Fishaudio S2 Pro MLX 4bit
fish-audio-research
Model source
- Kind
- Other
- File size
- 1.74–2.39 GB
- Base model
- fishaudio/s2-pro
- Version
- vRevision 8c4d2e0
- Updated
- Oct 19, 2026
- Downloads
- 58
- Source
- Hugging Face
Source description
Fish Audio S2 Pro MLX 4-bit
MLX 4-bit quantization of fishaudio/s2-pro, produced with mlx-audio on Apple Silicon.
Built with Fish Audio.
Provenance
Converted directly from fishaudio/s2-pro bf16 safetensors via python -m mlx_audio.convert ... -q --q-bits 4 --q-group-size 64. The codec.pth audio decoder, tokenizer, and chat template are preserved alongside the quantized LLM weights.
Quantization
- Method: MLX affine quantization, group_size=64, 4-bit
- Actual bits per weight after conversion: ~5.0 (some small projections are skipped and remain bf16 — this is expected behavior for mlx.nn.quantize on multimodal models)
- Size on disk: ~2.4 GB (vs ~8 GB for bf16)
- Target: Apple Silicon with ≥ 16 GB unified memory (comfortable on M-series with 24 GB+)
Architecture
fish_qwen3_omni — a Qwen3-based decoder-only LLM that emits audio tokens consumed by the codec.pth DualAR codec. The text side is quantized; the codec is kept as-is.
Quickstart
from mlx_audio.tts import load
model = load("majentik/fishaudio-s2-pro-MLX-4bit")
audio = model.generate(
text="Hello, this is a test of Fish S2 Pro at 4-bit MLX.",
voice="en_default",
)
Fish S2 Pro supports zero-shot voice cloning from a reference audio sample; see the base model card for the reference-conditioning API.
License & Use Restrictions
This model is a derivative work of Fish Audio S2 Pro, licensed under the Fish Audio Research License.
- Research + Non-Commercial use: free, subject to the license.
- Commercial use: requires a separate written agreement with Fish Audio — see https://fish.audio or email
[email protected]. - Use restriction: outputs must not be used to create or improve any foundational generative AI model (excluding the Model itself or derivatives of it).
- Attribution: downstream use must include this NOTICE and display "Built with Fish Audio" in any product/service/interface that surfaces model outputs.
See LICENSE.md and NOTICE files bundled with this repository.
Languages
English, Chinese, Japanese, Korean, Spanish, Portuguese, Arabic, Russian, French, German (per base model card).
See also
- Base: fishaudio/s2-pro
- bf16 MLX: mlx-community/fish-audio-s2-pro-bf16
- 8-bit MLX: mlx-community/fishaudio-s2-pro-8bit-mlx
- mlx-audio: https://github.com/Blaizzy/mlx-audio
- Garden hub: majentik/garden
Sources
1 source
Sources
1 sourceVerified Oct 19
Model artifacts
2 artifacts
Model artifacts
2 artifactsSource excerpts
3 excerpts
Source excerpts
3 excerpts--- library_name: mlx-audio tags: - mlx - mlx-audio - text-to-speech - speech - speech-generation - voice-cloning - tts - fish-audio - s2-pro - quantized - 4bit - fish_qwen3_omni base_model: fishaudio/s2-pro license: other license_name: fish-audio-research license_link: https://huggingface.co/fishaudio/s2-pro/blob/main/LICENSE pipeline_tag: text-to-speech language: - en - zh - ja - ko - es - pt - ar - ru - fr - de --- # Fish Audio S2 Pro MLX 4-bit MLX 4-bit quantization of [fishaudio/s2-pro](https://huggingface.co/fishaudio/s2-pro), produced with [mlx-audio](https://github.com/Blaizzy/mlx-audio) on Apple Silicon. **Built with Fish Audio.** ## Provenance Converted directly from `fishaudio/s2-pro` bf16 safetensors via `python -m mlx_audio.convert ... -q --q-bits 4 --q-group-size 64`. The `codec.pth` audio decoder, tokenizer, and chat template are preserved alongside the quantized LLM weights. ## Quantization - Method: MLX affine quantization, group_size=64, 4-bit - Actual bits per weight after conversion: **~5.0** (some small projections are skipped and remain bf16 — this is expected behavior for mlx.nn.quantize on multimodal models) - Size on disk: ~2.4 GB (vs ~8 GB for bf16) - Target: Apple Silicon with ≥ 16 GB unified memory (comfortable on M-series with 24 GB+) ## Architecture `fish_qwen3_omni` — a Qwen3-based decoder-only LLM that emits audio tokens consumed by the `codec.pth` DualAR codec. The text side is quantized; the codec is kept as-is. ## Quickstart ```python from mlx_audio.tts import load model = load("majentik/fishaudio-s2-pro-MLX-4bit") audio = model.generate( text="Hello, this is a test of Fish S2 Pro at 4-bit MLX.", voice="en_default", ) ``` Fish S2 Pro supports zero-shot voice cloning from a reference audio sample; see the base model card for the reference-conditioning API. ## License & Use Restrictions This model is a derivative work of **Fish Audio S2 Pro**, licensed under the **Fish Audio Research License**. - **Research + Non-Commercial use:** free, subject to the license. - **Commercial use:** requires a separate written agreement with Fish Audio — see https://fish.audio or email `[email protected]`. - **Use restriction:** outputs must not be used to create or improve any foundational generative AI model (excluding the Model itself or derivatives of it). - **Attribution:** downstream use must include this NOTICE an...
Source context: 58 downloads · 0 likes · Pipeline text-to-speech · Library mlx-audio · Repo 3z1mmaj3/fishaudio-s2-pro-MLX-4bit
Source context: 77 downloads · 0 likes · Pipeline text-to-speech · Library mlx-audio · Repo 3z1mmaj3/fishaudio-s2-pro-MLX-4bit
