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Split WAV files at silence
#!/usr/bin/env python
from import wavfile
import os
import numpy as np
import argparse
from tqdm import tqdm
# Utility functions
def windows(signal, window_size, step_size):
if type(window_size) is not int:
raise AttributeError("Window size must be an integer.")
if type(step_size) is not int:
raise AttributeError("Step size must be an integer.")
for i_start in xrange(0, len(signal), step_size):
i_end = i_start + window_size
if i_end >= len(signal):
yield signal[i_start:i_end]
def energy(samples):
return np.sum(np.power(samples, 2.)) / float(len(samples))
def rising_edges(binary_signal):
previous_value = 0
index = 0
for x in binary_signal:
if x and not previous_value:
yield index
previous_value = x
index += 1
# Process command line arguments
parser = argparse.ArgumentParser(description='Split a WAV file at silence.')
parser.add_argument('input_file', type=str, help='The WAV file to split.')
parser.add_argument('--output-dir', '-o', type=str, default='.', help='The output folder. Defaults to the current folder.')
parser.add_argument('--min-silence-length', '-m', type=float, default=3., help='The minimum length of silence at which a split may occur [seconds]. Defaults to 3 seconds.')
parser.add_argument('--silence-threshold', '-t', type=float, default=1e-6, help='The energy level (between 0.0 and 1.0) below which the signal is regarded as silent. Defaults to 1e-6 == 0.0001%.')
parser.add_argument('--step-duration', '-s', type=float, default=None, help='The amount of time to step forward in the input file after calculating energy. Smaller value = slower, but more accurate silence detection. Larger value = faster, but might miss some split opportunities. Defaults to (min-silence-length / 10.).')
parser.add_argument('--dry-run', '-n', action='store_true', help='Don\'t actually write any output files.')
args = parser.parse_args()
input_filename = args.input_file
window_duration = args.min_silence_length
if args.step_duration is None:
step_duration = window_duration / 10.
step_duration = args.step_duration
silence_threshold = args.silence_threshold
output_dir = args.output_dir
output_filename_prefix = os.path.splitext(os.path.basename(input_filename))[0]
dry_run = args.dry_run
print "Splitting {} where energy is below {}% for longer than {}s.".format(
silence_threshold * 100.,
# Read and split the file
sample_rate, samples =, mmap=True)
max_amplitude = np.iinfo(samples.dtype).max
max_energy = energy([max_amplitude])
window_size = int(window_duration * sample_rate)
step_size = int(step_duration * sample_rate)
signal_windows = windows(
window_energy = (energy(w) / max_energy for w in tqdm(
total=int(len(samples) / float(step_size))
window_silence = (e > silence_threshold for e in window_energy)
cut_times = (r * step_duration for r in rising_edges(window_silence))
# This is the step that takes long, since we force the generators to run.
print "Finding silences..."
cut_samples = [int(t * sample_rate) for t in cut_times]
cut_ranges = [(i, cut_samples[i], cut_samples[i+1]) for i in xrange(len(cut_samples) - 1)]
for i, start, stop in tqdm(cut_ranges):
output_file_path = "{}_{:03d}.wav".format(
os.path.join(output_dir, output_filename_prefix),
if not dry_run:
print "Writing file {}".format(output_file_path)
print "Not writing file {}".format(output_file_path)

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@sushant-kum sushant-kum commented Feb 8, 2018

How can i adapt this for live wav data being streamed live over socket?

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