voxtral-4b-tts-GGUF

Voxtral-4B-TTS-2603 — GGUF

GGUF / ggml conversions of mistralai/Voxtral-4B-TTS-2603 for use with the voxtral-tts backend of CrispStrobe/CrispASR — one C++ binary, no Python.

Text-to-speech across 9 languages (en, fr, de, es, it, pt, nl, ar, hi) with 20 preset voices, 24 kHz output.

Architecture

A three-component pipeline, all implemented as ggml compute graphs:

1. LLM backbone — Ministral-3B autoregressive decoder (26 layers, GQA 32/8, NORMAL/adjacent-pair RoPE θ=1e6). Conditioned on a preset voice prefix + text, it emits one hidden state per audio frame.
2. Flow-matching acoustic transformer — 3-layer bidirectional transformer (no positional encoding). Per frame: a semantic token (greedy argmax) plus 36 acoustic FSQ codes via an 8-step (7-interval) Euler ODE with classifier-free guidance (α=1.2).
3. Voxtral codec decoder — 292-d input (256-d semantic VQ + 36-d FSQ) → causal conv → 4× [2-layer ALiBi transformer + ConvTranspose1d upsampling] → 240 PCM samples/frame at 24 kHz.

Files

The semantic VQ codebook (codec.semantic_cb.weight) and preset voice embeddings are kept at F32 in every file.

Usage

# auto-downloads the Q4_K on first run
crispasr --backend voxtral-tts -m auto --auto-download \
    --tts "Bonjour le monde." --voice fr_female --tts-output out.wav

--voice accepts any preset (e.g. neutral_female, neutral_male, casual_female, cheerful_female, fr_female, de_male, es_female, it_male, pt_female, nl_male, ar_male, hi_female, …). Omit it for the default voice. Add --seed N for a reproducible acoustic sample.

License

The GGUF conversions inherit the base model's CC-BY-NC-4.0 license (non-commercial). See mistralai/Voxtral-4B-TTS-2603 for the original weights and terms.

Provenance and EU AI Act Art. 53 note

• Upstream model: mistralai/Voxtral-4B-TTS-2603 — published by mistralai.
• Upstream licence: cc-by-nc-4.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository. No training-content summary was found on the upstream model card at the time of writing; that documentation gap is upstream's and is not filled here.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.