mirror of
https://github.com/Jeuners/astra-local-voice.git
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a42b3e8d73
Lokaler deutscher Sprachagent für Apple Silicon: Pipecat-Pipeline mit Nemotron-ASR (MLX), Qwen über natives Ollama /api/chat, und Pocket TTS. Loopback-only WebRTC-Server mit Origin/Host-Härtung. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LVgSHNHdRx3UNTBodFmhRA
95 lines
4.1 KiB
Python
95 lines
4.1 KiB
Python
"""Download and warm local models; never opens the microphone."""
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import asyncio
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import json
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import time
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from pathlib import Path
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import httpx
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from astra.core import Settings, build_request
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from astra.inference import Models, Recognizer, on_executor
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async def main():
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import nltk
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nltk_path = Path(__file__).resolve().parent.parent / ".cache" / "nltk"
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nltk_path.mkdir(parents=True, exist_ok=True, mode=0o700)
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if not nltk.download("punkt_tab", download_dir=str(nltk_path), quiet=True):
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raise RuntimeError("Satzsegmentierung konnte nicht heruntergeladen werden")
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settings = Settings.from_env()
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models = Models(settings)
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try:
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print("Lade Nemotron für deutsche Streaming-Erkennung …", flush=True)
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await on_executor(models.stt_executor, models.load_stt)
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recognizer = await on_executor(models.stt_executor, Recognizer, models.stt)
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await on_executor(models.stt_executor, recognizer.push, bytes(16000), True)
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print("Nemotron bereit. Lade deutsche Pocket-TTS-Stimme …", flush=True)
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await on_executor(models.tts_executor, models.load_tts)
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def synthesize():
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start = time.monotonic()
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chunks = []
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first_ms = None
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for chunk in models.tts.generate_audio_stream(
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models.voice,
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"Hallo, ich bin Astra. Ich laufe vollständig auf deinem Mac.",
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copy_state=True,
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):
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if first_ms is None:
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first_ms = round((time.monotonic() - start) * 1000)
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chunks.append(chunk.numpy())
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import numpy as np
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samples = np.concatenate(chunks)
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return samples, {
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"first_audio_ms": first_ms,
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"seconds": round(len(samples) / models.tts.sample_rate, 2),
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"generation_ms": round((time.monotonic() - start) * 1000),
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}
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samples, metrics = await on_executor(models.tts_executor, synthesize)
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print(f"Pocket TTS: {json.dumps(metrics)}", flush=True)
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import numpy as np
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from scipy.signal import resample_poly
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audio16 = resample_poly(samples, 2, 3)
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pcm = (np.clip(audio16, -1, 1) * 32767).astype("<i2").tobytes()
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recognizer = await on_executor(models.stt_executor, Recognizer, models.stt)
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started = time.monotonic()
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partials = []
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for offset in range(0, len(pcm), 10240):
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text = await on_executor(
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models.stt_executor, recognizer.push, pcm[offset : offset + 10240], False
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)
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if text and (not partials or text != partials[-1]):
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partials.append(text)
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text = await on_executor(models.stt_executor, recognizer.push, b"", True)
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print(f"ASR-Rücktest: {text}", flush=True)
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print(
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f"ASR: {len(partials)} Live-Zwischenstände, {round((time.monotonic() - started) * 1000)} ms",
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flush=True,
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)
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if "astra" not in text.lower() or "mac" not in text.lower():
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raise RuntimeError("Deutscher Sprach-Rücktest fehlgeschlagen")
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async with httpx.AsyncClient(timeout=180) as client:
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payload = build_request(
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settings, [{"role": "user", "content": "Sage kurz Hallo auf Deutsch."}]
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)
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response = await client.post(f"{settings.ollama_url}/api/chat", json=payload)
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response.raise_for_status()
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events = [json.loads(line) for line in response.text.splitlines() if line]
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if any(e.get("message", {}).get("thinking") for e in events):
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raise RuntimeError("Thinking ist nicht deaktiviert")
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answer = "".join(e.get("message", {}).get("content", "") for e in events)
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if not answer or not events[-1].get("done"):
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raise RuntimeError("Ollama hat keine vollständige Antwort geliefert")
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print(f"Ollama ohne Thinking: {answer}", flush=True)
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print("Alle drei lokalen Modelle erfolgreich geprüft.", flush=True)
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finally:
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await models.close()
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if __name__ == "__main__":
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asyncio.run(main())
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