mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
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6a822b28c3
Refactor api_v2.py and api_v3.py to update sampling parameters and weight paths for better clarity and support for v3/v4 vocoders. Introduce new methods in PrepareCoordinator for handling empty text features and improve profiling capabilities. Additionally, update unified engine components to streamline audio processing and state management, enhancing overall performance and maintainability of the TTS system.
122 lines
3.9 KiB
Python
122 lines
3.9 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any, Callable, Dict, Generator, List, Optional
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from GPT_SoVITS.TTS_infer_pack.t2s_scheduler import SchedulerRequestSpec
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@dataclass
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class RuntimeControlCallbacks:
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restart: Callable[[], None] | None = None
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exit: Callable[[], None] | None = None
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@dataclass
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class DirectTTSExecution:
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media_type: str
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streaming: bool
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audio_generator: Optional[Generator[bytes, None, None]] = None
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audio_bytes: Optional[bytes] = None
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request_id: Optional[str] = None
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@dataclass
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class NormalizedEngineRequest:
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request_id: str
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text: str
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text_lang: str
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ref_audio_path: str
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prompt_lang: str
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prompt_text: str = ""
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aux_ref_audio_paths: List[str] | None = None
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top_k: int = 15
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top_p: float = 1.0
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temperature: float = 1.0
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repetition_penalty: float = 1.35
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early_stop_num: int = -1
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ready_step: int = 0
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text_split_method: str = "cut5"
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batch_size: int = 1
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batch_threshold: float = 0.75
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split_bucket: bool = False
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speed_factor: float = 1.0
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fragment_interval: float = 0.3
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seed: int = -1
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media_type: str = "wav"
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streaming_mode: bool | int = False
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return_fragment: bool = False
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fixed_length_chunk: bool = False
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response_streaming: bool = False
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parallel_infer: bool = False
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sample_steps: int = 32
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super_sampling: bool = False
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overlap_length: int = 2
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min_chunk_length: int = 16
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timeout_sec: float | None = None
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def to_payload(self) -> Dict[str, Any]:
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return {
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"request_id": self.request_id,
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"text": self.text,
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"text_lang": self.text_lang,
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"ref_audio_path": self.ref_audio_path,
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"aux_ref_audio_paths": list(self.aux_ref_audio_paths) if self.aux_ref_audio_paths else None,
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"prompt_text": self.prompt_text,
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"prompt_lang": self.prompt_lang,
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"top_k": self.top_k,
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"top_p": self.top_p,
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"temperature": self.temperature,
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"text_split_method": self.text_split_method,
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"batch_size": self.batch_size,
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"batch_threshold": self.batch_threshold,
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"speed_factor": self.speed_factor,
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"split_bucket": self.split_bucket,
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"fragment_interval": self.fragment_interval,
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"seed": self.seed,
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"media_type": self.media_type,
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"streaming_mode": self.streaming_mode,
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"return_fragment": self.return_fragment,
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"fixed_length_chunk": self.fixed_length_chunk,
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"response_streaming": self.response_streaming,
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"parallel_infer": self.parallel_infer,
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"repetition_penalty": self.repetition_penalty,
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"sample_steps": self.sample_steps,
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"super_sampling": self.super_sampling,
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"overlap_length": self.overlap_length,
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"min_chunk_length": self.min_chunk_length,
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"early_stop_num": self.early_stop_num,
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"ready_step": self.ready_step,
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"timeout_sec": self.timeout_sec,
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}
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def to_scheduler_spec(self) -> SchedulerRequestSpec:
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return SchedulerRequestSpec(
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request_id=self.request_id,
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ref_audio_path=Path(self.ref_audio_path),
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prompt_text=self.prompt_text,
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prompt_lang=self.prompt_lang,
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text=self.text,
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text_lang=self.text_lang,
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top_k=self.top_k,
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top_p=self.top_p,
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temperature=self.temperature,
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repetition_penalty=self.repetition_penalty,
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early_stop_num=self.early_stop_num,
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aux_ref_audio_paths=list(self.aux_ref_audio_paths or []),
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ready_step=self.ready_step,
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)
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@dataclass
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class SchedulerDebugExecution:
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payload: Dict[str, Any]
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@dataclass
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class SchedulerSubmitExecution:
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audio_bytes: bytes
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media_type: str
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headers: Dict[str, str]
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