wywsgs commited on
Commit
2892f55
·
verified ·
1 Parent(s): 8dde10a

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +5 -2
README.md CHANGED
@@ -1,3 +1,7 @@
 
 
 
 
1
  Muyan-TTS is a trainable TTS model designed for podcast applications within a $50,000 budget, which is pre-trained on over 100,000 hours of podcast audio data, enabling zero-shot TTS synthesis with high-quality voice generation. Furthermore, Muyan-TTS supports speaker adaptation with dozens of minutes of target speech, making it highly customizable for individual voices.
2
 
3
  ## Install
@@ -106,5 +110,4 @@ After generating ```data/tts_sft_data.json```, train.sh will automatically copy
106
  ```
107
  Finally, it will automatically execute the ```llamafactory-cli train``` command to start training. You can adjust training settings using ```training/sft.yaml```. By default, the trained weights will be saved to ```pretrained_models/Muyan-TTS-new-SFT```.
108
 
109
- You can directly deploy your trained model using the API tool above. During inference, you need to specify the ```model_type``` to be ```sft``` and replace the ```ref_wav_path``` and ```prompt text``` with a sample of the speaker's voice you trained on.
110
-
 
1
+ ---
2
+ tags:
3
+ - text-to-speech
4
+ ---
5
  Muyan-TTS is a trainable TTS model designed for podcast applications within a $50,000 budget, which is pre-trained on over 100,000 hours of podcast audio data, enabling zero-shot TTS synthesis with high-quality voice generation. Furthermore, Muyan-TTS supports speaker adaptation with dozens of minutes of target speech, making it highly customizable for individual voices.
6
 
7
  ## Install
 
110
  ```
111
  Finally, it will automatically execute the ```llamafactory-cli train``` command to start training. You can adjust training settings using ```training/sft.yaml```. By default, the trained weights will be saved to ```pretrained_models/Muyan-TTS-new-SFT```.
112
 
113
+ You can directly deploy your trained model using the API tool above. During inference, you need to specify the ```model_type``` to be ```sft``` and replace the ```ref_wav_path``` and ```prompt text``` with a sample of the speaker's voice you trained on.