change model location and rewrite readme

.gitignore

@@ -1 +1,2 @@
 .DS_Store
+G_98000.pth

README.md

@@ -1,89 +1,10 @@
-
-# This code is slightly modified on the [VITS repo](https://github.com/jaywalnut310/vits)
-## chinese train
-`python3 train.py -c configs/woman_csmsc.json -m woman_csmsc`
-
-## inference 
-you can run infer.py
-
-## switch english or chinese
-just modify 
-`chinese_mode = True`
-in ./text/__init__.py
-
-## data example
-copy **24000hz** data-baker datasets to ./test/csmsc
-
-**For copyright reasons, you can only download it yourself**
-
-## models
-you can get example model in ./logs/woman_csmsc/G*.pth
-
-## Prosody model
-you can try chinese Prosody model in this repo.
-
-
-
-======================================================================================================
-
-# VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
-
-### Jaehyeon Kim, Jungil Kong, and Juhee Son
-
-In our recent [paper](https://arxiv.org/abs/2106.06103), we propose VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech.
-
-Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and parallel sampling have been proposed, but their sample quality does not match that of two-stage TTS systems. In this work, we present a parallel end-to-end TTS method that generates more natural sounding audio than current two-stage models. Our method adopts variational inference augmented with normalizing flows and an adversarial training process, which improves the expressive power of generative modeling. We also propose a stochastic duration predictor to synthesize speech with diverse rhythms from input text. With the uncertainty modeling over latent variables and the stochastic duration predictor, our method expresses the natural one-to-many relationship in which a text input can be spoken in multiple ways with different pitches and rhythms. A subjective human evaluation (mean opinion score, or MOS) on the LJ Speech, a single speaker dataset, shows that our method outperforms the best publicly available TTS systems and achieves a MOS comparable to ground truth.
-
-Visit our [demo](https://jaywalnut310.github.io/vits-demo/index.html) for audio samples.
-
-We also provide the [pretrained models](https://drive.google.com/drive/folders/1ksarh-cJf3F5eKJjLVWY0X1j1qsQqiS2?usp=sharing).
-
-** Update note: Thanks to [Rishikesh (ऋषिकेश)](https://github.com/jaywalnut310/vits/issues/1), our interactive TTS demo is now available on [Colab Notebook](https://colab.research.google.com/drive/1CO61pZizDj7en71NQG_aqqKdGaA_SaBf?usp=sharing).
-
-<table style="width:100%">
-  <tr>
-    <th>VITS at training</th>
-    <th>VITS at inference</th>
-  </tr>
-  <tr>
-    <td><img src="resources/fig_1a.png" alt="VITS at training" height="400"></td>
-    <td><img src="resources/fig_1b.png" alt="VITS at inference" height="400"></td>
-  </tr>
-</table>
-
-
-## Pre-requisites
-0. Python >= 3.6
-0. Clone this repository
-0. Install python requirements. Please refer [requirements.txt](requirements.txt)
-    1. You may need to install espeak first: `apt-get install espeak`
-0. Download datasets
-    1. 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`
-    1. 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`
-0. Build Monotonic Alignment Search and run preprocessing if you use your own datasets.
-```sh
-# 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
-```
-
-
-## Training Exmaple
-```sh
-# LJ Speech
-python train.py -c configs/ljs_base.json -m ljs_base
-
-# VCTK
-python train_ms.py -c configs/vctk_base.json -m vctk_base
-
-
-
-```
-
-
-## Inference Example
-See [inference.ipynb](inference.ipynb)
+---
+title: Vits Chinese
+emoji: 🏃
+colorFrom: red
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.9
+app_file: app.py
+pinned: false
+---

app.py

@@ -28,7 +28,7 @@ net_g = SynthesizerTrn(
 _ = net_g.eval()
 
 # _ = utils.load_checkpoint("logs/woman_csmsc/G_100000.pth", net_g, None) 
-_ = utils.load_checkpoint("logs/G_98000.pth", net_g, None) 
+_ = utils.load_checkpoint("G_98000.pth", net_g, None) 
 
 def vc_fn(input):
     stn_tst = get_text(input, hps)