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https://github.com/shivammehta25/Matcha-TTS.git
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Shivam Mehta
217
matcha/hifigan/meldataset.py
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217
matcha/hifigan/meldataset.py
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""" from https://github.com/jik876/hifi-gan """
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import math
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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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from librosa.filters import mel as librosa_mel_fn
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from librosa.util import normalize
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from scipy.io.wavfile import read
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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 dynamic_range_compression(x, C=1, clip_val=1e-5):
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return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
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def dynamic_range_decompression(x, C=1):
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return np.exp(x) / C
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def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
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return torch.log(torch.clamp(x, min=clip_val) * C)
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def dynamic_range_decompression_torch(x, C=1):
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return torch.exp(x) / C
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def spectral_normalize_torch(magnitudes):
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output = dynamic_range_compression_torch(magnitudes)
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return output
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def spectral_de_normalize_torch(magnitudes):
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output = dynamic_range_decompression_torch(magnitudes)
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return output
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mel_basis = {}
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hann_window = {}
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def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
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if torch.min(y) < -1.0:
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print("min value is ", torch.min(y))
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if torch.max(y) > 1.0:
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print("max value is ", torch.max(y))
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global mel_basis, hann_window # pylint: disable=global-statement
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if fmax not in mel_basis:
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mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
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mel_basis[str(fmax) + "_" + str(y.device)] = torch.from_numpy(mel).float().to(y.device)
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hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
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y = torch.nn.functional.pad(
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y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect"
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)
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y = y.squeeze(1)
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spec = torch.view_as_real(
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torch.stft(
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y,
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n_fft,
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hop_length=hop_size,
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win_length=win_size,
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window=hann_window[str(y.device)],
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center=center,
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pad_mode="reflect",
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normalized=False,
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onesided=True,
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return_complex=True,
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)
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)
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spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
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spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec)
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spec = spectral_normalize_torch(spec)
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return spec
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def get_dataset_filelist(a):
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with open(a.input_training_file, encoding="utf-8") as fi:
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training_files = [
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os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0
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]
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with open(a.input_validation_file, encoding="utf-8") as fi:
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validation_files = [
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os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0
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]
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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__(
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self,
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training_files,
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segment_size,
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n_fft,
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num_mels,
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hop_size,
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win_size,
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sampling_rate,
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fmin,
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fmax,
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split=True,
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shuffle=True,
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n_cache_reuse=1,
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device=None,
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fmax_loss=None,
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fine_tuning=False,
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base_mels_path=None,
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):
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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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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(f"{sampling_rate} SR doesn't match target {self.sampling_rate} SR")
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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(
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audio,
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self.n_fft,
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self.num_mels,
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self.sampling_rate,
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self.hop_size,
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self.win_size,
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self.fmin,
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self.fmax,
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center=False,
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)
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else:
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mel = np.load(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(
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audio,
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self.n_fft,
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self.num_mels,
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self.sampling_rate,
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self.hop_size,
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self.win_size,
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self.fmin,
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self.fmax_loss,
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center=False,
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)
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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)
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