forked from mrq/ai-voice-cloning
VALL-E config edits
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@ -1,4 +1,33 @@
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{
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"optimizer": {
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"type": "AdamW",
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"params": {
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"lr": 2e-05,
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"betas": [
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0.9,
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0.96
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],
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"eps": 1e-07,
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"weight_decay": 0.01
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}
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},
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"scheduler":{
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"type":"WarmupLR",
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"params":{
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"warmup_min_lr":0,
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"warmup_max_lr":2e-5,
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"warmup_num_steps":100,
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"warmup_type":"linear"
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}
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},
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"fp16":{
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"enabled":true,
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"loss_scale":0,
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"loss_scale_window":1000,
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"initial_scale_power":16,
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"hysteresis":2,
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"min_loss_scale":1
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},
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"autotuning":{
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"enabled":false,
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"results_dir":"./config/autotune/results",
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@ -21,15 +50,6 @@
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},
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"zero_optimization":{
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"stage":0,
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"offload_param": {
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"device": "nvme",
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"nvme_path": "/tmp/zero/",
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"pin_memory": false,
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"buffer_count": 5,
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"buffer_size": 1e9,
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"max_in_cpu": 1e9
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},
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"overlap_comm": true,
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"reduce_bucket_size":"auto",
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"contiguous_gradients":true,
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"sub_group_size":1e8,
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@ -3,14 +3,17 @@ ckpt_root: ./training/${voice}/finetune/ckpt/
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log_root: ./training/${voice}/finetune/logs/
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data_dirs: [./training/${voice}/valle/]
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spkr_name_getter: "lambda p: p.parts[-3]"
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spkr_name_getter: "lambda p: p.parts[-3]" # "lambda p: p.parts[-1].split('-')[0]"
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model: ${model_name}
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batch_size: ${batch_size}
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eval_batch_size: ${validation_batch_size}
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gradient_accumulation_steps: ${gradient_accumulation_size}
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eval_batch_size: ${batch_size}
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max_iter: ${iterations}
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save_ckpt_every: ${save_rate}
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eval_every: ${validation_rate}
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max_phones: 256
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sampling_temperature: 1.0
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@ -488,7 +488,7 @@ def setup_gradio():
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)
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with gr.Row():
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TRAINING_SETTINGS["batch_size"] = gr.Number(label="Batch Size", value=128, precision=0)
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TRAINING_SETTINGS["gradient_accumulation_size"] = gr.Number(label="Gradient Accumulation Size", value=4, precision=0, visible=args.tts_backend=="tortoise")
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TRAINING_SETTINGS["gradient_accumulation_size"] = gr.Number(label="Gradient Accumulation Size", value=4, precision=0)
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with gr.Row():
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TRAINING_SETTINGS["save_rate"] = gr.Number(label="Save Frequency (in epochs)", value=5, precision=0)
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TRAINING_SETTINGS["validation_rate"] = gr.Number(label="Validation Frequency (in epochs)", value=5, precision=0)
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