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import argparse
import glob
import multiprocessing
import os
import pathlib
import torch
from tqdm import tqdm
from TTS.utils.vad import get_vad_model_and_utils, remove_silence
torch.set_num_threads(1)
def adjust_path_and_remove_silence(audio_path):
output_path = audio_path.replace(os.path.join(args.input_dir, ""), os.path.join(args.output_dir, ""))
# ignore if the file exists
if os.path.exists(output_path) and not args.force:
return output_path, False
# create all directory structure
pathlib.Path(output_path).parent.mkdir(parents=True, exist_ok=True)
# remove the silence and save the audio
output_path, is_speech = remove_silence(
model_and_utils,
audio_path,
output_path,
trim_just_beginning_and_end=args.trim_just_beginning_and_end,
use_cuda=args.use_cuda,
)
return output_path, is_speech
def preprocess_audios():
files = sorted(glob.glob(os.path.join(args.input_dir, args.glob), recursive=True))
print("> Number of files: ", len(files))
if not args.force:
print("> Ignoring files that already exist in the output idrectory.")
if args.trim_just_beginning_and_end:
print("> Trimming just the beginning and the end with nonspeech parts.")
else:
print("> Trimming all nonspeech parts.")
filtered_files = []
if files:
# create threads
# num_threads = multiprocessing.cpu_count()
# process_map(adjust_path_and_remove_silence, files, max_workers=num_threads, chunksize=15)
if args.num_processes > 1:
with multiprocessing.Pool(processes=args.num_processes) as pool:
results = list(
tqdm(
pool.imap_unordered(adjust_path_and_remove_silence, files),
total=len(files),
desc="Processing audio files",
)
)
for output_path, is_speech in results:
if not is_speech:
filtered_files.append(output_path)
else:
for f in tqdm(files):
output_path, is_speech = adjust_path_and_remove_silence(f)
if not is_speech:
filtered_files.append(output_path)
# write files that do not have speech
with open(os.path.join(args.output_dir, "filtered_files.txt"), "w", encoding="utf-8") as f:
for file in filtered_files:
f.write(str(file) + "\n")
else:
print("> No files Found !")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="python TTS/bin/remove_silence_using_vad.py -i=VCTK-Corpus/ -o=VCTK-Corpus-removed-silence/ -g=wav48_silence_trimmed/*/*_mic1.flac --trim_just_beginning_and_end True"
)
parser.add_argument("-i", "--input_dir", type=str, help="Dataset root dir", required=True)
parser.add_argument("-o", "--output_dir", type=str, help="Output Dataset dir", default="")
parser.add_argument("-f", "--force", default=False, action="store_true", help="Force the replace of exists files")
parser.add_argument(
"-g",
"--glob",
type=str,
default="**/*.wav",
help="path in glob format for acess wavs from input_dir. ex: wav48/*/*.wav",
)
parser.add_argument(
"-t",
"--trim_just_beginning_and_end",
type=bool,
default=True,
help="If True this script will trim just the beginning and end nonspeech parts. If False all nonspeech parts will be trim. Default True",
)
parser.add_argument(
"-c",
"--use_cuda",
type=bool,
default=False,
help="If True use cuda",
)
parser.add_argument(
"--use_onnx",
type=bool,
default=False,
help="If True use onnx",
)
parser.add_argument(
"--num_processes",
type=int,
default=1,
help="Number of processes to use",
)
args = parser.parse_args()
if args.output_dir == "":
args.output_dir = args.input_dir
# load the model and utils
model_and_utils = get_vad_model_and_utils(use_cuda=args.use_cuda, use_onnx=args.use_onnx)
preprocess_audios()
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