faddb0a8a8
Copying files from macOS to Linux would produce files starting with . _ that were incorrectly identified as model files.
332 lines
13 KiB
Python
332 lines
13 KiB
Python
# usage: .venv/bin/python convert_onnx.py --model model_assets/koharune-ami/koharune-ami.safetensors
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# usage: .venv/bin/python convert_onnx.py --model model_assets/ (All models in the directory will be converted)
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# ref: https://github.com/tuna2134/sbv2-api/blob/main/convert/convert_model.py
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import time
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from argparse import ArgumentParser
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from pathlib import Path
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from typing import cast
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import onnx
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import torch
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from onnxsim import model_info, simplify
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from rich import print
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from rich.rule import Rule
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from rich.style import Style
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from style_bert_vits2.constants import (
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DEFAULT_ASSIST_TEXT_WEIGHT,
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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.models.infer import get_text
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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.tts_model import TTSModel
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if __name__ == "__main__":
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start_time = time.time()
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parser = ArgumentParser()
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parser.add_argument(
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"--model", required=True, help="Path to the model file or directory"
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)
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parser.add_argument(
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"--force-convert",
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action="store_true",
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help="Already converted models will be overwritten",
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)
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args = parser.parse_args()
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# --model に指定されたパスがディレクトリの時、配下にある全ての .safetensors ファイルを対象に変換する
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model_paths: list[Path] = []
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if Path(args.model).is_dir():
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for path in Path(args.model).glob("**/*.safetensors"):
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# . から始まるファイルは除外
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if not path.name.startswith("."):
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model_paths.append(path)
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else:
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model_paths.append(Path(args.model))
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for model_path in model_paths:
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# モデルの入出力先ファイルパスを取得
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onnx_temp_model_path = model_path.parent / f"{model_path.stem}_temp.onnx"
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onnx_optimized_model_path = model_path.parent / f"{model_path.stem}.onnx"
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config_path = model_path.parent / "config.json"
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style_vec_path = model_path.parent / "style_vectors.npy"
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assert model_path.exists(), "Model file does not exist"
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assert config_path.exists(), "Config file does not exist"
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assert style_vec_path.exists(), "Style vector file does not exist"
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assert model_path.suffix != ".onnx", "Model file is already ONNX"
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print(Rule(characters="=", style=Style(color="blue")))
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print(f"[bold cyan]Model file:[/bold cyan] {model_path}")
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print(f"[bold cyan]Config file:[/bold cyan] {config_path}")
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print(f"[bold cyan]Style vector file:[/bold cyan] {style_vec_path}")
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print(Rule(characters="=", style=Style(color="blue")))
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# すでに ONNX モデルが存在する場合、--force-convert オプションが指定されていない場合はスキップ
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if onnx_optimized_model_path.exists() and not args.force_convert:
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print(
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f"[bold yellow]ONNX model already exists: {onnx_optimized_model_path}[/bold yellow]"
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)
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print(
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"[bold]If you want to overwrite it, use the --force-convert option.[/bold]"
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)
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print(Rule(characters="=", style=Style(color="blue")))
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continue
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# PyTorch モデルを読み込む
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device = "cpu"
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tts_model = TTSModel(
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model_path=model_path,
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config_path=config_path,
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style_vec_path=style_vec_path,
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device=device,
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)
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tts_model.load()
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style_id = tts_model.style2id[DEFAULT_STYLE]
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assert tts_model.net_g is not None, "Model is not loaded"
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# 音声合成に必要な BERT 特徴量・音素列・アクセント列・言語 ID を取得
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# JP-Extra モデルアーキテクチャの場合、bert (中国語の BERT 特徴量) や en_bert (英語の BERT 特徴量) は
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# torch.zeros() で適当に埋められており、推論には ja_bert (日本語の BERT 特徴量) のみが使用される
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bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
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"今日はいい天気ですね。",
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Languages.JP,
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tts_model.hyper_parameters,
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device,
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assist_text=None,
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assist_text_weight=DEFAULT_ASSIST_TEXT_WEIGHT,
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given_phone=None,
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given_tone=None,
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)
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# スタイルベクトルを取得
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style_vector = tts_model.get_style_vector(style_id, DEFAULT_STYLE_WEIGHT)
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# モデルの入力を作成
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x_tst = phones.to(device).unsqueeze(0)
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tones = tones.to(device).unsqueeze(0)
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lang_ids = lang_ids.to(device).unsqueeze(0)
