VoxCPM2 — MLX bf16

Full-precision MLX port for Apple Silicon.

MLX port of openbmb/VoxCPM2 — a 2B-parameter multilingual diffusion-autoregressive TTS model with 48 kHz studio-quality output, voice cloning, and instruction-driven voice design.

Part of soniqo.audio — an on-device speech toolkit for Apple Silicon. Consumed by the open-source speech-swift library (module VoxCPM2TTS).

Bundle size: 4.96 GB

Use cases

Speech generation — 48 kHz TTS with voice design and multilingual support.
Voice cloning — reference-audio cloning + ultimate cloning (audio + transcript).
CLI reference — speech speak --engine voxcpm2 ... flags.
Getting started — install speech-swift on macOS / iOS.

Variants

Variant	Size	Notes
bf16	~5.0 GB	Reference quality, no Linear quantization.
int8	~3.0 GB	8-bit group quantization. Mean rel-L2 0.53 % vs bf16.

Capabilities

30 languages including English, Chinese, Indonesian, Japanese, Korean
48 kHz output
Zero-shot synthesis — generate speech from text alone
Voice cloning — clone a target speaker from a single reference clip
Voice design — natural-language style control (e.g. "young female voice, warm and gentle")
Ultimate cloning — reference audio + transcript for prosody-preserving cloning
Streaming generation — patch-level decoding for low-latency synthesis

Precision

No quantization. All Linear weights stored as bfloat16. Use this variant for reference quality or when memory is not a constraint.

Usage with `speech-swift`

This bundle is consumed by soniqo/speech-swift's VoxCPM2TTS Swift module.

import VoxCPM2TTS

let model = try await VoxCPM2TTSModel.fromPretrained(
    modelId: "aufklarer/VoxCPM2-MLX-bf16"
)
let audio = try await model.generate(text: "Hello from VoxCPM2.", language: "english")

Or via the CLI:

speech speak "Hello from VoxCPM2." --engine voxcpm2 --voxcpm2-variant bf16 -o hi.wav

Source

This bundle is converted from the upstream PyTorch weights at openbmb/VoxCPM2.

License

Apache 2.0 — inherited from the upstream openbmb/VoxCPM2 model.

Responsible use

Voice cloning capability is included. Users are responsible for obtaining consent for any voice that is cloned and for not using the model to impersonate individuals without their permission, generate disinformation, or commit fraud.