VITS2: Improving Quality and Efficiency of Single-Stage Text-to-Speech with Adversarial Learning and Architecture Design
Jungil Kong, Jihoon Park, Beomjeong Kim, Jeongmin Kim, Dohee Kong, Sangjin Kim
Unofficial implementation of the VITS2 paper, sequel to VITS paper. (thanks to the authors for their work!)
Single-stage text-to-speech models have been actively studied recently, and their results have outperformed two-stage pipeline systems. Although the previous single-stage model has made great progress, there is room for improvement in terms of its intermittent unnaturalness, computational efficiency, and strong dependence on phoneme conversion. In this work, we introduce VITS2, a single-stage text-to-speech model that efficiently synthesizes a more natural speech by improving several aspects of the previous work. We propose improved structures and training mechanisms and present that the proposed methods are effective in improving naturalness, similarity of speech characteristics in a multi-speaker model, and efficiency of training and inference. Furthermore, we demonstrate that the strong dependence on phoneme conversion in previous works can be significantly reduced with our method, which allows a fully end-toend single-stage approach.
Credits
- We will build this repo based on the VITS repo. The goal is to make this model easier to transfer learning from VITS pretrained model!
- (08-17-2023) - The authors were really kind to guide me through the paper and answer my questions. I am open to discuss any changes or answer questions regarding the implementation. Please feel free to open an issue or contact me directly.
Pretrained checkpoints
Sample audio
Prerequisites
- Python >= 3.10
- Tested on Pytorch version 1.13.1 with Google Colab and LambdaLabs cloud.
- Clone this repository
- Install python requirements. Please refer requirements.txt
- You may need to install espeak first:
apt-get install espeak
- Download datasets
- Download and extract the LJ Speech dataset, then rename or create a link to the dataset folder:
ln -s /path/to/LJSpeech-1.1/wavs DUMMY1
- For mult-speaker setting, download and extract the VCTK dataset, and downsample wav files to 22050 Hz. Then rename or create a link to the dataset folder:
ln -s /path/to/VCTK-Corpus/downsampled_wavs DUMMY2
- Build Monotonic Alignment Search and run preprocessing if you use your own datasets.
# Cython-version Monotonoic Alignment Search
cd monotonic_align
python setup.py build_ext --inplace
# Preprocessing (g2p) for your own datasets. Preprocessed phonemes for LJ Speech and VCTK have been already provided.
# python preprocess.py --text_index 1 --filelists filelists/ljs_audio_text_train_filelist.txt filelists/ljs_audio_text_val_filelist.txt filelists/ljs_audio_text_test_filelist.txt
# python preprocess.py --text_index 2 --filelists filelists/vctk_audio_sid_text_train_filelist.txt filelists/vctk_audio_sid_text_val_filelist.txt filelists/vctk_audio_sid_text_test_filelist.txt
How to run (dry-run)
- model forward pass (dry-run)
import torch
from models import SynthesizerTrn
net_g = SynthesizerTrn(
n_vocab=256,
spec_channels=80, # <--- vits2 parameter (changed from 513 to 80)
segment_size=8192,
inter_channels=192,
hidden_channels=192,
filter_channels=768,
n_heads=2,
n_layers=6,
kernel_size=3,
p_dropout=0.1,
resblock="1",
resblock_kernel_sizes=[3, 7, 11],
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
upsample_rates=[8, 8, 2, 2],
upsample_initial_channel=512,
upsample_kernel_sizes=[16, 16, 4, 4],
n_speakers=0,
gin_channels=0,
use_sdp=True,
use_transformer_flows=True, # <--- vits2 parameter
# (choose from "pre_conv", "fft", "mono_layer_inter_residual", "mono_layer_post_residual")
transformer_flow_type="fft", # <--- vits2 parameter
use_spk_conditioned_encoder=True, # <--- vits2 parameter
use_noise_scaled_mas=True, # <--- vits2 parameter
use_duration_discriminator=True, # <--- vits2 parameter
)
x = torch.LongTensor([[1, 2, 3],[4, 5, 6]]) # token ids
x_lengths = torch.LongTensor([3, 2]) # token lengths
y = torch.randn(2, 80, 100) # mel spectrograms
y_lengths = torch.Tensor([100, 80]) # mel spectrogram lengths
net_g(
x=x,
x_lengths=x_lengths,
y=y,
y_lengths=y_lengths,
)
# calculate loss and backpropagate
Training Example
# LJ Speech
python train.py -c configs/vits2_ljs_nosdp.json -m ljs_base # no-sdp; (recommended)
python train.py -c configs/vits2_ljs_base.json -m ljs_base # with sdp;
# VCTK
python train_ms.py -c configs/vits2_vctk_base.json -m vctk_base
# for onnx export of trained models
python export_onnx.py --model-path="G_64000.pth" --config-path="config.json" --output="vits2.onnx"
python infer_onnx.py --model="vits2.onnx" --config-path="config.json" --output-wav-path="output.wav" --text="hello world, how are you?"
TODOs, features and notes
Duration predictor (fig 1a)
Speaker-conditioned text encoder (fig 1c)
Mel spectrogram posterior encoder (Section 3)
Training scripts
ONNX export
Gradio Demo
Special mentions
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