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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 utils.audio_utils import mel_spectrogram
MAX_WAV_VALUE = 32768.0
def load_wav(full_path):
sampling_rate, data = read(full_path)
return data, sampling_rate
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]
files = os.listdir(a.input_wavs_dir)
random.shuffle(files)
files = [os.path.join(a.input_wavs_dir, f) for f in files]
training_files = files[: -int(len(files) * 0.05)]
validation_files = files[-int(len(files) * 0.05):]
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
audio = np.load(filename)
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_path = os.path.join(self.base_mels_path, "mel" + "-" + filename.split("/")[-1].split("-")[-1])
mel = np.load(mel_path).T
#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) |