Palash Debnath
5cab8e0149
Renames what users see. The app, the installers, the window title, the
docs and all 21 locales now say VoiceStudio, with "(previously
OmniVoice-Studio)" noted near the title of each doc surface so people
recognise it.
Deliberately NOT renamed, because renaming any of them silently breaks
an existing install — there is no legacy-path fallback anywhere in this
codebase:
- bundle identifier com.debpalash.omnivoice-studio (MSI UpgradeCode,
macOS TCC grants, managed venv, WebView localStorage, the
single-instance lock)
- data directories OmniVoice / .omnivoice and omnivoice.db
- the ~150 OMNIVOICE_* environment variables
- the X-OmniVoice-* HTTP headers (a wire protocol)
- the published Docker image paths
- the OmniVoice ENGINE, which is a model name and not this product
tests/test_identity_paths_survive_the_rename.py pins every one of those
so a future well-meaning sweep cannot orphan a user's library.
Linux .deb users install a new package name and should apt remove
omnivoice-studio; that note is in the changelog.
1.6 KiB
1.6 KiB
Training
Training Config
All training is controlled by a JSON training config file and a JSON data config file.
See examples/config/ for ready-to-use configs.
Training config file on Emilia is: examples/config/train_config_emilia.json
Data config file for Emilia is: examples/config/data_config_emilia.json
Key fields in training config file:
| Field | Description | Default |
|---|---|---|
llm_name_or_path |
local LLM path or huggingface id | Qwen/Qwen3-0.6B |
steps |
Total training steps | 300,000 |
learning_rate |
Peak learning rate | 1e-4 |
batch_tokens |
Tokens per batch on each GPU | 8192 |
output_dir and data_config are passed via command line (see below).
Launching Training
accelerate launch \
--gpu_ids "0,1,2,3,4,5,6,7" \
--num_processes 8 \
-m omnivoice.cli.train \
--train_config config/train_config_emilia.json \
--data_config config/data_config_emilia.json \
--output_dir exp/omnivoice_emilia
Resuming Training
Set resume_from_checkpoint in your training config to resume from an existing checkpoint:
{
"resume_from_checkpoint": "exp/omnivoice/checkpoint-100000"
}
Initializing from a Pretrained Model
To start training from a pretrained VoiceStudio checkpoint (for fine-tuning):
{
"init_from_checkpoint": "exp/omnivoice/checkpoint-100000"
}
Monitoring
Training logs to TensorBoard:
tensorboard --logdir exp/omnivoice_emilia/tensorboard