|
|
|
|
|
|
|
|
|
|
|
|
| import math
|
| import os
|
| import random
|
| import torch
|
| import torch.utils.data
|
| import numpy as np
|
| from librosa.util import normalize
|
| from scipy.io.wavfile import read
|
| from librosa.filters import mel as librosa_mel_fn
|
| import pathlib
|
| from tqdm import tqdm
|
|
|
| MAX_WAV_VALUE = 32767.0
|
|
|
|
|
| def load_wav(full_path, sr_target):
|
| sampling_rate, data = read(full_path)
|
| if sampling_rate != sr_target:
|
| raise RuntimeError(
|
| f"Sampling rate of the file {full_path} is {sampling_rate} Hz, but the model requires {sr_target} Hz"
|
| )
|
| return data, sampling_rate
|
|
|
|
|
| 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):
|
| return dynamic_range_compression_torch(magnitudes)
|
|
|
|
|
| def spectral_de_normalize_torch(magnitudes):
|
| return dynamic_range_decompression_torch(magnitudes)
|
|
|
|
|
| mel_basis_cache = {}
|
| hann_window_cache = {}
|
|
|
|
|
| def mel_spectrogram(
|
| y: torch.Tensor,
|
| n_fft: int,
|
| num_mels: int,
|
| sampling_rate: int,
|
| hop_size: int,
|
| win_size: int,
|
| fmin: int,
|
| fmax: int = None,
|
| center: bool = False,
|
| ) -> torch.Tensor:
|
| """
|
| Calculate the mel spectrogram of an input signal.
|
| This function uses slaney norm for the librosa mel filterbank (using librosa.filters.mel) and uses Hann window for STFT (using torch.stft).
|
|
|
| Args:
|
| y (torch.Tensor): Input signal.
|
| n_fft (int): FFT size.
|
| num_mels (int): Number of mel bins.
|
| sampling_rate (int): Sampling rate of the input signal.
|
| hop_size (int): Hop size for STFT.
|
| win_size (int): Window size for STFT.
|
| fmin (int): Minimum frequency for mel filterbank.
|
| fmax (int): Maximum frequency for mel filterbank. If None, defaults to half the sampling rate (fmax = sr / 2.0) inside librosa_mel_fn
|
| center (bool): Whether to pad the input to center the frames. Default is False.
|
|
|
| Returns:
|
| torch.Tensor: Mel spectrogram.
|
| """
|
| if torch.min(y) < -1.0:
|
| print(f"[WARNING] Min value of input waveform signal is {torch.min(y)}")
|
| if torch.max(y) > 1.0:
|
| print(f"[WARNING] Max value of input waveform signal is {torch.max(y)}")
|
|
|
| device = y.device
|
| key = f"{n_fft}_{num_mels}_{sampling_rate}_{hop_size}_{win_size}_{fmin}_{fmax}_{device}"
|
|
|
| if key not in mel_basis_cache:
|
| mel = librosa_mel_fn(
|
| sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
|
| )
|
| mel_basis_cache[key] = torch.from_numpy(mel).float().to(device)
|
| hann_window_cache[key] = torch.hann_window(win_size).to(device)
|
|
|
| mel_basis = mel_basis_cache[key]
|
| hann_window = hann_window_cache[key]
|
|
|
| padding = (n_fft - hop_size) // 2
|
| y = torch.nn.functional.pad(
|
| y.unsqueeze(1), (padding, padding), mode="reflect"
|
| ).squeeze(1)
|
|
|
| spec = torch.stft(
|
| y,
|
| n_fft,
|
| hop_length=hop_size,
|
| win_length=win_size,
|
| window=hann_window,
|
| center=center,
|
| pad_mode="reflect",
|
| normalized=False,
|
| onesided=True,
|
| return_complex=True,
|
| )
|
| spec = torch.sqrt(torch.view_as_real(spec).pow(2).sum(-1) + 1e-9)
|
|
|
| mel_spec = torch.matmul(mel_basis, spec)
|
| mel_spec = spectral_normalize_torch(mel_spec)
|
|
|
| return mel_spec
|
|
|
|
|
| def get_mel_spectrogram(wav, h):
|
| """
|
| Generate mel spectrogram from a waveform using given hyperparameters.
|
|
|
| Args:
|
| wav (torch.Tensor): Input waveform.
|
| h: Hyperparameters object with attributes n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax.
|
|
|
| Returns:
|
| torch.Tensor: Mel spectrogram.
