"""Local on-device TTS engines for ``tools.tts_tool``: NeuTTS, Piper, KittenTTS. All three synthesize WAV natively; :func:`_finalize_wav_output` converts/renames to the requested container. Piper and KittenTTS keep loaded models in small LRU caches registered in ``_LOCAL_TTS_MODEL_CACHES`` so warm/release can pre-load or drop them. ``_import_piper`` / ``_import_kittentts`` are resolved through the origin module at call time (test monkeypatches). """ from __future__ import annotations import logging import subprocess import sys from pathlib import Path from typing import Any, Callable, Dict, Tuple from tools.tts_tool_delivery import _finalize_wav_output, _origin, _section, _wav_sidecar_path logger = logging.getLogger("tools.tts_tool") DEFAULT_KITTENTTS_MODEL = "KittenML/kitten-tts-nano-0.8-int8" # 25MB DEFAULT_KITTENTTS_VOICE = "Jasper" DEFAULT_PIPER_VOICE = "en_US-lessac-medium" # balanced size/quality _NEUTTS_SAMPLES = Path(__file__).parent / "neutts_samples" # --- Bounded model caches --- # Each entry is a whole loaded model (tens of MB); unbounded, one would be pinned per distinct # voice for the process lifetime. Most sessions use one or two voices; a cold reload is cheap. _TTS_MODEL_CACHE_MAX = 3 # Provider name -> the cache it populates (warm/release in tts_tool_lifecycle; a new local engine # adds a row here plus a loader in _local_tts_warmers()). Piper keyed on absolute .onnx path # (+cuda flag); KittenTTS on model name. _piper_voice_cache: Dict[str, Any] = {} _kittentts_model_cache: Dict[str, Any] = {} _LOCAL_TTS_MODEL_CACHES: Dict[str, Dict[str, Any]] = { "piper": _piper_voice_cache, "kittentts": _kittentts_model_cache} def _tts_cache_get_or_load(cache: Dict[str, Any], key: str, load: Callable[[], Any]) -> Any: """Get ``key`` from ``cache`` or load it, LRU-bounded at ``_TTS_MODEL_CACHE_MAX`` (a hit refreshes recency via pop + reinsert; eviction only releases the slot, not live references).""" if key in cache: cache[key] = cache.pop(key) return cache[key] value = load() cache[key] = value while len(cache) > _TTS_MODEL_CACHE_MAX: cache.pop(next(iter(cache)), None) return value def _run_helper(cmd: list, timeout: int) -> subprocess.CompletedProcess: return subprocess.run( cmd, capture_output=True, text=True, encoding='utf-8', errors='replace', timeout=timeout, stdin=subprocess.DEVNULL, ) # --- NeuTTS (subprocess via tools/neutts_synth.py so the ~500MB model exits after use) --- def _generate_neutts(text: str, output_path: str, tts_config: Dict[str, Any]) -> str: neutts_config = tts_config.get("neutts") or {} wav_path = _wav_sidecar_path(output_path) cmd = [ sys.executable, str(Path(__file__).parent / "neutts_synth.py"), "--text", text, "--out", wav_path, "--ref-audio", neutts_config.get("ref_audio", "") or str(_NEUTTS_SAMPLES / "jo.wav"), "--ref-text", neutts_config.get("ref_text", "") or str(_NEUTTS_SAMPLES / "jo.txt"), "--model", neutts_config.get("model", "neuphonic/neutts-air-q4-gguf"), "--device", neutts_config.get("device", "cpu")] result = _run_helper(cmd, 120) if result.returncode != 0: # the synth script reports success lines as "OK:" on stderr too error_lines = [l for l in result.stderr.strip().splitlines() if not l.startswith("OK:")] raise RuntimeError(f"NeuTTS synthesis failed: {chr(10).join(error_lines) or 'unknown error'}") return _finalize_wav_output(wav_path, output_path) # --- Piper (local neural VITS, 44 languages) --- def _get_piper_voices_dir() -> Path: """``/cache/piper-voices/`` so voice downloads follow profile boundaries.""" from hermes_constants import get_hermes_dir root = Path(get_hermes_dir("cache/piper-voices", "piper_voices_cache")) root.mkdir(parents=True, exist_ok=True) return root def _resolve_piper_voice_path(voice: str, download_dir: Path) -> str: """Resolve *voice* (an .onnx path or a name like ``en_US-lessac-medium``, downloaded into *download_dir* on first use) to a concrete .onnx file; RuntimeError when it can't be.""" voice = voice or DEFAULT_PIPER_VOICE candidate = Path(voice).expanduser() if candidate.suffix.lower() == ".onnx" and candidate.exists(): return str(candidate) cached = download_dir / f"{voice}.onnx" if cached.exists() and (download_dir / f"{voice}.onnx.json").exists(): return str(cached) logger.info("[Piper] Downloading voice '%s' to %s (first use)", voice, download_dir) try: result = _run_helper( [sys.executable, "-m", "piper.download_voices", voice, "--download-dir", str(download_dir)], 300, ) except subprocess.TimeoutExpired as exc: raise RuntimeError(f"Piper voice download timed out after 300s for '{voice}'") from exc if result.returncode != 0: stderr = (result.stderr or "").strip() or "no stderr output" raise RuntimeError(f"Piper voice download failed for '{voice}': {stderr[:400]}") if not cached.exists(): raise RuntimeError( f"Piper voice download completed but {cached} is missing — " f"check voice name (see: https://github.com/OHF-Voice/piper1-gpl/" f"blob/main/docs/VOICES.md)") return str(cached) def _load_piper_voice_for_config(tts_config: Dict[str, Any]) -> Tuple[Any, Dict[str, Any]]: """Resolve + load (or fetch from cache) the selected Piper voice -> ``(voice, piper_config)``. Shared by synthesis and ``warm_tts_provider`` so a warm-up fills exactly the slot synthesis hits.""" PiperVoice = _origin()._import_piper() piper_config = _section(tts_config, "piper") voice_name = piper_config.get("voice") or DEFAULT_PIPER_VOICE download_dir = Path(piper_config.get("voices_dir") or _get_piper_voices_dir()).expanduser() download_dir.mkdir(parents=True, exist_ok=True) use_cuda = bool(piper_config.get("use_cuda", False)) model_path = _resolve_piper_voice_path(voice_name, download_dir) def _load_piper_voice(): logger.info("[Piper] Loading voice: %s", model_path) v = PiperVoice.load(model_path, use_cuda=use_cuda) logger.info("[Piper] Voice loaded") return v # speaker_id is applied per call via syn_config, so one instance serves every speaker. cache_key = f"{model_path}::cuda={use_cuda}" return _tts_cache_get_or_load(_piper_voice_cache, cache_key, _load_piper_voice), piper_config _PIPER_ADVANCED_KNOBS = ("length_scale", "noise_scale", "noise_w_scale", "volume", "normalize_audio", "speaker_id") def _generate_piper_tts(text: str, output_path: str, tts_config: Dict[str, Any]) -> str: import wave voice, piper_config = _load_piper_voice_for_config(tts_config) # Bad speaker_id drops to 0 (Piper's default); bools are rejected (they'd coerce to 1/0). _raw_speaker = piper_config.get("speaker_id", 0) speaker_id = _raw_speaker if type(_raw_speaker) is int else 0 # Only build a SynthesisConfig when an advanced knob is configured, so we don't depend on a # newer piper-tts than the user's unless we must. syn_config = None if any(k in piper_config for k in _PIPER_ADVANCED_KNOBS): try: from piper import SynthesisConfig # type: ignore syn_config = SynthesisConfig( length_scale=float(piper_config.get("length_scale", 1.0)), noise_scale=float(piper_config.get("noise_scale", 0.667)), noise_w_scale=float(piper_config.get("noise_w_scale", 0.8)), volume=float(piper_config.get("volume", 1.0)), normalize_audio=bool(piper_config.get("normalize_audio", True)), speaker_id=speaker_id) except ImportError: logger.warning("[Piper] SynthesisConfig not available in this piper-tts version — advanced knobs ignored") wav_path = _wav_sidecar_path(output_path) with wave.open(wav_path, "wb") as wav_file: if syn_config is not None: voice.synthesize_wav(text, wav_file, syn_config=syn_config) else: voice.synthesize_wav(text, wav_file) return _finalize_wav_output(wav_path, output_path) # --- KittenTTS (local ONNX, 25-80MB models, CPU only) --- def _load_kittentts_model_for_config(tts_config: Dict[str, Any]) -> Tuple[Any, Dict[str, Any]]: """Load (or fetch from cache) the KittenTTS model; returns ``(model, kittentts_config)``.""" KittenTTS = _origin()._import_kittentts() kt_config = _section(tts_config, "kittentts") model_name = kt_config.get("model", DEFAULT_KITTENTTS_MODEL) def _load_kittentts_model(): logger.info("[KittenTTS] Loading model: %s", model_name) m = KittenTTS(model_name) logger.info("[KittenTTS] Model loaded successfully") return m return _tts_cache_get_or_load(_kittentts_model_cache, model_name, _load_kittentts_model), kt_config def _generate_kittentts(text: str, output_path: str, tts_config: Dict[str, Any]) -> str: model, kt_config = _load_kittentts_model_for_config(tts_config) audio = model.generate( # numpy array at 24kHz text, voice=kt_config.get("voice", DEFAULT_KITTENTTS_VOICE), speed=kt_config.get("speed", 1.0), clean_text=kt_config.get("clean_text", True)) import soundfile as sf wav_path = _wav_sidecar_path(output_path) sf.write(wav_path, audio, 24000) return _finalize_wav_output(wav_path, output_path)