update chute_config instasll

chute_config.yml

@@ -4,7 +4,7 @@
 Image:
   from_base: parachutes/base-python:3.12.9
   run_command:
-    - pip install torch torchaudio transformers accelerate huggingface_hub pyyaml soundfile
+    - pip install torch torchaudio transformers accelerate huggingface_hub pyyaml soundfile snac
   set_workdir: /app
 
 NodeSelector:

vocence_local_wrapper.py

@@ -0,0 +1,133 @@
+#!/usr/bin/env python3
+"""
+Local Vocence wrapper for testing miner.py without Chutes.
+
+Run:
+  python vocence_local_wrapper.py
+
+Then call:
+  GET  http://127.0.0.1:8000/health
+  POST http://127.0.0.1:8000/speak
+"""
+from __future__ import annotations
+
+import io
+import wave
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import uvicorn
+from fastapi import FastAPI, HTTPException, status
+from fastapi.responses import Response
+from pydantic import BaseModel, Field
+from yaml import safe_load
+
+from miner import Miner
+
+VOCENCE_MAX_AUDIO_SECONDS = 30
+VOCENCE_MAX_TEXT_LEN = 2000
+VOCENCE_MAX_INSTRUCTION_LEN = 600
+
+
+class VocenceSpeakRequest(BaseModel):
+    instruction: str = Field(..., min_length=1, max_length=VOCENCE_MAX_INSTRUCTION_LEN)
+    text: str = Field(..., min_length=1, max_length=VOCENCE_MAX_TEXT_LEN)
+
+
+class VocenceHealthResponse(BaseModel):
+    status: str
+    model_loaded: bool
+    sample_rate: Optional[int] = None
+    adapter: Optional[str] = None
+    repo_path: str
+
+
+def waveform_to_wav_bytes(waveform: np.ndarray, sample_rate: int) -> bytes:
+    if waveform.ndim != 1:
+        raise ValueError("waveform must be 1D mono")
+
+    if waveform.dtype != np.int16:
+        wf = np.asarray(waveform, dtype=np.float32)
+        wf = np.clip(wf, -1.0, 1.0)
+        wf = (wf * 32767.0).astype(np.int16)
+    else:
+        wf = waveform
+
+    buf = io.BytesIO()
+    with wave.open(buf, "wb") as wav:
+        wav.setnchannels(1)
+        wav.setsampwidth(2)
+        wav.setframerate(sample_rate)
+        wav.writeframes(wf.tobytes())
+    return buf.getvalue()
+
+
+repo_path = Path(__file__).resolve().parent
+app = FastAPI(title="Vocence Local Wrapper", version="0.1.0")
+
+
+@app.on_event("startup")
+async def startup_event() -> None:
+    app.state.status = "unknown"
+    app.state.sample_rate = None
+    app.state.adapter = None
+    app.state.tts_engine = None
+
+    try:
+        app.state.tts_engine = Miner(repo_path)
+        app.state.tts_engine.warmup()
+
+        vocence_yaml = repo_path / "vocence_config.yaml"
+        if vocence_yaml.exists():
+            with vocence_yaml.open("r", encoding="utf-8") as f:
+                cfg = safe_load(f) or {}
+            app.state.sample_rate = int(cfg.get("generation", {}).get("sample_rate", 24000))
+            app.state.adapter = str(cfg.get("runtime", {}).get("adapter", "unknown"))
+        else:
+            app.state.sample_rate = 24000
+            app.state.adapter = "unknown"
+
+        app.state.status = "healthy"
+    except Exception as exc:
+        app.state.status = f"startup_failed: {exc}"
+        app.state.tts_engine = None
+
+
+@app.get("/health")
+async def health() -> dict:
+    return VocenceHealthResponse(
+        status=getattr(app.state, "status", "unknown"),
+        model_loaded=getattr(app.state, "tts_engine", None) is not None,
+        sample_rate=getattr(app.state, "sample_rate", None),
+        adapter=getattr(app.state, "adapter", None),
+        repo_path=str(repo_path),
+    ).model_dump()
+
+
+@app.post("/speak", response_class=Response)
+async def speak(args: VocenceSpeakRequest):
+    engine = getattr(app.state, "tts_engine", None)
+    if engine is None:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="TTS engine not loaded",
+        )
+
+    waveform, sample_rate = engine.generate_wav(instruction=args.instruction, text=args.text)
+    waveform = np.asarray(waveform)
+    if waveform.ndim != 1 or waveform.size == 0:
+        raise HTTPException(status_code=400, detail="invalid waveform")
+
+    duration_sec = float(waveform.shape[0]) / float(sample_rate)
+    if duration_sec <= 0 or duration_sec > VOCENCE_MAX_AUDIO_SECONDS:
+        raise HTTPException(status_code=400, detail="invalid duration")
+
+    return Response(
+        content=waveform_to_wav_bytes(waveform, sample_rate),
+        media_type="audio/wav",
+    )
+
+
+if __name__ == "__main__":
+    uvicorn.run("vocence_local_wrapper:app", host="127.0.0.1", port=8000, reload=False)