training"
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./local/utils/generate_transcript.py --lang-dir data/lang
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fi
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fi
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# Stage 3: Train BPE models
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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log "Stage 3: Train BPE models"
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for vocab_size in "${vocab_sizes[@]}"; do
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bpe_dir=data/lang_bpe_${vocab_size}
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log "Stage 3: Prepare BPE tokenizer"
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for vocab_size in ${vocab_sizes[@]}; do
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log "Training BPE model with vocab_size=${vocab_size}"
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bpe_dir=data/lang/bpe_${vocab_size}
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mkdir -p $bpe_dir
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if [ ! -f $bpe_dir/bpe.model ]; then
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./local/train_bpe_model.py \
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--lang-dir $bpe_dir \
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--lang-dir $bpe_dir \
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--vocab-size $vocab_size \
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--transcript data/lang/transcript.txt
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fi
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done
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fi
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log "MLS English data preparation completed successfully"
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log "MLS English data preparation completed successfully"
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