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import numpy as np
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import gradio as gr
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import torch
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import os
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import warnings
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from gradio.processing_utils import convert_to_16_bit_wav
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from typing import Dict, List, Optional, Union
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import utils
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from infer import get_net_g, infer
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from models import SynthesizerTrn
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from models_jp_extra import SynthesizerTrn as SynthesizerTrnJPExtra
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from .log import logger
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from .constants import (
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DEFAULT_ASSIST_TEXT_WEIGHT,
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DEFAULT_LENGTH,
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DEFAULT_LINE_SPLIT,
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DEFAULT_NOISE,
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DEFAULT_NOISEW,
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DEFAULT_SDP_RATIO,
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DEFAULT_SPLIT_INTERVAL,
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DEFAULT_STYLE,
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DEFAULT_STYLE_WEIGHT,
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)
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class Model:
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def __init__(
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self, model_path: str, config_path: str, style_vec_path: str, device: str
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):
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self.model_path: str = model_path
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self.config_path: str = config_path
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self.device: str = device
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self.style_vec_path: str = style_vec_path
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self.hps: utils.HParams = utils.get_hparams_from_file(self.config_path)
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self.spk2id: Dict[str, int] = self.hps.data.spk2id
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self.id2spk: Dict[int, str] = {v: k for k, v in self.spk2id.items()}
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self.num_styles: int = self.hps.data.num_styles
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if hasattr(self.hps.data, "style2id"):
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self.style2id: Dict[str, int] = self.hps.data.style2id
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else:
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self.style2id: Dict[str, int] = {str(i): i for i in range(self.num_styles)}
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if len(self.style2id) != self.num_styles:
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raise ValueError(
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f"Number of styles ({self.num_styles}) does not match the number of style2id ({len(self.style2id)})"
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)
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self.style_vectors: np.ndarray = np.load(self.style_vec_path)
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if self.style_vectors.shape[0] != self.num_styles:
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raise ValueError(
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f"The number of styles ({self.num_styles}) does not match the number of style vectors ({self.style_vectors.shape[0]})"
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)
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self.net_g: Union[SynthesizerTrn, SynthesizerTrnJPExtra, None] = None
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def load_net_g(self):
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self.net_g = get_net_g(
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model_path=self.model_path,
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version=self.hps.version,
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device=self.device,
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hps=self.hps,
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)
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def get_style_vector(self, style_id: int, weight: float = 1.0) -> np.ndarray:
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mean = self.style_vectors[0]
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style_vec = self.style_vectors[style_id]
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style_vec = mean + (style_vec - mean) * weight
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return style_vec
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def get_style_vector_from_audio(
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self, audio_path: str, weight: float = 1.0
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) -> np.ndarray:
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from style_gen import get_style_vector
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xvec = get_style_vector(audio_path)
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mean = self.style_vectors[0]
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xvec = mean + (xvec - mean) * weight
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return xvec
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def infer(
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self,
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text: str,
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language: str = "JP",
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sid: int = 0,
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reference_audio_path: Optional[str] = None,
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sdp_ratio: float = DEFAULT_SDP_RATIO,
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noise: float = DEFAULT_NOISE,
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noisew: float = DEFAULT_NOISEW,
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length: float = DEFAULT_LENGTH,
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line_split: bool = DEFAULT_LINE_SPLIT,
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split_interval: float = DEFAULT_SPLIT_INTERVAL,
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assist_text: Optional[str] = None,
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assist_text_weight: float = DEFAULT_ASSIST_TEXT_WEIGHT,
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use_assist_text: bool = False,
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style: str = DEFAULT_STYLE,
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style_weight: float = DEFAULT_STYLE_WEIGHT,
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given_tone: Optional[list[int]] = None,
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) -> tuple[int, np.ndarray]:
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logger.info(f"Start generating audio data from text:\n{text}")
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if language != "JP" and self.hps.version.endswith("JP-Extra"):
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raise ValueError(
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"The model is trained with JP-Extra, but the language is not JP"
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)
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if reference_audio_path == "":
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reference_audio_path = None
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if assist_text == "" or not use_assist_text:
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assist_text = None
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if self.net_g is None:
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self.load_net_g()
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if reference_audio_path is None:
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style_id = self.style2id[style]
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style_vector = self.get_style_vector(style_id, style_weight)
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else:
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style_vector = self.get_style_vector_from_audio(
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reference_audio_path, style_weight
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)
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if not line_split:
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with torch.no_grad():
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audio = infer(
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text=text,
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sdp_ratio=sdp_ratio,
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noise_scale=noise,
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noise_scale_w=noisew,
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length_scale=length,
