Onnx导出拓展以及WebUI支持 (#140)
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export_onnx.py
118
export_onnx.py
@ -1,47 +1,81 @@
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from infer_pack.models_onnx import SynthesizerTrnMs256NSFsid
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from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM
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from infer_pack.models_onnx import SynthesizerTrnMs256NSFsidO
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import torch
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person = "Shiroha/shiroha.pth"
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exported_path = "model.onnx"
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if __name__ == '__main__':
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MoeVS = True #模型是否为MoeVoiceStudio(原MoeSS)使用
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ModelPath = "Shiroha/shiroha.pth" #模型路径
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ExportedPath = "model.onnx" #输出路径
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hidden_channels = 256 # hidden_channels,为768Vec做准备
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cpt = torch.load(ModelPath, map_location="cpu")
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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print(*cpt["config"])
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cpt = torch.load(person, map_location="cpu")
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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print(*cpt["config"])
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=False)
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net_g.load_state_dict(cpt["weight"], strict=False)
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test_phone = torch.rand(1, 200, hidden_channels) # hidden unit
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test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度(貌似没啥用)
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test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频(单位赫兹)
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test_pitchf = torch.rand(1, 200) # nsf基频
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test_ds = torch.LongTensor([0]) # 说话人ID
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test_rnd = torch.rand(1, 192, 200) # 噪声(加入随机因子)
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test_phone = torch.rand(1, 200, 256)
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test_phone_lengths = torch.tensor([200]).long()
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test_pitch = torch.randint(size=(1, 200), low=5, high=255)
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test_pitchf = torch.rand(1, 200)
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test_ds = torch.LongTensor([0])
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test_rnd = torch.rand(1, 192, 200)
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input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
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output_names = [
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"audio",
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]
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device = "cpu"
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torch.onnx.export(
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net_g,
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(
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test_phone.to(device),
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test_phone_lengths.to(device),
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test_pitch.to(device),
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test_pitchf.to(device),
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test_ds.to(device),
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test_rnd.to(device),
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),
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exported_path,
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dynamic_axes={
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"phone": [1],
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"pitch": [1],
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"pitchf": [1],
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"rnd": [2],
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},
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do_constant_folding=False,
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opset_version=16,
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verbose=False,
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input_names=input_names,
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output_names=output_names,
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)
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device = "cpu" #导出时设备(不影响使用模型)
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if MoeVS:
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net_g = SynthesizerTrnMs256NSFsidM(*cpt["config"], is_half=False) # fp32导出(C++要支持fp16必须手动将内存重新排列所以暂时不用fp16)
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net_g.load_state_dict(cpt["weight"], strict=False)
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input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
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output_names = [
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"audio",
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]
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torch.onnx.export(
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net_g,
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(
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test_phone.to(device),
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test_phone_lengths.to(device),
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test_pitch.to(device),
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test_pitchf.to(device),
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test_ds.to(device),
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test_rnd.to(device),
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),
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ExportedPath,
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dynamic_axes={
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"phone": [1],
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"pitch": [1],
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"pitchf": [1],
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"rnd": [2],
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},
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do_constant_folding=False,
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opset_version=16,
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verbose=False,
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input_names=input_names,
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output_names=output_names,
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)
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else:
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net_g = SynthesizerTrnMs256NSFsidO(*cpt["config"], is_half=False) # fp32导出(C++要支持fp16必须手动将内存重新排列所以暂时不用fp16)
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net_g.load_state_dict(cpt["weight"], strict=False)
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input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds"]
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output_names = [
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"audio",
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]
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torch.onnx.export(
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net_g,
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(
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test_phone.to(device),
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test_phone_lengths.to(device),
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test_pitch.to(device),
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test_pitchf.to(device),
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test_ds.to(device),
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),
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ExportedPath,
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dynamic_axes={
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"phone": [1],
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"pitch": [1],
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"pitchf": [1],
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},
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do_constant_folding=False,
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opset_version=16,
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verbose=False,
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input_names=input_names,
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output_names=output_names,
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)
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47
export_onnx_old.py
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47
export_onnx_old.py
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@ -0,0 +1,47 @@
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from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM
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import torch
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person = "Shiroha/shiroha.pth"
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exported_path = "model.onnx"
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cpt = torch.load(person, map_location="cpu")
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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print(*cpt["config"])
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net_g = SynthesizerTrnMs256NSFsidM(*cpt["config"], is_half=False)
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net_g.load_state_dict(cpt["weight"], strict=False)
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test_phone = torch.rand(1, 200, 256)
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test_phone_lengths = torch.tensor([200]).long()
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test_pitch = torch.randint(size=(1, 200), low=5, high=255)
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test_pitchf = torch.rand(1, 200)
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test_ds = torch.LongTensor([0])
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test_rnd = torch.rand(1, 192, 200)
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input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
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output_names = [
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"audio",
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]
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device = "cpu"
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torch.onnx.export(
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net_g,
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(
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test_phone.to(device),
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test_phone_lengths.to(device),
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test_pitch.to(device),
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test_pitchf.to(device),
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test_ds.to(device),
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test_rnd.to(device),
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),
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exported_path,
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dynamic_axes={
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"phone": [1],
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"pitch": [1],
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"pitchf": [1],
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"rnd": [2],
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},
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do_constant_folding=False,
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opset_version=16,
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verbose=False,
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input_names=input_names,
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output_names=output_names,
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)
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@ -95,5 +95,10 @@
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"性能设置": "Performance settings",
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"开始音频转换": "Start Audio Conversion",
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"停止音频转换": "Stop Audio Conversion",
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"推理时间(ms):": "Infer Time(ms):"
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"推理时间(ms):": "Infer Time(ms):",
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"Onnx导出": "Onnx",
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"RVC模型路径": "RVC Path",
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"Onnx输出路径": "Onnx Export Path",
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"导出Onnx模型": "Export Onnx Model",
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"MoeVS模型": "MoeSS?"
