mirror of
https://github.com/denizsafak/abogen.git
synced 2026-07-22 07:10:28 +02:00
feat: migrate remaining consumers to new Plugin Architecture
- Add compatibility functions to tts_plugin/compat.py: - get_metadata(): returns TTSBackendMetadata with voices - is_registered_backend(): checks if plugin is loaded - resolve_backend_for_voice(): resolves backend for voice spec - get_default_voice(): gets default voice for backend - Update tts_plugin/__init__.py to export new functions - Migrate all consumers from old tts_backend_registry: - WebUI: conversion_runner, debug_tts_runner, routes/api, routes/utils/* - PyQt UI: gui, predownload_gui, voice_formula_gui - Voice utilities: voice_cache, voice_formulas, voice_profiles - Other: subtitle_utils, utils, predownload_gui (root) - Update tests to use new plugin architecture Old architecture remains intact as fallback.
This commit is contained in:
Artem Akymenko
+230
-230
@@ -1,230 +1,230 @@
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import json
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import os
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from typing import Any, Dict, Iterable, List, Tuple
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from abogen.tts_backend_registry import get_metadata, is_registered_backend
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from abogen.utils import get_user_config_path
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def _get_profiles_path():
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config_path = get_user_config_path()
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config_dir = os.path.dirname(config_path)
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return os.path.join(config_dir, "voice_profiles.json")
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def load_profiles():
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"""Load all voice profiles from JSON file."""
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path = _get_profiles_path()
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if os.path.exists(path):
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try:
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with open(path, "r", encoding="utf-8") as f:
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data = json.load(f)
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# always expect abogen_voice_profiles wrapper
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if isinstance(data, dict) and "abogen_voice_profiles" in data:
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return data["abogen_voice_profiles"]
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# fallback: treat as profiles dict
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if isinstance(data, dict):
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return data
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except Exception:
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return {}
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return {}
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def save_profiles(profiles):
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"""Save all voice profiles to JSON file."""
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path = _get_profiles_path()
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os.makedirs(os.path.dirname(path), exist_ok=True)
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with open(path, "w", encoding="utf-8") as f:
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# always save with abogen_voice_profiles wrapper
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json.dump({"abogen_voice_profiles": profiles}, f, indent=2)
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def delete_profile(name):
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"""Remove a profile by name."""
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profiles = load_profiles()
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if name in profiles:
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del profiles[name]
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save_profiles(profiles)
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def duplicate_profile(src, dest):
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"""Duplicate an existing profile."""
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profiles = load_profiles()
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if src in profiles and dest:
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profiles[dest] = profiles[src]
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save_profiles(profiles)
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def export_profiles(export_path):
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"""Export all profiles to specified JSON file."""
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profiles = load_profiles()
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with open(export_path, "w", encoding="utf-8") as f:
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json.dump({"abogen_voice_profiles": profiles}, f, indent=2)
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def serialize_profiles() -> Dict[str, Dict[str, Iterable[Tuple[str, float]]]]:
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"""Return profiles in canonical dictionary form."""
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return load_profiles()
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def _normalize_supertonic_voice(value: Any) -> str:
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raw = str(value or "").strip().upper()
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supertonic_voices = get_metadata("supertonic").voices
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return raw if raw in supertonic_voices else "M1"
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def _coerce_supertonic_steps(value: Any) -> int:
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try:
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steps = int(value)
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except (TypeError, ValueError):
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return 5
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return max(2, min(15, steps))
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def _coerce_supertonic_speed(value: Any) -> float:
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try:
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speed = float(value)
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except (TypeError, ValueError):
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return 1.0
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return max(0.7, min(2.0, speed))
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def normalize_profile_entry(entry: Any) -> Dict[str, Any]:
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"""Normalize a stored profile entry.
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Backwards compatible:
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- Legacy Kokoro-only entries: {language, voices}
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- New entries: include provider.
