vllm-omni/recipes/rednote-hilab/dots.tts.md

dots.tts

Offline continuous-AR TTS at 48 kHz (rednote-hilab)

Summary

• Vendor: rednote-hilab
• Model: rednote-hilab/dots.tts-soar
• Task: Text-to-speech, zero-shot synthesis only
• Mode: Offline end-to-end example only (not yet wired for online serving — see Known limitations)
• Maintainer: Community

When to use this recipe

Use this recipe as a known-good starting point for running rednote-hilab/dots.tts-soar offline on vLLM-Omni on consumer-class GPUs. dots.tts is a ~1.7B-parameter continuous-AR TTS model (Qwen2.5-1.5B base LM

• 344M DiT flow-matching head + 180M AudioVAE) that emits 48 kHz mono audio. It follows the same "vLLM-native base LM + side-path computation" pattern as VoxCPM2 — single-stage pipeline Qwen2.5-1.5B base LM → DiT (10-step Euler flow matching) → patch_encoder AR loopback → AudioVAE (streaming decode) — with a plain Qwen2 backbone instead of MiniCPM4, and no FSQ / residual-LM stage.

This is an early integration (see Known limitations below before depending on it for anything beyond offline experimentation).

References

• Offline end-to-end script: examples/offline_inference/text_to_speech/dots_tts/end2end.py
• Example guide: examples/offline_inference/text_to_speech/README.md
• Default deploy config: vllm_omni/deploy/dots_tts.yaml
• Talker / pipeline source: vllm_omni/model_executor/models/dots_tts/
• Upstream: rednote-hilab/dots.tts

Hardware Support

This recipe documents one tested 16 GB consumer-GPU configuration. Other vendor sections (ROCm, NPU) and larger-VRAM configurations are welcome as community validation lands.

GPU

1 x RTX 5080 16GB (Single GPU, Minimum Recommended)

dots.tts (~1.7B params across the base LM + DiT + AudioVAE + CAM++ speaker encoder, bfloat16) fits comfortably on a single 16 GB GPU. The bundled default config at vllm_omni/deploy/dots_tts.yaml (gpu_memory_utilization: 0.8, max_num_seqs: 4, enforce_eager: true, enable_prefix_caching: false) loads cleanly with ~5.1 GiB for model weights and ~0.3 GiB peak activation; the remainder of the configured budget is available for KV cache. Total resident footprint at idle is roughly 7-8 GiB / 16 GB — the only tight spot in the full CUDA-Graph roadmap would be step 8's graph capture (not implemented yet; this release runs enforce_eager: true, so it doesn't apply today).

Environment

• OS: Linux (WSL2)
• Python: 3.12
• Driver / runtime: NVIDIA driver 595.95
• torch: 2.11.0+cu130
• vLLM: 0.26.0
• vLLM-Omni: 0.22.1.dev (current main)

Command

python examples/offline_inference/text_to_speech/dots_tts/end2end.py \
    --model rednote-hilab/dots.tts-soar \
    --text "Hello, this is a test of dots TTS running on vLLM Omni."

The deploy config at vllm_omni/deploy/dots_tts.yaml is loaded automatically by the model registry (HF model_type=dots_tts). Pass --deploy-config <path> to override.

Verification

T1 — offline zero-shot synthesis:

python examples/offline_inference/text_to_speech/dots_tts/end2end.py \
    --model rednote-hilab/dots.tts-soar \
    --text "Hello, this is a test of dots TTS running on vLLM Omni."

Observed: output_audio/output.wav, 3.52 s @ 48 kHz mono. Single one-shot process (init → one generate() → exit), so the reported numbers include engine init and first-request warmup, not just steady-state per-step throughput — same caveat as VoxCPM2's recipe. Inference: 5.52s, RTF: 1.569.

Weight-loading breakdown from the same run (all tensors matched, no missing/extra keys): 951/951 AudioVAE, 244/244 DiT, 270/270 patch_encoder, 198 Qwen2, 938/938 CAM++ speaker encoder.

Whisper transcription of the output matched the input text with no dropped leading word (confirms the streaming-vocoder patch-boundary fix described in Known limitations).

Notes

• Output: 48 kHz mono WAV.
• Checkpoints: rednote-hilab/dots.tts-soar is the validated default used throughout this recipe. rednote-hilab/dots.tts-base shares the same architecture but is unvalidated in this repo. rednote-hilab/dots.tts-mf (MeanFlow, 2-4 step) is not supported — see below.
• enforce_eager: true and enable_prefix_caching: false in the deploy config are load-bearing, not just conservative defaults: with prefix caching enabled, vLLM-Omni's prefix-cache multimodal-output merge path does not preserve the sparse_audio marker this model relies on to route audio output correctly, and generation silently truncates to a single ~160 ms patch. Do not override enable_prefix_caching for this model until that framework-level gap is fixed.

Known limitations

• No online serving yet. dots.tts is not registered in vllm_omni/entrypoints/openai/tts_adapters/ (the framework other TTS models in this repo use for /v1/audio/speech), so vllm serve rednote-hilab/dots.tts-soar --omni will start but the OpenAI-compatible speech endpoint does not know how to build request params for it yet. Offline Omni() (as used in end2end.py) is the only supported path today.
• Voice cloning is not wired. The CAM++ x-vector speaker encoder weights load and are exercised by load_weights(), but nothing in the prompt builder consumes a reference audio yet — generation is zero-shot only. end2end.py has no --ref-audio/--ref-text flags.
• dots.tts-mf (MeanFlow, 2-4 step) checkpoint is not supported. Only the fixed 10-step Euler DiT sampler used by dots.tts-soar / dots.tts-base is implemented.
• No CUDA graph capture. The talker runs fully eager. voxcpm2's three captured graphs (base LM decode, CFM solver, VAE decode) have no dots.tts equivalent yet.
• Concurrent requests do not scale. Each request's 10-step DiT Euler integration runs serially in the side path (no cross-request batching, unlike voxcpm2's enable_batched_cfm). A community review of this integration measured no throughput gain at c=4 concurrent requests versus c=1.
• SamplingParams.seed does not control audio-generation randomness. The DiT's flow-matching noise is deterministically derived per-request from a fixed internal seed (reproducible run-to-run, matching voxcpm2's deterministic_cfm_seed convention) rather than from the caller-supplied seed field — the same limitation voxcpm2 has today.