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}
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@ -95,5 +95,10 @@
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"性能设置": "パフォーマンス設定",
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"开始音频转换": "音声変換を開始",
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"停止音频转换": "音声変換を停止",
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"推理时间(ms):": "推論時間(ms):"
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"推理时间(ms):": "推論時間(ms):",
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"Onnx导出": "Onnx",
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"RVC模型路径": "RVCルパス",
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"Onnx输出路径": "Onnx出力パス",
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"导出Onnx模型": "Onnxに変換",
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"MoeVS模型": "MoeSS?"
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}
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@ -95,5 +95,10 @@
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"性能设置": "性能设置",
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"开始音频转换": "开始音频转换",
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"停止音频转换": "停止音频转换",
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"推理时间(ms):": "推理时间(ms):"
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"推理时间(ms):": "推理时间(ms):",
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"Onnx导出": "Onnx导出",
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"RVC模型路径": "RVC模型路径",
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"Onnx输出路径": "Onnx输出路径",
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"导出Onnx模型": "导出Onnx模型",
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"MoeVS模型": "MoeSS模型"
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}
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@ -95,5 +95,10 @@
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"性能设置": "效能設定",
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"开始音频转换": "開始音訊轉換",
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"停止音频转换": "停止音訊轉換",
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"推理时间(ms):": "推理時間(ms):"
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"推理时间(ms):": "推理時間(ms):",
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"Onnx导出": "Onnx导出",
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"RVC模型路径": "RVC模型路径",
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"Onnx输出路径": "Onnx输出路径",
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"导出Onnx模型": "导出Onnx模型",
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"MoeVS模型": "MoeSS模型"
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}
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@ -95,5 +95,10 @@
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"性能设置": "效能設定",
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"开始音频转换": "開始音訊轉換",
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"停止音频转换": "停止音訊轉換",
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"推理时间(ms):": "推理時間(ms):"
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"推理时间(ms):": "推理時間(ms):",
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"Onnx导出": "Onnx导出",
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"RVC模型路径": "RVC模型路径",
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"Onnx输出路径": "Onnx输出路径",
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"导出Onnx模型": "导出Onnx模型",
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"MoeVS模型": "MoeSS模型"
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}
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@ -95,5 +95,10 @@
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"性能设置": "效能設定",
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"开始音频转换": "開始音訊轉換",
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"停止音频转换": "停止音訊轉換",
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"推理时间(ms):": "推理時間(ms):"
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"推理时间(ms):": "推理時間(ms):",
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"Onnx导出": "Onnx导出",
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"RVC模型路径": "RVC模型路径",
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"Onnx输出路径": "Onnx输出路径",
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"导出Onnx模型": "导出Onnx模型",
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"MoeVS模型": "MoeSS模型"
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}
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90
infer-web.py
90
infer-web.py
@ -119,6 +119,7 @@ for name in os.listdir(weight_uvr5_root):
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uvr5_names.append(name.replace(".pth", ""))
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def vc_single(
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sid,
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input_audio,
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@ -885,6 +886,83 @@ def change_info_(ckpt_path):
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return {"__type__": "update"}, {"__type__": "update"}
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from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM
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from infer_pack.models_onnx import SynthesizerTrnMs256NSFsidO
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def export_onnx(ModelPath, ExportedPath, MoeVS=True):
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hidden_channels = 256 # hidden_channels,为768Vec做准备
