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import subprocess
import matplotlib
import os
matplotlib.use('Agg')
import librosa
import librosa.filters
import numpy as np
from scipy import signal
from scipy.io import wavfile
def save_wav(wav, path, sr, norm=False):
if norm:
wav = wav / np.abs(wav).max()
wav *= 32767
# proposed by @dsmiller
wavfile.write(path, sr, wav.astype(np.int16))
def get_hop_size(hparams):
hop_size = hparams['hop_size']
if hop_size is None:
assert hparams['frame_shift_ms'] is not None
hop_size = int(hparams['frame_shift_ms'] / 1000 * hparams['audio_sample_rate'])
return hop_size
###########################################################################################
def _stft(y, hparams):
return librosa.stft(y=y, n_fft=hparams['fft_size'], hop_length=get_hop_size(hparams),
win_length=hparams['win_size'], pad_mode='constant')
def _istft(y, hparams):
return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams['win_size'])
def librosa_pad_lr(x, fsize, fshift, pad_sides=1):
'''compute right padding (final frame) or both sides padding (first and final frames)
'''
assert pad_sides in (1, 2)
# return int(fsize // 2)
pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0]
if pad_sides == 1:
return 0, pad
else:
return pad // 2, pad // 2 + pad % 2
# Conversions
def amp_to_db(x):
return 20 * np.log10(np.maximum(1e-5, x))
def normalize(S, hparams):
return (S - hparams['min_level_db']) / -hparams['min_level_db']
def denormalize(D, hparams):
return (D * -hparams['min_level_db']) + hparams['min_level_db']
def rnnoise(filename, out_fn=None, verbose=False, out_sample_rate=22050):
assert os.path.exists('./rnnoise/examples/rnnoise_demo'), INSTALL_STR
if out_fn is None:
out_fn = f"{filename[:-4]}.denoised.wav"
out_48k_fn = f"{out_fn}.48000.wav"
tmp0_fn = f"{out_fn}.0.wav"
tmp1_fn = f"{out_fn}.1.wav"
tmp2_fn = f"{out_fn}.2.raw"
tmp3_fn = f"{out_fn}.3.raw"
if verbose:
print("Pre-processing audio...") # wav to pcm raw
subprocess.check_call(
f'sox "{filename}" -G -r48000 "{tmp0_fn}"', shell=True, stdin=subprocess.PIPE) # convert to raw
subprocess.check_call(
f'sox -v 0.95 "{tmp0_fn}" "{tmp1_fn}"', shell=True, stdin=subprocess.PIPE) # convert to raw
subprocess.check_call(
f'ffmpeg -y -i "{tmp1_fn}" -loglevel quiet -f s16le -ac 1 -ar 48000 "{tmp2_fn}"',
shell=True, stdin=subprocess.PIPE) # convert to raw
if verbose:
print("Applying rnnoise algorithm to audio...") # rnnoise
subprocess.check_call(
f'./rnnoise/examples/rnnoise_demo "{tmp2_fn}" "{tmp3_fn}"', shell=True)
if verbose:
print("Post-processing audio...") # pcm raw to wav
if filename == out_fn:
subprocess.check_call(f'rm -f "{out_fn}"', shell=True)
subprocess.check_call(
f'sox -t raw -r 48000 -b 16 -e signed-integer -c 1 "{tmp3_fn}" "{out_48k_fn}"', shell=True)
subprocess.check_call(f'sox "{out_48k_fn}" -G -r{out_sample_rate} "{out_fn}"', shell=True)
subprocess.check_call(f'rm -f "{tmp0_fn}" "{tmp1_fn}" "{tmp2_fn}" "{tmp3_fn}" "{out_48k_fn}"', shell=True)
if verbose:
print("Audio-filtering completed!") |