Medix-AI/coqui-tts
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coqui-tts
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Update import statements

This commit is contained in:
Eren Gölge 2022-04-19 09:16:03 +02:00 committed by Eren G??lge
parent 9f8d86b716
commit 5f9d559419
29 changed files with 31 additions and 363 deletions
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2
TTS/encoder/models/resnet.py
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@ -1,7 +1,7 @@
import torch
from torch import nn
# from TTS.utils.audio import TorchSTFT
# from TTS.utils.audio.torch_transforms import TorchSTFT
from TTS.encoder.models.base_encoder import BaseEncoder

4
TTS/tts/models/vits.py
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@ -155,10 +155,10 @@ def spec_to_mel(spec, n_fft, num_mels, sample_rate, fmin, fmax):
def wav_to_mel(y, n_fft, num_mels, sample_rate, hop_length, win_length, fmin, fmax, center=False):
"""
Args Shapes:
- y : :math:`[B, 1, T]`
- y : :math:`[B, 1, T_y]`
Return Shapes:
- spec : :math:`[B,C,T]`
- spec : :math:`[B,C,T_spec]`
"""
y = y.squeeze(1)

1
TTS/utils/audio/__init__.py Normal file
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@ -0,0 +1 @@
from TTS.utils.audio.processor import AudioProcessor