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cpt = torch.load(ModelPath, map_location="cpu")
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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print(*cpt["config"])
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test_phone = torch.rand(1, 200, hidden_channels) # hidden unit
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test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度(貌似没啥用)
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test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频(单位赫兹)
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test_pitchf = torch.rand(1, 200) # nsf基频
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test_ds = torch.LongTensor([0]) # 说话人ID
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test_rnd = torch.rand(1, 192, 200) # 噪声(加入随机因子)
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device = "cpu" #导出时设备(不影响使用模型)
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if MoeVS:
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net_g = SynthesizerTrnMs256NSFsidM(*cpt["config"], is_half=False) # fp32导出(C++要支持fp16必须手动将内存重新排列所以暂时不用fp16)
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net_g.load_state_dict(cpt["weight"], strict=False)
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input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
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output_names = [
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"audio",
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]
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torch.onnx.export(
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net_g,
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(
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test_phone.to(device),
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test_phone_lengths.to(device),
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test_pitch.to(device),
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test_pitchf.to(device),
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test_ds.to(device),
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test_rnd.to(device),
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),
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ExportedPath,
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dynamic_axes={
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"phone": [1],
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"pitch": [1],
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"pitchf": [1],
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"rnd": [2],
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},
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do_constant_folding=False,
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opset_version=16,
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verbose=False,
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input_names=input_names,
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output_names=output_names,
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)
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else:
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net_g = SynthesizerTrnMs256NSFsidO(*cpt["config"], is_half=False) # fp32导出(C++要支持fp16必须手动将内存重新排列所以暂时不用fp16)
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net_g.load_state_dict(cpt["weight"], strict=False)
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input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds"]
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output_names = [
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"audio",
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]
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torch.onnx.export(
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net_g,
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(
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test_phone.to(device),
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test_phone_lengths.to(device),
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test_pitch.to(device),
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test_pitchf.to(device),
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test_ds.to(device),
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),
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ExportedPath,
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dynamic_axes={
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"phone": [1],
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"pitch": [1],
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"pitchf": [1],
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},
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do_constant_folding=False,
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opset_version=16,
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verbose=False,
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input_names=input_names,
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output_names=output_names,
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)
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return "Finished"
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with gr.Blocks() as app:
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gr.Markdown(
