icefall/egs/zipvoice/local/preprocess_emilia.py
Wei Kang 762f965cf7
[zipvoice] Add requirements.txt and pinyin.txt, remove k2 from pretrained model inference. (#1965)
* Add requirements.txt and pinyin.txt needed by zipvoice

* simplify the requirements for pretrained model inference
2025-06-18 18:38:46 +08:00

156 lines
4.8 KiB
Python

#!/usr/bin/env python3
# Copyright 2024 Xiaomi Corp. (authors: Zengwei Yao,
# Zengrui Jin,
# Wei Kang)
# 2024 Tsinghua University (authors: Zengrui Jin,)
#
# 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.
"""
This file reads the texts in given manifest and save the cleaned new cuts.
"""
import argparse
import glob
import logging
import os
from concurrent.futures import ProcessPoolExecutor as Pool
from pathlib import Path
from typing import List
from lhotse import CutSet, load_manifest_lazy
from tokenizer import (
is_alphabet,
is_chinese,
is_hangul,
is_japanese,
tokenize_by_CJK_char,
)
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--subset",
type=str,
help="Subset of emilia, (ZH, EN, etc.)",
)
parser.add_argument(
"--jobs",
type=int,
default=20,
help="Number of jobs to processing.",
)
parser.add_argument(
"--source-dir",
type=str,
default="data/manifests/splits_raw",
help="The source directory of manifest files.",
)
parser.add_argument(
"--dest-dir",
type=str,
default="data/manifests/splits",
help="The destination directory of manifest files.",
)
return parser.parse_args()
def preprocess_emilia(file_name: str, input_dir: Path, output_dir: Path):
logging.info(f"Processing {file_name}")
if (output_dir / file_name).is_file():
logging.info(f"{file_name} exists, skipping.")
return
def _filter_cut(cut):
text = cut.supervisions[0].text
duration = cut.supervisions[0].duration
chinese = []
english = []
# only contains chinese and space and alphabets
clean_chars = []
for x in text:
if is_hangul(x):
logging.warning(f"Delete cut with text containing Korean : {text}")
return False
if is_japanese(x):
logging.warning(f"Delete cut with text containing Japanese : {text}")
return False
if is_chinese(x):
chinese.append(x)
clean_chars.append(x)
if is_alphabet(x):
english.append(x)
clean_chars.append(x)
if x == " ":
clean_chars.append(x)
if len(english) + len(chinese) == 0:
logging.warning(f"Delete cut with text has no valid chars : {text}")
return False
words = tokenize_by_CJK_char("".join(clean_chars))
for i in range(len(words) - 10):
if words[i : i + 10].count(words[i]) == 10:
logging.warning(f"Delete cut with text with too much repeats : {text}")
return False
# word speed, 20 - 600 / minute
if duration < len(words) / 600 * 60 or duration > len(words) / 20 * 60:
logging.warning(
f"Delete cut with audio text mismatch, duration : {duration}s, "
f"words : {len(words)}, text : {text}"
)
return False
return True
try:
cut_set = load_manifest_lazy(input_dir / file_name)
cut_set = cut_set.filter(_filter_cut)
cut_set.to_file(output_dir / file_name)
except Exception as e:
logging.error(f"Manifest {file_name} failed with error: {e}")
os.remove(str(output_dir / file_name))
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
args = get_args()
input_dir = Path(args.source_dir)
output_dir = Path(args.dest_dir)
output_dir.mkdir(parents=True, exist_ok=True)
cut_files = glob.glob(f"{args.source_dir}/emilia_cuts_{args.subset}.*.jsonl.gz")
with Pool(max_workers=args.jobs) as pool:
futures = [
pool.submit(
preprocess_emilia, filename.split("/")[-1], input_dir, output_dir
)
for filename in cut_files
]
for f in futures:
f.result()
f.done()
logging.info("Processing done.")