refactor: rename canto-backend → backend, canto-frontend → frontend
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
354
backend/indextts/s2mel/modules/bigvgan/meldataset.py
Normal file
354
backend/indextts/s2mel/modules/bigvgan/meldataset.py
Normal file
@@ -0,0 +1,354 @@
|
||||
# Copyright (c) 2024 NVIDIA CORPORATION.
|
||||
# Licensed under the MIT license.
|
||||
|
||||
# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
|
||||
# LICENSE is in incl_licenses directory.
|
||||
|
||||
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 # NOTE: 32768.0 -1 to prevent int16 overflow (results in popping sound in corner cases)
|
||||
|
||||
|
||||
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: # Validation step
|
||||
# Match audio length to self.hop_size * n for evaluation
|
||||
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)
|
||||
Reference in New Issue
Block a user