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value=i18n(
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@ -1361,6 +1439,18 @@ with gr.Blocks() as app:
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info7,
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)
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with gr.TabItem(i18n("Onnx导出")):
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with gr.Row():
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ckpt_dir = gr.Textbox(label=i18n("RVC模型路径"), value="", interactive=True)
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with gr.Row():
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onnx_dir = gr.Textbox(label=i18n("Onnx输出路径"), value="", interactive=True)
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with gr.Row():
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moevs = gr.Checkbox(label=i18n("MoeVS模型"), value=True)
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infoOnnx = gr.Label(label="Null")
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with gr.Row():
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butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary")
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butOnnx.click(export_onnx, [ckpt_dir, onnx_dir, moevs], infoOnnx)
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# with gr.TabItem(i18n("招募音高曲线前端编辑器")):
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# gr.Markdown(value=i18n("加开发群联系我xxxxx"))
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# with gr.TabItem(i18n("点击查看交流、问题反馈群号")):
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@ -527,7 +527,7 @@ sr2sr = {
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}
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class SynthesizerTrnMs256NSFsid(nn.Module):
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class SynthesizerTrnMs256NSFsidO(nn.Module):
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def __init__(
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self,
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spec_channels,
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@ -612,104 +612,15 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
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self.flow.remove_weight_norm()
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self.enc_q.remove_weight_norm()
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def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
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def forward(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
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g = self.emb_g(sid).unsqueeze(-1)
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m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
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z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
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z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
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z = self.flow(z_p, x_mask, g=g, reverse=True)
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o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
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return o
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class SynthesizerTrnMs256NSFsid_sim(nn.Module):
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"""
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Synthesizer for Training
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"""
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def __init__(
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self,
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spec_channels,
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segment_size,
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inter_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
spk_embed_dim,
|
||||
# hop_length,
|
||||
gin_channels=0,
|
||||
use_sdp=True,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__()
|
||||
self.spec_channels = spec_channels
|
||||
self.inter_channels = inter_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.resblock = resblock
|
||||
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||||
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||||
self.upsample_rates = upsample_rates
|
||||
self.upsample_initial_channel = upsample_initial_channel
|
||||
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||||
self.segment_size = segment_size
|
||||
self.gin_channels = gin_channels
|
||||
# self.hop_length = hop_length#
|
||||
self.spk_embed_dim = spk_embed_dim
|
||||
self.enc_p = TextEncoder256Sim(
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
)
|
||||
self.dec = GeneratorNSF(
|
||||
inter_channels,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
gin_channels=gin_channels,
|
||||
is_half=kwargs["is_half"],
|
||||
)
|
||||
|
||||
self.flow = ResidualCouplingBlock(
|
||||
inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
|
||||
)
|
||||
self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
|
||||
print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
|
||||
|
||||
def remove_weight_norm(self):
|
||||
self.dec.remove_weight_norm()
|
||||
self.flow.remove_weight_norm()
|
||||
self.enc_q.remove_weight_norm()
|
||||
|
||||
def forward(
|
||||
self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
