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Improve: .safetensors models under a directory specified with --model can be automatically converted to ONNX

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This commit is contained in:
tsukumi
2024-09-22 16:27:22 +09:00
parent 291e635e9e
commit 16115a366b
2 changed files with 193 additions and 163 deletions
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22
convert_bert_onnx.py
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@@ -21,7 +21,7 @@ from style_bert_vits2.nlp import bert_models
if __name__ == "__main__":
start_time = time.time()
parser = ArgumentParser()
parser.add_argument("--language", default=Languages.JP)
parser.add_argument("--language", default=Languages.JP, help="Language of the BERT model to be converted")
args = parser.parse_args()
# モデルの入出力先ファイルパスを取得
@@ -70,11 +70,19 @@ if __name__ == "__main__":
export_start_time = time.time()
torch.onnx.export(
model=model,
args=(inputs["input_ids"], inputs["token_type_ids"], inputs["attention_mask"]),
args=(
inputs["input_ids"],
inputs["token_type_ids"],
inputs["attention_mask"],
),
f=str(onnx_temp_model_path),
input_names=["input_ids", "token_type_ids", "attention_mask"],
verbose=False,
input_names=[
"input_ids",
"token_type_ids",
"attention_mask",
],
output_names=["output"],
verbose=True,
dynamic_axes={
"input_ids": {1: "batch_size"},
"attention_mask": {1: "batch_size"},
@@ -92,13 +100,15 @@ if __name__ == "__main__":
onnx_model = onnx.load(onnx_temp_model_path)
simplified_onnx_model, check = simplify(onnx_model)
onnx.save(simplified_onnx_model, onnx_optimized_model_path)
onnx_temp_model_path.unlink()
print(
f"[bold green]ONNX model optimized and saved to {onnx_optimized_model_path} ({time.time() - optimize_start_time:.2f}s)[/bold green]"
)
print(
f"[bold]Total Time: {time.time() - start_time:.2f}s / Size: {onnx_optimized_model_path.stat().st_size / 1024 / 1024:.2f}MB[/bold]"
f"[bold]Total Time: {time.time() - start_time:.2f}s / "
f"Size: {onnx_temp_model_path.stat().st_size / 1000 / 1000:.2f}MB -> "
f"{onnx_optimized_model_path.stat().st_size / 1000 / 1000:.2f}MB[/bold]"
)
onnx_temp_model_path.unlink()
print(Rule(characters="=", style=Style(color="blue")))
print("[bold cyan]Optimized model info:[/bold cyan]")
model_info.print_simplifying_info(onnx_model, simplified_onnx_model)

54
convert_onnx.py
View File

@@ -1,4 +1,5 @@
# usage: .venv/bin/python convert_onnx.py --model model_assets/koharune-ami/koharune-ami.safetensors
# usage: .venv/bin/python convert_onnx.py --model model_assets/ (All models in the directory will be converted)
# ref: https://github.com/tuna2134/sbv2-api/blob/main/convert/convert_model.py
import time
@@ -29,15 +30,25 @@ from style_bert_vits2.tts_model import TTSModel
if __name__ == "__main__":
start_time = time.time()
parser = ArgumentParser()
parser.add_argument("--model", required=True)
parser.add_argument("--model", required=True, help="Path to the model file or directory")
parser.add_argument("--force-convert", action="store_true", help="Already converted models will be overwritten")
args = parser.parse_args()
# --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
model_paths: list[Path] = []
if Path(args.model).is_dir():
for path in Path(args.model).glob("**/*.safetensors"):
model_paths.append(path)
else:
model_paths.append(Path(args.model))
for model_path in model_paths:
# モデルの入出力先ファイルパスを取得
model_path = Path(args.model)
onnx_temp_model_path = Path(args.model).parent / f"{model_path.stem}_temp.onnx"
onnx_optimized_model_path = Path(args.model).parent / f"{model_path.stem}.onnx"
config_path = Path(args.model).parent / "config.json"
style_vec_path = Path(args.model).parent / "style_vectors.npy"
onnx_temp_model_path = model_path.parent / f"{model_path.stem}_temp.onnx"
onnx_optimized_model_path = model_path.parent / f"{model_path.stem}.onnx"
config_path = model_path.parent / "config.json"
style_vec_path = model_path.parent / "style_vectors.npy"
assert model_path.exists(), "Model file does not exist"
assert config_path.exists(), "Config file does not exist"
assert style_vec_path.exists(), "Style vector file does not exist"
@@ -48,6 +59,13 @@ if __name__ == "__main__":
print(f"[bold cyan]Style vector file:[/bold cyan] {style_vec_path}")
print(Rule(characters="=", style=Style(color="blue")))
# 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
if onnx_optimized_model_path.exists() and not args.force_convert:
print(f"[bold yellow]ONNX model already exists: {onnx_optimized_model_path}[/bold yellow]")
print("[bold]If you want to overwrite it, use the --force-convert option.[/bold]")
print(Rule(characters="=", style=Style(color="blue")))
continue
# PyTorch モデルを読み込む
device = "cpu"
tts_model = TTSModel(
@@ -150,15 +168,6 @@ if __name__ == "__main__":
),
f=str(onnx_temp_model_path),
verbose=False,
dynamic_axes={
"x_tst": {0: "batch_size", 1: "x_tst_max_length"},
"x_tst_lengths": {0: "batch_size"},
"sid": {0: "batch_size"},
"tones": {0: "batch_size", 1: "x_tst_max_length"},
"language": {0: "batch_size", 1: "x_tst_max_length"},
"bert": {0: "batch_size", 2: "x_tst_max_length"},
"style_vec": {0: "batch_size"},
},
input_names=[
"x_tst",
"x_tst_lengths",
@@ -173,6 +182,15 @@ if __name__ == "__main__":
"noise_scale_w",
],
output_names=["output"],
dynamic_axes={
"x_tst": {0: "batch_size", 1: "x_tst_max_length"},
"x_tst_lengths": {0: "batch_size"},
"sid": {0: "batch_size"},
"tones": {0: "batch_size", 1: "x_tst_max_length"},
"language": {0: "batch_size", 1: "x_tst_max_length"},
"bert": {0: "batch_size", 2: "x_tst_max_length"},
"style_vec": {0: "batch_size"},
},
)
print(
f"[bold green]ONNX model exported to {onnx_temp_model_path} ({time.time() - export_start_time:.2f}s)[/bold green]"
@@ -191,13 +209,15 @@ if __name__ == "__main__":
onnx_model = onnx.load(onnx_temp_model_path)
simplified_onnx_model, check = simplify(onnx_model)
onnx.save(simplified_onnx_model, onnx_optimized_model_path)
onnx_temp_model_path.unlink()
print(
f"[bold green]ONNX model optimized and saved to {onnx_optimized_model_path} ({time.time() - optimize_start_time:.2f}s)[/bold green]"
)
print(
f"[bold]Total Time: {time.time() - start_time:.2f}s / Size: {onnx_optimized_model_path.stat().st_size / 1000 / 1000:.2f}MB[/bold]"
f"[bold]Total Time: {time.time() - start_time:.2f}s / "
f"Size: {onnx_temp_model_path.stat().st_size / 1000 / 1000:.2f}MB -> "
f"{onnx_optimized_model_path.stat().st_size / 1000 / 1000:.2f}MB[/bold]"
)
onnx_temp_model_path.unlink()
print(Rule(characters="=", style=Style(color="blue")))
print("[bold cyan]Optimized model info:[/bold cyan]")
model_info.print_simplifying_info(onnx_model, simplified_onnx_model)