forked from mrq/ai-voice-cloning
139 lines
3.9 KiB
YAML
Executable File
139 lines
3.9 KiB
YAML
Executable File
name: '${voice}'
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model: extensibletrainer
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scale: 1
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gpu_ids: [0] # Manually edit this if the GPU you want to train on is not your primary, as this will set the env var that exposes CUDA devices
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start_step: 0
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checkpointing_enabled: true
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fp16: ${half_p}
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bitsandbytes: ${bitsandbytes}
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gpus: ${gpus}
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datasets:
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train:
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name: training
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n_workers: ${workers}
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batch_size: ${batch_size}
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mode: paired_voice_audio
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path: ${dataset_path}
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fetcher_mode: ['lj']
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phase: train
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max_wav_length: 255995 # ~11.6 seconds
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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
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n_workers: ${workers}
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batch_size: ${validation_batch_size}
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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
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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
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# Generally follows the recipe from the DALLE paper.
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optimizer: ${optimizer} # 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} # originally: 1e-4
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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:
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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"
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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: ${text_lr_weight}
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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: ${mel_lr_weight}
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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
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kwargs:
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layers: 30 # originally: 8
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model_dim: 1024 # originally: 512
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heads: 16 # originally: 8
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max_text_tokens: 402 # originally: 120
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max_mel_tokens: 604 # originally: 250
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max_conditioning_inputs: 2 # originally: 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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tortoise_compat: True
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# freeze_everything_but_position_embeddings: True
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path:
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strict_load: true
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${source_model}
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${resume_state}
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train:
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niter: ${iterations}
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warmup_iter: -1
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mega_batch_factor: ${gradient_accumulation_size}
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val_freq: ${validation_rate}
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ema_enabled: false # I really don't think EMA matters
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${learning_rate_scheme}
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eval:
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pure: ${validation_enabled}
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output_state: gen
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logger:
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save_checkpoint_freq: ${save_rate}
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visuals: [gen, mel]
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visual_debug_rate: ${save_rate}
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is_mel_spectrogram: true |