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vanch007/mlx-indextts2-standard-8bit
mlx-indextts2-standard-8bit is a text-to-speech model from vanch007. Use it when you need text read aloud. It is set up for mlx. The card lists the license as mit.
This is a converted MLX IndexTTS2 model for Apple Silicon inference with solar2ain/mlx-indextts.
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.safetensors3 GB · 100%
From the Hugging Face model README
This is a converted MLX IndexTTS2 model for Apple Silicon inference with solar2ain/mlx-indextts.
It was prepared for the local /Users/vanch/index-tts IndexTTS2 optimization project, where the goal was stable Vietnamese and multilingual TTS on an M3 Max Mac without PyTorch MPS memory crashes.
/Users/vanch/index-tts/checkpointsmlx-indextts convert --quantize 8. In the current upstream implementation this quantizes GPT only; S2Mel and BigVGAN stay fp32.The repository root is a ready-to-use MLX IndexTTS2 model directory:
gpt.safetensorss2mel.safetensorsbigvgan.safetensorsvq2emb.safetensorstokenizer.modelconfig.yamlconfig.jsonfeat1.ptfeat2.ptwav2vec2bert_stats.ptInstall and use mlx-indextts:
git clone https://github.com/solar2ain/mlx-indextts.git
cd mlx-indextts
uv sync --extra convert --extra v2
huggingface-cli download vanch007/mlx-indextts2-standard-8bit \
--local-dir models/mlx-indextts2-standard-8bit \
--local-dir-use-symlinks False
uv run mlx-indextts generate \
-m models/mlx-indextts2-standard-8bit \
-r /path/to/reference_or_speaker.npz \
-t "Your text here" \
-o output.wav \
--memory-limit 24 \
--diffusion-steps 16
For repeated generation, precompute speaker conditioning first:
uv run mlx-indextts speaker \
-m models/mlx-indextts2-standard-8bit \
-r /path/to/reference.wav \
-o speaker.npz \
--memory-limit 24
Benchmarked on a 128GB unified-memory M3 Max Mac using:
mlx-indextts from solar2ain/mlx-indextts.npz speaker conditioningmemory_limit=24GBdiffusion_steps=16calm, emo_alpha=0.6RTF lower is faster:
| Case | fp32 MLX RTF | fp16 MLX RTF | 8bit MLX RTF | PyTorch MPS RTF |
|---|---|---|---|---|
| zh short | 1.127 | 1.538 | 0.966 | 1.446 |
| zh long | 1.232 | 1.584 | 1.035 | 1.699 |
| en short | 1.157 | 1.462 | 0.914 | 2.192 |
| en long | 1.193 | 1.511 | 0.956 | 1.783 |
Summary from the local comparison:
ASR validation with local mlx_whisper + whisper-large-v3-turbo found no empty audio, wrong-language output, or obvious missing sentences. Chinese long-form ASR showed a minor 她/他 homophone difference; English long-form 8-bit ASR showed a minor tense difference.
ASR was used only as an automated sanity check. Final production selection should still include human listening, especially for long-form Vietnamese narration.
This is an MLX conversion for local Apple Silicon inference, not the original PyTorch release. The original implementation and model family are associated with IndexTTS / IndexTTS2; the MLX runtime used here is solar2ain/mlx-indextts.
The benchmark numbers are environment-specific and should be treated as local M3 Max results, not universal performance guarantees.