Commit
•
a4df380
1
Parent(s):
3cd0f38
Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,101 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: mit
|
3 |
+
tags:
|
4 |
+
- vits
|
5 |
+
pipeline_tag: text-to-speech
|
6 |
+
---
|
7 |
+
|
8 |
+
# VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
|
9 |
+
|
10 |
+
VITS is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a
|
11 |
+
conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. This repository
|
12 |
+
contains the weights for the official VITS checkpoint trained on the [LJ Speech](https://huggingface.co/datasets/lj_speech) dataset.
|
13 |
+
|
14 |
+
## Model Details
|
15 |
+
|
16 |
+
VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end
|
17 |
+
speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational
|
18 |
+
autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
|
19 |
+
|
20 |
+
A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
|
21 |
+
text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
|
22 |
+
much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text
|
23 |
+
input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to
|
24 |
+
synthesise speech with different rhythms from the same input text.
|
25 |
+
|
26 |
+
The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training.
|
27 |
+
To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During
|
28 |
+
inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the
|
29 |
+
waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
|
30 |
+
the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
|
31 |
+
|
32 |
+
There are two variants of the VITS model: one is trained on the [LJ Speech](https://huggingface.co/datasets/lj_speech) dataset,
|
33 |
+
and the other is trained on the [VCTK](https://huggingface.co/datasets/vctk) dataset. LJ Speech dataset consists of 13,100 short
|
34 |
+
audio clips of a single speaker with a total length of approximately 24 hours. The VCTK dataset consists of approximately 44,000
|
35 |
+
short audio clips uttered by 109 native English speakers with various accents. The total length of the audio clips is approximately
|
36 |
+
44 hours.
|
37 |
+
|
38 |
+
| Checkpoint | Train Hours | Speakers |
|
39 |
+
|------------|-------------|----------|
|
40 |
+
| [vits-ljs](https://huggingface.co/kakao-enterprise/vits-ljs) | 24 | 1 |
|
41 |
+
| [vits-vctk](https://huggingface.co/kakao-enterprise/vits-vctk) | 44 | 109 |
|
42 |
+
|
43 |
+
## Usage
|
44 |
+
|
45 |
+
VITS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint,
|
46 |
+
first install the latest version of the library:
|
47 |
+
|
48 |
+
```
|
49 |
+
pip install --upgrade transformers accelerate
|
50 |
+
```
|
51 |
+
|
52 |
+
Then, run inference with the following code-snippet:
|
53 |
+
|
54 |
+
```python
|
55 |
+
from transformers import VitsModel, AutoTokenizer
|
56 |
+
import torch
|
57 |
+
|
58 |
+
model = VitsModel.from_pretrained("kakao-enterprise/vits-ljs")
|
59 |
+
tokenizer = AutoTokenizer.from_pretrained("kakao-enterprise/vits-ljs")
|
60 |
+
|
61 |
+
text = "Hey, it's Hugging Face on the phone"
|
62 |
+
inputs = tokenizer(text, return_tensors="pt")
|
63 |
+
|
64 |
+
with torch.no_grad():
|
65 |
+
output = model(**inputs).waveform
|
66 |
+
```
|
67 |
+
|
68 |
+
The resulting waveform can be saved as a `.wav` file:
|
69 |
+
|
70 |
+
```python
|
71 |
+
import scipy
|
72 |
+
|
73 |
+
scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
|
74 |
+
```
|
75 |
+
|
76 |
+
Or displayed in a Jupyter Notebook / Google Colab:
|
77 |
+
|
78 |
+
```python
|
79 |
+
from IPython.display import Audio
|
80 |
+
|
81 |
+
Audio(output, rate=model.config.sampling_rate)
|
82 |
+
```
|
83 |
+
|
84 |
+
## BibTex citation
|
85 |
+
|
86 |
+
This model was developed by Jaehyeon Kim et al. from Kakao Enterprise. If you use the model, consider citing the VITS paper:
|
87 |
+
|
88 |
+
```
|
89 |
+
@inproceedings{kim2021conditional,
|
90 |
+
title={"Conditional Variational Autoencoder with Adversarial Learning for End-to-end Text-to-speech"},
|
91 |
+
author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee},
|
92 |
+
booktitle={International Conference on Machine Learning},
|
93 |
+
pages={5530--5540},
|
94 |
+
year={2021},
|
95 |
+
organization={PMLR}
|
96 |
+
}
|
97 |
+
```
|
98 |
+
|
99 |
+
## License
|
100 |
+
|
101 |
+
The model is licensed as [**MIT**](https://github.com/jaywalnut310/vits/blob/main/LICENSE).
|