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Fix onnx_export to support v2 (#1604)
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zzz
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@ -13,7 +13,9 @@ from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
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from module.commons import init_weights, get_padding
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from module.mrte_model import MRTE
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from module.quantize import ResidualVectorQuantizer
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from text import symbols
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# from text import symbols
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from text import symbols as symbols_v1
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from text import symbols2 as symbols_v2
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from torch.cuda.amp import autocast
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@ -182,6 +184,7 @@ class TextEncoder(nn.Module):
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kernel_size,
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p_dropout,
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latent_channels=192,
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version="v2",
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):
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super().__init__()
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self.out_channels = out_channels
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@ -192,6 +195,7 @@ class TextEncoder(nn.Module):
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.latent_channels = latent_channels
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self.version = version
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self.ssl_proj = nn.Conv1d(768, hidden_channels, 1)
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@ -207,6 +211,11 @@ class TextEncoder(nn.Module):
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self.encoder_text = attentions.Encoder(
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hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
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)
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if self.version == "v1":
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symbols = symbols_v1.symbols
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else:
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symbols = symbols_v2.symbols
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self.text_embedding = nn.Embedding(len(symbols), hidden_channels)
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self.mrte = MRTE()
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@ -817,6 +826,7 @@ class SynthesizerTrn(nn.Module):
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use_sdp=True,
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semantic_frame_rate=None,
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freeze_quantizer=None,
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version="v2",
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**kwargs
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):
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super().__init__()
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@ -837,6 +847,7 @@ class SynthesizerTrn(nn.Module):
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self.segment_size = segment_size
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self.n_speakers = n_speakers
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self.gin_channels = gin_channels
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self.version = version
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self.use_sdp = use_sdp
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self.enc_p = TextEncoder(
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@ -847,6 +858,7 @@ class SynthesizerTrn(nn.Module):
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n_layers,
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kernel_size,
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p_dropout,
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version=version,
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)
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self.dec = Generator(
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inter_channels,
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@ -871,9 +883,11 @@ class SynthesizerTrn(nn.Module):
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inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
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)
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self.ref_enc = modules.MelStyleEncoder(
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spec_channels, style_vector_dim=gin_channels
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)
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# self.version=os.environ.get("version","v1")
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if self.version == "v1":
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self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)
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else:
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self.ref_enc = modules.MelStyleEncoder(704, style_vector_dim=gin_channels)
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ssl_dim = 768
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self.ssl_dim = ssl_dim
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@ -894,7 +908,10 @@ class SynthesizerTrn(nn.Module):
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def forward(self, codes, text, refer):
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refer_mask = torch.ones_like(refer[:1,:1,:])
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ge = self.ref_enc(refer * refer_mask, refer_mask)
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if (self.version == "v1"):
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ge = self.ref_enc(refer * refer_mask, refer_mask)
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else:
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ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
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quantized = self.quantizer.decode(codes)
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if self.semantic_frame_rate == "25hz":
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@ -1,11 +1,12 @@
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from module.models_onnx import SynthesizerTrn, symbols
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from module.models_onnx import SynthesizerTrn, symbols_v1, symbols_v2
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from AR.models.t2s_lightning_module_onnx import Text2SemanticLightningModule
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import torch
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import torchaudio
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from torch import nn
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from feature_extractor import cnhubert
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cnhubert_base_path = "pretrained_models/chinese-hubert-base"
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cnhubert.cnhubert_base_path=cnhubert_base_path
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cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
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cnhubert.cnhubert_base_path = cnhubert_base_path
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ssl_model = cnhubert.get_model()
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from text import cleaned_text_to_sequence
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import soundfile
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@ -196,6 +197,11 @@ class VitsModel(nn.Module):
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super().__init__()
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dict_s2 = torch.load(vits_path,map_location="cpu")
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self.hps = dict_s2["config"]
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if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
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self.hps["model"]["version"] = "v1"
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else:
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self.hps["model"]["version"] = "v2"
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self.hps = DictToAttrRecursive(self.hps)
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self.hps.model.semantic_frame_rate = "25hz"
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self.vq_model = SynthesizerTrn(
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@ -267,13 +273,13 @@ class SSLModel(nn.Module):
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return self.ssl.model(ref_audio_16k)["last_hidden_state"].transpose(1, 2)
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def export(vits_path, gpt_path, project_name):
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def export(vits_path, gpt_path, project_name, vits_model="v2"):
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vits = VitsModel(vits_path)
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gpt = T2SModel(gpt_path, vits)
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gpt_sovits = GptSoVits(vits, gpt)
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ssl = SSLModel()
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ref_seq = torch.LongTensor([cleaned_text_to_sequence(["n", "i2", "h", "ao3", ",", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4"])])
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text_seq = torch.LongTensor([cleaned_text_to_sequence(["w", "o3", "sh", "i4", "b", "ai2", "y", "e4", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4"])])
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ref_seq = torch.LongTensor([cleaned_text_to_sequence(["n", "i2", "h", "ao3", ",", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4"],version=vits_model)])
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text_seq = torch.LongTensor([cleaned_text_to_sequence(["w", "o3", "sh", "i4", "b", "ai2", "y", "e4", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4"],version=vits_model)])
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ref_bert = torch.randn((ref_seq.shape[1], 1024)).float()
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text_bert = torch.randn((text_seq.shape[1], 1024)).float()
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ref_audio = torch.randn((1, 48000 * 5)).float()
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@ -287,34 +293,38 @@ def export(vits_path, gpt_path, project_name):
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pass
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ssl_content = ssl(ref_audio_16k).float()
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debug = False
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# debug = False
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debug = True
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# gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content, project_name)
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if debug:
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a, b = gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content, debug=debug)
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soundfile.write("out1.wav", a.cpu().detach().numpy(), vits.hps.data.sampling_rate)
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soundfile.write("out2.wav", b[0], vits.hps.data.sampling_rate)
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return
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a = gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content).detach().cpu().numpy()
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else:
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a = gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content).detach().cpu().numpy()
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soundfile.write("out.wav", a, vits.hps.data.sampling_rate)
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soundfile.write("out.wav", a, vits.hps.data.sampling_rate)
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gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content, project_name)
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if vits_model == "v1":
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symbols = symbols_v1
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else:
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symbols = symbols_v2
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MoeVSConf = {
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"Folder" : f"{project_name}",
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"Name" : f"{project_name}",
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"Type" : "GPT-SoVits",
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"Rate" : vits.hps.data.sampling_rate,
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"NumLayers": gpt.t2s_model.num_layers,
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"EmbeddingDim": gpt.t2s_model.embedding_dim,
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"Dict": "BasicDict",
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"BertPath": "chinese-roberta-wwm-ext-large",
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"Symbol": symbols,
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"AddBlank": False
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}
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"Folder": f"{project_name}",
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"Name": f"{project_name}",
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"Type": "GPT-SoVits",
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"Rate": vits.hps.data.sampling_rate,
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"NumLayers": gpt.t2s_model.num_layers,
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"EmbeddingDim": gpt.t2s_model.embedding_dim,
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"Dict": "BasicDict",
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"BertPath": "chinese-roberta-wwm-ext-large",
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# "Symbol": symbols,
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"AddBlank": False,
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}
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MoeVSConfJson = json.dumps(MoeVSConf)
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with open(f"onnx/{project_name}.json", 'w') as MoeVsConfFile:
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json.dump(MoeVSConf, MoeVsConfFile, indent = 4)
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