162 lines
4.8 KiB
Python
162 lines
4.8 KiB
Python
import argparse
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import os
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import shutil
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from pathlib import Path
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import soundfile as sf
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import torch
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import yaml
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from tqdm import tqdm
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from common.log import logger
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from common.stdout_wrapper import SAFE_STDOUT
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vad_model, utils = torch.hub.load(
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repo_or_dir="snakers4/silero-vad",
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model="silero_vad",
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onnx=True,
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trust_repo=True,
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)
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(get_speech_timestamps, _, read_audio, *_) = utils
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def get_stamps(
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audio_file, min_silence_dur_ms: int = 700, min_sec: float = 2, max_sec: float = 12
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):
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"""
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min_silence_dur_ms: int (ミリ秒):
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このミリ秒数以上を無音だと判断する。
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逆に、この秒数以下の無音区間では区切られない。
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小さくすると、音声がぶつ切りに小さくなりすぎ、
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大きくすると音声一つ一つが長くなりすぎる。
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データセットによってたぶん要調整。
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min_sec: float (秒):
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この秒数より小さい発話は無視する。
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max_sec: float (秒):
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この秒数より大きい発話は無視する。
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"""
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sampling_rate = 16000 # 16kHzか8kHzのみ対応
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min_ms = int(min_sec * 1000)
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wav = read_audio(audio_file, sampling_rate=sampling_rate)
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speech_timestamps = get_speech_timestamps(
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wav,
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vad_model,
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sampling_rate=sampling_rate,
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min_silence_duration_ms=min_silence_dur_ms,
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min_speech_duration_ms=min_ms,
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max_speech_duration_s=max_sec,
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)
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return speech_timestamps
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def split_wav(
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audio_file,
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target_dir="raw",
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min_sec=2,
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max_sec=12,
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min_silence_dur_ms=700,
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):
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margin = 200 # ミリ秒単位で、音声の前後に余裕を持たせる
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speech_timestamps = get_stamps(
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audio_file,
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min_silence_dur_ms=min_silence_dur_ms,
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min_sec=min_sec,
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max_sec=max_sec,
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)
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data, sr = sf.read(audio_file)
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total_ms = len(data) / sr * 1000
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file_name = os.path.basename(audio_file).split(".")[0]
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os.makedirs(target_dir, exist_ok=True)
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total_time_ms = 0
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count = 0
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# タイムスタンプに従って分割し、ファイルに保存
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for i, ts in enumerate(speech_timestamps):
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start_ms = max(ts["start"] / 16 - margin, 0)
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end_ms = min(ts["end"] / 16 + margin, total_ms)
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start_sample = int(start_ms / 1000 * sr)
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end_sample = int(end_ms / 1000 * sr)
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segment = data[start_sample:end_sample]
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sf.write(os.path.join(target_dir, f"{file_name}-{i}.wav"), segment, sr)
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total_time_ms += end_ms - start_ms
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count += 1
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return total_time_ms / 1000, count
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--min_sec", "-m", type=float, default=2, help="Minimum seconds of a slice"
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)
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parser.add_argument(
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"--max_sec", "-M", type=float, default=12, help="Maximum seconds of a slice"
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)
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parser.add_argument(
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"--input_dir",
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"-i",
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type=str,
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default="inputs",
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help="Directory of input wav files",
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)
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parser.add_argument(
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"--model_name",
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"-m",
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type=str,
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required=True,
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help="The result will be in Data/{model_name}/raw/ (if Data is dataset_root in configs/paths.yml)",
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)
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parser.add_argument(
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"--min_silence_dur_ms",
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"-s",
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type=int,
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default=700,
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help="Silence above this duration (ms) is considered as a split point.",
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)
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args = parser.parse_args()
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with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f:
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path_config: dict[str, str] = yaml.safe_load(f.read())
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dataset_root = path_config["dataset_root"]
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input_dir = args.input_dir
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output_dir = os.path.join(dataset_root, args.model_name, "raw")
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min_sec = args.min_sec
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max_sec = args.max_sec
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min_silence_dur_ms = args.min_silence_dur_ms
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wav_files = Path(input_dir).glob("**/*.wav")
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wav_files = list(wav_files)
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logger.info(f"Found {len(wav_files)} wav files.")
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if os.path.exists(output_dir):
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logger.warning(f"Output directory {output_dir} already exists, deleting...")
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shutil.rmtree(output_dir)
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total_sec = 0
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total_count = 0
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for wav_file in tqdm(wav_files, file=SAFE_STDOUT):
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time_sec, count = split_wav(
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audio_file=str(wav_file),
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target_dir=output_dir,
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min_sec=min_sec,
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max_sec=max_sec,
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min_silence_dur_ms=min_silence_dur_ms,
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)
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total_sec += time_sec
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total_count += count
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logger.info(
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f"Slice done! Total time: {total_sec / 60:.2f} min, {total_count} files."
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)
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