|
||||
): # y是spec不需要了现在
|
||||
g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1) # [b, 256, 1]##1是t,广播的
|
||||
x, x_mask = self.enc_p(phone, pitch, phone_lengths)
|
||||
x = self.flow(x, x_mask, g=g, reverse=True)
|
||||
o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
|
||||
return o
|
||||
|
||||
|
||||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(MultiPeriodDiscriminator, self).__init__()
|
||||
|
849
infer_pack/models_onnx_moess.py
Normal file
849
infer_pack/models_onnx_moess.py
Normal file
@ -0,0 +1,849 @@
|
||||
import math, pdb, os
|
||||
from time import time as ttime
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
from infer_pack import modules
|
||||
from infer_pack import attentions
|
||||
from infer_pack import commons
|
||||
from infer_pack.commons import init_weights, get_padding
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
from infer_pack.commons import init_weights
|
||||
import numpy as np
|
||||
from infer_pack import commons
|
||||
|
||||
|
||||
class TextEncoder256(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
f0=True,
|
||||
):
|
||||
super().__init__()
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.emb_phone = nn.Linear(256, hidden_channels)
|
||||
self.lrelu = nn.LeakyReLU(0.1, inplace=True)
|
||||
if f0 == True:
|
||||
self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
|
||||
self.encoder = attentions.Encoder(
|
||||
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
|
||||
)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
|
||||
def forward(self, phone, pitch, lengths):
|
||||
if pitch == None:
|
||||
x = self.emb_phone(phone)
|
||||
else:
|
||||
x = self.emb_phone(phone) + self.emb_pitch(pitch)
|
||||
x = x * math.sqrt(self.hidden_channels) # [b, t, h]
|
||||
x = self.lrelu(x)
|
||||
x = torch.transpose(x, 1, -1) # [b, h, t]
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
|
||||
x.dtype
|
||||
)
|
||||
x = self.encoder(x * x_mask, x_mask)
|
||||
stats = self.proj(x) * x_mask
|
||||
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
return m, logs, x_mask
|
||||
|
||||
|
||||
class TextEncoder256Sim(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
f0=True,
|
||||
):
|
||||
super().__init__()
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.emb_phone = nn.Linear(256, hidden_channels)
|
||||
self.lrelu = nn.LeakyReLU(0.1, inplace=True)
|
||||
if f0 == True:
|
||||
self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
|
||||
self.encoder = attentions.Encoder(
|
||||
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
|
||||
)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
||||
|
||||
def forward(self, phone, pitch, lengths):
|
||||
if pitch == None:
|
||||
x = self.emb_phone(phone)
|
||||
else:
|
||||
x = self.emb_phone(phone) + self.emb_pitch(pitch)
|
||||
x = x * math.sqrt(self.hidden_channels) # [b, t, h]
|
||||
x = self.lrelu(x)
|
||||
x = torch.transpose(x, 1, -1) # [b, h, t]
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
|
||||
x.dtype
|
||||
)
|
||||
x = self.encoder(x * x_mask, x_mask)
|
||||
x = self.proj(x) * x_mask
|
||||
return x, x_mask
|
||||
|
||||
|
||||
class ResidualCouplingBlock(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
n_flows=4,
|
||||
gin_channels=0,
|
||||
):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.n_flows = n_flows
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.flows = nn.ModuleList()
|
||||
for i in range(n_flows):
|
||||
self.flows.append(
|
||||
modules.ResidualCouplingLayer(
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=gin_channels,
|
||||
mean_only=True,
|
||||
)
|
||||
)
|
||||
self.flows.append(modules.Flip())
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
if not reverse:
|
||||
for flow in self.flows:
|
||||
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
||||
else:
|
||||
for flow in reversed(self.flows):
|
||||
x = flow(x, x_mask, g=g, reverse=reverse)
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for i in range(self.n_flows):
|
||||
self.flows[i * 2].remove_weight_norm()
|
||||
|
||||
|
||||
class PosteriorEncoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=0,
|
||||
):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
||||
self.enc = modules.WN(
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=gin_channels,
|
||||
)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
|
||||
def forward(self, x, x_lengths, g=None):
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
|
||||
x.dtype
|
||||
)
|
||||
x = self.pre(x) * x_mask
|
||||
x = self.enc(x, x_mask, g=g)
|
||||
stats = self.proj(x) * x_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
||||
return z, m, logs, x_mask
|
||||
|
||||
def remove_weight_norm(self):
|
||||
self.enc.remove_weight_norm()
|
||||
|
||||
|
||||
class Generator(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
initial_channel,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
gin_channels=0,
|
||||
):
|
||||
super(Generator, self).__init__()
|
||||
self.num_kernels = len(resblock_kernel_sizes)
|
||||
self.num_upsamples = len(upsample_rates)
|
||||
self.conv_pre = Conv1d(
|
||||
initial_channel, upsample_initial_channel, 7, 1, padding=3
|
||||
)
|
||||
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
|
||||
|
||||
self.ups = nn.ModuleList()
|
||||
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
||||
self.ups.append(
|
||||
weight_norm(
|
||||
ConvTranspose1d(
|
||||
upsample_initial_channel // (2**i),
|
||||