335
TTS/utils/audio.py → TTS/utils/audio/processor.py
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@ -1,177 +1,9 @@
from typing import Dict, Tuple
import librosa
import numpy as np
import pyworld as pw
import scipy.io.wavfile
import scipy.signal
import soundfile as sf
import torch
from torch import nn
from TTS.tts.utils.helpers import StandardScaler
class TorchSTFT(nn.Module): # pylint: disable=abstract-method
"""Some of the audio processing funtions using Torch for faster batch processing.
TODO: Merge this with audio.py
Args:
n_fft (int):
FFT window size for STFT.
hop_length (int):
number of frames between STFT columns.
win_length (int, optional):
STFT window length.
pad_wav (bool, optional):
If True pad the audio with (n_fft - hop_length) / 2). Defaults to False.
window (str, optional):
The name of a function to create a window tensor that is applied/multiplied to each frame/window. Defaults to "hann_window"
sample_rate (int, optional):
target audio sampling rate. Defaults to None.
mel_fmin (int, optional):
minimum filter frequency for computing melspectrograms. Defaults to None.
mel_fmax (int, optional):
maximum filter frequency for computing melspectrograms. Defaults to None.
n_mels (int, optional):
number of melspectrogram dimensions. Defaults to None.
use_mel (bool, optional):
If True compute the melspectrograms otherwise. Defaults to False.
do_amp_to_db_linear (bool, optional):
enable/disable amplitude to dB conversion of linear spectrograms. Defaults to False.
spec_gain (float, optional):
gain applied when converting amplitude to DB. Defaults to 1.0.
power (float, optional):
Exponent for the magnitude spectrogram, e.g., 1 for energy, 2 for power, etc. Defaults to None.
use_htk (bool, optional):
Use HTK formula in mel filter instead of Slaney.
mel_norm (None, 'slaney', or number, optional):
If 'slaney', divide the triangular mel weights by the width of the mel band
(area normalization).
If numeric, use `librosa.util.normalize` to normalize each filter by to unit l_p norm.
See `librosa.util.normalize` for a full description of supported norm values
(including `+-np.inf`).
Otherwise, leave all the triangles aiming for a peak value of 1.0. Defaults to "slaney".
"""
def __init__(
self,
n_fft,
hop_length,
win_length,
pad_wav=False,
window="hann_window",
sample_rate=None,
mel_fmin=0,
mel_fmax=None,
n_mels=80,
use_mel=False,
do_amp_to_db=False,
spec_gain=1.0,
power=None,
use_htk=False,
mel_norm="slaney",
):
super().__init__()
self.n_fft = n_fft
self.hop_length = hop_length
self.win_length = win_length
self.pad_wav = pad_wav
self.sample_rate = sample_rate
self.mel_fmin = mel_fmin
self.mel_fmax = mel_fmax
self.n_mels = n_mels
self.use_mel = use_mel
self.do_amp_to_db = do_amp_to_db
self.spec_gain = spec_gain
self.power = power
self.use_htk = use_htk
self.mel_norm = mel_norm
self.window = nn.Parameter(getattr(torch, window)(win_length), requires_grad=False)
self.mel_basis = None
if use_mel:
self._build_mel_basis()
def __call__(self, x):
"""Compute spectrogram frames by torch based stft.
Args:
x (Tensor): input waveform
Returns:
Tensor: spectrogram frames.
Shapes:
x: [B x T] or [:math:`[B, 1, T]`]
"""
if x.ndim == 2:
x = x.unsqueeze(1)
if self.pad_wav:
padding = int((self.n_fft - self.hop_length) / 2)
x = torch.nn.functional.pad(x, (padding, padding), mode="reflect")
# B x D x T x 2
o = torch.stft(
x.squeeze(1),
self.n_fft,
self.hop_length,
self.win_length,
self.window,
center=True,
pad_mode="reflect", # compatible with audio.py
normalized=False,
onesided=True,
return_complex=False,
)
M = o[:, :, :, 0]
P = o[:, :, :, 1]
S = torch.sqrt(torch.clamp(M**2 + P**2, min=1e-8))
if self.power is not None:
S = S**self.power
if self.use_mel:
S = torch.matmul(self.mel_basis.to(x), S)
if self.do_amp_to_db:
S = self._amp_to_db(S, spec_gain=self.spec_gain)
return S
def _build_mel_basis(self):
mel_basis = librosa.filters.mel(
self.sample_rate,
self.n_fft,
n_mels=self.n_mels,
fmin=self.mel_fmin,
fmax=self.mel_fmax,
htk=self.use_htk,
norm=self.mel_norm,
)
self.mel_basis = torch.from_numpy(mel_basis).float()
@staticmethod
def _amp_to_db(x, spec_gain=1.0):
return torch.log(torch.clamp(x, min=1e-5) * spec_gain)
@staticmethod
def _db_to_amp(x, spec_gain=1.0):
return torch.exp(x) / spec_gain
# pylint: disable=too-many-public-methods
@ -398,158 +230,6 @@ class AudioProcessor(object):
return AudioProcessor(verbose=verbose, **config)
### setting up the parameters ###
def _build_mel_basis(
self,
) -> np.ndarray:
"""Build melspectrogram basis.
Returns:
np.ndarray: melspectrogram basis.
"""
if self.mel_fmax is not None:
assert self.mel_fmax <= self.sample_rate // 2
return librosa.filters.mel(
self.sample_rate, self.fft_size, n_mels=self.num_mels, fmin=self.mel_fmin, fmax=self.mel_fmax
)
def _stft_parameters(
self,
) -> Tuple[int, int]:
"""Compute the real STFT parameters from the time values.
Returns:
Tuple[int, int]: hop length and window length for STFT.
"""
factor = self.frame_length_ms / self.frame_shift_ms
assert (factor).is_integer(), " [!] frame_shift_ms should divide frame_length_ms"