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sid=sid,
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language=language,
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hps=self.hps,
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net_g=self.net_g,
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device=self.device,
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assist_text=assist_text,
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assist_text_weight=assist_text_weight,
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style_vec=style_vector,
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given_tone=given_tone,
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)
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else:
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texts = text.split("\n")
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texts = [t for t in texts if t != ""]
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audios = []
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with torch.no_grad():
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for i, t in enumerate(texts):
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audios.append(
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infer(
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text=t,
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sdp_ratio=sdp_ratio,
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noise_scale=noise,
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noise_scale_w=noisew,
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length_scale=length,
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sid=sid,
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language=language,
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hps=self.hps,
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net_g=self.net_g,
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device=self.device,
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assist_text=assist_text,
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assist_text_weight=assist_text_weight,
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style_vec=style_vector,
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)
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)
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if i != len(texts) - 1:
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audios.append(np.zeros(int(44100 * split_interval)))
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audio = np.concatenate(audios)
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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audio = convert_to_16_bit_wav(audio)
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logger.info("Audio data generated successfully")
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return (self.hps.data.sampling_rate, audio)
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class ModelHolder:
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def __init__(self, root_dir: str, device: str):
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self.root_dir: str = root_dir
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self.device: str = device
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self.model_files_dict: Dict[str, List[str]] = {}
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self.current_model: Optional[Model] = None
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self.model_names: List[str] = []
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self.models: List[Model] = []
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self.refresh()
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def refresh(self):
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self.model_files_dict = {}
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self.model_names = []
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self.current_model = None
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model_dirs = [
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d
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for d in os.listdir(self.root_dir)
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if os.path.isdir(os.path.join(self.root_dir, d))
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]
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for model_name in model_dirs:
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model_dir = os.path.join(self.root_dir, model_name)
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model_files = [
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os.path.join(model_dir, f)
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for f in os.listdir(model_dir)
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if f.endswith(".pth") or f.endswith(".pt") or f.endswith(".safetensors")
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]
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if len(model_files) == 0:
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logger.warning(
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f"No model files found in {self.root_dir}/{model_name}, so skip it"
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)
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continue
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self.model_files_dict[model_name] = model_files
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self.model_names.append(model_name)
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def load_model_gr(
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self, model_name: str, model_path: str
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) -> tuple[gr.Dropdown, gr.Button, gr.Dropdown]:
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if model_name not in self.model_files_dict:
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raise ValueError(f"Model `{model_name}` is not found")
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if model_path not in self.model_files_dict[model_name]:
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raise ValueError(f"Model file `{model_path}` is not found")
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if (
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self.current_model is not None
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and self.current_model.model_path == model_path
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):
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speakers = list(self.current_model.spk2id.keys())
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styles = list(self.current_model.style2id.keys())
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return (
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gr.Dropdown(choices=styles, value=styles[0]),
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gr.Button(interactive=True, value="音声合成"),
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gr.Dropdown(choices=speakers, value=speakers[0]),
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)
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self.current_model = Model(
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model_path=model_path,
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config_path=os.path.join(self.root_dir, model_name, "config.json"),
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style_vec_path=os.path.join(self.root_dir, model_name, "style_vectors.npy"),
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device=self.device,
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)
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speakers = list(self.current_model.spk2id.keys())
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styles = list(self.current_model.style2id.keys())
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return (
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gr.Dropdown(choices=styles, value=styles[0]),
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gr.Button(interactive=True, value="音声合成"),
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gr.Dropdown(choices=speakers, value=speakers[0]),
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)
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def update_model_files_gr(self, model_name: str) -> gr.Dropdown:
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model_files = self.model_files_dict[model_name]
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return gr.Dropdown(choices=model_files, value=model_files[0])
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def update_model_names_gr(self) -> tuple[gr.Dropdown, gr.Dropdown, gr.Button]:
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self.refresh()
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initial_model_name = self.model_names[0]
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initial_model_files = self.model_files_dict[initial_model_name]
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return (
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gr.Dropdown(choices=self.model_names, value=initial_model_name),
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gr.Dropdown(choices=initial_model_files, value=initial_model_files[0]),
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gr.Button(interactive=False),
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)
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