webui/modules/implementations/rvc/data_utils.py

import os, traceback
import numpy as np
import scipy
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn


mel_basis = {}
hann_window = {}


def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
    """Convert waveform into Linear-frequency Linear-amplitude spectrogram.

    Args:
        y             :: (B, T) - Audio waveforms
        n_fft
        sampling_rate
        hop_size
        win_size
        center
    Returns:
        :: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
    """
    # Validation
    # if torch.min(y) < -1.07:
    #     print("min value is ", torch.min(y))
    # if torch.max(y) > 1.07:
    #     print("max value is ", torch.max(y))

    # Window - Cache if needed
    global hann_window
    dtype_device = str(y.dtype) + "_" + str(y.device)
    wnsize_dtype_device = str(win_size) + "_" + dtype_device
    if wnsize_dtype_device not in hann_window:
        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
            dtype=y.dtype, device=y.device
        )

    # Padding
    y = torch.nn.functional.pad(
        y.unsqueeze(1),
        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
        mode="reflect",
    )
    y = y.squeeze(1)

    # Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
    spec = torch.stft(
        y,
        n_fft,
        hop_length=hop_size,
        win_length=win_size,
        window=hann_window[wnsize_dtype_device],
        center=center,
        pad_mode="reflect",
        normalized=False,
        onesided=True,
        return_complex=False,
    )

    # Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
    return spec


def load_wav_to_torch(full_path):
    sampling_rate, data = scipy.io.wavfile.read(full_path)
    return torch.FloatTensor(data.astype(np.float32)), sampling_rate


# def load_filepaths_and_text(filename, split="|"):
#     with open(filename, encoding="utf-8") as f:
#         filepaths_and_text = [line.strip().split(split) for line in f]
#     return filepaths_and_text


class TextAudioLoaderMultiNSFsid(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.audiopaths_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.min_text_len = getattr(hparams, "min_text_len", 1)
        self.max_text_len = getattr(hparams, "max_text_len", 5000)
        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, pitch, pitchf, dv 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, pitch, pitchf, dv])
                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
        self.audiopaths_and_text = audiopaths_and_text_new
        self.lengths = lengths

    def get_sid(self, sid):
        sid = torch.LongTensor([int(sid)])
        return sid

    def get_audio_text_pair(self, audiopath_and_text):
        # separate filename and text
        file = audiopath_and_text[0]
        phone = audiopath_and_text[1]
        pitch = audiopath_and_text[2]
        pitchf = audiopath_and_text[3]
        dv = audiopath_and_text[4]

        phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf)
        spec, wav = self.get_audio(file)
        dv = self.get_sid(dv)

        len_phone = phone.size()[0]
        len_spec = spec.size()[-1]
        # print(123,phone.shape,pitch.shape,spec.shape)
        if len_phone != len_spec:
            len_min = min(len_phone, len_spec)
            # amor
            len_wav = len_min * self.hop_length

            spec = spec[:, :len_min]
            wav = wav[:, :len_wav]

            phone = phone[:len_min, :]
            pitch = pitch[:len_min]
            pitchf = pitchf[:len_min]

        return (spec, wav, phone, pitch, pitchf, dv)

    def get_labels(self, phone, pitch, pitchf):
        phone = np.load(phone)
        phone = np.repeat(phone, 2, axis=0)
        pitch = np.load(pitch)
        pitchf = np.load(pitchf)
        n_num = min(phone.shape[0], 900)  # DistributedBucketSampler
        # print(234,phone.shape,pitch.shape)
        phone = phone[:n_num, :]
        pitch = pitch[:n_num]
        pitchf = pitchf[:n_num]
        phone = torch.FloatTensor(phone)
        pitch = torch.LongTensor(pitch)
        pitchf = torch.FloatTensor(pitchf)
        return phone, pitch, pitchf

    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
        #        audio_norm = audio / self.max_wav_value
        #        audio_norm = audio / np.abs(audio).max()

        audio_norm = audio_norm.unsqueeze(0)
        spec_filename = filename.replace(".wav", ".spec.pt")
        if os.path.exists(spec_filename):
            try:
                spec = torch.load(spec_filename)
            except:
                print(spec_filename, traceback.format_exc())
                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, _use_new_zipfile_serialization=False)
        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, _use_new_zipfile_serialization=False)
        return spec, audio_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 TextAudioCollateMultiNSFsid:
    """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_wave_len = max([x[1].size(1) for x in batch])
        spec_lengths = torch.LongTensor(len(batch))
        wave_lengths = torch.LongTensor(len(batch))
        spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
        wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
        spec_padded.zero_()
        wave_padded.zero_()

        max_phone_len = max([x[2].size(0) for x in batch])
        phone_lengths = torch.LongTensor(len(batch))
        phone_padded = torch.FloatTensor(
            len(batch), max_phone_len, batch[0][2].shape[1]
        )  # (spec, wav, phone, pitch)
        pitch_padded = torch.LongTensor(len(batch), max_phone_len)
        pitchf_padded = torch.FloatTensor(len(batch), max_phone_len)
        phone_padded.zero_()
        pitch_padded.zero_()
        pitchf_padded.zero_()
        # dv = torch.FloatTensor(len(batch), 256)#gin=256
        sid = torch.LongTensor(len(batch))

        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)

            wave = row[1]
            wave_padded[i, :, : wave.size(1)] = wave
            wave_lengths[i] = wave.size(1)

            phone = row[2]
            phone_padded[i, : phone.size(0), :] = phone
            phone_lengths[i] = phone.size(0)

            pitch = row[3]
            pitch_padded[i, : pitch.size(0)] = pitch
            pitchf = row[4]
            pitchf_padded[i, : pitchf.size(0)] = pitchf

