feat: 添加导出 v3 的 script

GPT_SoVITS/export_torch_script.py

@@ -427,7 +427,7 @@ class T2SModel(nn.Module):
         self.top_k = int(raw_t2s.config["inference"]["top_k"])
         self.early_stop_num = torch.LongTensor([self.hz * self.max_sec])
     
-    def forward(self,prompts:LongTensor, ref_seq:LongTensor, text_seq:LongTensor, ref_bert:torch.Tensor, text_bert:torch.Tensor):
+    def forward(self,prompts:LongTensor, ref_seq:LongTensor, text_seq:LongTensor, ref_bert:torch.Tensor, text_bert:torch.Tensor,top_k:LongTensor):
         bert = torch.cat([ref_bert.T, text_bert.T], 1)
         all_phoneme_ids = torch.cat([ref_seq, text_seq], 1)
         bert = bert.unsqueeze(0)
@@ -472,12 +472,13 @@ class T2SModel(nn.Module):
                                                 .to(device=x.device, dtype=torch.bool)
         
         idx = 0
+        top_k = int(top_k)
         
         xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None)
 
         logits = self.ar_predict_layer(xy_dec[:, -1])
         logits = logits[:, :-1]
-        samples = sample(logits, y, top_k=self.top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0]
+        samples = sample(logits, y, top_k=top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0]
         y = torch.concat([y, samples], dim=1)
         y_emb = self.ar_audio_embedding(y[:, -1:])
         xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to(dtype=y_emb.dtype,device=y_emb.device)
@@ -493,7 +494,7 @@ class T2SModel(nn.Module):
             if(idx<11):###至少预测出10个token不然不给停止（0.4s）
                 logits = logits[:, :-1]
             
-            samples = sample(logits, y, top_k=self.top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0]
+            samples = sample(logits, y, top_k=top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0]
 
             y = torch.concat([y, samples], dim=1)
             

GPT_SoVITS/f5_tts/model/backbones/dit.py

@@ -138,7 +138,7 @@ class DiT(nn.Module):
         time: float["b"] | float[""],  # time step  # noqa: F821 F722
             dt_base_bootstrap,
         text0,  # : int["b nt"]  # noqa: F722#####condition feature
-        use_grad_ckpt,  # bool
+        use_grad_ckpt=False,  # bool
         ###no-use
         drop_audio_cond=False,  # cfg for cond audio
         drop_text=False,  # cfg for text

GPT_SoVITS/module/models_onnx.py

@@ -9,6 +9,8 @@ from module import commons
 from module import modules
 from module import attentions_onnx as attentions
 
+from f5_tts.model import DiT
+
 from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
 from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
 from module.commons import init_weights, get_padding
@@ -342,6 +344,37 @@ class PosteriorEncoder(nn.Module):
         return z, m, logs, x_mask
 