upsample_initial_channel // (2 ** (i + 1)),
|
||||
k,
|
||||
u,
|
||||
padding=(k - u) // 2,
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
self.resblocks = nn.ModuleList()
|
||||
for i in range(len(self.ups)):
|
||||
ch = upsample_initial_channel // (2 ** (i + 1))
|
||||
for j, (k, d) in enumerate(
|
||||
zip(resblock_kernel_sizes, resblock_dilation_sizes)
|
||||
):
|
||||
self.resblocks.append(resblock(ch, k, d))
|
||||
|
||||
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
||||
self.ups.apply(init_weights)
|
||||
|
||||
if gin_channels != 0:
|
||||
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
||||
|
||||
def forward(self, x, g=None):
|
||||
x = self.conv_pre(x)
|
||||
if g is not None:
|
||||
x = x + self.cond(g)
|
||||
|
||||
for i in range(self.num_upsamples):
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
x = self.ups[i](x)
|
||||
xs = None
|
||||
for j in range(self.num_kernels):
|
||||
if xs is None:
|
||||
xs = self.resblocks[i * self.num_kernels + j](x)
|
||||
else:
|
||||
xs += self.resblocks[i * self.num_kernels + j](x)
|
||||
x = xs / self.num_kernels
|
||||
x = F.leaky_relu(x)
|
||||
x = self.conv_post(x)
|
||||
x = torch.tanh(x)
|
||||
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.ups:
|
||||
remove_weight_norm(l)
|
||||
for l in self.resblocks:
|
||||
l.remove_weight_norm()
|
||||
|
||||
|
||||
class SineGen(torch.nn.Module):
|
||||
"""Definition of sine generator
|
||||
SineGen(samp_rate, harmonic_num = 0,
|
||||
sine_amp = 0.1, noise_std = 0.003,
|
||||
voiced_threshold = 0,
|
||||
flag_for_pulse=False)
|
||||
samp_rate: sampling rate in Hz
|
||||
harmonic_num: number of harmonic overtones (default 0)
|
||||
sine_amp: amplitude of sine-wavefrom (default 0.1)
|
||||
noise_std: std of Gaussian noise (default 0.003)
|
||||
voiced_thoreshold: F0 threshold for U/V classification (default 0)
|
||||
flag_for_pulse: this SinGen is used inside PulseGen (default False)
|
||||
Note: when flag_for_pulse is True, the first time step of a voiced
|
||||
segment is always sin(np.pi) or cos(0)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
samp_rate,
|
||||
harmonic_num=0,
|
||||
sine_amp=0.1,
|
||||
noise_std=0.003,
|
||||
voiced_threshold=0,
|
||||
flag_for_pulse=False,
|
||||
):
|
||||
super(SineGen, self).__init__()
|
||||
self.sine_amp = sine_amp
|
||||
self.noise_std = noise_std
|
||||
self.harmonic_num = harmonic_num
|
||||
self.dim = self.harmonic_num + 1
|
||||
self.sampling_rate = samp_rate
|
||||
self.voiced_threshold = voiced_threshold
|
||||
|
||||
def _f02uv(self, f0):
|
||||
# generate uv signal
|
||||
uv = torch.ones_like(f0)
|
||||
uv = uv * (f0 > self.voiced_threshold)
|
||||
return uv
|
||||
|
||||
def forward(self, f0, upp):
|
||||
"""sine_tensor, uv = forward(f0)
|
||||
input F0: tensor(batchsize=1, length, dim=1)
|
||||
f0 for unvoiced steps should be 0
|
||||
output sine_tensor: tensor(batchsize=1, length, dim)
|
||||
output uv: tensor(batchsize=1, length, 1)
|
||||
"""
|
||||
with torch.no_grad():
|
||||
f0 = f0[:, None].transpose(1, 2)
|
||||
f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
|
||||
# fundamental component
|
||||
f0_buf[:, :, 0] = f0[:, :, 0]
|
||||
for idx in np.arange(self.harmonic_num):
|
||||
f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
|
||||
idx + 2
|
||||
) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
|
||||
rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
|
||||
rand_ini = torch.rand(
|
||||
f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
|
||||
)
|
||||
rand_ini[:, 0] = 0
|
||||
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
|
||||
tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化
|
||||
tmp_over_one *= upp
|
||||
tmp_over_one = F.interpolate(
|
||||
tmp_over_one.transpose(2, 1),
|
||||
scale_factor=upp,
|
||||
mode="linear",
|
||||
align_corners=True,
|
||||
).transpose(2, 1)
|
||||
rad_values = F.interpolate(
|
||||
rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
|
||||
).transpose(
|
||||
2, 1
|
||||
) #######
|
||||
tmp_over_one %= 1
|
||||
tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
|
||||
cumsum_shift = torch.zeros_like(rad_values)
|
||||
cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
|
||||
sine_waves = torch.sin(
|
||||
torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
|
||||
)
|
||||
sine_waves = sine_waves * self.sine_amp
|
||||
uv = self._f02uv(f0)
|
||||
uv = F.interpolate(
|
||||
uv.transpose(2, 1), scale_factor=upp, mode="nearest"
|
||||
).transpose(2, 1)
|
||||
noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
|
||||
noise = noise_amp * torch.randn_like(sine_waves)
|
||||
sine_waves = sine_waves * uv + noise
|
||||
return sine_waves, uv, noise
|
||||
|
||||
|
||||
class SourceModuleHnNSF(torch.nn.Module):
|
||||
"""SourceModule for hn-nsf
|
||||
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
|
||||
add_noise_std=0.003, voiced_threshod=0)
|
||||
sampling_rate: sampling_rate in Hz
|
||||
harmonic_num: number of harmonic above F0 (default: 0)
|
||||
sine_amp: amplitude of sine source signal (default: 0.1)
|
||||
add_noise_std: std of additive Gaussian noise (default: 0.003)
|
||||
note that amplitude of noise in unvoiced is decided
|
||||
by sine_amp
|
||||
voiced_threshold: threhold to set U/V given F0 (default: 0)
|
||||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||||
F0_sampled (batchsize, length, 1)
|
||||
Sine_source (batchsize, length, 1)
|
||||
noise_source (batchsize, length 1)
|
