mirror of
https://github.com/Nighthawk42/llm-tts-factory.git
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Nighthawk
e7ee43de41
Major framework modernization and quality-of-life improvements: - Centralized Configuration: Replaced scattered, hardcoded hyperparameters and paths across all training/inference scripts with a single, documented `config.yaml` and `config_loader.py`. - OS-Aware Audio Pipeline: Introduced `utils/audio_utils.py` to handle cross-platform audio loading. Automatically routes Windows to a robust `ffmpeg` subprocess to bypass unstable Python audio bindings, while keeping `torchaudio` for Linux. - Dependency Management: Migrated from `requirements.txt` to `uv` with a fully configured `pyproject.toml`. Explicitly targets Python 3.12 and pulls PyTorch `cu128` wheels by default. - Dataset Fixes: Restored the missing `dataset_e2e.py` required for proper STFT/GAN decoder training and updated all dataloaders to utilize the new AudioPipeline. - Documentation & Housekeeping: Overhauled `README.md` with updated workflows, Windows instructions, and `uv` setup. Added a comprehensive `.gitignore` for virtual environments, model weights, and cache files.
97 lines
3.0 KiB
YAML
97 lines
3.0 KiB
YAML
# ==============================================================================
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# LLM-TTS-Factory Configuration
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# ==============================================================================
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# ------------------------------------------------------------------------------
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# Global Settings
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# ------------------------------------------------------------------------------
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global:
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seed: 1337
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device: "cuda:0"
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num_workers: 4
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use_wandb: true
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wandb_project: "soprano-tts"
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# ------------------------------------------------------------------------------
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# File Paths
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# Use forward slashes (/) for both Windows and Linux, or standard OS paths.
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# Relative paths are evaluated from the directory where the script is run.
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#
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# Examples:
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# Linux: "/home/ubuntu/data/lj_speech/LJSpeech-1.1"
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# Windows: "C:/Users/Name/Documents/datasets/LJSpeech-1.1"
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# Relative: "./datasets/LJSpeech-1.1"
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# ------------------------------------------------------------------------------
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paths:
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dataset_root: "./data/LJSpeech-1.1"
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# Base directory to save all checkpoints and logs
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save_dir: "./checkpoints"
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# Pretrained model paths (set to null if training from scratch)
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# Linux ex: "/home/ubuntu/soma/ckpt/suprano/codec/step_42000.pt"
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pretrained_codec_path: null
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pretrained_llm_path: null
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pretrained_decoder_path: null
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pretrained_discriminator_path: null
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# ------------------------------------------------------------------------------
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# Codec Training Configuration
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# ------------------------------------------------------------------------------
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codec:
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sample_rate: 32000
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batch_size: 16
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num_epochs: 100
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learning_rate: 1.0e-4
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freeze_encoder: false
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# ------------------------------------------------------------------------------
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# LLM Training Configuration
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# ------------------------------------------------------------------------------
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llm:
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from_scratch: false
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batch_size: 64
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max_steps: 150000
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max_lr: 2.0e-5
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min_lr_ratio: 0.3
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warmup_ratio: 0.3
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cooldown_ratio: 0.1
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grad_accum_steps: 1
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seq_len: 1024
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val_freq: 250
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save_freq: 5000
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text_factor: 0.5
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betas: [0.9, 0.95]
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weight_decay: 0.1
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# ------------------------------------------------------------------------------
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# Decoder (Vocos) Training Configuration
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# ------------------------------------------------------------------------------
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decoder:
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use_discriminator: true
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batch_size: 64
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max_steps: 200000
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max_lr: 2.0e-4
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min_lr_ratio: 0.1
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warmup_ratio: 0.2
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cooldown_ratio: 0.1
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grad_accum_steps: 1
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seq_len: 1024
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segment_size_samples: 32768 # ~1 sec (16 tokens)
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val_freq: 250
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text_factor: 0.0
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betas: [0.8, 0.99]
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weight_decay: 0.1
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# Loss Weights
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lambda_mel: 45.0
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lambda_fm: 2.0
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lambda_gen: 1.0
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lambda_stft: 1.0
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# ------------------------------------------------------------------------------
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# Data Generation Configuration (generate_dataset*.py)
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# ------------------------------------------------------------------------------
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data_generation:
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val_prop: 0.1
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val_max: 512 |