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Running on Zero
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03022ee | 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 | import torch
import torchaudio
from scipy import signal
import numpy as np
import random
from local.utils.fileio import load_wav_scp
def addreverb(wav, rir_wav):
# wav: [T,], rir_wav: [T,]
wav = wav.numpy()
rir_wav = rir_wav.numpy()
wav_len = wav.shape[0]
rir_wav = rir_wav / np.sqrt(np.sum(rir_wav**2))
out_wav = signal.convolve(wav, rir_wav,
mode='full')[:wav_len]
out_wav = out_wav / (np.max(np.abs(out_wav)) + 1e-6)
return torch.from_numpy(out_wav)
def addnoise(wav, noise=None, snr_high=15, snr_low=0):
# wav: [T,], noise: [T,]
if noise is None:
noise = torch.randn_like(wav)
noise = noise.numpy()
wav = wav.numpy()
wav_len = wav.shape[0]
noise_len = noise.shape[0]
if noise_len >= wav_len:
start = random.randint(0, noise_len - wav_len)
noise = noise[start:start + wav_len]
else:
noise = noise.repeat(wav_len // noise_len + 1)
noise = noise[:wav_len]
wav_db = 10 * np.log10(np.mean(wav**2) + 1e-6)
noise_db = 10 * np.log10(np.mean(noise**2) + 1e-6)
noise_snr = random.uniform(snr_low, snr_high)
noise = np.sqrt(10**(
(wav_db - noise_db - noise_snr) / 10)) * noise
out_wav = wav + noise
out_wav = out_wav / (np.max(np.abs(out_wav)) + 1e-6)
return torch.from_numpy(out_wav)
class NoiseReverbCorrupter(object):
def __init__(
self,
noise_prob=0.0,
reverb_prob=0.0,
noise_file=None,
reverb_file=None,
noise_snr_low=0,
noise_snr_high=15,
):
if reverb_prob > 0.0:
if reverb_file is None:
raise ValueError('Reverb_file not be assigned.')
self.add_reverb = addreverb
self.reverb_data = load_wav_scp(reverb_file)
self.reverb_data_keys = list(self.reverb_data.keys())
if noise_prob > 0.0:
if noise_file is None:
raise ValueError('Noise_file not be assigned.')
self.add_noise = addnoise
self.noise_data = load_wav_scp(noise_file)
self.noise_data_keys = list(self.noise_data.keys())
self.reverb_prob = reverb_prob
self.noise_prob = noise_prob
self.noise_snr_low = noise_snr_low
self.noise_snr_high = noise_snr_high
def __call__(self, wav, fs=16000):
if self.reverb_prob > random.random():
reverb_path = self.reverb_data[random.choice(self.reverb_data_keys)]
reverb, fs_rir = torchaudio.load(reverb_path)
assert fs_rir == fs
wav = self.add_reverb(wav, reverb[0])
if self.noise_prob > random.random():
noise_path = self.noise_data[random.choice(self.noise_data_keys)]
noise, fs_noise = torchaudio.load(noise_path)
assert fs_noise == fs
wav = self.add_noise(
wav, noise[0],
snr_high=self.noise_snr_high,
snr_low=self.noise_snr_low,)
return wav
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