| --- |
| license: apache-2.0 |
| tags: |
| - text-to-speech |
| - omnivoice |
| - lora |
| - low-resource |
| --- |
| |
| # Per-language LoRA adapters for OmniVoice — Hmong, Bahnar, Khmer |
|
|
| Three low-rank adapters over a **frozen** multilingual OmniVoice base. Each adapter |
| is ~114 MB; the 2.45 GB base is shared between all of them. |
|
|
| ```bash |
| pip install -r requirements.txt |
| |
| python infer_lora.py --adapter adapters/km --text "សួស្តី" --output out.wav \ |
| --ref_audio demo_voices/km_female.wav --ref_text "<the clip's transcript>" |
| ``` |
|
|
| Paths resolve relative to the repo, so nothing needs editing after cloning. |
|
|
| ## Adapters |
|
|
| | dir | language | code | notes | |
| |-----|----------|------|-------| |
| | `adapters/km` | Khmer | `km` | in the base model's training mix | |
| | `adapters/bdq` | Bahnar | `bdq` | in the base model's training mix | |
| | `adapters/hmongv` | Hmong | `hmongv` | **new language**, not seen by the base | |
|
|
| `hmongv` is the **Vietnam-based Latin orthography** for Hmong (Vietnamese diacritics |
| plus final-consonant tone letters, e.g. *"Thâuv txos cheix ntux yiêz"*). It is a |
| different writing system from RPA White Hmong (*"Txheeb xyuas cov kab lus"*), which |
| appears in the same source corpus under `hmongz`. The two are deliberately **not** |
| merged — one tag for two orthographies would give the model contradictory spellings |
| for identical sounds. |
|
|
| ## What is trained |
|
|
| | component | params | trained | |
| |---|---|---| |
| | Qwen3 backbone (28 layers) | 596M | frozen | |
| | LoRA r=16 on all attn + MLP projections | 10.1M | yes | |
| | `audio_embeddings` (8200×1024) | 8.4M | yes | |
| | `audio_heads` (1024×8200) | 8.4M | yes | |
| | text embedding (151676×1024) | 155M | frozen by default | |
|
|
| **26.9M / 639.5M trainable (4.2%).** The audio embedding and head tables are trained |
| in full because adapting to a new language is largely a matter of re-mapping which |
| audio tokens follow which text, and a rank-16 update inside the backbone is a narrow |
| channel for that. The text embedding is left frozen: it alone is nine times larger |
| than everything else trainable combined, and Qwen3's byte-level BPE tokenizes unseen |
| orthographies losslessly into known subwords. `train_lora.py --train_text_embed` |
| turns it on. |
|
|
| ## Contents |
|
|
| ``` |
| train_lora.py LoRA training (base frozen) |
| infer_lora.py inference; load_lora_model() is the reusable entry point |
| hmong_to_lhotse.py Hmong ASR corpus -> Lhotse Shar |
| parquet_to_lhotse.py elego VC parquet -> Lhotse Shar |
| extract_tokens_parquet.py Lhotse/parquet -> Higgs audio tokens |
| lhotse_dataset.py shared Lhotse reader |
| parquet_dataset.py shared parquet reader |
| registry.py corpus registry |
| adapters/{km,bdq,hmongv}/ adapter weights + adapter_meta.json |
| base/ frozen base checkpoint (inference weights) |
| demo_voices/ reference clips + transcripts |
| ``` |
|
|
| ## Reproducing |
|
|
| ```bash |
| # 1. data -> Lhotse -> audio tokens |
| python hmong_to_lhotse.py --max_hours 150 |
| python extract_tokens_parquet.py --lhotse_dir /path/lhotse/hmongv/train \ |
| --language_id hmongv --output_dir /path/tokens/hmongv/train --gpu_ids 0,1,2,3 |
| |
| # 2. one adapter per language, one GPU each |
| CUDA_VISIBLE_DEVICES=0 python train_lora.py --language hmongv \ |
| --output_dir exp/lora_hmongv --steps 8000 |
| ``` |
|
|
| ## Notes and gotchas |
|
|
| - **LoRA targets are a regex anchored on `llm.layers.N.`**, not bare module names. At |
| inference OmniVoice attaches the Higgs audio tokenizer, whose encoder also has |
| `q_proj`/`k_proj`/`v_proj`; bare names match those too and peft then injects |
| untrained adapters into the audio codec. |
| - **Ids must not contain dots.** WebDataset splits a member name at the first dot to |
| derive its key, so an id like `hmongv_413024.0_00000002` is truncated and every |
| label lookup fails — at training time, long after tokenization reports success. |
| - The Hmong training set excludes the ~44% of the source corpus that are synthetic |
| augmentations (reverb / pitch-shift / time-mask). Those are fine as ASR inputs and |
| ruinous as TTS targets — the model would learn to reproduce the reverb. |
| - Hmong speaker/gender metadata is largely absent, so its two reference clips are |
| labelled `voice1`/`voice2` rather than male/female. |
| - Use `sdpa` attention. `flex_attention`'s Triton kernels fail on torch 2.7.1+cu118. |
|
|
| ## Base model |
|
|
| OmniVoice (Qwen3-0.6B + Higgs audio tokens) finetuned jointly on ~1,820 h across ten |
| languages of Vietnam and Cambodia, 60k steps. Also published at |
| [`shadwl/voice-demo`](https://huggingface.co/datasets/shadwl/voice-demo). |
|
|