hop_length = int(self.frame_shift_ms / 1000.0 * self.sample_rate)
win_length = int(hop_length * factor)
return hop_length, win_length
### normalization ###
def normalize(self, S: np.ndarray) -> np.ndarray:
"""Normalize values into `[0, self.max_norm]` or `[-self.max_norm, self.max_norm]`
Args:
S (np.ndarray): Spectrogram to normalize.
Raises:
RuntimeError: Mean and variance is computed from incompatible parameters.
Returns:
np.ndarray: Normalized spectrogram.
"""
# pylint: disable=no-else-return
S = S.copy()
if self.signal_norm:
# mean-var scaling
if hasattr(self, "mel_scaler"):
if S.shape[0] == self.num_mels:
return self.mel_scaler.transform(S.T).T
elif S.shape[0] == self.fft_size / 2:
return self.linear_scaler.transform(S.T).T
else:
raise RuntimeError(" [!] Mean-Var stats does not match the given feature dimensions.")
# range normalization
S -= self.ref_level_db # discard certain range of DB assuming it is air noise
S_norm = (S - self.min_level_db) / (-self.min_level_db)
if self.symmetric_norm:
S_norm = ((2 * self.max_norm) * S_norm) - self.max_norm
if self.clip_norm:
S_norm = np.clip(
S_norm, -self.max_norm, self.max_norm # pylint: disable=invalid-unary-operand-type
)
return S_norm
else:
S_norm = self.max_norm * S_norm
if self.clip_norm:
S_norm = np.clip(S_norm, 0, self.max_norm)
return S_norm
else:
return S
def denormalize(self, S: np.ndarray) -> np.ndarray:
"""Denormalize spectrogram values.
Args:
S (np.ndarray): Spectrogram to denormalize.
Raises:
RuntimeError: Mean and variance are incompatible.
Returns:
np.ndarray: Denormalized spectrogram.
"""
# pylint: disable=no-else-return
S_denorm = S.copy()
if self.signal_norm:
# mean-var scaling
if hasattr(self, "mel_scaler"):
if S_denorm.shape[0] == self.num_mels:
return self.mel_scaler.inverse_transform(S_denorm.T).T
elif S_denorm.shape[0] == self.fft_size / 2:
return self.linear_scaler.inverse_transform(S_denorm.T).T
else:
raise RuntimeError(" [!] Mean-Var stats does not match the given feature dimensions.")
if self.symmetric_norm:
if self.clip_norm:
S_denorm = np.clip(
S_denorm, -self.max_norm, self.max_norm # pylint: disable=invalid-unary-operand-type
)
S_denorm = ((S_denorm + self.max_norm) * -self.min_level_db / (2 * self.max_norm)) + self.min_level_db
return S_denorm + self.ref_level_db
else:
if self.clip_norm:
S_denorm = np.clip(S_denorm, 0, self.max_norm)
S_denorm = (S_denorm * -self.min_level_db / self.max_norm) + self.min_level_db
return S_denorm + self.ref_level_db
else:
return S_denorm
### Mean-STD scaling ###
def load_stats(self, stats_path: str) -> Tuple[np.array, np.array, np.array, np.array, Dict]:
"""Loading mean and variance statistics from a `npy` file.
Args:
stats_path (str): Path to the `npy` file containing
Returns:
Tuple[np.array, np.array, np.array, np.array, Dict]: loaded statistics and the config used to
compute them.
"""
stats = np.load(stats_path, allow_pickle=True).item() # pylint: disable=unexpected-keyword-arg
mel_mean = stats["mel_mean"]
mel_std = stats["mel_std"]
linear_mean = stats["linear_mean"]
linear_std = stats["linear_std"]
stats_config = stats["audio_config"]
# check all audio parameters used for computing stats
skip_parameters = ["griffin_lim_iters", "stats_path", "do_trim_silence", "ref_level_db", "power"]
for key in stats_config.keys():
if key in skip_parameters:
continue
if key not in ["sample_rate", "trim_db"]:
assert (
stats_config[key] == self.__dict__[key]
), f" [!] Audio param {key} does not match the value used for computing mean-var stats. {stats_config[key]} vs {self.__dict__[key]}"
return mel_mean, mel_std, linear_mean, linear_std, stats_config
# pylint: disable=attribute-defined-outside-init
def setup_scaler(
self, mel_mean: np.ndarray, mel_std: np.ndarray, linear_mean: np.ndarray, linear_std: np.ndarray
) -> None:
"""Initialize scaler objects used in mean-std normalization.
Args:
mel_mean (np.ndarray): Mean for melspectrograms.
mel_std (np.ndarray): STD for melspectrograms.
linear_mean (np.ndarray): Mean for full scale spectrograms.
linear_std (np.ndarray): STD for full scale spectrograms.
"""
self.mel_scaler = StandardScaler()
self.mel_scaler.set_stats(mel_mean, mel_std)
self.linear_scaler = StandardScaler()
self.linear_scaler.set_stats(linear_mean, linear_std)
### DB and AMP conversion ###
# pylint: disable=no-self-use
@ -737,8 +417,7 @@ class AudioProcessor(object):
Examples:
>>> WAV_FILE = filename = librosa.util.example_audio_file()
>>> from TTS.config import BaseAudioConfig
>>> from TTS.utils.audio import AudioProcessor
>>> conf = BaseAudioConfig(pitch_fmax=8000)
>>> from TTS.utils.audio.processor import AudioProcessor >>> conf = BaseAudioConfig(pitch_fmax=8000)
>>> ap = AudioProcessor(**conf)
>>> wav = ap.load_wav(WAV_FILE, sr=22050)[:5 * 22050]
>>> pitch = ap.compute_f0(wav)
@ -913,15 +592,3 @@ class AudioProcessor(object):
def dequantize(x, bits):
"""Dequantize a waveform from the given number of bits."""
return 2 * x / (2**bits - 1) - 1
def _log(x, base):
if base == 10:
return np.log10(x)
return np.log(x)
def _exp(x, base):
if base == 10:
return np.power(10, x)
return np.exp(x)

