Experimental pure C inference engine for Mistral's Voxtral-4B-TTS text-to-speech model. Zero external dependencies beyond the C standard library and math. Reads weights directly from safetensors via memory-mapped I/O.

Note: This project is an experiment to create a pure C implementation of Voxtral TTS. It is not production-ready and no further optimization work is planned (even if I'm considering to base it on ggml). Contributions are welcome and encouraged!

Sample: Hello world

• Single-file model loading from consolidated.safetensors
• BF16 weights accessed directly from mmap (no full conversion needed)
• 20 preset voices across 9 languages
• WAV output at 24kHz
• Optional BLAS acceleration (OpenBLAS, Apple Accelerate)
• NEON-optimized BF16 matvec on ARM

Voxtral TTS is a three-stage pipeline:

Text --> LLM Backbone (3.4B, 26-layer Mistral)
     --> Flow-Matching Acoustic Transformer (390M, 3 layers, 8 Euler steps)
     --> Audio Codec Decoder (300M, 4-stage conv + ALiBi transformer)
     --> 24kHz waveform

The LLM autoregressively generates hidden states conditioned on text and a voice prompt. The acoustic transformer converts each hidden state to 37 audio codes (1 semantic + 36 acoustic) via flow matching with classifier-free guidance. The codec decoder converts all collected codes into a raw waveform.

# Linux with OpenBLAS (recommended)
make blas

# CUDA + OpenBLAS (requires NVIDIA GPU + CUDA toolkit)
make cuda

# Specify GPU architecture (default: sm_80 for Ampere)
make cuda CUDA_ARCH=sm_89   # Ada Lovelace (RTX 4090)
make cuda CUDA_ARCH=sm_90   # Hopper (H100)
make cuda CUDA_ARCH=sm_100  # Blackwell (B200)

# macOS with Accelerate
make apple

# Portable (no BLAS, slower)
make noblas

The CUDA build uploads all model weights to GPU VRAM and runs the full 26-layer LLM forward pass on GPU using cuBLAS for GEMM and custom CUDA kernels for RMS norm, RoPE, attention, and SwiGLU activation.

# Requires hf CLI (pip install huggingface_hub[cli]) or wget
./download_model.sh voxtral-tts-model

This downloads the model weights (~8GB), tokenizer, and voice embeddings from HuggingFace.

./voxtral_tts -d voxtral-tts-model -v neutral_female -o output.wav "Hello world"

Usage: ./voxtral_tts [options] "text to speak"

  -d <dir>        Model directory (required)
  -v <voice>      Voice name (default: neutral_female)
  -o <file>       Output WAV file (default: output.wav)
  -s <seed>       Random seed for reproducibility
  --verbose       Enable verbose output
  --inspect       Print model tensor info and exit

DGX Spark (NVIDIA GB10 Blackwell, 128GB unified memory, ARM Grace CPU). LLM decode on GPU via cuBLAS + custom CUDA kernels, prefill and codec on CPU.

• ~0.4s per audio frame for decode (12x faster than CPU)
• RTF ~7-10x for longer texts (fixed ~40s overhead for model load + prefill)
• Further speedups possible: GPU prefill, GPU codec, CUDA graphs

AMD Ryzen 9 9950X3D (16-core), 84GB RAM, OpenBLAS 0.3.26. Pure CPU inference.

• ~4.8s per audio frame (each frame = 80ms of audio at 12.5 Hz)
• RTF ~58x for typical inputs

• Peak RSS: ~7.8 GB (8GB model weights mmap'd)
• Binary size: 86 KB
• Each audio frame requires a full 26-layer LLM forward pass (3.4B parameters) plus 14 acoustic transformer forward passes (7 Euler steps x 2 for classifier-free guidance)

Run ./bench.sh to reproduce these benchmarks on your machine.

voxtral_tts.h                 Main header (constants, structs, API)
voxtral_tts.c                 Model loading and inference orchestrator
voxtral_tts_llm.c             26-layer Mistral decoder with KV cache
voxtral_tts_acoustic.c        Flow-matching acoustic transformer
voxtral_tts_codec.c           Audio codec decoder (ALiBi + weight_norm)
voxtral_tts_kernels.{c,h}     Math kernels (matmul, attention, conv, RoPE, ...)
voxtral_tts_tokenizer.{c,h}   Tekken BPE tokenizer (encode + decode)
voxtral_tts_voice.c           Voice embedding loader (.pt) + audio codebook embeddings
voxtral_tts_wav.c             WAV file writer
voxtral_tts_safetensors.{c,h} Safetensors mmap reader
main.c                        CLI entry point

• inspect_weights -- dump tensor names/shapes from safetensors (make inspect)
• convert_voice.py -- convert .pt voice embeddings to raw binary
• download_model.sh -- download model from HuggingFace

The prompt format follows mistral_common's encode_speech_request:

[BOS] [BEGIN_AUDIO] [voice_embedding x N] [/INST] text_tokens [INST] [BEGIN_AUDIO]

Voice embeddings are pre-computed BF16 tensors of shape [N, 3072] that replace audio token placeholder positions. After prefill, the model enters an autoregressive loop:

1. LLM produces a hidden state
2. Acoustic transformer predicts a semantic code (greedy argmax) and 36 acoustic codes (flow matching with 8 Euler ODE steps and CFG alpha=1.2)
3. The 37 codes are embedded back into LLM input space via multi-vocabulary embeddings (sum across codebooks)
4. Repeat until [END_AUDIO] is generated
5. All collected codes are decoded by the audio codec into a 24kHz waveform

• C11 compiler (gcc, clang)
• ~10GB RAM (8GB mmap'd weights + working memory)
• Optional: OpenBLAS or Apple Accelerate for faster matrix operations

MIT License. See LICENSE.

Note: The Voxtral-4B-TTS model weights are released by Mistral AI under CC BY-NC 4.0. This inference engine is MIT-licensed but the model weights have their own license terms.

Ettore Di Giacinto (@mudler)

This project builds on the work of several open-source projects:

• voxtral.c by Salvatore Sanfilippo (antirez) -- Pure C inference engine for Voxtral Realtime (ASR). The safetensors reader, math kernels, Mistral decoder implementation, and overall architecture of this project are directly adapted from voxtral.c. The project demonstrated that a full transformer inference engine can be written in clean, dependency-free C.

• vLLM and vLLM-Omni -- The reference Python implementation for Voxtral TTS inference. The flow-matching acoustic transformer, audio codec decoder, and the overall TTS pipeline were implemented based on the vLLM-Omni model code. The prompt format, voice embedding handling, and audio code generation logic follow vLLM-Omni's implementation.

• Mistral AI -- For developing and open-sourcing the Voxtral TTS model and the mistral_common tokenizer library.