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Plachta
2023-06-14 15:36:59 +08:00
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@@ -24,7 +24,7 @@
``` ```
6. Download auxiliary data for training 6. Download auxiliary data for training
``` ```
!mkdir pretrained_models mkdir pretrained_models
# download data for fine-tuning # download data for fine-tuning
wget https://huggingface.co/datasets/Plachta/sampled_audio4ft/resolve/main/sampled_audio4ft_v2.zip wget https://huggingface.co/datasets/Plachta/sampled_audio4ft/resolve/main/sampled_audio4ft_v2.zip
unzip sampled_audio4ft_v2.zip unzip sampled_audio4ft_v2.zip
@@ -94,10 +94,10 @@
If not, run `python3.8 preprocess_v2.py --languages "{PRETRAINED_MODEL}"` If not, run `python3.8 preprocess_v2.py --languages "{PRETRAINED_MODEL}"`
Do replace `"{PRETRAINED_MODEL}"` with one of `{CJ, CJE, C}` according to your previous model choice. Do replace `"{PRETRAINED_MODEL}"` with one of `{CJ, CJE, C}` according to your previous model choice.
11. Start Training. 11. Start Training.
Run `python finetune_speaker_v2.py -m "./OUTPUT_MODEL" --max_epochs "{Maximum_epochs}" --drop_speaker_embed True` Run `python finetune_speaker_v2.py -m ./OUTPUT_MODEL --max_epochs "{Maximum_epochs}" --drop_speaker_embed True`
Do replace `{Maximum_epochs}` with your desired number of epochs. Empirically, 100 or more is recommended. Do replace `{Maximum_epochs}` with your desired number of epochs. Empirically, 100 or more is recommended.
To continue training on previous checkpoint, change the training command to: `python finetune_speaker_v2.py -m "./OUTPUT_MODEL" --max_epochs "{Maximum_epochs}" --drop_speaker_embed True --cont True`. Before you do this, make sure you have previous `G_latest.pth` and `D_latest.pth` under `./OUTPUT_MODEL/` directory. To continue training on previous checkpoint, change the training command to: `python finetune_speaker_v2.py -m ./OUTPUT_MODEL --max_epochs "{Maximum_epochs}" --drop_speaker_embed True --cont True`. Before you do this, make sure you have previous `G_latest.pth` and `D_latest.pth` under `./OUTPUT_MODEL/` directory.
To view training progress, open a new terminal and `cd` to the project root directory, run `tensorboard --logdir="./OUTPUT_MODEL"`, then visit `localhost:6006` with your web browser. To view training progress, open a new terminal and `cd` to the project root directory, run `tensorboard --logdir=./OUTPUT_MODEL`, then visit `localhost:6006` with your web browser.
12. After training is completed, you can use your model by running: 12. After training is completed, you can use your model by running:
`python VC_inference.py --model_dir ./OUTPUT_MODEL/G_latest.pth --share True` `python VC_inference.py --model_dir ./OUTPUT_MODEL/G_latest.pth --share True`
13. To clear all audio data, run: 13. To clear all audio data, run: