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https://github.com/RVC-Boss/GPT-SoVITS.git
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800f01790e
Rename several methods in EngineApiFacade to follow a consistent private naming convention, enhancing code clarity. Update EngineApiDelegates to remove redundant method definitions, streamlining the interface. Introduce EnginePublicInterface to encapsulate public API methods, improving organization and maintainability of the TTS system. Additionally, update the EngineCompositionBuilder to use the new scheduler worker state retrieval method.
47 lines
1.8 KiB
Python
47 lines
1.8 KiB
Python
from __future__ import annotations
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import os
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from typing import Sequence
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from GPT_SoVITS.TTS_infer_pack.TTS import TTS
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from GPT_SoVITS.TTS_infer_pack.unified_engine_builder import EngineCompositionBuilder
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from GPT_SoVITS.TTS_infer_pack.unified_engine_components import RuntimeControlCallbacks
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from GPT_SoVITS.TTS_infer_pack.unified_engine_delegates import EngineApiDelegates, EngineBridgeDelegates, EngineRuntimeDelegates
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from GPT_SoVITS.TTS_infer_pack.unified_engine_public import EngineCompatInterface, EnginePublicInterface
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class UnifiedTTSEngine(EnginePublicInterface, EngineCompatInterface, EngineBridgeDelegates, EngineApiDelegates, EngineRuntimeDelegates):
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@staticmethod
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def _env_flag(name: str, default: bool) -> bool:
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value = os.environ.get(name)
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if value is None:
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return bool(default)
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return str(value).strip().lower() not in {"0", "false", "no", "off", ""}
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@staticmethod
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def _env_int(name: str, default: int) -> int:
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value = os.environ.get(name)
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if value in [None, ""]:
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return int(default)
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return int(value)
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@staticmethod
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def _env_float(name: str, default: float) -> float:
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value = os.environ.get(name)
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if value in [None, ""]:
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return float(default)
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return float(value)
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def __init__(
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self,
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tts: TTS,
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cut_method_names: Sequence[str],
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control_callbacks: RuntimeControlCallbacks | None = None,
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max_steps: int = 1500,
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micro_batch_wait_ms: int = 5,
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) -> None:
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self.tts = tts
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self.cut_method_names = set(cut_method_names)
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self.control_callbacks = control_callbacks or RuntimeControlCallbacks()
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EngineCompositionBuilder(self).build(max_steps=max_steps, micro_batch_wait_ms=micro_batch_wait_ms)
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