dubbing-ai/SiangTTS-VoxCPM2-Thai-LoRA

SiangTTS — Thai Voice-Cloning TTS (VoxCPM2 LoRA)

SiangTTS (เสียง = voice) is a LoRA adapter for openbmb/VoxCPM2 that gives it clear, natural Thai speech with zero-shot voice cloning, trained on a single RTX 3090 (24 GB).

It's a parameter-efficient (LoRA) fine-tune of VoxCPM2 — small enough to train on one consumer GPU while keeping the base model's voice-design and cloning abilities. The Thai-adaptation approach is inspired by JaiTTS (a separate, closed-source Thai VoxCPM model by others); SiangTTS is an independent open reproduction at LoRA scale and is not affiliated with it.

🔊 Listen / compare (ref vs ground-truth vs base vs SiangTTS): https://dubbing-ai.github.io/VoxCPM-thai/ · Code: https://github.com/dubbing-ai/VoxCPM-thai

Results

Measured with Typhoon-Whisper-Large-v3 (Thai ASR; CER) and WavLM x-vectors (speaker SIM) on small eval sets, so numbers are directional. CER is an upper bound on error — the ASR judge itself mis-recognises some rare/archaic Thai words the model pronounces correctly.

VoxCPM2's base is already a capable Thai speaker (it reads numerals and handles long-form). SiangTTS clones at essentially the real same-speaker similarity ceiling and is as intelligible as the original recordings, with much lower CER than the base. The GT column is the real recording, included as the reference.

Voice cloning (80 prompts)	GT (real rec.)	Base	SiangTTS
Intelligibility — CER ↓	0.97%	3.26%	0.84%
Speaker similarity — SIM ↑	0.913	0.906	0.909

(CER ≤ GT reflects clean, ASR-friendly synthesis plus the ASR judge's own floor; SIM ≈ GT means cloning is about as close as two real recordings of one speaker.) Separately, short-form Thai CER 5.7%→3.8% and long-form 2.7%→1.6% (small 5 / 2 -prompt sets — directional).

Trained 2 epochs over ~205 h: Common Voice Thai (diverse speakers) + porjai_central (studio-clean) + a LibriTTS-R English slice (retains English & code-switching). Audio encoded at 16 kHz, generated at 48 kHz (VoxCPM2 design).

Usage

from voxcpm import VoxCPM
from voxcpm.model.voxcpm2 import LoRAConfig
import json

cfg = json.load(open("lora_config.json"))["lora_config"]
model = VoxCPM.from_pretrained(
    "openbmb/VoxCPM2",
    lora_config=LoRAConfig(**cfg),
    lora_weights_path=".",        # dir holding lora_weights.safetensors
)

# Plain TTS
wav = model.generate(text="สวัสดีครับ ยินดีที่ได้รู้จัก", cfg_value=2.5, inference_timesteps=10)

# Voice cloning from a 3-10 s reference clip
wav = model.generate(text="ทดสอบการโคลนเสียง", reference_wav_path="ref.wav",
                     cfg_value=2.5, inference_timesteps=10)

Or via the CLI: voxcpm clone --text "..." --reference-audio ref.wav --lora-path . -o out.wav

Limitations

• Rare archaic / liturgical Pali-Sanskrit vocabulary may occasionally be mispronounced (no phonetic-respelling dict is used — raw-text approach).
• Eval prompt sets are small; numbers are directional.
• Best for everyday/conversational Thai; not tuned for specific domains.

License

cc-by-sa-4.0 - inherited from the most restrictive training-data license (porjai_central is CC-BY-SA-4.0; Common Voice is CC0; LibriTTS-R is CC-BY-4.0). Commercial use is permitted under share-alike. The base model openbmb/VoxCPM2 is Apache-2.0; SiangTTS code is Apache-2.0.