Spaces:
Running
Running
File size: 5,450 Bytes
bfd2c22 |
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 |
import math
import os
os.environ["LRU_CACHE_CAPACITY"] = "3"
import random
import torch
import torch.utils.data
import numpy as np
import librosa
from librosa.util import normalize
from librosa.filters import mel as librosa_mel_fn
from scipy.io.wavfile import read
import soundfile as sf
import torch.nn.functional as F
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
sampling_rate = None
try:
data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
except Exception as ex:
print(f"'{full_path}' failed to load.\nException:")
print(ex)
if return_empty_on_exception:
return [], sampling_rate or target_sr or 48000
else:
raise Exception(ex)
if len(data.shape) > 1:
data = data[:, 0]
assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
if np.issubdtype(data.dtype, np.integer): # if audio data is type int
max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
else: # if audio data is type fp32
max_mag = max(np.amax(data), -np.amin(data))
max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
data = torch.FloatTensor(data.astype(np.float32))/max_mag
if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
return [], sampling_rate or target_sr or 48000
if target_sr is not None and sampling_rate != target_sr:
data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
sampling_rate = target_sr
return data, sampling_rate
def dynamic_range_compression(x, C=1, clip_val=1e-5):
return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
def dynamic_range_decompression(x, C=1):
return np.exp(x) / C
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompression_torch(x, C=1):
return torch.exp(x) / C
class STFT():
def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
self.target_sr = sr
self.n_mels = n_mels
self.n_fft = n_fft
self.win_size = win_size
self.hop_length = hop_length
self.fmin = fmin
self.fmax = fmax
self.clip_val = clip_val
self.mel_basis = {}
self.hann_window = {}
def get_mel(self, y, keyshift=0, speed=1, center=False):
sampling_rate = self.target_sr
n_mels = self.n_mels
n_fft = self.n_fft
win_size = self.win_size
hop_length = self.hop_length
fmin = self.fmin
fmax = self.fmax
clip_val = self.clip_val
factor = 2 ** (keyshift / 12)
n_fft_new = int(np.round(n_fft * factor))
win_size_new = int(np.round(win_size * factor))
hop_length_new = int(np.round(hop_length * speed))
if torch.min(y) < -1.:
print('min value is ', torch.min(y))
if torch.max(y) > 1.:
print('max value is ', torch.max(y))
mel_basis_key = str(fmax)+'_'+str(y.device)
if mel_basis_key not in self.mel_basis:
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
self.mel_basis[mel_basis_key] = torch.from_numpy(mel).float().to(y.device)
keyshift_key = str(keyshift)+'_'+str(y.device)
if keyshift_key not in self.hann_window:
self.hann_window[keyshift_key] = torch.hann_window(win_size_new).to(y.device)
pad_left = (win_size_new - hop_length_new) //2
pad_right = max((win_size_new- hop_length_new + 1) //2, win_size_new - y.size(-1) - pad_left)
if pad_right < y.size(-1):
mode = 'reflect'
else:
mode = 'constant'
y = torch.nn.functional.pad(y.unsqueeze(1), (pad_left, pad_right), mode = mode)
y = y.squeeze(1)
spec = torch.stft(y, n_fft_new, hop_length=hop_length_new, win_length=win_size_new, window=self.hann_window[keyshift_key],
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
# print(111,spec)
spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
if keyshift != 0:
size = n_fft // 2 + 1
resize = spec.size(1)
if resize < size:
spec = F.pad(spec, (0, 0, 0, size-resize))
spec = spec[:, :size, :] * win_size / win_size_new
# print(222,spec)
spec = torch.matmul(self.mel_basis[mel_basis_key], spec)
# print(333,spec)
spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
# print(444,spec)
return spec
def __call__(self, audiopath):
audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
return spect
stft = STFT()
|