Improve the consistency between ONNX and torch

GPT_SoVITS/AR/models/t2s_model_onnx.py

@@ -69,7 +69,8 @@ def logits_to_probs(
 def multinomial_sample_one_no_sync(
     probs_sort
 ):  # Does multinomial sampling without a cuda synchronization
-    q = torch.randn_like(probs_sort)
+    lambda_ = 1.0
+    q = -torch.log(torch.rand_like(probs_sort)) / lambda_
     return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
 
 
@@ -152,6 +153,7 @@ class T2SFirstStageDecoder(nn.Module):
 
         xy_dec = self.h(xy_pos, mask=xy_attn_mask, cache=cache)
         logits = self.ar_predict_layer(xy_dec[:, -1])
+        logits = logits[:, :-1]  ###刨除1024终止符号的概率
         samples = sample(logits[0], y, top_k=self.top_k, top_p=1.0, repetition_penalty=1.35)[0].unsqueeze(0)
 
         y = torch.concat([y, samples], dim=1)

GPT_SoVITS/AR/modules/embedding_onnx.py

@@ -50,10 +50,15 @@ class SinePositionalEmbedding(nn.Module):
         self.div_term = torch.exp(torch.arange(0, self.embedding_dim, 2) * -(math.log(10000.0) / self.embedding_dim))
 
     def extend_pe(self, x):
-        position = torch.cumsum(torch.ones_like(x[:,:,0]), dim=1).transpose(0, 1)
+        position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
-        scpe = (position * self.div_term).unsqueeze(0)
+        div_term = torch.exp(
-        pe = torch.cat([torch.sin(scpe), torch.cos(scpe)]).permute(1, 2, 0)
+            torch.arange(0, self.embedding_dim, 2, dtype=torch.float32)
-        pe = pe.contiguous().view(1, -1, self.embedding_dim)
+            * -(math.log(10000.0) / self.embedding_dim)
+        )
+        pe = torch.zeros(x.size(1), self.embedding_dim)
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
         return pe
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:

GPT_SoVITS/module/models_onnx.py

@@ -892,7 +892,7 @@ class SynthesizerTrn(nn.Module):
             # self.enc_p.encoder_text.requires_grad_(False)
             # self.enc_p.mrte.requires_grad_(False)
 
-    def forward(self, codes, text, refer):
+    def forward(self, codes, text, refer, noise_scale=0.5):
         refer_mask = torch.ones_like(refer[:1,:1,:])
         ge = self.ref_enc(refer * refer_mask, refer_mask)
 
@@ -905,7 +905,7 @@ class SynthesizerTrn(nn.Module):
             quantized, text, ge
         )
         
-        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p)
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
 
         z = self.flow(z_p, y_mask, g=ge, reverse=True)
 

GPT_SoVITS/onnx_export.py

@@ -4,7 +4,7 @@ import torch
 import torchaudio
 from torch import nn
 from feature_extractor import cnhubert
-cnhubert_base_path = "pretrained_models/chinese-hubert-base"
+cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
 cnhubert.cnhubert_base_path=cnhubert_base_path
 ssl_model = cnhubert.get_model()
 from text import cleaned_text_to_sequence
@@ -266,6 +266,22 @@ class SSLModel(nn.Module):
     def forward(self, ref_audio_16k):
         return self.ssl.model(ref_audio_16k)["last_hidden_state"].transpose(1, 2)
 
+    def export(self, ref_audio_16k, project_name):
+        self.ssl.model.eval()
+        torch.onnx.export(
+            self,
+            (ref_audio_16k),
+            f"onnx/{project_name}/{project_name}_cnhubert.onnx",
+            input_names=["ref_audio_16k"],
+            output_names=["last_hidden_state"],
+            dynamic_axes={
+                "ref_audio_16k": {1 : "text_length"},
+                "last_hidden_state": {2 : "pred_length"}
+            },
+            opset_version=17,
+            verbose=False
+        )
+
 
 def export(vits_path, gpt_path, project_name):
     vits = VitsModel(vits_path)
@@ -300,6 +316,7 @@ def export(vits_path, gpt_path, project_name):
 
     soundfile.write("out.wav", a, vits.hps.data.sampling_rate)
 
+    ssl.export(ref_audio_16k, project_name)
     gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content, project_name)
 
     MoeVSConf = {
@@ -326,8 +343,8 @@ if __name__ == "__main__":
     except:
         pass
 
-    gpt_path = "GPT_weights/nahida-e25.ckpt"
+    gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
-    vits_path = "SoVITS_weights/nahida_e30_s3930.pth"
+    vits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth"
     exp_path = "nahida"
     export(vits_path, gpt_path, exp_path)