bilibili.model.use.license.agreement
Source du modèle
Description de la source
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 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.
Sources
1 sourceVérifié 13 sept.
Artefacts du modèle
1 artefactExtraits de sources
2 extraitsIndexTeam/IndexTTS-2IndexTeam/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