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Fishaudio S2 Pro MLX 4bit

fish-audio-research

KindOtherBase modelfishaudio/s2-proVersionRevision 8c4d2e0Cgrade

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

Sources

1 source
Hugging FacePrimaryVersion 8c4d2e0192c18a579f91a9ee67c005fa58d40a49
huggingface.co/3z1mmaj3/fishaudio-s2-pro-MLX-4bit/tree/8c4d2e0192c18a579f91a9ee67c005fa58d40a49

Verified Oct 19

Model artifacts

2 artifacts

codec.pth

pth · 1.74 GB · SHA-256 74fc41c5a715…bb2f · Hugging Face

Download

model.safetensors

safetensors · 2.39 GB · SHA-256 1cbb30334f1b…34c2 · Hugging Face

Download

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