dots.tts-soar — GGUF (CrispASR)
GGUF conversions of dots.tts-soar,
a continuous-latent autoregressive text-to-speech model, for use with
CrispASR — a portable
C/C++ speech engine built on ggml. No Python or PyTorch needed at
inference time; runs on CPU (Metal/CUDA/Vulkan optional).
dots.tts generates 48 kHz speech patch-by-patch in a continuous latent space (no discrete audio codec). A Qwen2.5-1.5B language model drives a flow-matching DiT head that predicts acoustic latents, which a BigVGAN vocoder renders to waveform.
Files
| File | Size | Contents | Use |
|---|---|---|---|
dots-tts-soar-f16.gguf | 4.6 GB | Full model, F16 | Reference quality |
dots-tts-soar-q8_0.gguf | 3.1 GB | Mixed-quant (Q8_0 LLM + PatchEncoder, F16 DiT) | Recommended default |
dots-tts-soar-q4_k.gguf | 2.0 GB | Mixed-quant (Q4_K LLM + PatchEncoder, F16 DiT) | Smallest footprint |
dots-tts-soar-vocoder-f16.gguf | 0.36 GB | BigVGAN 48 kHz vocoder | Required companion |
dots-tts-soar-spk-f16.gguf | 0.01 GB | CAM++ speaker encoder | Optional (voice cloning) |
Each core GGUF needs the vocoder companion. Pick one core + the vocoder.
⚠️ Why the quants are "mixed"
The flow-matching DiT head is kept at F16 in every quant. It runs in a classifier-free-guidance Euler ODE loop (≈16 steps × 18 layers × 2 CFG = hundreds of forwards per utterance); per-step quantization noise compounds and derails generation — validated: a fully-Q8 DiT pushes the flow-match cosine to ~0.994 and produces no-EOS runaway / garbled audio.
The LLM (cos 0.999 quantized) and PatchEncoder (cos 0.9999 quantized) are
robust, so the quants shrink those layers while leaving the DiT,
projections, embeddings and denormalization statistics at source precision.
Result: q8_0 and q4_k are ASR-roundtrip-verbatim against F16, at a
fraction of the size. Do not re-quantize the DiT yourself.
Usage (CrispASR)
# Build CrispASR (see repo README), then:
./build/bin/crispasr --backend dots-tts \
-m dots-tts-soar-q8_0.gguf \
--tts "Hello world." \
--tts-output out.wav
The vocoder companion is auto-discovered as a sibling file next to the
core model (dots-tts-soar-vocoder-*.gguf in the same directory), so no
extra flag is needed. To point at a vocoder elsewhere, pass
--codec-model dots-tts-soar-vocoder-f16.gguf (there is no
--tts-vocoder flag). The optional CAM++ speaker encoder
(dots-tts-soar-spk-f16.gguf) is discovered the same way and only used
when you pass --voice for cloning.
Or let the model registry fetch the default (F16 core + vocoder)
automatically with -m auto --backend dots-tts.
Tuning knobs (env)
| Variable | Default | Effect |
|---|---|---|
CRISPASR_DOTS_MAX_PATCHES | 200 | Hard cap on generated audio patches |
CRISPASR_DOTS_ODE_STEPS | 16 | Flow-matching Euler steps (quality vs speed) |
CRISPASR_DOTS_EOS_THRESHOLD | 0.5 | End-of-speech probability threshold |
Architecture
- LLM: Qwen2.5-1.5B (28 layers, 12 Q / 2 KV heads, hidden 1536, head_dim 128, RoPE θ=1e6) — autoregressive driver.
- PatchEncoder: 24-layer VAE semantic encoder (RMSNorm, no RoPE, no QK-norm) — re-encodes generated latents back into the LLM stream.
- DiT flow-matching head: 18 layers (RoPE + AdaLN), CFG Euler ODE — predicts continuous acoustic latents. Kept at F16.
- Vocoder: BigVGAN, 48 kHz output.
- Speaker: CAM++ (optional, for voice conditioning).
patch_size=4, latent_dim=128, out_ds_rate=2, dit_dim=1024.
License
Apache-2.0, inherited from the upstream dots/dots.tts-soar model.
Conversion
Converted and validated stage-by-stage (per-component cosine against the
PyTorch reference) with the CrispASR diff harness. Mixed-quant produced by
crispasr-quantize, which automatically preserves the DiT and all
sampling-critical tensors at F16.
Provenance and EU AI Act Art. 53 note
- Upstream model: dots/dots.tts-soar.
- Upstream licence:
apache-2.0. 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). 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.
- 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.