import logging import warnings import librosa warnings.filterwarnings('ignore') # Configure logging at the top of your slicer.py logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) class Slicer: def __init__(self, sr: int, threshold: float = -30., min_length: int = 3000, min_interval: int = 100, hop_size: int = 20, max_sil_kept: int = 5000): if not min_length >= min_interval >= hop_size: raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size') if not max_sil_kept >= hop_size: raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size') min_interval = sr * min_interval / 1000 self.sr = sr self.threshold = 10 ** (threshold / 20.) self.hop_size = round(sr * hop_size / 1000) self.win_size = min(round(min_interval), 4 * self.hop_size) self.min_length = round(sr * min_length / 1000 / self.hop_size) self.min_interval = round(min_interval / self.hop_size) self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size) def _apply_slice(self, waveform, begin, end): if len(waveform.shape) > 1: return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)] else: return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)] def slice(self, waveform): if len(waveform.shape) > 1: samples = librosa.to_mono(waveform) else: samples = waveform if samples.shape[0] <= self.min_length: # Return the entire audio as a single chunk return [(0, waveform)] rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0) sil_tags = [] silence_start = None clip_start = 0 for i, rms in enumerate(rms_list): # Keep looping while frame is silent. if rms < self.threshold: # Record start of silent frames. if silence_start is None: silence_start = i continue # Keep looping while frame is not silent and silence start has not been recorded. if silence_start is None: continue # Clear recorded silence start if interval is not enough or clip is too short is_leading_silence = silence_start == 0 and i > self.max_sil_kept need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length if not is_leading_silence and not need_slice_middle: silence_start = None continue # Need slicing. Record the range of silent frames to be removed. if i - silence_start <= self.max_sil_kept: pos = rms_list[silence_start: i + 1].argmin() + silence_start if silence_start == 0: sil_tags.append((0, pos)) else: sil_tags.append((pos, pos)) clip_start = pos elif i - silence_start <= self.max_sil_kept * 2: pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin() pos += i - self.max_sil_kept pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept if silence_start == 0: sil_tags.append((0, pos_r)) clip_start = pos_r else: sil_tags.append((min(pos_l, pos), max(pos_r, pos))) clip_start = max(pos_r, pos) else: pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept if silence_start == 0: sil_tags.append((0, pos_r)) else: sil_tags.append((pos_l, pos_r)) clip_start = pos_r silence_start = None # Deal with trailing silence. total_frames = rms_list.shape[0] if silence_start is not None and total_frames - silence_start >= self.min_interval: silence_end = min(total_frames, silence_start + self.max_sil_kept) pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start sil_tags.append((pos, total_frames + 1)) # Apply and return slices. if len(sil_tags) == 0: # Return the entire audio as a single chunk if no silence detected return [(0, waveform)] # Extract non-silence chunks non_silence_chunks = [] # Add first non-silence chunk if it exists if sil_tags[0][0] > 0: start_pos = 0 end_frame = sil_tags[0][0] chunk = self._apply_slice(waveform, 0, end_frame) non_silence_chunks.append((start_pos, chunk)) # Add middle non-silence chunks for i in range(1, len(sil_tags)): start_frame = sil_tags[i-1][1] end_frame = sil_tags[i][0] if start_frame < end_frame: # Only add if there's actual non-silence content start_pos = start_frame * self.hop_size chunk = self._apply_slice(waveform, start_frame, end_frame) non_silence_chunks.append((start_pos, chunk)) # Add last non-silence chunk if it exists if sil_tags[-1][1] * self.hop_size < len(waveform): start_frame = sil_tags[-1][1] start_pos = start_frame * self.hop_size chunk = self._apply_slice(waveform, start_frame, total_frames) non_silence_chunks.append((start_pos, chunk)) for i, (start_pos, chunk) in enumerate(non_silence_chunks): # Calculate start and end times in seconds start_time_sec = start_pos / self.sr end_time_sec = start_pos / self.sr + len(chunk) / self.sr if len(chunk.shape) == 1 else start_pos / self.sr + chunk.shape[1] / self.sr duration_sec = end_time_sec - start_time_sec # Format start and end times as mm:ss start_min, start_sec = divmod(start_time_sec, 60) end_min, end_sec = divmod(end_time_sec, 60) # Log the information logger.info(f"Chunk {i}: Start={int(start_min):02d}:{start_sec:05.2f}, End={int(end_min):02d}:{end_sec:05.2f}, Duration={duration_sec:.2f}s") return non_silence_chunks def main(): import os.path from argparse import ArgumentParser import librosa import soundfile from pathlib import Path parser = ArgumentParser() parser.add_argument('audio', type=str, help='The audio file or directory to be sliced') parser.add_argument('--out', type=str, help='Output directory of the sliced audio clips') parser.add_argument('--db_thresh', type=float, required=False, default=-30, help='The dB threshold for silence detection') parser.add_argument('--min_length', type=int, required=False, default=3000, help='The minimum milliseconds required for each sliced audio clip') parser.add_argument('--min_interval', type=int, required=False, default=100, help='The minimum milliseconds for a silence part to be sliced') parser.add_argument('--hop_size', type=int, required=False, default=20, help='Frame length in milliseconds') parser.add_argument('--max_sil_kept', type=int, required=False, default=5000, help='The maximum silence length kept around the sliced clip, presented in milliseconds') args = parser.parse_args() # Determine if the input is a file or directory audio_path = Path(args.audio) is_directory = audio_path.is_dir() # Prepare output directory out = args.out if out is None: if is_directory: out = os.path.abspath(args.audio) else: out = os.path.dirname(os.path.abspath(args.audio)) if not os.path.exists(out): os.makedirs(out) # Audio file extensions to process audio_extensions = ['.wav', '.mp3', '.flac', '.ogg', '.m4a'] # Process a single file or all files in a directory if is_directory: logger.info(f"Processing all audio files in directory: {args.audio}") audio_files = [] for ext in audio_extensions: audio_files.extend(list(audio_path.glob(f'*{ext}'))) if not audio_files: logger.warning(f"No audio files found in {args.audio}") return logger.info(f"Found {len(audio_files)} audio files to process") for audio_file in audio_files: process_audio_file(audio_file, out, args) else: # Process a single audio file logger.info(f"Processing single audio file: {args.audio}") process_audio_file(audio_path, out, args) def process_audio_file(audio_file, out_dir, args): """Process a single audio file with the given parameters""" import os.path import librosa import soundfile try: logger.info(f"Loading audio file: {audio_file}") audio, sr = librosa.load(str(audio_file), sr=None, mono=False) slicer = Slicer( sr=sr, threshold=args.db_thresh, min_length=args.min_length, min_interval=args.min_interval, hop_size=args.hop_size, max_sil_kept=args.max_sil_kept ) # Get non-silence chunks with their positions chunks_with_pos = slicer.slice(audio) file_basename = os.path.basename(str(audio_file)).rsplit('.', maxsplit=1)[0] logger.info(f"Saving {len(chunks_with_pos)} non-silence audio chunks from {file_basename}...") for i, (pos, chunk) in enumerate(chunks_with_pos): if len(chunk.shape) > 1: chunk = chunk.T output_file = os.path.join(out_dir, f'{file_basename}_{i}_pos_{pos}.wav') soundfile.write(output_file, chunk, sr) logger.info(f"Finished processing {audio_file}") except Exception as e: logger.error(f"Error processing {audio_file}: {str(e)}") if __name__ == '__main__': main()