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bert = bert.to(device).unsqueeze(0)
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ja_bert = ja_bert.to(device).unsqueeze(0)
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en_bert = en_bert.to(device).unsqueeze(0)
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
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style_vec_tensor = torch.from_numpy(style_vector).to(device).unsqueeze(0)
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sid = 0
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sid_tensor = torch.LongTensor([sid]).to(device)
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length_scale = torch.tensor(1.0)
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sdp_ratio = torch.tensor(0.0)
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noise_scale = torch.tensor(0.667)
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noise_scale_w = torch.tensor(0.8)
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# JP-Extra モデルアーキテクチャ向けの ONNX 変換ロジック
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if isinstance(tts_model.net_g, SynthesizerTrnJPExtra):
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# SynthesizerTrnJPExtra の forward メソッドをオーバーライド
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def forward_jp_extra(
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x: torch.Tensor,
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x_lengths: torch.Tensor,
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sid: torch.Tensor,
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tone: torch.Tensor,
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language: torch.Tensor,
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bert: torch.Tensor,
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style_vec: torch.Tensor,
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length_scale: float = 1.0,
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sdp_ratio: float = 0.0,
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noise_scale: float = 0.667,
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noise_scale_w: float = 0.8,
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) -> tuple[
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torch.Tensor, torch.Tensor, torch.Tensor, tuple[torch.Tensor, ...]
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]:
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return cast(SynthesizerTrnJPExtra, tts_model.net_g).infer(
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x,
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x_lengths,
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sid,
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tone,
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language,
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bert,
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style_vec,
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length_scale=length_scale,
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sdp_ratio=sdp_ratio,
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noise_scale=noise_scale,
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noise_scale_w=noise_scale_w,
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)
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tts_model.net_g.forward = forward_jp_extra # type: ignore
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# モデルを ONNX に変換
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print(Rule(characters="=", style=Style(color="blue")))
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print(
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f"[bold cyan]Exporting ONNX model... (Architecture: JP-Extra)[/bold cyan]"
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)
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print(Rule(characters="=", style=Style(color="blue")))
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export_start_time = time.time()
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torch.onnx.export(
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model=tts_model.net_g,
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args=(
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x_tst,
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x_tst_lengths,
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sid_tensor,
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tones,
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lang_ids,
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ja_bert,
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style_vec_tensor,
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length_scale,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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),
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f=str(onnx_temp_model_path),
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verbose=False,
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input_names=[
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"x_tst",
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"x_tst_lengths",
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"sid",
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"tones",
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"language",
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"bert",
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"style_vec",
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"length_scale",
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"sdp_ratio",
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"noise_scale",
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"noise_scale_w",
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],
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output_names=["output"],
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dynamic_axes={
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"x_tst": {0: "batch_size", 1: "x_tst_max_length"},
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"x_tst_lengths": {0: "batch_size"},
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"sid": {0: "batch_size"},
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"tones": {0: "batch_size", 1: "x_tst_max_length"},
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"language": {0: "batch_size", 1: "x_tst_max_length"},
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"bert": {0: "batch_size", 2: "x_tst_max_length"},
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"style_vec": {0: "batch_size"},
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},
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)
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print(
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f"[bold green]ONNX model exported to {onnx_temp_model_path} ({time.time() - export_start_time:.2f}s)[/bold green]"
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)
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# 非 JP-Extra モデルアーキテクチャ向けの ONNX 変換ロジック
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else:
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# SynthesizerTrn の forward メソッドをオーバーライド
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def forward_non_jp_extra(
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x: torch.Tensor,
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x_lengths: torch.Tensor,
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sid: torch.Tensor,
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tone: torch.Tensor,
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language: torch.Tensor,
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bert: torch.Tensor,
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ja_bert: torch.Tensor,
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en_bert: torch.Tensor,
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style_vec: torch.Tensor,
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length_scale: float = 1.0,
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sdp_ratio: float = 0.0,
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noise_scale: float = 0.667,
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noise_scale_w: float = 0.8,
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) -> tuple[
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torch.Tensor, torch.Tensor, torch.Tensor, tuple[torch.Tensor, ...]
