186 lines
6.9 KiB
Python
186 lines
6.9 KiB
Python
import os
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import random
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import numpy as np
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import torch
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import torch.utils.data
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import utils
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from modules.mel_processing import spectrogram_torch
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from utils import load_filepaths_and_text, load_wav_to_torch
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# import h5py
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"""Multi speaker version"""
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class TextAudioSpeakerLoader(torch.utils.data.Dataset):
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"""
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1) loads audio, speaker_id, text pairs
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2) normalizes text and converts them to sequences of integers
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3) computes spectrograms from audio files.
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"""
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def __init__(self, audiopaths, hparams, all_in_mem: bool = False, vol_aug: bool = True):
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self.audiopaths = load_filepaths_and_text(audiopaths)
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self.hparams = hparams
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self.max_wav_value = hparams.data.max_wav_value
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self.sampling_rate = hparams.data.sampling_rate
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self.filter_length = hparams.data.filter_length
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self.hop_length = hparams.data.hop_length
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self.win_length = hparams.data.win_length
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self.unit_interpolate_mode = hparams.data.unit_interpolate_mode
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self.sampling_rate = hparams.data.sampling_rate
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self.use_sr = hparams.train.use_sr
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self.spec_len = hparams.train.max_speclen
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self.spk_map = hparams.spk
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self.vol_emb = hparams.model.vol_embedding
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self.vol_aug = hparams.train.vol_aug and vol_aug
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random.seed(1234)
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random.shuffle(self.audiopaths)
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self.all_in_mem = all_in_mem
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if self.all_in_mem:
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self.cache = [self.get_audio(p[0]) for p in self.audiopaths]
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def get_audio(self, filename):
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filename = filename.replace("\\", "/")
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audio, sampling_rate = load_wav_to_torch(filename)
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if sampling_rate != self.sampling_rate:
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raise ValueError(
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"Sample Rate not match. Expect {} but got {} from {}".format(
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self.sampling_rate, sampling_rate, filename))
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audio_norm = audio / self.max_wav_value
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audio_norm = audio_norm.unsqueeze(0)
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spec_filename = filename.replace(".wav", ".spec.pt")
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# Ideally, all data generated after Mar 25 should have .spec.pt
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if os.path.exists(spec_filename):
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spec = torch.load(spec_filename)
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else:
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spec = spectrogram_torch(audio_norm, self.filter_length,
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self.sampling_rate, self.hop_length, self.win_length,
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center=False)
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spec = torch.squeeze(spec, 0)
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torch.save(spec, spec_filename)
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spk = filename.split("/")[-2]
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spk = torch.LongTensor([self.spk_map[spk]])
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f0, uv = np.load(filename + ".f0.npy",allow_pickle=True)
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f0 = torch.FloatTensor(np.array(f0,dtype=float))
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uv = torch.FloatTensor(np.array(uv,dtype=float))
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c = torch.load(filename+ ".soft.pt")
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c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[0], mode=self.unit_interpolate_mode)
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if self.vol_emb:
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volume_path = filename + ".vol.npy"
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volume = np.load(volume_path)
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volume = torch.from_numpy(volume).float()
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else:
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volume = None
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lmin = min(c.size(-1), spec.size(-1))
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assert abs(c.size(-1) - spec.size(-1)) < 3, (c.size(-1), spec.size(-1), f0.shape, filename)
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assert abs(audio_norm.shape[1]-lmin * self.hop_length) < 3 * self.hop_length
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spec, c, f0, uv = spec[:, :lmin], c[:, :lmin], f0[:lmin], uv[:lmin]
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audio_norm = audio_norm[:, :lmin * self.hop_length]
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if volume is not None:
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volume = volume[:lmin]
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return c, f0, spec, audio_norm, spk, uv, volume
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def random_slice(self, c, f0, spec, audio_norm, spk, uv, volume):
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# if spec.shape[1] < 30:
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# print("skip too short audio:", filename)
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# return None
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if random.choice([True, False]) and self.vol_aug and volume is not None:
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max_amp = float(torch.max(torch.abs(audio_norm))) + 1e-5
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max_shift = min(1, np.log10(1/max_amp))
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log10_vol_shift = random.uniform(-1, max_shift)
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audio_norm = audio_norm * (10 ** log10_vol_shift)
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volume = volume * (10 ** log10_vol_shift)
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spec = spectrogram_torch(audio_norm,
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self.hparams.data.filter_length,
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self.hparams.data.sampling_rate,
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self.hparams.data.hop_length,
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self.hparams.data.win_length,
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center=False)[0]
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if spec.shape[1] > 800:
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start = random.randint(0, spec.shape[1]-800)
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end = start + 790
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spec, c, f0, uv = spec[:, start:end], c[:, start:end], f0[start:end], uv[start:end]
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audio_norm = audio_norm[:, start * self.hop_length : end * self.hop_length]
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if volume is not None:
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volume = volume[start:end]
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return c, f0, spec, audio_norm, spk, uv,volume
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def __getitem__(self, index):
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if self.all_in_mem:
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return self.random_slice(*self.cache[index])
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else:
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return self.random_slice(*self.get_audio(self.audiopaths[index][0]))
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def __len__(self):
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return len(self.audiopaths)
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class TextAudioCollate:
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def __call__(self, batch):
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batch = [b for b in batch if b is not None]
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input_lengths, ids_sorted_decreasing = torch.sort(
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torch.LongTensor([x[0].shape[1] for x in batch]),
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dim=0, descending=True)
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max_c_len = max([x[0].size(1) for x in batch])
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max_wav_len = max([x[3].size(1) for x in batch])
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lengths = torch.LongTensor(len(batch))
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c_padded = torch.FloatTensor(len(batch), batch[0][0].shape[0], max_c_len)
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f0_padded = torch.FloatTensor(len(batch), max_c_len)
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spec_padded = torch.FloatTensor(len(batch), batch[0][2].shape[0], max_c_len)
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wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
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spkids = torch.LongTensor(len(batch), 1)
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uv_padded = torch.FloatTensor(len(batch), max_c_len)
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volume_padded = torch.FloatTensor(len(batch), max_c_len)
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c_padded.zero_()
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spec_padded.zero_()
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f0_padded.zero_()
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wav_padded.zero_()
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uv_padded.zero_()
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volume_padded.zero_()
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for i in range(len(ids_sorted_decreasing)):
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row = batch[ids_sorted_decreasing[i]]
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c = row[0]
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c_padded[i, :, :c.size(1)] = c
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lengths[i] = c.size(1)
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f0 = row[1]
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f0_padded[i, :f0.size(0)] = f0
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spec = row[2]
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spec_padded[i, :, :spec.size(1)] = spec
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wav = row[3]
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wav_padded[i, :, :wav.size(1)] = wav
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spkids[i, 0] = row[4]
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uv = row[5]
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uv_padded[i, :uv.size(0)] = uv
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volume = row[6]
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if volume is not None:
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volume_padded[i, :volume.size(0)] = volume
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else :
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volume_padded = None
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return c_padded, f0_padded, spec_padded, wav_padded, spkids, lengths, uv_padded, volume_padded
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