dataset.py

import math
import os
import random
import torch
import torch.utils.data
import numpy as np
from librosa.util import normalize
from librosa.filters import mel as librosa_mel_fn
import librosa
import torchaudio
import torch.nn as nn
from pghipy import pghi


def load_wav(full_path, sample_rate):
    data, _ = librosa.load(full_path, sr=sample_rate, mono=True)
    return data


def dynamic_range_compression(x, C=1, clip_val=1e-5):
    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)


def dynamic_range_decompression(x, C=1):
    return np.exp(x) / C


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


def dynamic_range_decompression_torch(x, C=1):
    return torch.exp(x) / C


def spectral_normalize_torch(magnitudes):
    output = dynamic_range_compression_torch(magnitudes)
    return output


def spectral_de_normalize_torch(magnitudes):
    output = dynamic_range_decompression_torch(magnitudes)
    return output


mel_window = {}
inv_mel_window = {}


def param_string(sampling_rate, n_fft, num_mels, fmin, fmax, win_size, device):
    return f"{sampling_rate}-{n_fft}-{num_mels}-{fmin}-{fmax}-{win_size}-{device}"


def mel_spectrogram(
    y,
    n_fft,
    num_mels,
    sampling_rate,
    hop_size,
    win_size,
    fmin,
    fmax,
    center=True,
    in_dataset=False,
):
    global mel_window
    device = torch.device("cpu") if in_dataset else y.device
    ps = param_string(sampling_rate, n_fft, num_mels, fmin, fmax, win_size, device)
    if ps in mel_window:
        mel_basis, hann_window = mel_window[ps]
        # print(mel_basis, hann_window)
        # mel_basis, hann_window = mel_basis.to(y.device), hann_window.to(y.device)
    else:
        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
        mel_basis = torch.from_numpy(mel).float().to(device)
        hann_window = torch.hann_window(win_size).to(device)
        mel_window[ps] = (mel_basis.clone(), hann_window.clone())

    spec = torch.stft(
        y.to(device),
        n_fft,
        hop_length=hop_size,
        win_length=win_size,
        window=hann_window.to(device),
        center=True,
        return_complex=True,
    )

    spec = mel_basis.to(device) @ spec.abs()
    spec = spectral_normalize_torch(spec)

    return spec  # [batch_size,n_fft/2+1,frames]


def inverse_mel(
    mel,
    n_fft,
    num_mels,
    sampling_rate,
    hop_size,
    win_size,
    fmin,
    fmax,
    in_dataset=False,
):
    global inv_mel_window, mel_window
    device = torch.device("cpu") if in_dataset else mel.device
    ps = param_string(sampling_rate, n_fft, num_mels, fmin, fmax, win_size, device)
    if ps in inv_mel_window:
        inv_basis = inv_mel_window[ps]
    else:
        if ps in mel_window:
            mel_basis, _ = mel_window[ps]
        else:
            mel_np = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
            mel_basis = torch.from_numpy(mel_np).float().to(device)
            hann_window = torch.hann_window(win_size).to(device)
            mel_window[ps] = (mel_basis.clone(), hann_window.clone())
        inv_basis = mel_basis.pinverse()
        inv_mel_window[ps] = inv_basis.clone()
    return inv_basis.to(device) @ spectral_de_normalize_torch(mel.to(device))


def amp_pha_specturm(y, n_fft, hop_size, win_size):
    hann_window = torch.hann_window(win_size).to(y.device)

    stft_spec = torch.stft(
        y,
        n_fft,
        hop_length=hop_size,
        win_length=win_size,
        window=hann_window,
        center=True,
        return_complex=True,
    )  # [batch_size, n_fft//2+1, frames, 2]

    log_amplitude = torch.log(
        stft_spec.abs() + 1e-5
    )  # [batch_size, n_fft//2+1, frames]
    phase = stft_spec.angle()  # [batch_size, n_fft//2+1, frames]

