e8a76e547b
Since the BERT features of the dataset are pre-extracted by bert_gen.py, there is no need to load the BERT model at training time.
436 lines
19 KiB
Python
436 lines
19 KiB
Python
import warnings
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from pathlib import Path
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from typing import Any, Optional, Union
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import gradio as gr
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import numpy as np
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import pyannote.audio
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import torch
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from gradio.processing_utils import convert_to_16_bit_wav
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from numpy.typing import NDArray
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from pydantic import BaseModel
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from style_bert_vits2.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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Languages,
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)
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from style_bert_vits2.logging import logger
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from style_bert_vits2.models.hyper_parameters import HyperParameters
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from style_bert_vits2.models.infer import get_net_g, infer
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from style_bert_vits2.models.models import SynthesizerTrn
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from style_bert_vits2.models.models_jp_extra import (
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SynthesizerTrn as SynthesizerTrnJPExtra,
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)
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from style_bert_vits2.nlp import bert_models
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from style_bert_vits2.voice import adjust_voice
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class TTSModel:
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"""
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Style-Bert-Vits2 の音声合成モデルを操作するクラス。
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モデル/ハイパーパラメータ/スタイルベクトルのパスとデバイスを指定して初期化し、model.infer() メソッドを呼び出すと音声合成を行える。
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"""
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def __init__(
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self, model_path: Path, config_path: Path, style_vec_path: Path, device: str
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) -> None:
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"""
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Style-Bert-Vits2 の音声合成モデルを初期化する。
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この時点ではモデルはロードされていない (明示的にロードしたい場合は model.load() を呼び出す)。
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Args:
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model_path (Path): モデル (.safetensors) のパス
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config_path (Path): ハイパーパラメータ (config.json) のパス
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style_vec_path (Path): スタイルベクトル (style_vectors.npy) のパス
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device (str): 音声合成時に利用するデバイス (cpu, cuda, mps など)
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"""
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self.model_path: Path = model_path
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self.config_path: Path = config_path
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self.style_vec_path: Path = style_vec_path
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self.device: str = device
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self.hyper_parameters: HyperParameters = HyperParameters.load_from_json(
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self.config_path
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)
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self.spk2id: dict[str, int] = self.hyper_parameters.data.spk2id
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self.id2spk: dict[int, str] = {v: k for k, v in self.spk2id.items()}
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num_styles: int = self.hyper_parameters.data.num_styles
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if hasattr(self.hyper_parameters.data, "style2id"):
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self.style2id: dict[str, int] = self.hyper_parameters.data.style2id
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else:
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self.style2id: dict[str, int] = {str(i): i for i in range(num_styles)}
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if len(self.style2id) != num_styles:
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raise ValueError(
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f"Number of styles ({num_styles}) does not match the number of style2id ({len(self.style2id)})"
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)
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self.__style_vector_inference: Optional[pyannote.audio.Inference] = None
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self.__style_vectors: NDArray[Any] = np.load(self.style_vec_path)
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if self.__style_vectors.shape[0] != num_styles:
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raise ValueError(
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f"The number of styles ({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(self) -> None:
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"""
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音声合成モデルをデバイスにロードする。
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"""
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self.__net_g = get_net_g(
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model_path=str(self.model_path),
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version=self.hyper_parameters.version,
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device=self.device,
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hps=self.hyper_parameters,
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)
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def __get_style_vector(self, style_id: int, weight: float = 1.0) -> NDArray[Any]:
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"""
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スタイルベクトルを取得する。
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Args:
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style_id (int): スタイル ID (0 から始まるインデックス)
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weight (float, optional): スタイルベクトルの重み. Defaults to 1.0.
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Returns:
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NDArray[Any]: スタイルベクトル
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"""
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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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) -> NDArray[Any]:
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"""
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音声からスタイルベクトルを推論する。
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Args:
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audio_path (str): 音声ファイルのパス
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weight (float, optional): スタイルベクトルの重み. Defaults to 1.0.
