120 lines
4.9 KiB
Python
120 lines
4.9 KiB
Python
import math
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import os
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import random
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import torch
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import torch.utils.data
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import numpy as np
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from librosa.util import normalize
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from scipy.io.wavfile import read
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from utils.audio_utils import mel_spectrogram
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MAX_WAV_VALUE = 32768.0
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def load_wav(full_path):
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sampling_rate, data = read(full_path)
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return data, sampling_rate
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def get_dataset_filelist(a):
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#with open(a.input_training_file, 'r', encoding='utf-8') as fi:
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# training_files = [os.path.join(a.input_wavs_dir, x.split('|')[0] + '.wav')
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# for x in fi.read().split('\n') if len(x) > 0]
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#with open(a.input_validation_file, 'r', encoding='utf-8') as fi:
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# validation_files = [os.path.join(a.input_wavs_dir, x.split('|')[0] + '.wav')
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# for x in fi.read().split('\n') if len(x) > 0]
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files = os.listdir(a.input_wavs_dir)
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random.shuffle(files)
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files = [os.path.join(a.input_wavs_dir, f) for f in files]
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training_files = files[: -int(len(files) * 0.05)]
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validation_files = files[-int(len(files) * 0.05):]
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return training_files, validation_files
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class MelDataset(torch.utils.data.Dataset):
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def __init__(self, training_files, segment_size, n_fft, num_mels,
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hop_size, win_size, sampling_rate, fmin, fmax, split=True, shuffle=True, n_cache_reuse=1,
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device=None, fmax_loss=None, fine_tuning=False, base_mels_path=None):
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self.audio_files = training_files
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random.seed(1234)
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if shuffle:
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random.shuffle(self.audio_files)
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self.segment_size = segment_size
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self.sampling_rate = sampling_rate
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self.split = split
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self.n_fft = n_fft
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self.num_mels = num_mels
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self.hop_size = hop_size
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self.win_size = win_size
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self.fmin = fmin
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self.fmax = fmax
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self.fmax_loss = fmax_loss
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self.cached_wav = None
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self.n_cache_reuse = n_cache_reuse
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self._cache_ref_count = 0
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self.device = device
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self.fine_tuning = fine_tuning
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self.base_mels_path = base_mels_path
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def __getitem__(self, index):
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filename = self.audio_files[index]
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if self._cache_ref_count == 0:
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#audio, sampling_rate = load_wav(filename)
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#audio = audio / MAX_WAV_VALUE
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audio = np.load(filename)
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if not self.fine_tuning:
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audio = normalize(audio) * 0.95
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self.cached_wav = audio
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#if sampling_rate != self.sampling_rate:
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# raise ValueError("{} SR doesn't match target {} SR".format(
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# sampling_rate, self.sampling_rate))
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self._cache_ref_count = self.n_cache_reuse
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else:
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audio = self.cached_wav
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self._cache_ref_count -= 1
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audio = torch.FloatTensor(audio)
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audio = audio.unsqueeze(0)
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if not self.fine_tuning:
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if self.split:
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if audio.size(1) >= self.segment_size:
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max_audio_start = audio.size(1) - self.segment_size
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audio_start = random.randint(0, max_audio_start)
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audio = audio[:, audio_start:audio_start+self.segment_size]
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else:
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audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), 'constant')
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mel = mel_spectrogram(audio, self.n_fft, self.num_mels,
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self.sampling_rate, self.hop_size, self.win_size, self.fmin, self.fmax,
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center=False)
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else:
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mel_path = os.path.join(self.base_mels_path, "mel" + "-" + filename.split("/")[-1].split("-")[-1])
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mel = np.load(mel_path).T
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#mel = np.load(
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# os.path.join(self.base_mels_path, os.path.splitext(os.path.split(filename)[-1])[0] + '.npy'))
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mel = torch.from_numpy(mel)
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if len(mel.shape) < 3:
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mel = mel.unsqueeze(0)
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if self.split:
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frames_per_seg = math.ceil(self.segment_size / self.hop_size)
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if audio.size(1) >= self.segment_size:
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mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1)
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mel = mel[:, :, mel_start:mel_start + frames_per_seg]
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audio = audio[:, mel_start * self.hop_size:(mel_start + frames_per_seg) * self.hop_size]
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else:
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mel = torch.nn.functional.pad(mel, (0, frames_per_seg - mel.size(2)), 'constant')
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audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), 'constant')
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mel_loss = mel_spectrogram(audio, self.n_fft, self.num_mels,
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self.sampling_rate, self.hop_size, self.win_size, self.fmin, self.fmax_loss,
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center=False)
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return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze())
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def __len__(self):
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return len(self.audio_files) |