    return log_amplitude, phase, stft_spec.real, stft_spec.imag


def get_dataset_filelist(input_training_wav_list, input_validation_wav_list):
    training_files = []
    filelist = os.listdir(input_training_wav_list)
    for files in filelist:
        src = os.path.join(input_training_wav_list, files)
        training_files.append(src)

    validation_files = []
    filelist = os.listdir(input_validation_wav_list)
    for files in filelist:
        src = os.path.join(input_validation_wav_list, files)
        validation_files.append(src)

    return training_files, validation_files


class Dataset(torch.utils.data.Dataset):
    def __init__(
        self,
        training_files,
        segment_size,
        n_fft,
        num_mels,
        hop_size,
        win_size,
        sampling_rate,
        fmin,
        fmax,
        meloss,
        split=True,
        shuffle=True,
        n_cache_reuse=1,
        device=None,
        inv_mel=False,
        use_pghi=False,
    ):
        self.audio_files = training_files
        random.seed(1234)
        if shuffle:
            random.shuffle(self.audio_files)
        self.segment_size = segment_size
        self.sampling_rate = sampling_rate
        self.split = split
        self.n_fft = n_fft
        self.num_mels = num_mels
        self.hop_size = hop_size
        self.win_size = win_size
        self.fmin = fmin
        self.fmax = fmax
        self.cached_wav = None
        self.n_cache_reuse = n_cache_reuse
        self._cache_ref_count = 0
        self.device = device
        self.meloss = meloss
        self.inv_mel = inv_mel
        self.pghi = use_pghi

    def __getitem__(self, index):
        filename = self.audio_files[index]
        if self._cache_ref_count == 0:
            audio = load_wav(filename, self.sampling_rate)
            self.cached_wav = audio
            self._cache_ref_count = self.n_cache_reuse
        else:
            audio = self.cached_wav
            self._cache_ref_count -= 1

        audio = torch.FloatTensor(audio)  # [T]
        audio = audio.unsqueeze(0)  # [1,T]

        if self.split:
            if audio.size(1) >= self.segment_size:
                max_audio_start = audio.size(1) - self.segment_size
                audio_start = random.randint(0, max_audio_start)
                audio = audio[:, audio_start : audio_start + self.segment_size]  # [1,T]
            else:
                audio = torch.nn.functional.pad(
                    audio, (0, self.segment_size - audio.size(1)), "constant"
                )

        mel = mel_spectrogram(
            audio,
            self.n_fft,
            self.num_mels,
            self.sampling_rate,
            self.hop_size,
            self.win_size,
            self.fmin,
            self.fmax,
            center=True,
            in_dataset=True,
        )
        meloss1 = mel_spectrogram(
            audio,
            self.n_fft,
            self.num_mels,
            self.sampling_rate,
            self.hop_size,
            self.win_size,
            self.fmin,
            self.meloss,
            center=True,
            in_dataset=True,
        )
        log_amplitude, phase, rea, imag = amp_pha_specturm(
            audio, self.n_fft, self.hop_size, self.win_size
        )  # [1,n_fft/2+1,frames]
        inv_mel = (
            inverse_mel(
                mel,
                self.n_fft,
                self.num_mels,
                self.sampling_rate,
                self.hop_size,
                self.win_size,
                self.fmin,
                self.fmax,
            )
            .abs()
            .clamp_min(1e-5)
            .squeeze()
            if self.inv_mel
            else torch.tensor([0])
        )
        if self.pghi:
            pghid = torch.tensor(
                pghi(inv_mel.squeeze(0).T.numpy(), self.win_size, self.hop_size)
            ).T
            pghid = torch.polar(torch.ones_like(pghid), pghid).angle()
        else:
            pghid = torch.tensor([0])

        # print(pghid)
        return (
            mel.squeeze(),
            log_amplitude.squeeze(),
            phase.squeeze(),
            rea.squeeze(),
            imag.squeeze(),
            audio.squeeze(0),
            meloss1.squeeze(),
            inv_mel,
            pghid,
        )

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