2
TTS/utils/synthesizer.py
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@ -11,7 +11,7 @@ from TTS.tts.models import setup_model as setup_tts_model
# pylint: disable=unused-wildcard-import
# pylint: disable=wildcard-import
from TTS.tts.utils.synthesis import synthesis, transfer_voice, trim_silence
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
from TTS.vocoder.models import setup_model as setup_vocoder_model
from TTS.vocoder.utils.generic_utils import interpolate_vocoder_input

2
TTS/vocoder/layers/losses.py
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@ -4,7 +4,7 @@ import torch
from torch import nn
from torch.nn import functional as F
from TTS.utils.audio import TorchSTFT
from TTS.utils.audio.torch_transforms import TorchSTFT
from TTS.vocoder.utils.distribution import discretized_mix_logistic_loss, gaussian_loss
#################################

2
TTS/vocoder/models/univnet_discriminator.py
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@ -3,7 +3,7 @@ import torch.nn.functional as F
from torch import nn
from torch.nn.utils import spectral_norm, weight_norm
from TTS.utils.audio import TorchSTFT
from TTS.utils.audio.torch_transforms import TorchSTFT
from TTS.vocoder.models.hifigan_discriminator import MultiPeriodDiscriminator
LRELU_SLOPE = 0.1

2
recipes/ljspeech/align_tts/train_aligntts.py
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@ -7,7 +7,7 @@ from TTS.tts.configs.shared_configs import BaseDatasetConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.align_tts import AlignTTS
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))

2
recipes/ljspeech/fast_pitch/train_fast_pitch.py
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@ -7,7 +7,7 @@ from TTS.tts.configs.fast_pitch_config import FastPitchConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.forward_tts import ForwardTTS
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
from TTS.utils.manage import ModelManager
output_path = os.path.dirname(os.path.abspath(__file__))

2
recipes/ljspeech/fast_speech/train_fast_speech.py
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@ -7,7 +7,7 @@ from TTS.tts.configs.fast_speech_config import FastSpeechConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.forward_tts import ForwardTTS
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
from TTS.utils.manage import ModelManager
output_path = os.path.dirname(os.path.abspath(__file__))

2
recipes/ljspeech/glow_tts/train_glowtts.py
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@ -12,7 +12,7 @@ from TTS.tts.configs.shared_configs import BaseDatasetConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.glow_tts import GlowTTS
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
# we use the same path as this script as our training folder.
output_path = os.path.dirname(os.path.abspath(__file__))

2
recipes/ljspeech/hifigan/train_hifigan.py
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@ -2,7 +2,7 @@ import os
from trainer import Trainer, TrainerArgs
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
from TTS.vocoder.configs import HifiganConfig
from TTS.vocoder.datasets.preprocess import load_wav_data
from TTS.vocoder.models.gan import GAN

