icefall/egs/librispeech/SSL/local/process_librispeech4finetune.py
Yifan Yang 87843e9382
k2SSL: a Faster and Better Framework for Self-Supervised Speech Representation Learning (#1500)
* Add k2SSL

* fix flake8

* fix for black

* fix for black

* fix for black

* Update ssl_datamodule.py

* Fix bugs in HubertDataset

* update comments

* add librilight

* add checkpoint convert script

* format

---------

Co-authored-by: yifanyeung <yifanyeung@yifanyeung.local>
Co-authored-by: zzasdf <15218404468@163.com>
2024-04-04 23:29:16 +08:00

108 lines
3.0 KiB
Python

#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import logging
import os
from pathlib import Path
from typing import Optional
import torch
from lhotse import CutSet
from lhotse.recipes.utils import read_manifests_if_cached
from icefall.utils import str2bool
# Torch's multithreaded behavior needs to be disabled or
# it wastes a lot of CPU and slow things down.
# Do this outside of main() in case it needs to take effect
# even when we are not invoking the main (e.g. when spawning subprocesses).
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--dataset",
type=str,
help="""Dataset parts to compute fbank. If None, we will use all""",
)
return parser.parse_args()
def process_wav_librispeech(
dataset: Optional[str] = None,
):
src_dir = Path("data/manifests")
output_dir = Path("data/wav")
if dataset is None:
dataset_parts = (
"dev-clean",
"dev-other",
"test-clean",
"test-other",
"train-clean-100",
"train-clean-360",
"train-other-500",
)
else:
dataset_parts = dataset.split(" ", -1)
prefix = "librispeech"
suffix = "jsonl.gz"
manifests = read_manifests_if_cached(
dataset_parts=dataset_parts,
output_dir=src_dir,
prefix=prefix,
suffix=suffix,
)
assert manifests is not None
assert len(manifests) == len(dataset_parts), (
len(manifests),
len(dataset_parts),
list(manifests.keys()),
dataset_parts,
)
for partition, m in manifests.items():
cuts_filename = f"{prefix}_cuts_{partition}.{suffix}"
if (output_dir / cuts_filename).is_file():
logging.info(f"{partition} already exists - skipping.")
continue
logging.info(f"Processing {partition}")
cut_set = CutSet.from_manifests(
recordings=m["recordings"],
supervisions=m["supervisions"],
)
cut_set.to_file(output_dir / cuts_filename)
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
args = get_args()
logging.info(vars(args))
process_wav_librispeech(
dataset=args.dataset,
)