update train.py cli argument, load a wavenet model

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chenfeiyu 2020-02-28 04:07:32 +00:00
parent 44e61e0781
commit 51fee9912d
1 changed files with 10 additions and 13 deletions

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@ -28,21 +28,18 @@ Train the model using train.py, follow the usage displayed by `python train.py -
```text
usage: train.py [-h] [--config CONFIG] [--device DEVICE] [--output OUTPUT]
[--data DATA] [--resume RESUME] [--conditioner CONDITIONER]
[--teacher TEACHER]
[--data DATA] [--resume RESUME] [--wavenet WAVENET]
train a clarinet model with LJspeech and a trained wavenet model.
optional arguments:
-h, --help show this help message and exit
--config CONFIG path of the config file.
--device DEVICE device to use.
--output OUTPUT path to save student.
--data DATA path of LJspeech dataset.
--resume RESUME checkpoint to load from.
--conditioner CONDITIONER
conditioner checkpoint to use.
--teacher TEACHER teacher checkpoint to use.
-h, --help show this help message and exit
--config CONFIG path of the config file.
--device DEVICE device to use.
--output OUTPUT path to save student.
--data DATA path of LJspeech dataset.
--resume RESUME checkpoint to load from.
--wavenet WAVENET wavenet checkpoint to use.
```
1. `--config` is the configuration file to use. The provided configurations can be used directly. And you can change some values in the configuration file and train the model with a different config.
@ -57,8 +54,8 @@ optional arguments:
```
5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
6. `--conditioner` is the path of the checkpoint to load for the `conditioner` part of clarinet. if you do not specify `--resume`, then this must be provided.
7. `--teacher` is the path of the checkpoint to load for the `teacher` part of clarinet. if you do not specify `--resume`, then this must be provided.
6. `--wavenet` is the path of the wavenet checkpoint to load. if you do not specify `--resume`, then this must be provided.
Before you start training a clarinet model, you should have trained a wavenet model with single gaussian as output distribution. Make sure the config for teacher matches that for the trained model.