2
recipes/ljspeech/multiband_melgan/train_multiband_melgan.py
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@ -2,7 +2,7 @@ import os
from trainer import Trainer, TrainerArgs
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
from TTS.vocoder.configs import MultibandMelganConfig
from TTS.vocoder.datasets.preprocess import load_wav_data
from TTS.vocoder.models.gan import GAN

2
recipes/ljspeech/speedy_speech/train_speedy_speech.py
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@ -7,7 +7,7 @@ from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.forward_tts import ForwardTTS
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(

2
recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py
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@ -8,7 +8,7 @@ from TTS.tts.configs.tacotron2_config import Tacotron2Config
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.tacotron2 import Tacotron2
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
# from TTS.tts.datasets.tokenizer import Tokenizer

2
recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py
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@ -8,7 +8,7 @@ from TTS.tts.configs.tacotron2_config import Tacotron2Config
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.tacotron2 import Tacotron2
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
# from TTS.tts.datasets.tokenizer import Tokenizer

2
recipes/ljspeech/univnet/train.py
View File

@ -2,7 +2,7 @@ import os
from trainer import Trainer, TrainerArgs
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
from TTS.vocoder.configs import UnivnetConfig
from TTS.vocoder.datasets.preprocess import load_wav_data
from TTS.vocoder.models.gan import GAN

4
recipes/ljspeech/vits_tts/train_vits.py
View File

@ -8,7 +8,7 @@ from TTS.tts.configs.vits_config import VitsConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.vits import Vits
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(
@ -37,7 +37,7 @@ config = VitsConfig(
batch_size=32,
eval_batch_size=16,
batch_group_size=5,
num_loader_workers=0,
num_loader_workers=8,
num_eval_loader_workers=4,
run_eval=True,
test_delay_epochs=-1,

2
recipes/ljspeech/wavegrad/train_wavegrad.py
View File

@ -2,7 +2,7 @@ import os
from trainer import Trainer, TrainerArgs
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
from TTS.vocoder.configs import WavegradConfig
from TTS.vocoder.datasets.preprocess import load_wav_data
from TTS.vocoder.models.wavegrad import Wavegrad

2
recipes/ljspeech/wavernn/train_wavernn.py
View File

@ -2,7 +2,7 @@ import os
from trainer import Trainer, TrainerArgs
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
from TTS.vocoder.configs import WavernnConfig
from TTS.vocoder.datasets.preprocess import load_wav_data
from TTS.vocoder.models.wavernn import Wavernn

2
recipes/multilingual/vits_tts/train_vits_tts.py
View File

@ -11,7 +11,7 @@ from TTS.tts.models.vits import CharactersConfig, Vits, VitsArgs
from TTS.tts.utils.languages import LanguageManager
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))

2
recipes/vctk/fast_pitch/train_fast_pitch.py
View File

@ -8,7 +8,7 @@ from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.forward_tts import ForwardTTS
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))

2
recipes/vctk/fast_speech/train_fast_speech.py
View File

@ -8,7 +8,7 @@ from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.forward_tts import ForwardTTS
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))

2
recipes/vctk/glow_tts/train_glow_tts.py
View File

@ -9,7 +9,7 @@ from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.glow_tts import GlowTTS
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
# set experiment paths
output_path = os.path.dirname(os.path.abspath(__file__))

2
recipes/vctk/speedy_speech/train_speedy_speech.py
View File

@ -8,7 +8,7 @@ from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.forward_tts import ForwardTTS
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))

2
recipes/vctk/tacotron-DDC/train_tacotron-DDC.py
View File

@ -9,7 +9,7 @@ from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.tacotron import Tacotron
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))

2
recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py
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@ -9,7 +9,7 @@ from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.tacotron2 import Tacotron2
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))

2
recipes/vctk/tacotron2/train_tacotron2.py
View File

@ -9,7 +9,7 @@ from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.tacotron2 import Tacotron2
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))

2
recipes/vctk/vits/train_vits.py
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@ -9,7 +9,7 @@ from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.vits import Vits, VitsArgs
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.processor import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(