Stable Audio Open Small — Mobile Optimized (ONNX)
This repository hosts mobile-optimized runtime artifacts and converted weights for Stable Audio Open Small (341M parameters), adapted for on-device generative audio synthesis in the Neural Beats mobile app (iOS & Android, via ONNX Runtime Mobile).
Model Architecture & Overview
- Base Architecture: Stable Audio Open Small (Stability AI / Arm), split into 3 independently exported ONNX graphs run in sequence:
- T5 text encoder (
t5_text_encoder.onnx) — encodes the text prompt into cross-attention conditioning embeddings. - DiT step model (
dit_step.onnx) — a single denoising step of the diffusion transformer; invoked repeatedly (8 rectified-flow pingpong steps) by the on-device sampler. - Oobleck VAE decoder (
vae_decoder.onnx) — decodes the 64-channel latent produced by the sampler into 44.1 kHz stereo audio (2048× upsampling).
- T5 text encoder (
- Segment Output: Generates ~10.0-second seamless stereo audio passes at 44.1 kHz (215 latent steps × 2048 downsampling ratio ÷ 44.1 kHz).
Weight Precision (INT8 storage)
All three ONNX models use weight-only INT8 storage: every weight tensor above a small-tensor threshold is symmetrically quantized to int8 (per-output-channel scale for 2D+ weight matrices, per-tensor scale for 1D tensors), with an explicit DequantizeLinear(int8→fp32) op inserted ahead of each tensor's original consumer(s). The compute graph itself is untouched — every op still runs in float32 exactly as before, and both model inputs/outputs remain float32 — so there is no runtime behavior change for native iOS/Android inference code, only a much smaller download (roughly a quarter of the original FP32 size, half of the previous FP16-storage size). Quantization introduces more noise than FP16 storage but remains well within an acceptable range for ambient/generative audio use cases (validated via onnxruntime: no clipping, no NaNs, >0.98 waveform correlation vs. the FP16-storage version on identical inputs).
Each .onnx file is a small graph-structure file; the bulk of each model's weight data lives in a companion .onnx.data external-data file with the same base name (ONNX Runtime resolves this automatically as long as both files are downloaded into the same directory).
Hosted Files & Formats
| File | Size (approx.) | Description |
|---|---|---|
t5_text_encoder.onnx |
~330 KB | T5-base text encoder graph structure. |
t5_text_encoder.onnx.data |
~105 MB | T5 text encoder weights (INT8 storage). |
dit_step.onnx |
~910 KB | Diffusion transformer single-step graph structure. |
dit_step.onnx.data |
~326 MB | Diffusion transformer weights (INT8 storage). |
vae_decoder.onnx |
~245 KB | Oobleck VAE decoder graph structure. |
vae_decoder.onnx.data |
~75 MB | Oobleck VAE decoder weights (INT8 storage). |
seconds_embedder.json |
~4.2 MB | Tiny seconds_total conditioning embedder weights (FP32 JSON). |
t5_tokenizer_json.json |
~2.3 MB | T5 fast tokenizer definition. |
t5_tokenizer_config.json |
~2.4 KB | T5 tokenizer configuration. |
model_config.json |
~5.5 KB | Complete model architecture hyperparameters & conditioning/sampling configuration. |
model_weights_meta.json |
~0.6 KB | Fast-load metadata summary (sample rate, latent shape, diffusion steps, sampler, component list) for mobile platform channels. |
checksums.sha256 |
~1 KB | SHA-256 checksums for all files above, verified after each file downloads on-device. |
Total download size: ~0.5 GB (down from ~1.0 GB at FP16-storage precision, ~2.0 GB at full FP32 precision).
License & Attribution
This model is derived from Stability AI's Stable Audio Open Small and is distributed under the Stability AI Community License.