Create README.md

README.md

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+---
+license: mit
+tags:
+- vits
+pipeline_tag: text-to-speech
+---
+
+# VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
+
+VITS is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a 
+conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. This repository 
+contains the weights for the official VITS checkpoint trained on the [VCTK](https://huggingface.co/datasets/vctk) dataset. 
+
+## Model Details
+
+VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end 
+speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational 
+autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
+
+A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
+text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
+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 
+input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to 
+synthesise speech with different rhythms from the same input text. 
+
+The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training. 
+To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During 
+inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the 
+waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
+the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
+
+There are two variants of the VITS model: one is trained on the [LJ Speech](https://huggingface.co/datasets/lj_speech) dataset, 
+and the other is trained on the [VCTK](https://huggingface.co/datasets/vctk) dataset. LJ Speech dataset consists of 13,100 short 
+audio clips of a single speaker with a total length of approximately 24 hours. The VCTK dataset consists of approximately 44,000 
+short audio clips uttered by 109 native English speakers with various accents. The total length of the audio clips is approximately 
+44 hours.
+
+| Checkpoint | Train Hours | Speakers |
+|------------|-------------|----------|
+| [vits-ljs](https://huggingface.co/kakao-enterprise/vits-ljs)   | 24          | 1        |
+| [vits-vctk](https://huggingface.co/kakao-enterprise/vits-vctk)  | 44          | 109      |
+
+## Usage
+
+VITS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint, 
+first install the latest version of the library:
+
+```
+pip install --upgrade transformers accelerate
+```
+
+Then, run inference with the following code-snippet:
+
+```python
+from transformers import VitsModel, AutoTokenizer
+import torch
+
+model = VitsModel.from_pretrained("kakao-enterprise/vits-vctk")
+tokenizer = AutoTokenizer.from_pretrained("kakao-enterprise/vits-vctk")
+
+text = "Hey, it's Hugging Face on the phone"
+inputs = tokenizer(text, return_tensors="pt")
+
+with torch.no_grad():
+    output = model(**inputs).waveform
+```
+
+The resulting waveform can be saved as a `.wav` file:
+
+```python
+import scipy
+
+scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
+```
+
+Or displayed in a Jupyter Notebook / Google Colab:
+
+```python
+from IPython.display import Audio
+
+Audio(output, rate=model.config.sampling_rate)
+```
+
+## BibTex citation
+
+This model was developed by Jaehyeon Kim et al. from Kakao Enterprise. If you use the model, consider citing the VITS paper:
+
+```
+@inproceedings{kim2021conditional,
+  title={"Conditional Variational Autoencoder with Adversarial Learning for End-to-end Text-to-speech"},
+  author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee},
+  booktitle={International Conference on Machine Learning},
+  pages={5530--5540},
+  year={2021},
+  organization={PMLR}
+}
+```
+
+## License
+
+The model is licensed as [**MIT**](https://github.com/jaywalnut310/vits/blob/main/LICENSE).