forked from mrq/ai-voice-cloning
146 lines
5.1 KiB
YAML
Executable File
146 lines
5.1 KiB
YAML
Executable File
name: ${name}
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model: extensibletrainer
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scale: 1
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gpu_ids: [0] # <-- unless you have multiple gpus, use this
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start_step: -1
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checkpointing_enabled: true # <-- Gradient checkpointing. Enable for huge GPU memory savings. Disable for distributed training.
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fp16: false # might want to check this out
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wandb: false # <-- enable to log to wandb. tensorboard logging is always enabled.
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use_tb_logger: true
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datasets:
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train:
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name: ${dataset_name}
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n_workers: 8 # idk what this does
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batch_size: ${batch_size} # This leads to ~16GB of vram usage on my 3090.
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mode: paired_voice_audio
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path: ${dataset_path}
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fetcher_mode: ['lj'] # CHANGEME if your dataset isn't in LJSpeech format
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phase: train
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max_wav_length: 255995
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max_text_length: 200
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sample_rate: 22050
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load_conditioning: True
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num_conditioning_candidates: 2
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conditioning_length: 44000
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use_bpe_tokenizer: True
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tokenizer_vocab: ./models/tortoise/bpe_lowercase_asr_256.json
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load_aligned_codes: False
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val:
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name: ${validation_name}
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n_workers: 1
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batch_size: 32 # this could be higher probably
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mode: paired_voice_audio
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path: ${validation_path}
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fetcher_mode: ['lj']
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phase: val # might be broken idk
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max_wav_length: 255995
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max_text_length: 200
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sample_rate: 22050
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load_conditioning: True
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num_conditioning_candidates: 2
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conditioning_length: 44000
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use_bpe_tokenizer: True
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tokenizer_vocab: ./models/tortoise/bpe_lowercase_asr_256.json
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load_aligned_codes: False
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steps:
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gpt_train:
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training: gpt
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loss_log_buffer: 500 # no idea what this does
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# Generally follows the recipe from the DALLE paper.
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optimizer: adamw # this should be adamw_zero if you're using distributed training
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optimizer_params:
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lr: !!float ${learning_rate} # CHANGEME: this was originally 1e-4; I reduced it to 1e-5 because it's fine-tuning, but **you should experiment with this value**
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weight_decay: !!float 1e-2
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beta1: 0.9
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beta2: 0.96
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clip_grad_eps: 4
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injectors: # TODO: replace this entire sequence with the GptVoiceLatentInjector
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paired_to_mel:
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type: torch_mel_spectrogram
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mel_norm_file: ./models/tortoise/clips_mel_norms.pth
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in: wav
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out: paired_mel
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paired_cond_to_mel:
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type: for_each
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subtype: torch_mel_spectrogram
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mel_norm_file: ./models/tortoise/clips_mel_norms.pth
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in: conditioning
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out: paired_conditioning_mel
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to_codes:
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type: discrete_token
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in: paired_mel
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out: paired_mel_codes
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dvae_config: "./models/tortoise/train_diffusion_vocoder_22k_level.yml" # EXTREMELY IMPORTANT
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paired_fwd_text:
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type: generator
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generator: gpt
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in: [paired_conditioning_mel, padded_text, text_lengths, paired_mel_codes, wav_lengths]
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out: [loss_text_ce, loss_mel_ce, logits]
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losses:
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text_ce:
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type: direct
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weight: .01
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key: loss_text_ce
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mel_ce:
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type: direct
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weight: 1
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key: loss_mel_ce
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networks:
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gpt:
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type: generator
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which_model_G: unified_voice2 # none of the unified_voice*.py files actually match the tortoise inference code... 4 and 3 have "alignment_head" (wtf is that?), 2 lacks the types=1 parameter.
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kwargs:
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layers: 30 # WAS 8
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model_dim: 1024 # WAS 512
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heads: 16 # WAS 8
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max_text_tokens: 402 # WAS 120
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max_mel_tokens: 604 # WAS 250
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max_conditioning_inputs: 2 # WAS 1
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mel_length_compression: 1024
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number_text_tokens: 256 # supposed to be 255 for newer unified_voice files
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number_mel_codes: 8194
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start_mel_token: 8192
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stop_mel_token: 8193
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start_text_token: 255
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train_solo_embeddings: False # missing in uv3/4
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use_mel_codes_as_input: True # ditto
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checkpointing: True
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#types: 1 # this is MISSING, but in my analysis 1 is equivalent to not having it.
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#only_alignment_head: False # uv3/4
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path:
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pretrain_model_gpt: './models/tortoise/autoregressive.pth' # CHANGEME: copy this from tortoise cache
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strict_load: true
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#resume_state: ./models/tortoise/train_imgnet_vqvae_stage1/training_state/0.state # <-- Set this to resume from a previous training state.
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# afaik all units here are measured in **steps** (i.e. one batch of batch_size is 1 unit)
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train: # CHANGEME: ALL OF THESE PARAMETERS SHOULD BE EXPERIMENTED WITH
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niter: ${iterations}
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warmup_iter: -1
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mega_batch_factor: 4 # <-- Gradient accumulation factor. If you are running OOM, increase this to [2,4,8].
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val_freq: 500
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default_lr_scheme: MultiStepLR
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gen_lr_steps: [500, 1000, 1400, 1800] #[50000, 100000, 140000, 180000]
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lr_gamma: 0.5
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eval:
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output_state: gen
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injectors:
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gen_inj_eval:
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type: generator
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generator: generator
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in: hq
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out: [gen, codebook_commitment_loss]
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logger:
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print_freq: ${print_rate}
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save_checkpoint_freq: ${save_rate} # CHANGEME: especially you should increase this it's really slow
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visuals: [gen, mel]
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visual_debug_rate: ${print_rate}
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is_mel_spectrogram: true |