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import math
import os
import random
import torch
import torch.utils.data
import numpy as np
from librosa.util import normalize
from scipy.io.wavfile import read
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
def load_wav(full_path):
sampling_rate, data = read(full_path)
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
def spectral_normalize_torch(magnitudes):
output = dynamic_range_compression_torch(magnitudes)
return output
def spectral_de_normalize_torch(magnitudes):
output = dynamic_range_decompression_torch(magnitudes)
return output
mel_basis = {}
hann_window = {}
def mel_spectrogram(
y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
):
if torch.min(y) < -1.0:
print("min value is ", torch.min(y))
if torch.max(y) > 1.0:
print("max value is ", torch.max(y))
global mel_basis, hann_window
if fmax not in mel_basis:
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
mel_basis[str(fmax) + "_" + str(y.device)] = (
torch.from_numpy(mel).float().to(y.device)
)
hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
spec = torch.stft(
y,
n_fft,
hop_length=hop_size,
win_length=win_size,
window=hann_window[str(y.device)],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
)
spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec)
spec = spectral_normalize_torch(spec)
return spec
def get_dataset_filelist(a):
with open(a.input_training_file, "r", encoding="utf-8") as fi:
training_files = [
os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav")
for x in fi.read().split("\n")
if len(x) > 0
]
with open(a.input_validation_file, "r", encoding="utf-8") as fi:
validation_files = [
os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav")
for x in fi.read().split("\n")
if len(x) > 0
]
return training_files, validation_files
class MelDataset(torch.utils.data.Dataset):
def __init__(
self,
training_files,
segment_size,
n_fft,
num_mels,
hop_size,
win_size,
sampling_rate,
fmin,
fmax,
split=True,
shuffle=True,
n_cache_reuse=1,
device=None,
fmax_loss=None,
fine_tuning=False,
base_mels_path=None,
):
self.audio_files = training_files
random.seed(1234)
if shuffle:
random.shuffle(self.audio_files)
self.segment_size = segment_size
self.sampling_rate = sampling_rate
self.split = split
self.n_fft = n_fft
self.num_mels = num_mels
self.hop_size = hop_size
self.win_size = win_size
self.fmin = fmin
self.fmax = fmax
self.fmax_loss = fmax_loss
self.cached_wav = None
self.n_cache_reuse = n_cache_reuse
self._cache_ref_count = 0
self.device = device
self.fine_tuning = fine_tuning
self.base_mels_path = base_mels_path
def __getitem__(self, index):
filename = self.audio_files[index]
if self._cache_ref_count == 0:
audio, sampling_rate = load_wav(filename)
audio = audio / MAX_WAV_VALUE
if not self.fine_tuning:
audio = normalize(audio) * 0.95
self.cached_wav = audio
if sampling_rate != self.sampling_rate:
raise ValueError(
"{} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate
)
)
self._cache_ref_count = self.n_cache_reuse
else:
audio = self.cached_wav
self._cache_ref_count -= 1
audio = torch.FloatTensor(audio)
audio = audio.unsqueeze(0)
if not self.fine_tuning:
if self.split:
if audio.size(1) >= self.segment_size:
max_audio_start = audio.size(1) - self.segment_size
audio_start = random.randint(0, max_audio_start)
audio = audio[:, audio_start : audio_start + self.segment_size]
else:
audio = torch.nn.functional.pad(
audio, (0, self.segment_size - audio.size(1)), "constant"
)
mel = mel_spectrogram(
audio,
self.n_fft,
self.num_mels,
self.sampling_rate,
self.hop_size,
self.win_size,
self.fmin,
self.fmax,
center=False,
)
else:
mel = np.load(
os.path.join(
self.base_mels_path,
os.path.splitext(os.path.split(filename)[-1])[0] + ".npy",
)
)
mel = torch.from_numpy(mel)
if len(mel.shape) < 3:
mel = mel.unsqueeze(0)
if self.split:
frames_per_seg = math.ceil(self.segment_size / self.hop_size)
if audio.size(1) >= self.segment_size:
mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1)
mel = mel[:, :, mel_start : mel_start + frames_per_seg]
audio = audio[
:,
mel_start
* self.hop_size : (mel_start + frames_per_seg)
* self.hop_size,
]
else:
mel = torch.nn.functional.pad(
mel, (0, frames_per_seg - mel.size(2)), "constant"
)
audio = torch.nn.functional.pad(
audio, (0, self.segment_size - audio.size(1)), "constant"
)
mel_loss = mel_spectrogram(
audio,
self.n_fft,
self.num_mels,
self.sampling_rate,
self.hop_size,
self.win_size,
self.fmin,
self.fmax_loss,
center=False,
)
return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze())
def __len__(self):
return len(self.audio_files)
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