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"""
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if not isinstance(entry, dict):
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return {}
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provider = str(entry.get("provider") or "kokoro").strip().lower()
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if not is_registered_backend(provider):
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provider = "kokoro"
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language = str(entry.get("language") or "a").strip().lower() or "a"
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if provider == "supertonic":
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return {
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"provider": "supertonic",
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"language": language,
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"voice": _normalize_supertonic_voice(
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entry.get("voice") or entry.get("voice_name") or entry.get("name")
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),
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"total_steps": _coerce_supertonic_steps(
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entry.get("total_steps")
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or entry.get("supertonic_total_steps")
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or entry.get("quality")
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),
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"speed": _coerce_supertonic_speed(
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entry.get("speed") or entry.get("supertonic_speed")
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),
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}
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voices = _normalize_voice_entries(entry.get("voices", []))
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if not voices:
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return {}
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return {
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"provider": "kokoro",
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"language": language,
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"voices": voices,
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}
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def _normalize_voice_entries(entries: Iterable) -> List[Tuple[str, float]]:
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normalized: List[Tuple[str, float]] = []
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kokoro_voices = get_metadata("kokoro").voices
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for item in entries or []:
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if isinstance(item, dict):
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voice = item.get("id") or item.get("voice")
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weight = item.get("weight")
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elif isinstance(item, (list, tuple)) and len(item) >= 2:
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voice, weight = item[0], item[1]
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else:
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continue
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if voice not in kokoro_voices:
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continue
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if weight is None:
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continue
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try:
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weight_val = float(weight)
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except (TypeError, ValueError):
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continue
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if weight_val <= 0:
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continue
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normalized.append((voice, weight_val))
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return normalized
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def normalize_voice_entries(entries: Iterable) -> List[Tuple[str, float]]:
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"""Public helper to normalize voice-weight pairs from arbitrary payloads."""
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return _normalize_voice_entries(entries)
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def save_profile(name: str, *, language: str, voices: Iterable) -> None:
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"""Persist a single profile after validating its data."""
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name = (name or "").strip()
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if not name:
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raise ValueError("Profile name is required")
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normalized = _normalize_voice_entries(voices)
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if not normalized:
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raise ValueError("At least one voice with a weight above zero is required")
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if not language:
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language = "a"
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profiles = load_profiles()
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profiles[name] = {"provider": "kokoro", "language": language, "voices": normalized}
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save_profiles(profiles)
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def remove_profile(name: str) -> None:
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delete_profile(name)
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def import_profiles_data(data: Dict, *, replace_existing: bool = False) -> List[str]:
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"""Merge profiles from a dictionary structure and persist them.
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Returns the list of profile names that were added or updated.
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"""
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if not isinstance(data, dict):
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raise ValueError("Invalid profile payload")
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if "abogen_voice_profiles" in data:
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data = data["abogen_voice_profiles"]
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if not isinstance(data, dict):
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raise ValueError("Invalid profile payload")
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current = load_profiles()
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updated: List[str] = []
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for name, entry in data.items():
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normalized = normalize_profile_entry(entry)
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if not normalized:
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continue
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if name in current and not replace_existing:
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# skip duplicates unless explicit replacement is requested
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continue
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current[name] = normalized
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updated.append(name)
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if updated:
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save_profiles(current)
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return updated
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def export_profiles_payload(names: Iterable[str] | None = None) -> Dict[str, Dict]:
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"""Return profiles limited to the provided names for download/export."""
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profiles = load_profiles()
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if names is None:
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subset = profiles
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else:
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subset = {name: profiles[name] for name in names if name in profiles}
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return {"abogen_voice_profiles": subset}
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import json
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import os
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from typing import Any, Dict, Iterable, List, Tuple
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from abogen.tts_plugin.compat import get_metadata, is_registered_backend
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from abogen.utils import get_user_config_path
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def _get_profiles_path():
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config_path = get_user_config_path()
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config_dir = os.path.dirname(config_path)
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return os.path.join(config_dir, "voice_profiles.json")
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def load_profiles():
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"""Load all voice profiles from JSON file."""
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path = _get_profiles_path()
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if os.path.exists(path):
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try:
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with open(path, "r", encoding="utf-8") as f:
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data = json.load(f)
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# always expect abogen_voice_profiles wrapper
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if isinstance(data, dict) and "abogen_voice_profiles" in data:
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return data["abogen_voice_profiles"]
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# fallback: treat as profiles dict
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if isinstance(data, dict):
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return data
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except Exception:
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return {}
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return {}
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def save_profiles(profiles):
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"""Save all voice profiles to JSON file."""
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path = _get_profiles_path()
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os.makedirs(os.path.dirname(path), exist_ok=True)
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with open(path, "w", encoding="utf-8") as f:
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# always save with abogen_voice_profiles wrapper
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json.dump({"abogen_voice_profiles": profiles}, f, indent=2)
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def delete_profile(name):
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"""Remove a profile by name."""
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profiles = load_profiles()
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if name in profiles:
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del profiles[name]
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save_profiles(profiles)
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def duplicate_profile(src, dest):
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"""Duplicate an existing profile."""
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profiles = load_profiles()
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if src in profiles and dest:
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profiles[dest] = profiles[src]
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save_profiles(profiles)
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def export_profiles(export_path):
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"""Export all profiles to specified JSON file."""
