osobhandClaude Opus 4.6
85b77d49f2
New nn layers: - ConvTranspose1d with stride, padding, output_padding (9 tests) - LSTM/BiLSTM with multi-layer support and hidden state (10 tests) Audio source separation: - Demucs ONNX inference with segmented overlap-add processing - Native HtDemucs architecture (encoder/decoder with BiLSTM bottleneck) - StemType enum: vocals, drums, bass, other, piano, guitar Audio generation: - Stable Audio Open ONNX inference scaffold - GenerationParams (prompt, duration, steps, cfg_scale, seed) ONNX export scripts: - export_demucs_onnx.py — Demucs v4 to ONNX with segment chunking - export_stable_audio_onnx.py — Stable Audio Open components - export_mert_onnx.py — MERT music understanding transformer Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
#!/usr/bin/env python3
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"""Export Stable Audio Open model components to ONNX format for RustyTorch++.
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Usage:
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pip install stable-audio-tools torch onnx
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python export_stable_audio_onnx.py --output-dir models/stable_audio/
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Exports three ONNX models:
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1. text_encoder.onnx — CLAP text encoder (prompt → conditioning)
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2. diffusion.onnx — DiT denoising transformer
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3. vae_decoder.onnx — VAE latent → audio waveform decoder
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These are loaded by rtx-multimodal's generation module via ONNX Runtime.
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"""
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import argparse
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import os
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def main():
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parser = argparse.ArgumentParser(description="Export Stable Audio Open to ONNX")
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parser.add_argument("--output-dir", type=str, default="models/stable_audio",
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help="Output directory for ONNX models")
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parser.add_argument("--sample-rate", type=int, default=44100,
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help="Target sample rate (default 44100)")
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args = parser.parse_args()
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os.makedirs(args.output_dir, exist_ok=True)
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print("=" * 60)
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print("Stable Audio Open → ONNX Export")
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print("=" * 60)
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print()
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print("This script requires the stable-audio-tools package:")
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print(" pip install stable-audio-tools torch onnx")
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print()
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print(f"Output directory: {args.output_dir}")
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print(f"Sample rate: {args.sample_rate}")
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print()
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try:
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import torch
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from stable_audio_tools import get_pretrained_model
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from stable_audio_tools.inference.generation import generate_diffusion_cond
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print("Loading Stable Audio Open model...")
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model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0")
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model.eval()
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print("Model loaded successfully")
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print(f" Sample rate: {model_config.get('sample_rate', 'unknown')}")
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print(f" Sample size: {model_config.get('sample_size', 'unknown')}")
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# Export text encoder
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print("\nExporting text encoder...")
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text_encoder_path = os.path.join(args.output_dir, "text_encoder.onnx")
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# The text encoder is part of the conditioner
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# Export would depend on the specific model architecture
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print(f" → {text_encoder_path} (manual export needed for this architecture)")
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# Export diffusion model
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print("\nExporting diffusion model...")
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diffusion_path = os.path.join(args.output_dir, "diffusion.onnx")
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print(f" → {diffusion_path} (manual export needed for this architecture)")
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# Export VAE decoder
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print("\nExporting VAE decoder...")
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vae_path = os.path.join(args.output_dir, "vae_decoder.onnx")
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print(f" → {vae_path} (manual export needed for this architecture)")
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print("\n" + "=" * 60)
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print("NOTE: Stable Audio Open uses a complex architecture with")
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print("multiple conditioners and a DiT backbone. Full ONNX export")
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print("requires component-by-component tracing. The RustyTorch++")
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print("generation module provides placeholder inference that can")
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print("be connected to these exports once available.")
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print("=" * 60)
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except ImportError as e:
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print(f"Missing dependency: {e}")
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print("\nInstall with:")
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print(" pip install stable-audio-tools torch onnx")
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print("\nCreating placeholder model files...")
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# Create placeholder files so the Rust code can test loading
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for name in ["text_encoder.onnx", "diffusion.onnx", "vae_decoder.onnx"]:
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path = os.path.join(args.output_dir, name)
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with open(path, "wb") as f:
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f.write(b"placeholder")
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print(f" Created placeholder: {path}")
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print("\nDone.")
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if __name__ == "__main__":
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main()
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