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VALL-E config edits

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mrq 2023-03-20 01:22:53 +00:00
parent 2e33bf071a
commit 34ef0467b9
3 changed files with 62 additions and 39 deletions
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90
config/ds_config.json
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@ -1,41 +1,61 @@
{
"autotuning": {
"enabled": false,
"results_dir": "./config/autotune/results",
"exps_dir": "./config/autotune/exps",
"overwrite": false,
"metric": "throughput",
"start_profile_step": 10,
"end_profile_step": 20,
"fast": false,
"max_train_batch_size": 32,
"mp_size": 1,
"num_tuning_micro_batch_sizes": 3,
"tuner_type": "model_based",
"tuner_early_stopping": 5,
"tuner_num_trials": 50,
"arg_mappings": {
"train_micro_batch_size_per_gpu": "--per_device_train_batch_size",
"gradient_accumulation_steps ": "--gradient_accumulation_steps"
"optimizer": {
"type": "AdamW",
"params": {
"lr": 2e-05,
"betas": [
0.9,
0.96
],
"eps": 1e-07,
"weight_decay": 0.01
}
},
"zero_optimization": {
"stage": 0,
"offload_param": {
"device": "nvme",
"nvme_path": "/tmp/zero/",
"pin_memory": false,
"buffer_count": 5,
"buffer_size": 1e9,
"max_in_cpu": 1e9
"scheduler":{
"type":"WarmupLR",
"params":{
"warmup_min_lr":0,
"warmup_max_lr":2e-5,
"warmup_num_steps":100,
"warmup_type":"linear"
}
},
"overlap_comm": true,
"reduce_bucket_size": "auto",
"contiguous_gradients": true,
"sub_group_size": 1e8,
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": "auto",
"stage3_max_reuse_distance": "auto"
"fp16":{
"enabled":true,
"loss_scale":0,
"loss_scale_window":1000,
"initial_scale_power":16,
"hysteresis":2,
"min_loss_scale":1
},
"autotuning":{
"enabled":false,
"results_dir":"./config/autotune/results",
"exps_dir":"./config/autotune/exps",
"overwrite":false,
"metric":"throughput",
"start_profile_step":10,
"end_profile_step":20,
"fast":false,
"max_train_batch_size":32,
"mp_size":1,
"num_tuning_micro_batch_sizes":3,
"tuner_type":"model_based",
"tuner_early_stopping":5,
"tuner_num_trials":50,
"arg_mappings":{
"train_micro_batch_size_per_gpu":"--per_device_train_batch_size",
"gradient_accumulation_steps ":"--gradient_accumulation_steps"
}
},
"zero_optimization":{
"stage":0,
"reduce_bucket_size":"auto",
"contiguous_gradients":true,
"sub_group_size":1e8,
"stage3_prefetch_bucket_size":"auto",
"stage3_param_persistence_threshold":"auto",
"stage3_max_live_parameters":"auto",
"stage3_max_reuse_distance":"auto"
}
}

7
models/.template.valle.yaml
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@ -3,14 +3,17 @@ ckpt_root: ./training/${voice}/finetune/ckpt/
log_root: ./training/${voice}/finetune/logs/
data_dirs: [./training/${voice}/valle/]
spkr_name_getter: "lambda p: p.parts[-3]"
spkr_name_getter: "lambda p: p.parts[-3]" # "lambda p: p.parts[-1].split('-')[0]"
model: ${model_name}
batch_size: ${batch_size}
eval_batch_size: ${validation_batch_size}
gradient_accumulation_steps: ${gradient_accumulation_size}
eval_batch_size: ${batch_size}
max_iter: ${iterations}
save_ckpt_every: ${save_rate}
eval_every: ${validation_rate}
max_phones: 256
sampling_temperature: 1.0

2
src/webui.py
View File

@ -488,7 +488,7 @@ def setup_gradio():
)
with gr.Row():
TRAINING_SETTINGS["batch_size"] = gr.Number(label="Batch Size", value=128, precision=0)
TRAINING_SETTINGS["gradient_accumulation_size"] = gr.Number(label="Gradient Accumulation Size", value=4, precision=0, visible=args.tts_backend=="tortoise")
TRAINING_SETTINGS["gradient_accumulation_size"] = gr.Number(label="Gradient Accumulation Size", value=4, precision=0)
with gr.Row():
TRAINING_SETTINGS["save_rate"] = gr.Number(label="Save Frequency (in epochs)", value=5, precision=0)
TRAINING_SETTINGS["validation_rate"] = gr.Number(label="Validation Frequency (in epochs)", value=5, precision=0)