data_utils.py

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

import commons
from utils import load_wav_to_torch, load_filepaths_and_text
from text import cleaned_text_to_sequence


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.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", 100)

        # shuffle is not nead for single speaker
        # 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, spec, bert, text in self.audiopaths_and_text:
            length = len(text.split())
            if self.min_text_len <= length and length <= self.max_text_len:
                audiopaths_and_text_new.append([audiopath, spec, bert, 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, spec = audiopath_and_text[0], audiopath_and_text[1]
        bert, text = audiopath_and_text[2], audiopath_and_text[3]
        wave = self.get_audio(audiopath)
        spec = torch.load(spec)
        text = self.get_text(text)
        bert = self.get_bert(bert)
        return (spec, wave, text, bert)

    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)
        return audio_norm

    def get_bert(self, bert):
        bert_embed = np.load(bert)
        bert_embed = bert_embed.astype(np.float32)
        bert_embed = torch.FloatTensor(bert_embed)
        return bert_embed

    def get_text(self, text):
        text_norm = cleaned_text_to_sequence(text)
        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 __init__(self, return_ids=False):
        self.return_ids = return_ids

    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[0].size(1) for x in batch]), dim=0, descending=True
        )

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

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

        spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
        text_padded = torch.LongTensor(len(batch), max_text_len)
        bert_padded = torch.FloatTensor(len(batch), max_text_len, 256)

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

            spec = row[0]
            spec_padded[i, :, :spec.size(1)] = spec
            spec_lengths[i] = spec.size(1)

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

            text = row[2]
            text_padded[i, :text.size(0)] = text
            text_lengths[i] = text.size(0)

            bert = row[3]
            bert_padded[i, :bert.size(0), :] = bert

        if self.return_ids:
            return text_padded, text_lengths, bert_padded, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing
        return text_padded, text_lengths, bert_padded, spec_padded, spec_lengths, wav_padded, wav_lengths


class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
    """
    Maintain similar input lengths in a batch.
    Length groups are specified by boundaries.
    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.

    It removes samples which are not included in the boundaries.
    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
    """

    def __init__(
        self,
        dataset,
        batch_size,
        boundaries,
        num_replicas=None,
        rank=None,
        shuffle=True,
    ):
        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
        self.lengths = dataset.lengths
        self.batch_size = batch_size
        self.boundaries = boundaries

        self.buckets, self.num_samples_per_bucket = self._create_buckets()
        self.total_size = sum(self.num_samples_per_bucket)
        self.num_samples = self.total_size // self.num_replicas

    def _create_buckets(self):
        buckets = [[] for _ in range(len(self.boundaries) - 1)]
        for i in range(len(self.lengths)):
            length = self.lengths[i]
            idx_bucket = self._bisect(length)
            if idx_bucket != -1:
                buckets[idx_bucket].append(i)

        for i in range(len(buckets) - 1, 0, -1):
            if len(buckets[i]) == 0:
                buckets.pop(i)
                self.boundaries.pop(i + 1)

        num_samples_per_bucket = []
        for i in range(len(buckets)):
            len_bucket = len(buckets[i])
            total_batch_size = self.num_replicas * self.batch_size
            rem = (
                total_batch_size - (len_bucket % total_batch_size)
            ) % total_batch_size
            num_samples_per_bucket.append(len_bucket + rem)
        return buckets, num_samples_per_bucket

    def __iter__(self):
        # deterministically shuffle based on epoch
        g = torch.Generator()
        g.manual_seed(self.epoch)

        indices = []
        if self.shuffle:
            for bucket in self.buckets:
                indices.append(torch.randperm(len(bucket), generator=g).tolist())
        else:
            for bucket in self.buckets:
                indices.append(list(range(len(bucket))))

        batches = []
        for i in range(len(self.buckets)):
            bucket = self.buckets[i]
            len_bucket = len(bucket)
            ids_bucket = indices[i]
            num_samples_bucket = self.num_samples_per_bucket[i]

            # add extra samples to make it evenly divisible
            rem = num_samples_bucket - len_bucket
            ids_bucket = (
                ids_bucket
                + ids_bucket * (rem // len_bucket)
                + ids_bucket[: (rem % len_bucket)]
            )

            # subsample
            ids_bucket = ids_bucket[self.rank :: self.num_replicas]

            # batching
            for j in range(len(ids_bucket) // self.batch_size):
                batch = [
                    bucket[idx]
                    for idx in ids_bucket[
                        j * self.batch_size : (j + 1) * self.batch_size
                    ]
                ]
                batches.append(batch)

        if self.shuffle:
            batch_ids = torch.randperm(len(batches), generator=g).tolist()
            batches = [batches[i] for i in batch_ids]
        self.batches = batches

        assert len(self.batches) * self.batch_size == self.num_samples
        return iter(self.batches)

    def _bisect(self, x, lo=0, hi=None):
        if hi is None:
            hi = len(self.boundaries) - 1

        if hi > lo:
            mid = (hi + lo) // 2
            if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
                return mid
            elif x <= self.boundaries[mid]:
                return self._bisect(x, lo, mid)
            else:
                return self._bisect(x, mid + 1, hi)
        else:
            return -1

    def __len__(self):
        return self.num_samples // self.batch_size