||||
uv (batchsize, length, 1)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
sampling_rate,
|
||||
harmonic_num=0,
|
||||
sine_amp=0.1,
|
||||
add_noise_std=0.003,
|
||||
voiced_threshod=0,
|
||||
is_half=True,
|
||||
):
|
||||
super(SourceModuleHnNSF, self).__init__()
|
||||
|
||||
self.sine_amp = sine_amp
|
||||
self.noise_std = add_noise_std
|
||||
self.is_half = is_half
|
||||
# to produce sine waveforms
|
||||
self.l_sin_gen = SineGen(
|
||||
sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
|
||||
)
|
||||
|
||||
# to merge source harmonics into a single excitation
|
||||
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
|
||||
self.l_tanh = torch.nn.Tanh()
|
||||
|
||||
def forward(self, x, upp=None):
|
||||
sine_wavs, uv, _ = self.l_sin_gen(x, upp)
|
||||
if self.is_half:
|
||||
sine_wavs = sine_wavs.half()
|
||||
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
|
||||
return sine_merge, None, None # noise, uv
|
||||
|
||||
|
||||
class GeneratorNSF(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
initial_channel,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
gin_channels,
|
||||
sr,
|
||||
is_half=False,
|
||||
):
|
||||
super(GeneratorNSF, self).__init__()
|
||||
self.num_kernels = len(resblock_kernel_sizes)
|
||||
self.num_upsamples = len(upsample_rates)
|
||||
|
||||
self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
|
||||
self.m_source = SourceModuleHnNSF(
|
||||
sampling_rate=sr, harmonic_num=0, is_half=is_half
|
||||
)
|
||||
self.noise_convs = nn.ModuleList()
|
||||
self.conv_pre = Conv1d(
|
||||
initial_channel, upsample_initial_channel, 7, 1, padding=3
|
||||
)
|
||||
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
|
||||
|
||||
self.ups = nn.ModuleList()
|
||||
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
||||
c_cur = upsample_initial_channel // (2 ** (i + 1))
|
||||
self.ups.append(
|
||||
weight_norm(
|
||||
ConvTranspose1d(
|
||||
upsample_initial_channel // (2**i),
|
||||
upsample_initial_channel // (2 ** (i + 1)),
|
||||
k,
|
||||
u,
|
||||
padding=(k - u) // 2,
|
||||
)
|
||||
)
|
||||
)
|
||||
if i + 1 < len(upsample_rates):
|
||||
stride_f0 = np.prod(upsample_rates[i + 1 :])
|
||||
self.noise_convs.append(
|
||||
Conv1d(
|
||||
1,
|
||||
c_cur,
|
||||
kernel_size=stride_f0 * 2,
|
||||
stride=stride_f0,
|
||||
padding=stride_f0 // 2,
|
||||
)
|
||||
)
|
||||
else:
|
||||
self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
|
||||
|
||||
self.resblocks = nn.ModuleList()
|
||||
for i in range(len(self.ups)):
|
||||
ch = upsample_initial_channel // (2 ** (i + 1))
|
||||
for j, (k, d) in enumerate(
|
||||
zip(resblock_kernel_sizes, resblock_dilation_sizes)
|
||||
):
|
||||
self.resblocks.append(resblock(ch, k, d))
|
||||
|
||||
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
||||
self.ups.apply(init_weights)
|
||||
|
||||
if gin_channels != 0:
|
||||
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
||||
|
||||
self.upp = np.prod(upsample_rates)
|
||||
|
||||
def forward(self, x, f0, g=None):
|
||||
har_source, noi_source, uv = self.m_source(f0, self.upp)
|
||||
har_source = har_source.transpose(1, 2)
|
||||
x = self.conv_pre(x)
|
||||
if g is not None:
|
||||
x = x + self.cond(g)
|
||||
|
||||
for i in range(self.num_upsamples):
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
x = self.ups[i](x)
|
||||
x_source = self.noise_convs[i](har_source)
|
||||
x = x + x_source
|
||||
xs = None
|
||||
for j in range(self.num_kernels):
|
||||
if xs is None:
|
||||
xs = self.resblocks[i * self.num_kernels + j](x)
|
||||
else:
|
||||
xs += self.resblocks[i * self.num_kernels + j](x)
|
||||
x = xs / self.num_kernels
|
||||
x = F.leaky_relu(x)
|
||||
x = self.conv_post(x)
|
||||
x = torch.tanh(x)
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.ups:
|
||||
remove_weight_norm(l)
|
||||
for l in self.resblocks:
|
||||
l.remove_weight_norm()
|
||||
|
||||
|
||||
sr2sr = {
|
||||
"32k": 32000,
|
||||
"40k": 40000,
|
||||
"48k": 48000,
|
||||
}
|
||||
|
||||
|
||||
class SynthesizerTrnMs256NSFsidM(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
spec_channels,
|
||||
segment_size,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
spk_embed_dim,
|
||||
gin_channels,
|
||||
sr,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__()
|
||||
if type(sr) == type("strr"):
|
||||
sr = sr2sr[sr]
|
||||
self.spec_channels = spec_channels
|
||||
self.inter_channels = inter_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.resblock = resblock
|
||||
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||||
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||||
self.upsample_rates = upsample_rates
|
||||
self.upsample_initial_channel = upsample_initial_channel
|
||||
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||||
self.segment_size = segment_size
|
||||
self.gin_channels = gin_channels
|
||||
# self.hop_length = hop_length#
|
||||
self.spk_embed_dim = spk_embed_dim
|
||||
self.enc_p = TextEncoder256(
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
)
|
||||
self.dec = GeneratorNSF(
|
||||
inter_channels,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
gin_channels=gin_channels,
|
||||
sr=sr,
|
||||
is_half=kwargs["is_half"],
|
||||
)
|
||||
self.enc_q = PosteriorEncoder(
|
||||
spec_channels,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
5,
|
||||
1,
|
||||
16,
|
||||
gin_channels=gin_channels,
|
||||
)
|
||||
self.flow = ResidualCouplingBlock(
|
||||
inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
