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"""
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Run NSNet inference using onnxruntime.
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"""
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import os
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import math
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import logging
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import numpy as np
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import soundfile as sf
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import onnxruntime
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import audiolib
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class NSNetInference:
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"Apply NSNet ONNX model to WAV files"
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def __init__(self, model_path, window_length, hop_fraction,
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dft_size, sampling_rate, output_dir=None,
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spectral_floor=-120.0, timesignal_floor=1e-12):
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self.hop_fraction = hop_fraction
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self.dft_size = dft_size
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self.sampling_rate = sampling_rate
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self.output_dir = output_dir
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self.spectral_floor = spectral_floor
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self.timesignal_floor = timesignal_floor
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self.framesize = int(window_length * sampling_rate)
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self.wind = audiolib.hamming(self.framesize, hop=hop_fraction)
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self.model = onnxruntime.InferenceSession(model_path)
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def __call__(self, noisy_speech_filename, output_dir=None):
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"Apply NSNet model to one file and produce an output file with clean speech."
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enhanced_filename = os.path.join(output_dir or self.output_dir,
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os.path.basename(noisy_speech_filename))
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logging.info("NSNet inference: %s", noisy_speech_filename)
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sig, sample_rate = sf.read(noisy_speech_filename)
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ssize = len(sig)
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print('ssize:', ssize)
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fsize = len(self.wind)
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hsize = int(self.hop_fraction * self.framesize)
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sstart = hsize - fsize
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print('sstart:', sstart)
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send = ssize
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nframe = math.ceil((send - sstart) / hsize)
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zpleft = -sstart
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zpright = (nframe - 1) * hsize + fsize - zpleft - ssize
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if zpleft > 0 or zpright > 0:
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sigpad = np.zeros(ssize + zpleft + zpright)
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sigpad[zpleft:len(sigpad)-zpright] = sig
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else:
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sigpad = sig
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sout = np.zeros(nframe * hsize)
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x_old = np.zeros(hsize)
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model_input_names = [inp.name for inp in self.model.get_inputs()]
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model_inputs = {
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inp.name: np.zeros(
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[dim if isinstance(dim, int) else 1 for dim in inp.shape],
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dtype=np.float32)
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for inp in self.model.get_inputs()[1:]}
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mu = None
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sigmasquare = None
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frame_count = 0
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for frame_sampleindex in range(0, nframe * hsize, hsize):
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sigpadframe = sigpad[frame_sampleindex:frame_sampleindex + fsize] * self.wind
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xmag, xphs = audiolib.magphasor(audiolib.stft(
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sigpadframe, self.sampling_rate, self.wind,
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self.hop_fraction, self.dft_size, synth=True, zphase=False))
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feat = audiolib.logpow(xmag, floor=self.spectral_floor)
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if frame_sampleindex == 0:
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mu = feat
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sigmasquare = feat**2
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norm_feat, mu, sigmasquare, frame_count = audiolib.onlineMVN_perframe(
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feat, frame_counter=frame_count, mu=mu, sigmasquare=sigmasquare,
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frameshift=0.01, tauFeat=3., tauFeatInit=0.1, t_init=0.1)
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norm_feat = norm_feat[np.newaxis, np.newaxis, :]
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model_inputs['input'] = np.float32(norm_feat)
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model_outputs = self.model.run(None, model_inputs)
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model_inputs = dict(zip(model_input_names, model_outputs))
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mask = model_outputs[0].squeeze()
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x_enh = audiolib.istft(
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(xmag * mask) * xphs, sample_rate, self.wind, self.dft_size, zphase=False)
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sout[frame_sampleindex:frame_sampleindex + hsize] = x_old + x_enh[0:hsize]
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x_old = x_enh[hsize:fsize]
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xfinal = sout
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audiolib.audiowrite(xfinal, sample_rate, enhanced_filename, norm=False)
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