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Improve: ONNX conversion logic improved, and support for noise_scale(_w) model argument

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tsukumi
2024-09-22 16:14:25 +09:00
parent 9b27ba9777
commit 291e635e9e
3 changed files with 104 additions and 76 deletions
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174
convert_onnx.py
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@@ -1,4 +1,4 @@
# usage: .venv/bin/python convert_onnx.py --model model_assets/amitaro/amitaro.safetensors
# usage: .venv/bin/python convert_onnx.py --model model_assets/koharune-ami/koharune-ami.safetensors
# ref: https://github.com/tuna2134/sbv2-api/blob/main/convert/convert_model.py
import time
@@ -59,37 +59,10 @@ if __name__ == "__main__":
tts_model.load()
style_id = tts_model.style2id[DEFAULT_STYLE]
assert tts_model.net_g is not None, "Model is not loaded"
assert (
tts_model.hyper_parameters.data.use_jp_extra is True
), "Normal model is not supported yet"
# SynthesizerTrnJPExtra の forward メソッドをオーバーライド
def forward(
x: torch.Tensor,
x_lengths: torch.Tensor,
sid: torch.Tensor,
tone: torch.Tensor,
language: torch.Tensor,
bert: torch.Tensor,
style_vec: torch.Tensor,
length_scale: float = 1.0,
sdp_ratio: float = 0.0,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, tuple[torch.Tensor, ...]]:
return cast(SynthesizerTrnJPExtra, tts_model.net_g).infer(
x,
x_lengths,
sid,
tone,
language,
bert,
style_vec,
sdp_ratio=sdp_ratio,
length_scale=length_scale,
)
tts_model.net_g.forward = forward # type: ignore
# 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
# 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
# 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
bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
"今日はいい天気ですね。",
Languages.JP,
@@ -104,6 +77,7 @@ if __name__ == "__main__":
# スタイルベクトルを取得
style_vector = tts_model.get_style_vector(style_id, DEFAULT_STYLE_WEIGHT)
# モデルの入力を作成
x_tst = phones.to(device).unsqueeze(0)
tones = tones.to(device).unsqueeze(0)
lang_ids = lang_ids.to(device).unsqueeze(0)
@@ -112,50 +86,102 @@ if __name__ == "__main__":
en_bert = en_bert.to(device).unsqueeze(0)
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
style_vec_tensor = torch.from_numpy(style_vector).to(device).unsqueeze(0)
sid = 0
sid_tensor = torch.LongTensor([sid]).to(device)
length_scale = torch.tensor(1.0)
sdp_ratio = torch.tensor(0.0)
noise_scale = torch.tensor(0.667)
noise_scale_w = torch.tensor(0.8)
# モデルを ONNX に変換
print(Rule(characters="=", style=Style(color="blue")))
print(f"[bold cyan]Exporting ONNX model...[/bold cyan]")
print(Rule(characters="=", style=Style(color="blue")))
export_start_time = time.time()
torch.onnx.export(
model=tts_model.net_g,
args=(
x_tst,
x_tst_lengths,
torch.LongTensor([0]).to(device),
tones,
lang_ids,
bert,
style_vec_tensor,
torch.tensor(1.0),
torch.tensor(0.0),
),
f=str(onnx_temp_model_path),
verbose=True,
dynamic_axes={
"x_tst": {1: "batch_size"},
"x_tst_lengths": {0: "batch_size"},
"tones": {1: "batch_size"},
"language": {1: "batch_size"},
"bert": {2: "batch_size"},
},
input_names=[
"x_tst",
"x_tst_lengths",
"sid",
"tones",
"language",
"bert",
"style_vec",
"length_scale",
"sdp_ratio",
],
output_names=["output"],
)
print(
f"[bold green]ONNX model exported to {onnx_temp_model_path} ({time.time() - export_start_time:.2f}s)[/bold green]"
)
# JP-Extra モデルアーキテクチャ向けの ONNX 変換ロジック
if tts_model.hyper_parameters.data.use_jp_extra is True:
# SynthesizerTrnJPExtra の forward メソッドをオーバーライド
def forward(
x: torch.Tensor,
x_lengths: torch.Tensor,
sid: torch.Tensor,
tone: torch.Tensor,
language: torch.Tensor,
bert: torch.Tensor,
style_vec: torch.Tensor,
length_scale: float = 1.0,
sdp_ratio: float = 0.0,
noise_scale: float = 0.667,
noise_scale_w: float = 0.8,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, tuple[torch.Tensor, ...]]:
return cast(SynthesizerTrnJPExtra, tts_model.net_g).infer(
x,
x_lengths,
sid,
tone,
language,
bert,
style_vec,
length_scale=length_scale,
sdp_ratio=sdp_ratio,
noise_scale=noise_scale,
noise_scale_w=noise_scale_w,
)
tts_model.net_g.forward = forward # type: ignore
# モデルを ONNX に変換
print(Rule(characters="=", style=Style(color="blue")))
print(
f"[bold cyan]Exporting ONNX model... (Architecture: JP-Extra)[/bold cyan]"
)
print(Rule(characters="=", style=Style(color="blue")))
export_start_time = time.time()
torch.onnx.export(
model=tts_model.net_g,
args=(
x_tst,
x_tst_lengths,
sid_tensor,
tones,
lang_ids,
ja_bert,
style_vec_tensor,
length_scale,
sdp_ratio,
noise_scale,
noise_scale_w,
),
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",
"sid",
"tones",
"language",
"bert",
"style_vec",
"length_scale",
"sdp_ratio",
"noise_scale",
"noise_scale_w",
],
output_names=["output"],
)
print(
f"[bold green]ONNX model exported to {onnx_temp_model_path} ({time.time() - export_start_time:.2f}s)[/bold green]"
)
else:
raise NotImplementedError(
"non-JP-Extra model architecture is not implemented yet"
)
# ONNX モデルを最適化
print(Rule(characters="=", style=Style(color="blue")))
@@ -170,7 +196,7 @@ if __name__ == "__main__":
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 / Size: {onnx_optimized_model_path.stat().st_size / 1000 / 1000:.2f}MB[/bold]"
)
print(Rule(characters="=", style=Style(color="blue")))
print("[bold cyan]Optimized model info:[/bold cyan]")