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This repository contains a converted native MIT2 model bundle for
IndexTTS2, prepared for the index-tts2-metal Apple Silicon runtime.
The weights are derived from IndexTeam/IndexTTS-2
and converted from the original PyTorch checkpoint layout into the MIT2 bundle
format used by the native Metal runtime. This is not a fine-tuned model and does
not change the model architecture or training data; it is a runtime-specific
conversion for local inference on Apple Silicon.
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1 fonteVerificado 13 de set.
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechosIndexTeam/IndexTTS-2metal-indextts2The converted bundle is expected to contain:
bin/
manifest.json
weights.bin
tokenizer/
bpe.model
pinyin.vocab
config.yaml
configuration.json
bigvgan_config.json
Depending on how the bundle was converted, it may also include optional clone-time components such as semantic codec, BigVGAN, CAMPPlus, W2V-BERT, and W2V-BERT statistics.
Voice profiles are separate from the model bundle. Use a converted voice bundle or create one from reference audio with the native runtime.
This bundle is intended for local text-to-speech inference with
metal-indextts2 on Apple Silicon. Typical use cases include:
This repository is not intended to be loaded directly with transformers,
diffusers, or the default Hugging Face Inference API.
metal-indextts2 native runtime built from sourceBuild the runtime:
cd index-tts2-metal
./build.sh
Download this model repository so the converted bundle is available locally.
If the repository stores the bundle under bin/, pass that directory to the
runtime.
./build/mtts --server \
--host 127.0.0.1 \
--port 3456 \
--model_bundle /path/to/model-repo/bin
./build/mtts --web \
--host 127.0.0.1 \
--port 3456 \
--model_bundle /path/to/model-repo/bin
Then open:
http://127.0.0.1:3456/web
./build/mtts --tts \
/path/to/model-repo/bin \
/path/to/voice-bundle \
"今天的天气不错,我们去划船吧。" \
out.wav
--cfm_steps controls the number of CFM synthesis steps. Lower values are
faster; higher values usually preserve more acoustic quality.
./build/mtts --cfm_steps 16 --tts \
/path/to/model-repo/bin \
/path/to/voice-bundle \
"今天的天气不错,我们去划船吧。" \
out.wav
./build/mtts --clone \
/path/to/model-repo/bin \
reference.wav \
/path/to/output-voice-bundle
The bundle can be regenerated from the original PyTorch checkpoint with:
python -m metal_indextts2.tools.convert_model \
--checkpoint-dir /path/to/IndexTTS-2 \
--output bin \
--force-dtype f16
Add optional conversion arguments if you need native voice cloning support:
python -m metal_indextts2.tools.convert_model \
--checkpoint-dir /path/to/IndexTTS-2 \
--output bin \
--force-dtype f16 \
--semantic-codec /path/to/semantic_codec/model.safetensors \
--bigvgan-checkpoint /path/to/bigvgan/generator.pth \
--bigvgan-config /path/to/bigvgan/config.json \
--campplus-checkpoint /path/to/campplus.ckpt \
--w2v-bert-dir /path/to/w2v-bert-2.0 \
--w2v-stats /path/to/wav2vec2bert_stats.pt
metal-indextts2; it is not a standard
transformers checkpoint.The original model is distributed by Index Team / bilibili under the
bilibili Model Use License Agreement.
This converted bundle is a derivative/runtime-format conversion of the original
weights, so users should review and comply with the original license terms.
If you redistribute this converted bundle, include the original license files
from IndexTeam/IndexTTS-2 in the model repository.
If you use this model, cite the original IndexTTS2 work:
@article{zhou2025indextts2,
title={IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speech},
author={Siyi Zhou and Yiquan Zhou and Yi He and Xun Zhou and Jinchao Wang and Wei Deng and Jingchen Shu},
journal={arXiv preprint arXiv:2506.21619},
year={2025}
}
@article{deng2025indextts,
title={IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System},
author={Wei Deng and Siyi Zhou and Jingchen Shu and Jinchao Wang and Lu Wang},
journal={arXiv preprint arXiv:2502.05512},
year={2025},
doi={10.48550/arXiv.2502.05512},
url={https://arxiv.org/abs/2502.05512}
}
This conversion targets the Apple Silicon runtime in metal-indextts2 and is
based on the original IndexTTS2 model released by Index Team.
--- license: other license_name: bilibili.model.use.license.agreement license_link: https://huggingface.co/IndexTeam/IndexTTS-2/blob/main/LICENSE.txt language: - zh - en pipeline_tag: text-to-speech library_name: metal-indextts2 base_model: IndexTeam/IndexTTS-2 base_model_relation: quantized tags: - text-to-speech - tts - zero-shot-tts - voice-cloning - indextts2 - index-tts2 - apple-silicon - metal - mps - mit2 - converted - fp16 - macos inference: false --- # IndexTTS2 Metal Native Bundle This repository contains a converted native MIT2 model bundle for IndexTTS2, prepared for the [`index-tts2-metal`](https://github.com/raoqu/index-tts2-metal) Apple Silicon runtime. The weights are derived from [`IndexTeam/IndexTTS-2`](https://huggingface.co/IndexTeam/IndexTTS-2) and converted from the original PyTorch checkpoint layout into the MIT2 bundle format used by the native Metal runtime. This is not a fine-tuned model and does not change the model architecture or training data; it is a runtime-specific conversion for local inference on Apple Silicon. ## Model Details - **Model type:** text-to-speech / zero-shot voice cloning - **Base model:** [`IndexTeam/IndexTTS-2`](https://huggingface.co/IndexTeam/IndexTTS-2) - **Runtime:** `metal-indextts2` - **Target hardware:** Apple Silicon Macs - **Target OS:** macOS with Metal and MetalPerformanceShaders - **Bundle format:** MIT2 native bundle - **Expected precision:** fp16 converted weights, with runtime-specific fp32 accumulation where implemented - **Hosted inference:** disabled; this bundle requires the native runtime ## What Is Included The converted bundle is expected to contain: ```text bin/ manifest.json weights.bin tokenizer/ bpe.model pinyin.vocab config.yaml configuration.json bigvgan_config.json ``` Depending on how the bundle was converted, it may also include optional clone-time components such as semantic codec, BigVGAN, CAMPPlus, W2V-BERT, and W2V-BERT statistics. Voice profiles are separate from the model bundle. Use a converted voice bundle or create one from reference audio with the native runtime. ## Intended Use This bundle is intended for local text-to-speech inference with `metal-indextts2` on Apple Silicon. Typical use cases include: - local CJK text-to-speech generation, - voice cloning from a reference WAV file, - running an HTTP TTS service on macOS, - exp...
Source context: 2 downloads · 0 likes · Pipeline text-to-speech · Library metal-indextts2 · Repo raoqu/index-tts2-metal