            # dv[i] = row[5]
            sid[i] = row[5]

        return (
            phone_padded,
            phone_lengths,
            pitch_padded,
            pitchf_padded,
            spec_padded,
            spec_lengths,
            wave_padded,
            wave_lengths,
            # dv
            sid,
        )


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.audiopaths_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.min_text_len = getattr(hparams, "min_text_len", 1)
        self.max_text_len = getattr(hparams, "max_text_len", 5000)
        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, dv 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, dv])
                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
        self.audiopaths_and_text = audiopaths_and_text_new
        self.lengths = lengths

    def get_sid(self, sid):
        sid = torch.LongTensor([int(sid)])
        return sid

    def get_audio_text_pair(self, audiopath_and_text):
        # separate filename and text
        file = audiopath_and_text[0]
        phone = audiopath_and_text[1]
        dv = audiopath_and_text[2]

        phone = self.get_labels(phone)
        spec, wav = self.get_audio(file)
        dv = self.get_sid(dv)

        len_phone = phone.size()[0]
        len_spec = spec.size()[-1]
        if len_phone != len_spec:
            len_min = min(len_phone, len_spec)
            len_wav = len_min * self.hop_length
            spec = spec[:, :len_min]
            wav = wav[:, :len_wav]
            phone = phone[:len_min, :]
        return (spec, wav, phone, dv)

    def get_labels(self, phone):
        phone = np.load(phone)
        phone = np.repeat(phone, 2, axis=0)
        n_num = min(phone.shape[0], 900)  # DistributedBucketSampler
        phone = phone[:n_num, :]
        phone = torch.FloatTensor(phone)
        return phone

    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
        #        audio_norm = audio / self.max_wav_value
        #        audio_norm = audio / np.abs(audio).max()

        audio_norm = audio_norm.unsqueeze(0)
        spec_filename = filename.replace(".wav", ".spec.pt")
        if os.path.exists(spec_filename):
            try:
                spec = torch.load(spec_filename)
            except:
                print(spec_filename, traceback.format_exc())
                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, _use_new_zipfile_serialization=False)
        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, _use_new_zipfile_serialization=False)
        return spec, audio_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_wave_len = max([x[1].size(1) for x in batch])
        spec_lengths = torch.LongTensor(len(batch))
        wave_lengths = torch.LongTensor(len(batch))
        spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
        wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
        spec_padded.zero_()
        wave_padded.zero_()

        max_phone_len = max([x[2].size(0) for x in batch])
        phone_lengths = torch.LongTensor(len(batch))
        phone_padded = torch.FloatTensor(
            len(batch), max_phone_len, batch[0][2].shape[1]
        )
        phone_padded.zero_()
        sid = torch.LongTensor(len(batch))

        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)

            wave = row[1]
            wave_padded[i, :, : wave.size(1)] = wave
            wave_lengths[i] = wave.size(1)

            phone = row[2]
            phone_padded[i, : phone.size(0), :] = phone
            phone_lengths[i] = phone.size(0)

            sid[i] = row[3]

        return (
            phone_padded,
            phone_lengths,
            spec_padded,
            spec_lengths,
            wave_padded,
            wave_lengths,
            sid,
        )


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, -1, -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

def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
    """
    PARAMS
    ------
    C: compression factor
    """
    return torch.log(torch.clamp(x, min=clip_val) * C)


def spectral_normalize_torch(magnitudes):
    return dynamic_range_compression_torch(magnitudes)


def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
    """Convert waveform into Linear-frequency Linear-amplitude spectrogram.

    Args:
        y             :: (B, T) - Audio waveforms
        n_fft
        sampling_rate
        hop_size
        win_size
        center
    Returns:
        :: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
    """
    # Validation
    if torch.min(y) < -1.07:
        print("min value is ", torch.min(y))
    if torch.max(y) > 1.07:
        print("max value is ", torch.max(y))

    # Window - Cache if needed
    global hann_window
    dtype_device = str(y.dtype) + "_" + str(y.device)
    wnsize_dtype_device = str(win_size) + "_" + dtype_device
    if wnsize_dtype_device not in hann_window:
        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
            dtype=y.dtype, device=y.device
        )

    # Padding
    y = torch.nn.functional.pad(
        y.unsqueeze(1),
        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
        mode="reflect",
    )
    y = y.squeeze(1)

    # Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
    spec = torch.stft(
        y,
        n_fft,
        hop_length=hop_size,
        win_length=win_size,
        window=hann_window[wnsize_dtype_device],
        center=center,
        pad_mode="reflect",
        normalized=False,
        onesided=True,
        return_complex=False,
    )

    # Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
    return spec


def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
    # MelBasis - Cache if needed
    global mel_basis
    dtype_device = str(spec.dtype) + "_" + str(spec.device)
    fmax_dtype_device = str(fmax) + "_" + dtype_device
    if fmax_dtype_device not in mel_basis:
        mel = librosa_mel_fn(
            sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
        )
        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
            dtype=spec.dtype, device=spec.device
        )

    # Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame)
    melspec = torch.matmul(mel_basis[fmax_dtype_device], spec)
    melspec = spectral_normalize_torch(melspec)
    return melspec


def mel_spectrogram_torch(
    y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
):
    """Convert waveform into Mel-frequency Log-amplitude spectrogram.

    Args:
        y       :: (B, T)           - Waveforms
    Returns:
        melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram
    """
    # Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame)
    spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center)

    # Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame)
    melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax)

    return melspec