F5-TTS v1 Base — GGUF
Native C++ GGUF conversion of SWivid/F5-TTS (MIT license) for the CrispASR runtime.
Model file
| File | Size | Description |
|---|---|---|
f5-tts-v1-base-f16.gguf | 953 MB | F16 weights for DiT/Vocos, F32 for critical paths (AdaLN, RoPE, time embed) |
Note on quantization: F5-TTS uses a 32-step iterative ODE solver
where each step runs the full 22-layer DiT twice (for CFG). This means
every weight matrix is used 1408 times per synthesis. Q8_0's ~0.5%
per-operation error compounds multiplicatively across these passes,
producing unintelligible output — even when the conditioning pathway
(AdaLN, timestep MLP) is kept at F32. F16's ~0.001% error survives
the 1408× accumulation. This is inherent to flow-matching architecture,
not a ggml limitation. The converter supports --quant q8_0 for
experimentation, but F16 is the only recommended format.
Architecture
- DiT backbone: 22-layer Diffusion Transformer with AdaLN-Zero (330M params)
- Text encoder: Character-level ConvNeXtV2 (4 blocks, 512-d)
- Vocoder: Vocos (8× ConvNeXt + ISTFTHead, 13M params)
- ODE solver: 32-step Euler with CFG (strength=2.0, sway=-1.0)
- Output: 24 kHz mono PCM
- Voice cloning: Zero-shot from 3-15s reference audio + transcript
Single GGUF contains both DiT and Vocos — no separate codec model needed.
Usage
# Install / build CrispASR
git clone https://github.com/CrispStrobe/CrispASR && cd CrispASR
cmake -B build && cmake --build build -j$(nproc) --target crispasr-cli
# Synthesize with voice cloning
./build/bin/crispasr --backend f5-tts -m auto \
--voice reference.wav \
--ref-text "Transcript of the reference audio" \
--tts "Hello, how are you today?" \
--tts-output output.wav --seed 42
The --ref-text flag is required — F5-TTS conditions on both audio and its transcript for voice cloning.
Conversion
Converted from SWivid/F5-TTS safetensors using:
python models/convert-f5-tts-to-gguf.py \
--model-dir /path/to/f5-tts \
--output f5-tts-v1-base-f16.gguf
Quantization beyond F16 is not recommended for flow-matching models.
License
MIT (same as upstream SWivid/F5-TTS).
Licence — CC-BY-NC-4.0 (corrected 2026-08-02)
This repository previously declared license: mit. That was wrong, and the
mistake is worth naming because it is easy to repeat: the F5-TTS code is
MIT, the F5-TTS weights are not. The MIT licence on
github.com/SWivid/F5-TTS covers the
training and inference implementation. The checkpoint this GGUF was converted
from, SWivid/F5-TTS, is published
under CC-BY-NC-4.0.
Converting the weights to GGUF changes their numeric representation, not their licence. So this build is non-commercial: you may use, modify and redistribute it for non-commercial purposes with attribution to the upstream authors, and you may not use it commercially. If you need a commercially usable TTS model, this is not it.