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from pydantic import BaseModel, Field
from typing import Any, Dict, List, Optional, Union
class EdgeProcessorData(BaseModel):
"""
:param text: 生成文本
:param tts_speaker: 讲话人id
:param rate: 语速
:param volume: 语气轻重
"""
"""生成文本"""
text: str
"""讲话人id"""
tts_speaker: int
"""语速"""
rate: str
"""语气轻重"""
volume: str
class BarkProcessorData(BaseModel):
"""
:param text: 生成文本
:param speaker_history_prompt: 音频预设npz文件
:param text_temp: 提示特殊标记程序，趋近于1，提示词特殊标记越明显
:param waveform_temp: 提示隐藏空间转音频参数比例
"""
"""生成文本"""
text: str = Field(default="你好")
"""音频预设npz文件"""
speaker_history_prompt: str = Field(default='zh_speaker_2')
"""提示特殊标记程序，趋近于1，提示词特殊标记越明显"""
text_temp: float = Field(default=1)
"""提示隐藏空间转音频参数比例"""
waveform_temp: float = Field(default=0.9)
class VitsProcessorData(BaseModel):
"""生成文本"""
text: str = Field(default="你好")
"""语言- 序号 ['日本語', '简体中文', 'English', 'Mix'] """
language: int = Field(default=1)
"""讲话人id"""
speaker_id: int = Field(default=0)
""" noise_scale(控制感情变化程度)"""
noise_scale: float = Field(default=0.5)
"""length_scale(控制整体语速)"""
speed: int = Field(default=1)
"""noise_scale_w(控制音素发音长度)"""
noise_scale_w: float = Field(default=1)
class RvcProcessorData(BaseModel):
model_index: int = Field(default=0)
""" 变调(整数, 半音数量, 升八度12降八度-12)"""
f0_up_key: int = Field(default=0)
""" F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"""
f0_method: str = Field(default="rmvpe")
"""检索特征占比"""
index_rate: float = Field(default=0.9)
""" >=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"""
filter_radius: int = Field(default=1)
"""输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"""
rms_mix_rate: int = Field(default=1)
"""后处理重采样至最终采样率，0为不进行重采样"""
resample_sr: float = Field(default=0)
"""保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"""
protect: float = Field(
default=0.33
)
f0_file: str = Field(
default=None
)
class BaseFlowData(BaseModel):
"""任务创建时间"""
created_at: float = Field(default=0)
"""任务请求时间"""
requested_at: float = Field(default=0)
class VitsVoiceFlowData(BaseModel):
vits: VitsProcessorData
rvc: RvcProcessorData
class RunnerParameter(BaseModel):
task_name: str = Field(default="vits_voice_task")
class BarkVoiceFlowData(BaseModel):
bark: BarkProcessorData
rvc: RvcProcessorData
class EdgeVoiceFlowData(BaseModel):
edge: EdgeProcessorData
rvc: RvcProcessorData
class PayLoad(BaseFlowData):
parameter: RunnerParameter
payload: Union[Dict, EdgeVoiceFlowData, BarkVoiceFlowData, VitsVoiceFlowData]
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