Update README.md

README.md

@@ -5,3 +5,39 @@ language:
 library_name: transformers.js
 ---
 
+# 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.
+
+## Model Details
+
+Languages: Chinese
+
+Dataset: THCHS-30
+
+Speakers: 44
+
+Training Hours: 48
+
+## Usage
+
+Using this checkpoint from Hugging Face Transformers:
+
+```py
+from transformers import VitsModel, VitsTokenizer
+from pypinyin import lazy_pinyin, Style
+import torch
+
+model = VitsModel.from_pretrained("BricksDisplay/vits-cmn")
+tokenizer = VitsTokenizer.from_pretrained("BricksDisplay/vits-cmn")
+
+text = "中文"
+payload = ''.join(lazy_pinyin(text, style=Style.TONE, tone_sandhi=True))
+inputs = tokenizer(payload, return_tensors="pt")
+
+with torch.no_grad():
+    output = model(**inputs, speaker_id=0)
+
+from IPython.display import Audio
+Audio(output.audio[0], rate=16000)
+```