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Returns:
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NDArray[Any]: スタイルベクトル
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"""
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# スタイルベクトルを取得するための推論モデルを初期化
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if self.__style_vector_inference is None:
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self.__style_vector_inference = pyannote.audio.Inference(
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model=pyannote.audio.Model.from_pretrained(
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"pyannote/wespeaker-voxceleb-resnet34-LM"
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),
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window="whole",
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)
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self.__style_vector_inference.to(torch.device(self.device))
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# 音声からスタイルベクトルを推論
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xvec = self.__style_vector_inference(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: Languages = Languages.JP,
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speaker_id: 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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noise_w: 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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pitch_scale: float = 1.0,
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intonation_scale: float = 1.0,
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) -> tuple[int, NDArray[Any]]:
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"""
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テキストから音声を合成する。
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Args:
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text (str): 読み上げるテキスト
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language (Languages, optional): 言語. Defaults to Languages.JP.
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speaker_id (int, optional): 話者 ID. Defaults to 0.
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reference_audio_path (Optional[str], optional): 音声スタイルの参照元の音声ファイルのパス. Defaults to None.
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sdp_ratio (float, optional): DP と SDP の混合比。0 で DP のみ、1で SDP のみを使用 (値を大きくするとテンポに緩急がつく). Defaults to DEFAULT_SDP_RATIO.
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noise (float, optional): DP に与えられるノイズ. Defaults to DEFAULT_NOISE.
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noise_w (float, optional): SDP に与えられるノイズ. Defaults to DEFAULT_NOISEW.
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length (float, optional): 生成音声の長さ（話速）のパラメータ。大きいほど生成音声が長くゆっくり、小さいほど短く早くなる。 Defaults to DEFAULT_LENGTH.
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line_split (bool, optional): テキストを改行ごとに分割して生成するかどうか. Defaults to DEFAULT_LINE_SPLIT.
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split_interval (float, optional): 改行ごとに分割する場合の無音 (秒). Defaults to DEFAULT_SPLIT_INTERVAL.
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assist_text (Optional[str], optional): 感情表現の参照元の補助テキスト. Defaults to None.
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assist_text_weight (float, optional): 感情表現の補助テキストを適用する強さ. Defaults to DEFAULT_ASSIST_TEXT_WEIGHT.
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use_assist_text (bool, optional): 音声合成時に感情表現の補助テキストを使用するかどうか. Defaults to False.
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style (str, optional): 音声スタイル (Neutral, Happy など). Defaults to DEFAULT_STYLE.
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style_weight (float, optional): 音声スタイルを適用する強さ. Defaults to DEFAULT_STYLE_WEIGHT.
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given_tone (Optional[list[int]], optional): アクセントのトーンのリスト. Defaults to None.
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pitch_scale (float, optional): ピッチの高さ (1.0 から変更すると若干音質が低下する). Defaults to 1.0.
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intonation_scale (float, optional): イントネーションの高さ (1.0 から変更すると若干音質が低下する). Defaults to 1.0.
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Returns:
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tuple[int, NDArray[Any]]: サンプリングレートと音声データ (16bit PCM)
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"""
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logger.info(f"Start generating audio data from text:\n{text}")
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if language != "JP" and self.hyper_parameters.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()
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assert self.__net_g is not None
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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=noise_w,
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length_scale=length,
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sid=speaker_id,
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language=language,
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hps=self.hyper_parameters,
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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=noise_w,
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length_scale=length,
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sid=speaker_id,
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language=language,
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hps=self.hyper_parameters,
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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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logger.info("Audio data generated successfully")
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if not (pitch_scale == 1.0 and intonation_scale == 1.0):
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_, audio = adjust_voice(
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fs=self.hyper_parameters.data.sampling_rate,
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wave=audio,
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pitch_scale=pitch_scale,
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intonation_scale=intonation_scale,
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)
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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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return (self.hyper_parameters.data.sampling_rate, audio)
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class TTSModelInfo(BaseModel):
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name: str
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files: list[str]
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styles: list[str]
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speakers: list[str]
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class TTSModelHolder:
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"""
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Style-Bert-Vits2 の音声合成モデルを管理するクラス。
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model_holder.models_info から指定されたディレクトリ内にある音声合成モデルの一覧を取得できる。
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"""
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def __init__(self, model_root_dir: Path, device: str) -> None:
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"""
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Style-Bert-Vits2 の音声合成モデルを管理するクラスを初期化する。
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音声合成モデルは下記のように配置されていることを前提とする (.safetensors のファイル名は自由) 。
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```
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model_root_dir
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├── model-name-1
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│ ├── config.json
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│ ├── model-name-1_e160_s14000.safetensors
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│ └── style_vectors.npy
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├── model-name-2
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│ ├── config.json
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│ ├── model-name-2_e160_s14000.safetensors
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│ └── style_vectors.npy
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└── ...
