183 行
6.3 KiB
Python
183 行
6.3 KiB
Python
"""
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Various functions for finding/manipulating silence in AudioSegments
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"""
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import itertools
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from .utils import db_to_float
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def detect_silence(audio_segment, min_silence_len=1000, silence_thresh=-16, seek_step=1):
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"""
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Returns a list of all silent sections [start, end] in milliseconds of audio_segment.
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Inverse of detect_nonsilent()
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audio_segment - the segment to find silence in
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min_silence_len - the minimum length for any silent section
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silence_thresh - the upper bound for how quiet is silent in dFBS
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seek_step - step size for interating over the segment in ms
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"""
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seg_len = len(audio_segment)
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# you can't have a silent portion of a sound that is longer than the sound
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if seg_len < min_silence_len:
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return []
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# convert silence threshold to a float value (so we can compare it to rms)
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silence_thresh = db_to_float(silence_thresh) * audio_segment.max_possible_amplitude
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# find silence and add start and end indicies to the to_cut list
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silence_starts = []
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# check successive (1 sec by default) chunk of sound for silence
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# try a chunk at every "seek step" (or every chunk for a seek step == 1)
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last_slice_start = seg_len - min_silence_len
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slice_starts = range(0, last_slice_start + 1, seek_step)
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# guarantee last_slice_start is included in the range
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# to make sure the last portion of the audio is searched
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if last_slice_start % seek_step:
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slice_starts = itertools.chain(slice_starts, [last_slice_start])
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for i in slice_starts:
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audio_slice = audio_segment[i:i + min_silence_len]
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if audio_slice.rms <= silence_thresh:
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silence_starts.append(i)
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# short circuit when there is no silence
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if not silence_starts:
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return []
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# combine the silence we detected into ranges (start ms - end ms)
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silent_ranges = []
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prev_i = silence_starts.pop(0)
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current_range_start = prev_i
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for silence_start_i in silence_starts:
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continuous = (silence_start_i == prev_i + seek_step)
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# sometimes two small blips are enough for one particular slice to be
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# non-silent, despite the silence all running together. Just combine
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# the two overlapping silent ranges.
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silence_has_gap = silence_start_i > (prev_i + min_silence_len)
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if not continuous and silence_has_gap:
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silent_ranges.append([current_range_start,
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prev_i + min_silence_len])
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current_range_start = silence_start_i
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prev_i = silence_start_i
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silent_ranges.append([current_range_start,
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prev_i + min_silence_len])
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return silent_ranges
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def detect_nonsilent(audio_segment, min_silence_len=1000, silence_thresh=-16, seek_step=1):
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"""
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Returns a list of all nonsilent sections [start, end] in milliseconds of audio_segment.
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Inverse of detect_silent()
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audio_segment - the segment to find silence in
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min_silence_len - the minimum length for any silent section
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silence_thresh - the upper bound for how quiet is silent in dFBS
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seek_step - step size for interating over the segment in ms
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"""
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silent_ranges = detect_silence(audio_segment, min_silence_len, silence_thresh, seek_step)
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len_seg = len(audio_segment)
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# if there is no silence, the whole thing is nonsilent
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if not silent_ranges:
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return [[0, len_seg]]
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# short circuit when the whole audio segment is silent
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if silent_ranges[0][0] == 0 and silent_ranges[0][1] == len_seg:
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return []
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prev_end_i = 0
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nonsilent_ranges = []
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for start_i, end_i in silent_ranges:
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nonsilent_ranges.append([prev_end_i, start_i])
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prev_end_i = end_i
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if end_i != len_seg:
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nonsilent_ranges.append([prev_end_i, len_seg])
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if nonsilent_ranges[0] == [0, 0]:
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nonsilent_ranges.pop(0)
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return nonsilent_ranges
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def split_on_silence(audio_segment, min_silence_len=1000, silence_thresh=-16, keep_silence=100,
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seek_step=1):
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"""
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Returns list of audio segments from splitting audio_segment on silent sections
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audio_segment - original pydub.AudioSegment() object
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min_silence_len - (in ms) minimum length of a silence to be used for
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a split. default: 1000ms
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silence_thresh - (in dBFS) anything quieter than this will be
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considered silence. default: -16dBFS
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keep_silence - (in ms or True/False) leave some silence at the beginning
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and end of the chunks. Keeps the sound from sounding like it
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is abruptly cut off.
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When the length of the silence is less than the keep_silence duration
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it is split evenly between the preceding and following non-silent
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segments.
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If True is specified, all the silence is kept, if False none is kept.
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default: 100ms
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seek_step - step size for interating over the segment in ms
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"""
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# from the itertools documentation
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def pairwise(iterable):
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"s -> (s0,s1), (s1,s2), (s2, s3), ..."
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a, b = itertools.tee(iterable)
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next(b, None)
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return zip(a, b)
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if isinstance(keep_silence, bool):
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keep_silence = len(audio_segment) if keep_silence else 0
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output_ranges = [
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[ start - keep_silence, end + keep_silence ]
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for (start,end)
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in detect_nonsilent(audio_segment, min_silence_len, silence_thresh, seek_step)
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]
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for range_i, range_ii in pairwise(output_ranges):
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last_end = range_i[1]
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next_start = range_ii[0]
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if next_start < last_end:
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range_i[1] = (last_end+next_start)//2
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range_ii[0] = range_i[1]
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return [
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audio_segment[ max(start,0) : min(end,len(audio_segment)) ]
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for start,end in output_ranges
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]
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def detect_leading_silence(sound, silence_threshold=-50.0, chunk_size=10):
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"""
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Returns the millisecond/index that the leading silence ends.
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audio_segment - the segment to find silence in
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silence_threshold - the upper bound for how quiet is silent in dFBS
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chunk_size - chunk size for interating over the segment in ms
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"""
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trim_ms = 0 # ms
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assert chunk_size > 0 # to avoid infinite loop
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while sound[trim_ms:trim_ms+chunk_size].dBFS < silence_threshold and trim_ms < len(sound):
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trim_ms += chunk_size
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# if there is no end it should return the length of the segment
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return min(trim_ms, len(sound))
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