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Build error
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5ff96b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | import os
import hydra
import logging
logger = logging.getLogger(__name__)
def run(args):
import unet
import tensorflow as tf
import soundfile as sf
import numpy as np
from tqdm import tqdm
import scipy.signal
path_experiment=str(args.path_experiment)
unet_model = unet.build_model_denoise(unet_args=args.unet)
ckpt=os.path.join(os.path.dirname(os.path.abspath(__file__)),path_experiment, 'checkpoint')
unet_model.load_weights(ckpt)
def do_stft(noisy):
window_fn = tf.signal.hamming_window
win_size=args.stft.win_size
hop_size=args.stft.hop_size
stft_signal_noisy=tf.signal.stft(noisy,frame_length=win_size, window_fn=window_fn, frame_step=hop_size, pad_end=True)
stft_noisy_stacked=tf.stack( values=[tf.math.real(stft_signal_noisy), tf.math.imag(stft_signal_noisy)], axis=-1)
return stft_noisy_stacked
def do_istft(data):
window_fn = tf.signal.hamming_window
win_size=args.stft.win_size
hop_size=args.stft.hop_size
inv_window_fn=tf.signal.inverse_stft_window_fn(hop_size, forward_window_fn=window_fn)
pred_cpx=data[...,0] + 1j * data[...,1]
pred_time=tf.signal.inverse_stft(pred_cpx, win_size, hop_size, window_fn=inv_window_fn)
return pred_time
audio=str(args.inference.audio)
data, samplerate = sf.read(audio)
print(data.dtype)
#Stereo to mono
if len(data.shape)>1:
data=np.mean(data,axis=1)
if samplerate!=44100:
print("Resampling")
data=scipy.signal.resample(data, int((44100 / samplerate )*len(data))+1)
segment_size=44100*5 #20s segments
length_data=len(data)
overlapsize=2048 #samples (46 ms)
window=np.hanning(2*overlapsize)
window_right=window[overlapsize::]
window_left=window[0:overlapsize]
audio_finished=False
pointer=0
denoised_data=np.zeros(shape=(len(data),))
residual_noise=np.zeros(shape=(len(data),))
numchunks=int(np.ceil(length_data/segment_size))
for i in tqdm(range(numchunks)):
if pointer+segment_size<length_data:
segment=data[pointer:pointer+segment_size]
#dostft
segment_TF=do_stft(segment)
segment_TF_ds=tf.data.Dataset.from_tensors(segment_TF)
pred = unet_model.predict(segment_TF_ds.batch(1))
pred=pred[0]
residual=segment_TF-pred[0]
residual=np.array(residual)
pred_time=do_istft(pred[0])
residual_time=do_istft(residual)
residual_time=np.array(residual_time)
if pointer==0:
pred_time=np.concatenate((pred_time[0:int(segment_size-overlapsize)], np.multiply(pred_time[int(segment_size-overlapsize):segment_size],window_right)), axis=0)
residual_time=np.concatenate((residual_time[0:int(segment_size-overlapsize)], np.multiply(residual_time[int(segment_size-overlapsize):segment_size],window_right)), axis=0)
else:
pred_time=np.concatenate((np.multiply(pred_time[0:int(overlapsize)], window_left), pred_time[int(overlapsize):int(segment_size-overlapsize)], np.multiply(pred_time[int(segment_size-overlapsize):int(segment_size)],window_right)), axis=0)
residual_time=np.concatenate((np.multiply(residual_time[0:int(overlapsize)], window_left), residual_time[int(overlapsize):int(segment_size-overlapsize)], np.multiply(residual_time[int(segment_size-overlapsize):int(segment_size)],window_right)), axis=0)
denoised_data[pointer:pointer+segment_size]=denoised_data[pointer:pointer+segment_size]+pred_time
residual_noise[pointer:pointer+segment_size]=residual_noise[pointer:pointer+segment_size]+residual_time
pointer=pointer+segment_size-overlapsize
else:
segment=data[pointer::]
lensegment=len(segment)
segment=np.concatenate((segment, np.zeros(shape=(int(segment_size-len(segment)),))), axis=0)
audio_finished=True
#dostft
segment_TF=do_stft(segment)
segment_TF_ds=tf.data.Dataset.from_tensors(segment_TF)
pred = unet_model.predict(segment_TF_ds.batch(1))
pred=pred[0]
residual=segment_TF-pred[0]
residual=np.array(residual)
pred_time=do_istft(pred[0])
pred_time=np.array(pred_time)
pred_time=pred_time[0:segment_size]
residual_time=do_istft(residual)
residual_time=np.array(residual_time)
residual_time=residual_time[0:segment_size]
if pointer==0:
pred_time=pred_time
residual_time=residual_time
else:
pred_time=np.concatenate((np.multiply(pred_time[0:int(overlapsize)], window_left), pred_time[int(overlapsize):int(segment_size)]),axis=0)
residual_time=np.concatenate((np.multiply(residual_time[0:int(overlapsize)], window_left), residual_time[int(overlapsize):int(segment_size)]),axis=0)
denoised_data[pointer::]=denoised_data[pointer::]+pred_time[0:lensegment]
residual_noise[pointer::]=residual_noise[pointer::]+residual_time[0:lensegment]
basename=os.path.splitext(audio)[0]
wav_noisy_name=basename+"_noisy_input"+".wav"
sf.write(wav_noisy_name, data, 44100)
wav_output_name=basename+"_denoised"+".wav"
sf.write(wav_output_name, denoised_data, 44100)
wav_output_name=basename+"_residual"+".wav"
sf.write(wav_output_name, residual_noise, 44100)
def _main(args):
global __file__
__file__ = hydra.utils.to_absolute_path(__file__)
run(args)
@hydra.main(config_path=".", config_name="conf")
def main(args):
try:
_main(args)
except Exception:
logger.exception("Some error happened")
# Hydra intercepts exit code, fixed in beta but I could not get the beta to work
os._exit(1)
if __name__ == "__main__":
main()
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