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sonselfa/CosyVoice2-KO-SFT-v6-epoch3
CosyVoice2-KO-SFT-v6-epoch3 is a text-to-speech model from sonselfa. Use it when you need text read aloud. It is set up for cosyvoice. The card lists the license as apache-2.0.
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Updated Jul 10, 2026
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From the Hugging Face model README
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한국어 억양과 감정·발화 스타일을 강화하기 위해 CosyVoice2-0.5B의 LLM 부분을 supervised fine-tuning한 community checkpoint입니다. Claude와 Codex에 연결하는 로컬 MCP는 cosyvoice2-ko-sft-mcp 저장소에서 제공합니다.
0.5B는 언어모델 코어의 규모를 뜻합니다. 전체 TTS 실행에는 flow, vocoder, tokenizer, speaker encoder 및 ONNX 구성요소가 추가로 필요합니다.
| GPU | Runtime | Result |
|---|---|---|
| RTX 5060 Ti 16GB | PyTorch 2.8.0, CUDA 12.8, FP16 | 7.04s WAV, RTF 1.31, peak allocated 3.38GiB, peak reserved 4.06GiB |
| RTX 3090 24GB | PyTorch 2.3.1, CUDA 12.1 | 7.76s WAV, RTF 1.07 |
| RTX 3060 12GB | 12GB-class target | Supported with memory headroom based on the measured 4.06GiB peak reserve |
Exact throughput depends on driver, PyTorch build, prompt length, and whether the model cache is warm.
| Item | Value |
|---|---|
| Base model | FunAudioLLM/CosyVoice2-0.5B |
| Fine-tuned component | CosyVoice2 LLM |
| Selected checkpoint | SFT v6 Epoch 3 |
| Training precision | BF16 autocast |
| Saved tensor dtype | FP32 |
| Gradient accumulation | 32 |
| Learning rate | 1e-5 |
| Checkpoint metadata | epoch 3, step 344 |
| State-dict tensor entries | 295 |
| State-dict parameter entries | 641,938,468 |
| Sample rate | 24 kHz |
Epoch 3 was selected after per-epoch validation and listening comparison. The recorded validation
snapshot reports loss 3.0528906387 and accuracy 0.2699536899. These training metrics are not a
standalone measure of perceived TTS quality; broader CER, speaker-similarity, RTF, and loudness
benchmarks will be added in a later model-card revision.
This repository intentionally contains only the fine-tuned LLM artifact. Download the remaining
components from the pinned FunAudioLLM/CosyVoice2-0.5B base model:
cosyvoice2.yamlCosyVoice-BlankEN/flow.pthift.ptcampplus.onnxspeech_tokenizer_v2.onnxThe companion MCP installer assembles the base snapshot and overlays llm.safetensors
automatically.
git clone --recursive https://github.com/feedingstick321-maker/cosyvoice2-ko-sft-mcp.git
cd cosyvoice2-ko-sft-mcp
powershell -ExecutionPolicy Bypass -File .\scripts\install.ps1
.\.venv\Scripts\cosyvoice-ko-prepare.exe
Then register the executable with an MCP client:
codex mcp add cosyvoice-ko -- "$PWD\.venv\Scripts\cosyvoice-ko-mcp.exe"
claude mcp add --transport stdio --scope user cosyvoice-ko -- "$PWD\.venv\Scripts\cosyvoice-ko-mcp.exe"
The model was fine-tuned using AI Hub's 감성 및 발화 스타일별 음성합성 데이터. The source audio, labels, parquet files, speech tokens, embeddings, and production audio are not included in this repository.
본 모델은 과학기술정보통신부의 재원으로 한국지능정보사회진흥원의 지원을 받아 구축된 AI Hub의 '감성 및 발화 스타일별 음성합성 데이터'를 활용하여 학습되었습니다.
Dataset page: https://www.aihub.or.kr/aihubdata/data/view.do?dataSetSn=466
spk2info.pt; use a reference
voice you have permission to use.Do not impersonate people, evade consent, or distribute generated audio deceptively. Users are responsible for the rights to every registered reference voice and for clearly disclosing synthetic audio where required.
Code and base model lineage are Apache-2.0. See LICENSE and NOTICE. This is a community model and
is not an official FunAudioLLM, Alibaba, NIA, Anthropic, or OpenAI release.