SEVITS_data_utils.py

import time
import os
import random
import numpy as np
import torch
import torch.utils.data

import commons 
from mel_processing import spectrogram_torch
from utils import load_wav_to_torch, load_filepaths_and_text
from text import text_to_sequence, cleaned_text_to_sequence

from encoder import generate_emb

class TextAudioLoader(torch.utils.data.Dataset):
    """
        1) loads audio, text pairs
        2) normalizes text and converts them to sequences of integers
        3) computes spectrograms from audio files.
    """
    def __init__(self, audiopaths_and_text, hparams):
        self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
        self.text_cleaners  = hparams.text_cleaners
        self.max_wav_value  = hparams.max_wav_value
        self.sampling_rate  = hparams.sampling_rate
        self.filter_length  = hparams.filter_length 
        self.hop_length     = hparams.hop_length 
        self.win_length     = hparams.win_length
        self.sampling_rate  = hparams.sampling_rate 

        self.cleaned_text = getattr(hparams, "cleaned_text", False)

        self.add_blank = hparams.add_blank
        self.min_text_len = getattr(hparams, "min_text_len", 1)
        self.max_text_len = getattr(hparams, "max_text_len", 190)

        random.seed(1234)
        random.shuffle(self.audiopaths_and_text)
        self._filter()


    def _filter(self):
        """
        Filter text & store spec lengths
        """
        # Store spectrogram lengths for Bucketing
        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
        # spec_length = wav_length // hop_length

        audiopaths_and_text_new = []
        lengths = []
        for audiopath, text in self.audiopaths_and_text:
            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
                audiopaths_and_text_new.append([audiopath, text])
                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
        self.audiopaths_and_text = audiopaths_and_text_new
        self.lengths = lengths

    def get_audio_text_pair(self, audiopath_and_text):
        # separate filename and text
        audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
        text = self.get_text(text)
        spec, wav = self.get_audio(audiopath)
        emb = self.get_emb(audiopath)
        return (text, spec, wav, emb)

    def get_emb(self, audiopath):
        if not os.path.exists(audiopath+".emb.npy"):
            generate_emb(audiopath)
        emb = np.load(audiopath+".emb.npy")
        return torch.from_numpy(emb)

    def get_audio(self, filename):
        audio, sampling_rate = load_wav_to_torch(filename)
        if sampling_rate != self.sampling_rate:
            raise ValueError("{} {} SR doesn't match target {} SR".format(
                sampling_rate, self.sampling_rate))
        audio_norm = audio / self.max_wav_value
        audio_norm = audio_norm.unsqueeze(0)
        spec_filename = filename.replace(".wav", ".spec.pt")
        if os.path.exists(spec_filename):
            spec = torch.load(spec_filename)
        else:
            spec = spectrogram_torch(audio_norm, self.filter_length,
                self.sampling_rate, self.hop_length, self.win_length,
                center=False)
            spec = torch.squeeze(spec, 0)
            torch.save(spec, spec_filename)
        return spec, audio_norm

    def get_text(self, text):
        if self.cleaned_text:
            text_norm = cleaned_text_to_sequence(text)
        else:
            text_norm = text_to_sequence(text, self.text_cleaners)
        if self.add_blank:
            text_norm = commons.intersperse(text_norm, 0)
        text_norm = torch.LongTensor(text_norm)
        return text_norm

    def __getitem__(self, index):
        return self.get_audio_text_pair(self.audiopaths_and_text[index])

    def __len__(self):
        return len(self.audiopaths_and_text)

class TextAudioCollate():
    """ Zero-pads model inputs and targets
    """

    def __call__(self, batch):
        """Collate's training batch from normalized text and aduio
        PARAMS
        ------
        batch: [text_normalized, spec_normalized, wav_normalized]
        """
        # Right zero-pad all one-hot text sequences to max input length
        _, ids_sorted_decreasing = torch.sort(
            torch.LongTensor([x[1].size(1) for x in batch]),
            dim=0, descending=True)

        max_text_len = max([len(x[0]) for x in batch])
        max_spec_len = max([x[1].size(1) for x in batch])
        max_wav_len = max([x[2].size(1) for x in batch])

        text_lengths = torch.LongTensor(len(batch))
        spec_lengths = torch.LongTensor(len(batch))
        wav_lengths = torch.LongTensor(len(batch))

        text_padded = torch.LongTensor(len(batch), max_text_len)
        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)

        emb = torch.FloatTensor(len(batch), len(batch[0][3]))

        text_padded.zero_()
        spec_padded.zero_()
        wav_padded.zero_()
        for i in range(len(ids_sorted_decreasing)):
            row = batch[ids_sorted_decreasing[i]]

            text = row[0]
            text_padded[i, :text.size(0)] = text
            text_lengths[i] = text.size(0)

            spec = row[1]
            spec_padded[i, :, :spec.size(1)] = spec
            spec_lengths[i] = spec.size(1)

            wav = row[2]
            wav_padded[i, :, :wav.size(1)] = wav
            wav_lengths[i] = wav.size(1)

            emb[i] = row[3]

        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, emb.unsqueeze(-1)