coqui-ai
/
TTS
mirror of https://github.com/coqui-ai/TTS.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

partial commit

pull/10/head
Eren Golge 2018-12-31 14:47:53 +01:00
parent 37476e136c
commit 084846f833
2 changed files with 5 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
README.md
View File

@ -106,6 +106,10 @@ You can also enjoy Tensorboard, if you point the Tensorboard argument```--logdir
## Testing ## Testing
Best way to test your pre-trained network is to use Notebooks under ```notebooks``` folder. Best way to test your pre-trained network is to use Notebooks under ```notebooks``` folder.
## Logging
# TODO
TTS enables intense logging on Tensorboard.
## What is new with TTS ## What is new with TTS
If you train TTS with LJSpeech dataset, you start to hear reasonable results after 12.5K iterations with batch size 32. This is the fastest training with character based methods up to our knowledge. Out implementation is also quite robust against long sentences. If you train TTS with LJSpeech dataset, you start to hear reasonable results after 12.5K iterations with batch size 32. This is the fastest training with character based methods up to our knowledge. Out implementation is also quite robust against long sentences.

2
config.json
View File

@ -49,7 +49,7 @@
"dataset": "tweb", // one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py "dataset": "tweb", // one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
"min_seq_len": 0, // minimum text length to use in training "min_seq_len": 0, // minimum text length to use in training
"max_seq_len": 300, // maximum text length "max_seq_len": 300, // maximum text length
"output_path": "../keep/", // output path for all training outputs. "output_path": "/media/erogol/data_ssd/Data/models/tweb_models/", // output path for all training outputs.
"num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values. "num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values.
"num_val_loader_workers": 4 // number of evaluation data loader processes. "num_val_loader_workers": 4 // number of evaluation data loader processes.
} }