Instructions to use vanch007/mlx-indextts2-2.5-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use vanch007/mlx-indextts2-2.5-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir mlx-indextts2-2.5-8bit vanch007/mlx-indextts2-2.5-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
mlx-indextts2-2.5-8bit
IndexTTS 2.5 converted to native MLX weights for Apple Silicon. Use it with
vanch007/mlx-indextts2.
Variant
- Precision / quantization: 8bit
- Approximate local size: 1.6GB
- Source model:
IndexTeam/IndexTTS-2.5 - Source revision:
d0aa86e75bb6f3437f3831e95056fa72842d89ef - Conversion status: strict manifest pass
- Details: GPT uses persistent 8-bit group quantization; the remaining components use float16. This is the recommended default variant.
The model supports Chinese, English, Japanese, Spanish, and Arabic, including cross-lingual voice transfer, separate speaker/emotion references, manual and text-derived emotion control, and Pinyin/CMU/Kana pronunciation annotations.
Download and Run
git clone https://github.com/vanch007/mlx-indextts2.git
cd mlx-indextts2
uv sync --extra v25
hf download vanch007/mlx-indextts2-2.5-8bit \
--local-dir models/mlx-indextts2-2.5-8bit
uv run mlx-indextts generate \
--profile v25 \
-m models/mlx-indextts2-2.5-8bit \
--language zh \
-r /path/to/reference.wav \
-t "你好,这是 IndexTTS 2.5 的 MLX 推理测试。" \
-o output.wav
See the project README and IndexTTS 2.5 guide for batch, API, WebUI, streaming, duration, pronunciation, and emotion-control examples.
Included Files
gpt.safetensorscodec.safetensorss2mel.safetensorsbigvgan.safetensorsmultilingual_zh_ja_yue_char_del.tiktokenconfig.yamlandconfig.jsonmodel_manifest.jsonandconversion_report.json- reference preprocessing statistics/features
LICENSE
The runtime additionally resolves the manifest-declared
facebook/w2v-bert-2.0 and funasr/campplus preprocessing dependencies.
Validation Boundary
The converted component coverage and strict load checks passed. The project validation matrix also covers five-language synthesis, ASR sanity checks, speaker similarity, emotion modes, cross-lingual generation, batch/API/WebUI, and completed-segment streaming. Results are hardware- and sample-specific; human listening remains recommended before production use.
License and Derivative Notice
The repository code is MIT-licensed, but these converted model weights are a Derivative Work governed by the included bilibili Model Use License Agreement. Review its commercial thresholds, downstream obligations, use restrictions, and prohibited high-risk scenarios before downloading or using the model.
Any modifications made to the original model in this Derivative Work are not endorsed, warranted, or guaranteed by the original right-holder of the original model, and the original right-holder disclaims all liability related to this Derivative Work.
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Base model
IndexTeam/IndexTTS-2.5