9f9d812320
Eight specialist agents over a 16-book verified WA law corpus (RAG with citations), per-user document vault, WA court-form PDF auto-fill, comms missions with DV safety guard, no-KYC auth, TTS. Self-hosted: Flask + SQLite + Ollama, stdlib-only RAG. Includes README, LICENSE (MIT + not-legal-advice notice), DEPLOY runbook, .gitignore.
34 lines
1.5 KiB
Python
34 lines
1.5 KiB
Python
# -*- coding: utf-8 -*-
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"""Astraea TTS — realistic neural voices via edge-tts (Microsoft neural, sounds human).
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Structured so a local GPU TTS (Kokoro/XTTS on the Windows box) can be swapped in later.
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"""
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import os
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import subprocess
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import sys
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import tempfile
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# Resolve edge-tts relative to the venv that runs this app (robust vs hardcoded paths).
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EDGE_TTS_BIN = os.path.join(os.path.dirname(sys.executable), "edge-tts")
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VOICES = {
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"aria": {"name": "en-US-AriaNeural", "label": "Aria — confident (F)"},
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"jenny": {"name": "en-US-JennyNeural", "label": "Jenny — warm (F)"},
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"ana": {"name": "en-US-AnaNeural", "label": "Ana — calm (F)"},
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"guy": {"name": "en-US-GuyNeural", "label": "Guy — professional (M)"},
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"christopher": {"name": "en-US-ChristopherNeural", "label": "Christopher — deep (M)"},
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"eric": {"name": "en-US-EricNeural", "label": "Eric — measured (M)"},
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}
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def synthesize(text, voice="aria", rate="-5%"):
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v = VOICES.get(voice, VOICES["aria"])["name"]
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text = text.replace("\n", " ").replace(" ", " ")[:4000]
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out = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False).name
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subprocess.run([EDGE_TTS_BIN, "--voice", v, f"--rate={rate}", "--text", text,
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"--write-media", out], capture_output=True, timeout=60, check=True)
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return out
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def list_voices():
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return [{"id": k, "label": v["label"]} for k, v in VOICES.items()]
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