144 lines
4.4 KiB
Python
144 lines
4.4 KiB
Python
import os
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import re
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import sys
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import json
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import librosa
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import argparse
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import numpy as np
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from tqdm import tqdm
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from glob import glob
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from pydub import silence
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from pydub import AudioSegment
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from functools import partial
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from hparams import hparams
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from utils import parallel_run, add_postfix
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from audio import load_audio, save_audio, get_duration, get_silence
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def abs_mean(x):
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return abs(x).mean()
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def remove_breath(audio):
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edges = librosa.effects.split(
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audio, top_db=40, frame_length=128, hop_length=32)
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for idx in range(len(edges)):
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start_idx, end_idx = edges[idx][0], edges[idx][1]
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if start_idx < len(audio):
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if abs_mean(audio[start_idx:end_idx]) < abs_mean(audio) - 0.05:
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audio[start_idx:end_idx] = 0
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return audio
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def split_on_silence_with_librosa(
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audio_path, top_db=40, frame_length=1024, hop_length=256,
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skip_idx=0, out_ext="wav",
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min_segment_length=3, max_segment_length=8,
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pre_silence_length=0, post_silence_length=0):
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filename = os.path.basename(audio_path).split('.', 1)[0]
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in_ext = audio_path.rsplit(".")[1]
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audio = load_audio(audio_path)
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edges = librosa.effects.split(audio,
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top_db=top_db, frame_length=frame_length, hop_length=hop_length)
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new_audio = np.zeros_like(audio)
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for idx, (start, end) in enumerate(edges[skip_idx:]):
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new_audio[start:end] = remove_breath(audio[start:end])
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save_audio(new_audio, add_postfix(audio_path, "no_breath"))
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audio = new_audio
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edges = librosa.effects.split(audio,
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top_db=top_db, frame_length=frame_length, hop_length=hop_length)
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audio_paths = []
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for idx, (start, end) in enumerate(edges[skip_idx:]):
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segment = audio[start:end]
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duration = get_duration(segment)
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if duration <= min_segment_length or duration >= max_segment_length:
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continue
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output_path = "{}/{}.{:04d}.{}".format(
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os.path.dirname(audio_path), filename, idx, out_ext)
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padded_segment = np.concatenate([
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get_silence(pre_silence_length),
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segment,
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get_silence(post_silence_length),
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])
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save_audio(padded_segment, output_path)
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audio_paths.append(output_path)
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return audio_paths
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def read_audio(audio_path):
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return AudioSegment.from_file(audio_path)
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def split_on_silence_with_pydub(
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audio_path, skip_idx=0, out_ext="wav",
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silence_thresh=-40, min_silence_len=400,
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silence_chunk_len=100, keep_silence=100):
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filename = os.path.basename(audio_path).split('.', 1)[0]
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in_ext = audio_path.rsplit(".")[1]
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audio = read_audio(audio_path)
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not_silence_ranges = silence.detect_nonsilent(
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audio, min_silence_len=silence_chunk_len,
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silence_thresh=silence_thresh)
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edges = [not_silence_ranges[0]]
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for idx in range(1, len(not_silence_ranges)-1):
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cur_start = not_silence_ranges[idx][0]
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prev_end = edges[-1][1]
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if cur_start - prev_end < min_silence_len:
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edges[-1][1] = not_silence_ranges[idx][1]
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else:
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edges.append(not_silence_ranges[idx])
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audio_paths = []
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for idx, (start_idx, end_idx) in enumerate(edges[skip_idx:]):
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start_idx = max(0, start_idx - keep_silence)
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end_idx += keep_silence
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target_audio_path = "{}/{}.{:04d}.{}".format(
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os.path.dirname(audio_path), filename, idx, out_ext)
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audio[start_idx:end_idx].export(target_audio_path, out_ext)
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audio_paths.append(target_audio_path)
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return audio_paths
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def split_on_silence_batch(audio_paths, method, **kargv):
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audio_paths.sort()
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method = method.lower()
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if method == "librosa":
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fn = partial(split_on_silence_with_librosa, **kargv)
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elif method == "pydub":
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fn = partial(split_on_silence_with_pydub, **kargv)
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parallel_run(fn, audio_paths,
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desc="Split on silence", parallel=False)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--audio_pattern', required=True)
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parser.add_argument('--out_ext', default='wav')
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parser.add_argument('--method', choices=['librosa', 'pydub'], required=True)
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config = parser.parse_args()
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audio_paths = glob(config.audio_pattern)
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split_on_silence_batch(
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audio_paths, config.method,
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out_ext=config.out_ext,
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)
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