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profiles = load_profiles()
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with open(export_path, "w", encoding="utf-8") as f:
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json.dump({"abogen_voice_profiles": profiles}, f, indent=2)
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def serialize_profiles() -> Dict[str, Dict[str, Iterable[Tuple[str, float]]]]:
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"""Return profiles in canonical dictionary form."""
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return load_profiles()
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def _normalize_supertonic_voice(value: Any) -> str:
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raw = str(value or "").strip().upper()
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supertonic_voices = get_metadata("supertonic").voices
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return raw if raw in supertonic_voices else "M1"
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def _coerce_supertonic_steps(value: Any) -> int:
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try:
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steps = int(value)
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except (TypeError, ValueError):
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return 5
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return max(2, min(15, steps))
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def _coerce_supertonic_speed(value: Any) -> float:
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try:
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speed = float(value)
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except (TypeError, ValueError):
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return 1.0
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return max(0.7, min(2.0, speed))
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def normalize_profile_entry(entry: Any) -> Dict[str, Any]:
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"""Normalize a stored profile entry.
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Backwards compatible:
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- Legacy Kokoro-only entries: {language, voices}
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- New entries: include provider.
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"""
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if not isinstance(entry, dict):
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return {}
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provider = str(entry.get("provider") or "kokoro").strip().lower()
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if not is_registered_backend(provider):
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provider = "kokoro"
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language = str(entry.get("language") or "a").strip().lower() or "a"
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if provider == "supertonic":
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return {
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"provider": "supertonic",
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"language": language,
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"voice": _normalize_supertonic_voice(
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entry.get("voice") or entry.get("voice_name") or entry.get("name")
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),
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"total_steps": _coerce_supertonic_steps(
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entry.get("total_steps")
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or entry.get("supertonic_total_steps")
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or entry.get("quality")
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),
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"speed": _coerce_supertonic_speed(
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entry.get("speed") or entry.get("supertonic_speed")
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),
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}
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voices = _normalize_voice_entries(entry.get("voices", []))
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if not voices:
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return {}
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return {
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"provider": "kokoro",
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"language": language,
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"voices": voices,
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}
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def _normalize_voice_entries(entries: Iterable) -> List[Tuple[str, float]]:
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normalized: List[Tuple[str, float]] = []
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kokoro_voices = get_metadata("kokoro").voices
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for item in entries or []:
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if isinstance(item, dict):
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voice = item.get("id") or item.get("voice")
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weight = item.get("weight")
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elif isinstance(item, (list, tuple)) and len(item) >= 2:
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voice, weight = item[0], item[1]
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else:
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continue
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if voice not in kokoro_voices:
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continue
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if weight is None:
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continue
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try:
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weight_val = float(weight)
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except (TypeError, ValueError):
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continue
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if weight_val <= 0:
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continue
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normalized.append((voice, weight_val))
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return normalized
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def normalize_voice_entries(entries: Iterable) -> List[Tuple[str, float]]:
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"""Public helper to normalize voice-weight pairs from arbitrary payloads."""
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return _normalize_voice_entries(entries)
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def save_profile(name: str, *, language: str, voices: Iterable) -> None:
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"""Persist a single profile after validating its data."""
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name = (name or "").strip()
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if not name:
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raise ValueError("Profile name is required")
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normalized = _normalize_voice_entries(voices)
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if not normalized:
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raise ValueError("At least one voice with a weight above zero is required")
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if not language:
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language = "a"
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profiles = load_profiles()
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profiles[name] = {"provider": "kokoro", "language": language, "voices": normalized}
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save_profiles(profiles)
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def remove_profile(name: str) -> None:
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delete_profile(name)
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def import_profiles_data(data: Dict, *, replace_existing: bool = False) -> List[str]:
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"""Merge profiles from a dictionary structure and persist them.
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Returns the list of profile names that were added or updated.
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"""
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if not isinstance(data, dict):
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raise ValueError("Invalid profile payload")
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if "abogen_voice_profiles" in data:
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data = data["abogen_voice_profiles"]
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if not isinstance(data, dict):
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raise ValueError("Invalid profile payload")
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current = load_profiles()
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updated: List[str] = []
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for name, entry in data.items():
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normalized = normalize_profile_entry(entry)
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if not normalized:
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continue
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if name in current and not replace_existing:
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# skip duplicates unless explicit replacement is requested
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continue
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current[name] = normalized
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updated.append(name)
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if updated:
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save_profiles(current)
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return updated
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def export_profiles_payload(names: Iterable[str] | None = None) -> Dict[str, Dict]:
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"""Return profiles limited to the provided names for download/export."""
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|
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profiles = load_profiles()
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if names is None:
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subset = profiles
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else:
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subset = {name: profiles[name] for name in names if name in profiles}
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return {"abogen_voice_profiles": subset}
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