|
| """
|
| return mel_spectrogram(
|
| wav,
|
| h.n_fft,
|
| h.num_mels,
|
| h.sampling_rate,
|
| h.hop_size,
|
| h.win_size,
|
| h.fmin,
|
| h.fmax,
|
| )
|
|
|
|
|
| def get_dataset_filelist(a):
|
| training_files = []
|
| validation_files = []
|
| list_unseen_validation_files = []
|
|
|
| with open(a.input_training_file, "r", encoding="utf-8") as fi:
|
| training_files = [
|
| os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav")
|
| for x in fi.read().split("\n")
|
| if len(x) > 0
|
| ]
|
| print(f"first training file: {training_files[0]}")
|
|
|
| with open(a.input_validation_file, "r", encoding="utf-8") as fi:
|
| validation_files = [
|
| os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav")
|
| for x in fi.read().split("\n")
|
| if len(x) > 0
|
| ]
|
| print(f"first validation file: {validation_files[0]}")
|
|
|
| for i in range(len(a.list_input_unseen_validation_file)):
|
| with open(a.list_input_unseen_validation_file[i], "r", encoding="utf-8") as fi:
|
| unseen_validation_files = [
|
| os.path.join(a.list_input_unseen_wavs_dir[i], x.split("|")[0] + ".wav")
|
| for x in fi.read().split("\n")
|
| if len(x) > 0
|
| ]
|
| print(
|
| f"first unseen {i}th validation fileset: {unseen_validation_files[0]}"
|
| )
|
| list_unseen_validation_files.append(unseen_validation_files)
|
|
|
| return training_files, validation_files, list_unseen_validation_files
|
|
|
|
|
| class MelDataset(torch.utils.data.Dataset):
|
| def __init__(
|
| self,
|
| training_files,
|
| hparams,
|
| segment_size,
|
| n_fft,
|
| num_mels,
|
| hop_size,
|
| win_size,
|
| sampling_rate,
|
| fmin,
|
| fmax,
|
| split=True,
|
| shuffle=True,
|
| n_cache_reuse=1,
|
| device=None,
|
| fmax_loss=None,
|
| fine_tuning=False,
|
| base_mels_path=None,
|
| is_seen=True,
|
| ):
|
| self.audio_files = training_files
|
| random.seed(1234)
|
| if shuffle:
|
| random.shuffle(self.audio_files)
|
| self.hparams = hparams
|
| self.is_seen = is_seen
|
| if self.is_seen:
|
| self.name = pathlib.Path(self.audio_files[0]).parts[0]
|
| else:
|
| self.name = "-".join(pathlib.Path(self.audio_files[0]).parts[:2]).strip("/")
|
|
|
| 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.fmax_loss = fmax_loss
|
| self.cached_wav = None
|
| self.n_cache_reuse = n_cache_reuse
|
| self._cache_ref_count = 0
|
| self.device = device
|
| self.fine_tuning = fine_tuning
|
| self.base_mels_path = base_mels_path
|
|
|
| print("[INFO] checking dataset integrity...")
|
| for i in tqdm(range(len(self.audio_files))):
|
| assert os.path.exists(
|
| self.audio_files[i]
|
| ), f"{self.audio_files[i]} not found"
|
|
|
| def __getitem__(self, index):
|
| filename = self.audio_files[index]
|
| if self._cache_ref_count == 0:
|
| audio, sampling_rate = load_wav(filename, self.sampling_rate)
|
| audio = audio / MAX_WAV_VALUE
|
| if not self.fine_tuning:
|
| audio = normalize(audio) * 0.95
|
| self.cached_wav = audio
|
| if sampling_rate != self.sampling_rate:
|
| raise ValueError(
|
| f"{sampling_rate} SR doesn't match target {self.sampling_rate} SR"
|
| )
|
| self._cache_ref_count = self.n_cache_reuse
|
| else:
|
| audio = self.cached_wav
|
| self._cache_ref_count -= 1
|
|
|
| audio = torch.FloatTensor(audio)
|
| audio = audio.unsqueeze(0)
|
|
|
| if not self.fine_tuning:
|
| 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]
|
| 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=False,
|
| )
|
| else:
|
|
|
| if (audio.size(1) % self.hop_size) != 0:
|
| audio = audio[:, : -(audio.size(1) % self.hop_size)]
|
| mel = mel_spectrogram(
|
| audio,
|
| self.n_fft,
|
| self.num_mels,
|
| self.sampling_rate,
|
| self.hop_size,
|
| self.win_size,
|
| self.fmin,
|
| self.fmax,
|
| center=False,
|
| )
|
| assert (
|
| audio.shape[1] == mel.shape[2] * self.hop_size
|
| ), f"audio shape {audio.shape} mel shape {mel.shape}"
|
|
|
| else:
|
| mel = np.load(
|
| os.path.join(
|
| self.base_mels_path,
|
| os.path.splitext(os.path.split(filename)[-1])[0] + ".npy",
|
| )
|
| )
|
| mel = torch.from_numpy(mel)
|
|
|
| if len(mel.shape) < 3:
|
| mel = mel.unsqueeze(0)
|
|
|
| if self.split:
|
| frames_per_seg = math.ceil(self.segment_size / self.hop_size)
|
|
|
| if audio.size(1) >= self.segment_size:
|
| mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1)
|
| mel = mel[:, :, mel_start : mel_start + frames_per_seg]
|
| audio = audio[
|
| :,
|
| mel_start
|
| * self.hop_size : (mel_start + frames_per_seg)
|
| * self.hop_size,
|
| ]
|
| else:
|
| mel = torch.nn.functional.pad(
|
| mel, (0, frames_per_seg - mel.size(2)), "constant"
|
| )
|
| audio = torch.nn.functional.pad(
|
| audio, (0, self.segment_size - audio.size(1)), "constant"
|
| )
|
|
|
| mel_loss = mel_spectrogram(
|
| audio,
|
| self.n_fft,
|
| self.num_mels,
|
| self.sampling_rate,
|
| self.hop_size,
|
| self.win_size,
|
| self.fmin,
|
| self.fmax_loss,
|
| center=False,
|
| )
|
|
|
| return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze())
|
|
|
| def __len__(self):
|
| return len(self.audio_files)
|
|
|