|
||||
)
|
||||
self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
|
||||
print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
|
||||
|
||||
def remove_weight_norm(self):
|
||||
self.dec.remove_weight_norm()
|
||||
self.flow.remove_weight_norm()
|
||||
self.enc_q.remove_weight_norm()
|
||||
|
||||
def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
|
||||
g = self.emb_g(sid).unsqueeze(-1)
|
||||
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
|
||||
z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
|
||||
z = self.flow(z_p, x_mask, g=g, reverse=True)
|
||||
o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
|
||||
return o
|
||||
|
||||
|
||||
class SynthesizerTrnMs256NSFsid_sim(nn.Module):
|
||||
"""
|
||||
Synthesizer for Training
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
spec_channels,
|
||||
segment_size,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
spk_embed_dim,
|
||||
# hop_length,
|
||||
gin_channels=0,
|
||||
use_sdp=True,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__()
|
||||
self.spec_channels = spec_channels
|
||||
self.inter_channels = inter_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.resblock = resblock
|
||||
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||||
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||||
self.upsample_rates = upsample_rates
|
||||
self.upsample_initial_channel = upsample_initial_channel
|
||||
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||||
self.segment_size = segment_size
|
||||
self.gin_channels = gin_channels
|
||||
# self.hop_length = hop_length#
|
||||
self.spk_embed_dim = spk_embed_dim
|
||||
self.enc_p = TextEncoder256Sim(
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
)
|
||||
self.dec = GeneratorNSF(
|
||||
inter_channels,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
gin_channels=gin_channels,
|
||||
is_half=kwargs["is_half"],
|
||||
)
|
||||
|
||||
self.flow = ResidualCouplingBlock(
|
||||
inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
|
||||
)
|
||||
self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
|
||||
print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
|
||||
|
||||
def remove_weight_norm(self):
|
||||
self.dec.remove_weight_norm()
|
||||
self.flow.remove_weight_norm()
|
||||
self.enc_q.remove_weight_norm()
|
||||
|
||||
def forward(
|
||||
self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
|
||||
): # y是spec不需要了现在
|
||||
g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1) # [b, 256, 1]##1是t,广播的
|
||||
x, x_mask = self.enc_p(phone, pitch, phone_lengths)
|
||||
x = self.flow(x, x_mask, g=g, reverse=True)
|
||||
o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
|
||||
return o
|
||||
|
||||
|
||||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(MultiPeriodDiscriminator, self).__init__()
|
||||
periods = [2, 3, 5, 7, 11, 17]
|
||||
# periods = [3, 5, 7, 11, 17, 23, 37]
|
||||
|
||||
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
||||
discs = discs + [
|
||||
DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
|
||||
]
|
||||
self.discriminators = nn.ModuleList(discs)
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = [] #
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
# for j in range(len(fmap_r)):
|
||||
# print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
|
||||
y_d_rs.append(y_d_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_rs.append(fmap_r)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
class DiscriminatorS(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(DiscriminatorS, self).__init__()
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList(
|
||||
[
|
||||
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
||||
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
||||
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
||||
]
|
||||
)
|
||||
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class DiscriminatorP(torch.nn.Module):
|
||||
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
||||
super(DiscriminatorP, self).__init__()
|
||||
self.period = period
|
||||
self.use_spectral_norm = use_spectral_norm
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList(
|
||||
[
|
||||
norm_f(
|
||||
Conv2d(
|
||||
1,
|
||||
32,
|
||||
(kernel_size, 1),
|
||||
(stride, 1),
|
||||
padding=(get_padding(kernel_size, 1), 0),
|
||||
)
|
||||
),
|
||||
norm_f(
|
||||
Conv2d(
|
||||
32,
|
||||
128,
|
||||
(kernel_size, 1),
|
||||
(stride, 1),
|
||||
padding=(get_padding(kernel_size, 1), 0),
|
||||
)
|
||||
),
|
||||
norm_f(
|
||||
Conv2d(
|
||||
128,
|
||||
512,
|
||||
(kernel_size, 1),
|
||||
(stride, 1),
|
||||
padding=(get_padding(kernel_size, 1), 0),
|
||||
)
|
||||
),
|
||||
norm_f(
|
||||
Conv2d(
|
||||
512,
|
||||
1024,
|
||||
(kernel_size, 1),
|
||||
(stride, 1),
|
||||
padding=(get_padding(kernel_size, 1), 0),
|
||||
)
|
||||
),
|
||||
norm_f(
|
||||
Conv2d(
|
||||
1024,
|
||||
1024,
|
||||
(kernel_size, 1),
|
||||
1,
|
||||
padding=(get_padding(kernel_size, 1), 0),
|
||||
)
|
||||
),
|
||||
]
|
||||
)
|
||||
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
# 1d to 2d
|
||||
b, c, t = x.shape
|
||||
if t % self.period != 0: # pad first
|
||||
n_pad = self.period - (t % self.period)
|
||||
x = F.pad(x, (0, n_pad), "reflect")
|
||||
t = t + n_pad
|
||||
x = x.view(b, c, t // self.period, self.period)
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
Loading…
Reference in New Issue
Block a user