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]:
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return cast(SynthesizerTrn, tts_model.net_g).infer(
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x,
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x_lengths,
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sid,
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tone,
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language,
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bert,
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ja_bert,
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en_bert,
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style_vec,
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length_scale=length_scale,
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sdp_ratio=sdp_ratio,
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noise_scale=noise_scale,
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noise_scale_w=noise_scale_w,
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)
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tts_model.net_g.forward = forward_non_jp_extra # type: ignore
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# モデルを ONNX に変換
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print(Rule(characters="=", style=Style(color="blue")))
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print(
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f"[bold cyan]Exporting ONNX model... (Architecture: Non-JP-Extra)[/bold cyan]"
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)
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print(Rule(characters="=", style=Style(color="blue")))
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export_start_time = time.time()
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torch.onnx.export(
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model=tts_model.net_g,
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args=(
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x_tst,
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x_tst_lengths,
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sid_tensor,
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tones,
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lang_ids,
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bert,
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ja_bert,
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en_bert,
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style_vec_tensor,
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length_scale,
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sdp_ratio,
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noise_scale,
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noise_scale_w,
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),
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f=str(onnx_temp_model_path),
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verbose=False,
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input_names=[
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"x_tst",
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"x_tst_lengths",
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"sid",
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"tones",
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"language",
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"bert",
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"ja_bert",
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"en_bert",
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"style_vec",
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"length_scale",
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"sdp_ratio",
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"noise_scale",
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"noise_scale_w",
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],
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output_names=["output"],
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dynamic_axes={
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"x_tst": {0: "batch_size", 1: "x_tst_max_length"},
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"x_tst_lengths": {0: "batch_size"},
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"sid": {0: "batch_size"},
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"tones": {0: "batch_size", 1: "x_tst_max_length"},
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"language": {0: "batch_size", 1: "x_tst_max_length"},
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"bert": {0: "batch_size", 2: "x_tst_max_length"},
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"ja_bert": {0: "batch_size", 2: "x_tst_max_length"},
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"en_bert": {0: "batch_size", 2: "x_tst_max_length"},
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"style_vec": {0: "batch_size"},
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},
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)
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print(
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f"[bold green]ONNX model exported to {onnx_temp_model_path} ({time.time() - export_start_time:.2f}s)[/bold green]"
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)
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# ONNX モデルを最適化
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print(Rule(characters="=", style=Style(color="blue")))
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print(f"[bold cyan]Optimizing ONNX model...[/bold cyan]")
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print(Rule(characters="=", style=Style(color="blue")))
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optimize_start_time = time.time()
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onnx_model = onnx.load(onnx_temp_model_path)
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simplified_onnx_model, check = simplify(onnx_model)
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onnx.save(simplified_onnx_model, onnx_optimized_model_path)
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print(
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f"[bold green]ONNX model optimized and saved to {onnx_optimized_model_path} ({time.time() - optimize_start_time:.2f}s)[/bold green]"
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)
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print(
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f"[bold]Total Time: {time.time() - start_time:.2f}s / "
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f"Size: {onnx_temp_model_path.stat().st_size / 1000 / 1000:.2f}MB -> "
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f"{onnx_optimized_model_path.stat().st_size / 1000 / 1000:.2f}MB[/bold]"
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)
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onnx_temp_model_path.unlink()
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print(Rule(characters="=", style=Style(color="blue")))
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print("[bold cyan]Optimized model info:[/bold cyan]")
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model_info.print_simplifying_info(onnx_model, simplified_onnx_model)
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print(Rule(characters="=", style=Style(color="blue")))
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