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072e103 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | #!/bin/bash
### Training parameters
# Model to train
QUALITY=high
# These two parameters will dictate, how much VRAM will be required
# NOTE: The following config will require around 20 GB VRAM (high model)
# Do not lower BATCHSIZE too much, as training will have a lot of "jitter".
# But reducing PHONEME_MAX too much will discard too long training data
# However: I was able to train on ~6-7 GB VRAM with BATCHSIZE=12 and PHONEME_MAX=400 (the jitter was clearly visible in tensorboard)
BATCHSIZE=32
PHONEME_MAX=400
# Base model to start training from. E.g. here from Thorsten Voice
BASE_CHKPOINT="./checkpoints/epoch=2665-step=1182078.ckpt"
# Start training from BASE_CHKPOINT
CHKPOINT=$BASE_CHKPOINT
# CONTINUE from an existing checkpoint.
# NOTE: It is good practice, to write down the last checkpoint, that was trained after you aborted training.
# The current checkpoint is automatically printed out by the training process.
#CHKPOINT="/training/traindata/lightning_logs/version_21/checkpoints/epoch=5661-step=1778926.ckpt"
python3 -m piper_train \
--dataset-dir ./traindata \
--accelerator 'gpu' \
--gpus 1 \
--batch-size ${BATCHSIZE} \
--validation-split 0.0 \
--num-test-examples 0 \
--max_epochs 6000 \
--resume_from_checkpoint "${CHKPOINT}" \
--checkpoint-epochs 1 \
--precision 16 \
--max-phoneme-ids ${PHONEME_MAX} \
--quality ${QUALITY}
# Batchsize: 32
# max-phenomene-ids: 400
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