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```
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Args:
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model_root_dir (Path): 音声合成モデルが配置されているディレクトリのパス
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device (str): 音声合成時に利用するデバイス (cpu, cuda, mps など)
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"""
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self.root_dir: Path = model_root_dir
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self.device: str = device
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self.model_files_dict: dict[str, list[Path]] = {}
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self.current_model: Optional[TTSModel] = None
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self.model_names: list[str] = []
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self.models_info: list[TTSModelInfo] = []
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self.refresh()
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def refresh(self) -> None:
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"""
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音声合成モデルの一覧を更新する。
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"""
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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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self.models_info = []
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model_dirs = [d for d in self.root_dir.iterdir() if d.is_dir()]
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for model_dir in model_dirs:
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model_files = [
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f
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for f in model_dir.iterdir()
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if f.suffix in [".pth", ".pt", ".safetensors"]
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]
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if len(model_files) == 0:
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logger.warning(f"No model files found in {model_dir}, so skip it")
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continue
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config_path = model_dir / "config.json"
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if not config_path.exists():
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logger.warning(
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f"Config file {config_path} not found, so skip {model_dir}"
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)
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continue
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self.model_files_dict[model_dir.name] = model_files
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self.model_names.append(model_dir.name)
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hyper_parameters = HyperParameters.load_from_json(config_path)
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style2id: dict[str, int] = hyper_parameters.data.style2id
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styles = list(style2id.keys())
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spk2id: dict[str, int] = hyper_parameters.data.spk2id
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speakers = list(spk2id.keys())
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self.models_info.append(
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TTSModelInfo(
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name=model_dir.name,
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files=[str(f) for f in model_files],
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styles=styles,
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speakers=speakers,
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)
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)
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def get_model(self, model_name: str, model_path_str: str) -> TTSModel:
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"""
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指定された音声合成モデルのインスタンスを取得する。
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この時点ではモデルはロードされていない (明示的にロードしたい場合は model.load() を呼び出す)。
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Args:
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model_name (str): 音声合成モデルの名前
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model_path_str (str): 音声合成モデルのファイルパス (.safetensors)
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Returns:
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TTSModel: 音声合成モデルのインスタンス
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"""
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model_path = Path(model_path_str)
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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 self.current_model is None or self.current_model.model_path != model_path:
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self.current_model = TTSModel(
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model_path=model_path,
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config_path=self.root_dir / model_name / "config.json",
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style_vec_path=self.root_dir / model_name / "style_vectors.npy",
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device=self.device,
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)
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return self.current_model
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def get_model_for_gradio(
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self, model_name: str, model_path_str: str
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) -> tuple[gr.Dropdown, gr.Button, gr.Dropdown]:
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bert_models.load_model(Languages.JP)
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bert_models.load_tokenizer(Languages.JP)
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bert_models.load_model(Languages.EN)
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bert_models.load_tokenizer(Languages.EN)
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bert_models.load_model(Languages.ZH)
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bert_models.load_tokenizer(Languages.ZH)
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model_path = Path(model_path_str)
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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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# Already loaded
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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]), # type: ignore
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gr.Button(interactive=True, value="音声合成"),
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gr.Dropdown(choices=speakers, value=speakers[0]), # type: ignore
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)
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self.current_model = TTSModel(
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model_path=model_path,
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config_path=self.root_dir / model_name / "config.json",
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style_vec_path=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]), # type: ignore
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gr.Button(interactive=True, value="音声合成"),
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gr.Dropdown(choices=speakers, value=speakers[0]), # type: ignore
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)
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def update_model_files_for_gradio(self, model_name: str) -> gr.Dropdown:
|
|
model_files = self.model_files_dict[model_name]
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|
return gr.Dropdown(choices=model_files, value=model_files[0]) # type: ignore
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|
|
|
def update_model_names_for_gradio(
|
|
self,
|
|
) -> tuple[gr.Dropdown, gr.Dropdown, gr.Button]:
|
|
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 (
|
|
gr.Dropdown(choices=self.model_names, value=initial_model_name), # type: ignore
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|
gr.Dropdown(choices=initial_model_files, value=initial_model_files[0]), # type: ignore
|
|
gr.Button(interactive=False), # For tts_button
|
|
)
|