preprocess_durations.py

import argparse
import text
from utils import load_filepaths_and_text

# from data_utils import (
#   TextAudioLoader,
# )
from text.symbols import symbols
from models import (
    SynthesizerTrn,
)
import utils
from torch.utils.data import DataLoader
import torch

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

import json


class TextAudioLoaderWithPath(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)
        return (text, spec, wav, audiopath)

    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 TextAudioCollateWithPath:
    """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)
        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)

        paths = [x[3] for x in batch]

        return (
            text_padded,
            text_lengths,
            spec_padded,
            spec_lengths,
            wav_padded,
            wav_lengths,
            paths,
        )


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--weights_path", default="./pretrained_ljs.pth")
    parser.add_argument(
        "--filelists",
        nargs="+",
        default=[
            "filelists/ljs_audio_text_val_filelist.txt",
            "filelists/ljs_audio_text_test_filelist.txt",
        ],
    )

    args = parser.parse_args()

    hps = utils.get_hparams_from_file("./configs/ljs_base.json")
    net_g = SynthesizerTrn(
        len(symbols),
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        **hps.model,
    ).cuda()
    _ = utils.load_checkpoint(args.weights_path, net_g, None)
    net_g.eval()

    print(args.filelists)

    os.makedirs("durations", exist_ok=True)

    for filelist in args.filelists:
        dataset = TextAudioLoaderWithPath(filelist, hps.data)
        dataloader = DataLoader(
            dataset,
            batch_size=1,
            shuffle=False,
            pin_memory=False,
            collate_fn=TextAudioCollateWithPath(),
        )

        with torch.no_grad():
            for batch_idx, (
                x,
                x_lengths,
                spec,
                spec_lengths,
                y,
                y_lengths,
                paths,
            ) in enumerate(dataloader):
                x, x_lengths = x.cuda(0), x_lengths.cuda(0)
                spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
                y, y_lengths = y.cuda(0), y_lengths.cuda(0)
                (path,) = paths

                (
                    y_hat,
                    l_length,
                    attn,
                    ids_slice,
                    x_mask,
                    z_mask,
                    (z, z_p, m_p, logs_p, m_q, logs_q),
                ) = net_g(x, x_lengths, spec, spec_lengths)

                w = attn.sum(2).flatten()
                x = x.flatten()

                save_path = os.path.join("durations", os.path.basename(path) + ".npy")
                np.save(save_path, w.cpu().numpy().astype(int))

                if batch_idx % 100 == 0:
                    print(f"{batch_idx} / {len(dataloader)}")