docs/Summary1.md

from pathlib import Path

content = """# ACE-Step 1.5 Deployment Context (Hugging Face Endpoint) — Handoff Notes

Objective

Set up ACE-Step/Ace-Step1.5 on Hugging Face so we can:

1. Serve music generations through a private endpoint (token-protected),
2. Run on GPU (A100 preferred),
3. Control costs aggressively (scale-to-zero / pause when idle),
4. Call the endpoint from local scripts/app backend,
5. Transition from a sine-wave smoke test to real ACE-Step generations.

---

Current State (What’s Already Done)

• Hugging Face auth is working in terminal (hf auth usable).
• A private dedicated endpoint exists:
  • https://xr81s77sis7hoggq.us-east-1.aws.endpoints.huggingface.cloud
• Endpoint is connected to a custom repo containing handler.py.
• Smoke-test handler.py was deployed and tested successfully:
  • Returns base64-encoded WAV generated as sine wave/noise.
• Local .bat + .ps1 testing flow works to hit endpoint and save .wav.

---

Key Constraint Discovered

ACE-Step/Ace-Step1.5 is not a one-click “Model Catalog verified” endpoint deployment. HF warning indicates:

• no verified config,
• missing handler.py if trying to deploy model repo directly.

Implication

Use a custom endpoint repo (our own repo) with:

• handler.py
• requirements.txt Then load ACE-Step/Ace-Step1.5 from code at runtime.

---

Important Product/Infra Notes

ZeroGPU vs Dedicated Endpoints

• ZeroGPU applies to Spaces (good for demos/prototypes).
• For production-like API serving, use Dedicated Inference Endpoints.

Idle Scaling

• In Dedicated Endpoints UI, minimum idle scale-to-zero window observed is 15 minutes.
• Faster than 15 min is not available in current UI setting.
• To stop all billing immediately, use Pause endpoint.
• Scale-to-zero (min replicas 0) is good for auto-wake behavior with cold starts.

---

Files in Custom Endpoint Repo (Expected)

• handler.py -> custom inference logic
• requirements.txt -> runtime dependencies
• README.md -> optional docs/config context

---

Smoke-Test Handler Behavior (Current)

Current handler:

• Does NOT load ACE-Step model.
• Generates synthetic audio via numpy sine + noise.
• Returns:
  • audio_base64_wav
  • sample_rate
  • duration_sec

This validated endpoint wiring, auth, request/response format, and client decode pipeline.

---

What Needs to Happen Next (Critical Path)

1) Replace fallback generation with real ACE-Step inference

In handler.py:

• __init__:

  • Load ACE-Step pipeline/model once at container startup.
  • Use model source ACE-Step/Ace-Step1.5.
  • Move model to CUDA when available.

• __call__:

  • Parse request inputs:
    • prompt
    • lyrics
    • duration_sec
    • sample_rate
    • seed
    • optional: guidance_scale, steps, use_lm
  • Execute ACE-Step generation.
  • Convert output waveform to WAV bytes.
  • Return base64 WAV in JSON.

2) Ensure dependencies are correct in requirements.txt

At minimum for current scaffold:

• numpy
• soundfile

Likely needed for ACE runtime:

• torch
• torchaudio
• transformers
• accelerate
• huggingface_hub
• any ACE-Step-specific package requirements from ACE docs/repo.

3) Push repo changes and redeploy/rebuild endpoint

• git add .
• git commit -m "..."
• git push
• wait for endpoint rebuild healthy status.

4) Run generation call with real payload

Use same client script and include prompt/lyrics.

---

Request Payload Contract (Target)

{
  "inputs": {
    "prompt": "upbeat pop rap with emotional guitar",
    "lyrics": "[Verse] city lights and midnight rain",
    "duration_sec": 12,
    "sample_rate": 44100,
    "seed": 42,
    "guidance_scale": 7.0,
    "steps": 50,
    "use_lm": true
  }
}