Dasheng-AudioGen — GGUF
GGUF weights for mispeech/Dasheng-AudioGen, a text→audio generation model, packaged for the C++/GGML runtime thinksound.cpp.
The port is verified faithful to the PyTorch reference (stage-by-stage parity, and the generated audio matches the reference character and amplitude). It runs on CPU, ROCm/HIP, and Vulkan.
Pipeline
text → Flan-T5-Large → content adapter (cross-attn + duration predictor)
→ LayerFusionAudioDiT (U-Net flow-matching, 25-step sway sampler, CFG)
→ Vocos decoder (×2 upsampler → ConvNeXt → ISTFT)
→ 16 kHz mono audio
The latent length is the model's predicted duration (from the adapter's
global_duration head); the requested duration does not force the output length.
Files
| File | Size | Contents |
|---|---|---|
dasheng-dit.gguf | 8.7 GB | LayerFusionAudioDiT backbone and the content adapter (loaded via tensor-name filter) |
dasheng-decoder.gguf | 696 MB | Vocos decoder (upsampler + ConvNeXt + ISTFT head) |
flan-t5-large-f32.gguf | 1.4 GB | Flan-T5-Large text encoder (google/flan-t5-large) |
flan-t5-tokenizer.gguf | 650 KB | T5 SentencePiece tokenizer |
Put all four in one directory.
Usage
Build thinksound.cpp, then:
# CLI
ts-dasheng-generate --dir /path/to/gguf --caption "a dog barking" -o out.wav
# HTTP server (POST /v1/dasheng/generate)
ts-server --dir /path/to/gguf
Feed the bare caption (e.g. a dog barking) — do not prepend a <|caption|>
tag; it is not a special token in this tokenizer and degrades the output.
License
Apache-2.0, inherited from the upstream mispeech/Dasheng-AudioGen. These are format-converted weights; all model credit goes to the original authors.