 
+class Encoder(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, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        if(g!=None):
+            g = g.detach()
+        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
+        return stats, x_mask
+
 class WNEncoder(nn.Module):
     def __init__(
         self,
@@ -917,3 +950,174 @@ class SynthesizerTrn(nn.Module):
         ssl = self.ssl_proj(x)
         quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
         return codes.transpose(0, 1)
+
+class CFM(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,dit
+    ):
+        super().__init__()
+        # self.sigma_min = 1e-6
+
+        self.estimator = dit
+
+        self.in_channels = in_channels
+
+        # self.criterion = torch.nn.MSELoss()
+
+    def forward(self, mu:torch.Tensor, x_lens:torch.LongTensor, prompt:torch.Tensor, n_timesteps:torch.LongTensor, temperature:float=1.0):
+        """Forward diffusion"""
+        B, T = mu.size(0), mu.size(1)
+        x = torch.randn([B, self.in_channels, T], device=mu.device,dtype=mu.dtype)
+
+        ntimesteps = int(n_timesteps)
+
+        prompt_len = prompt.size(-1)
+        prompt_x = torch.zeros_like(x,dtype=mu.dtype)
+        prompt_x[..., :prompt_len] = prompt[..., :prompt_len]
+        x[..., :prompt_len] = 0.0
+        mu=mu.transpose(2,1)
+        t = torch.tensor(0.0,dtype=x.dtype,device=x.device)
+        d = torch.tensor(1.0/ntimesteps,dtype=x.dtype,device=x.device)
+        d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d
+
+        for j in range(ntimesteps):
+            t_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * t
+            # d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d
+            # v_pred = model(x, t_tensor, d_tensor, **extra_args)
+            v_pred = self.estimator(x, prompt_x, x_lens, t_tensor,d_tensor, mu).transpose(2, 1)
+            # if inference_cfg_rate>1e-5:
+            #     neg = self.estimator(x, prompt_x, x_lens, t_tensor, d_tensor, mu, use_grad_ckpt=False, drop_audio_cond=True, drop_text=True).transpose(2, 1)
+            #     v_pred=v_pred+(v_pred-neg)*inference_cfg_rate
+            x = x + d * v_pred
+            t = t + d
+            x[:, :, :prompt_len] = 0.0
+        return x
+
+
+def set_no_grad(net_g):
+    for name, param in net_g.named_parameters():
+        param.requires_grad=False
+
+@torch.jit.script_if_tracing
+def compile_codes_length(codes):
+    y_lengths1 = torch.LongTensor([codes.size(2)]).to(codes.device)
+    return y_lengths1 * 2.5 * 1.5
+
+@torch.jit.script_if_tracing
+def compile_ref_length(refer):
+    refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
+    return refer_lengths
+
+class SynthesizerTrnV3(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,
+                 n_speakers=0,
+                 gin_channels=0,
+                 use_sdp=True,
+                 semantic_frame_rate=None,
+                 freeze_quantizer=None,
+                 version="v3",
+                 **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.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.version = version
+
+        self.model_dim=512
+        self.use_sdp = use_sdp
+        self.enc_p = TextEncoder(inter_channels,hidden_channels,filter_channels,n_heads,n_layers,kernel_size,p_dropout)
+        # self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)###Rollback
+        self.ref_enc = modules.MelStyleEncoder(704, style_vector_dim=gin_channels)###Rollback
+        # self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
+        #                      upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
+        # 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, 4, gin_channels=gin_channels)
+
+
+        ssl_dim = 768
+        assert semantic_frame_rate in ['25hz', "50hz"]
+        self.semantic_frame_rate = semantic_frame_rate
+        if semantic_frame_rate == '25hz':
+            self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2)
+        else:
+            self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1)
+
+        self.quantizer = ResidualVectorQuantizer(
+            dimension=ssl_dim,
+            n_q=1,
+            bins=1024
+        )
+        freeze_quantizer
+        inter_channels2=512
+        self.bridge=nn.Sequential(
+            nn.Conv1d(inter_channels, inter_channels2, 1, stride=1),
+            nn.LeakyReLU()
+        )
+        self.wns1=Encoder(inter_channels2, inter_channels2, inter_channels2, 5, 1, 8,gin_channels=gin_channels)
+        self.linear_mel=nn.Conv1d(inter_channels2,100,1,stride=1)
+        self.cfm = CFM(100,DiT(**dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=inter_channels2, conv_layers=4)),)#text_dim is condition feature dim
+        if freeze_quantizer==True:
+            set_no_grad(self.ssl_proj)
+            set_no_grad(self.quantizer)
+            set_no_grad(self.enc_p)
+
+    def create_ge(self, refer):
+        refer_lengths = compile_ref_length(refer)
+        refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype)
+        ge = self.ref_enc(refer[:,:704] * refer_mask, refer_mask)
+        return ge
+
+    def forward(self, codes, text,ge,speed=1):
+
+        y_lengths1=compile_codes_length(codes)
+        
+        quantized = self.quantizer.decode(codes)
+        if self.semantic_frame_rate == '25hz':
+            quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
+        x, m_p, logs_p, y_mask = self.enc_p(quantized, text, ge,speed)
+        fea=self.bridge(x)
+        fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
+        ####more wn paramter to learn mel
+        fea, y_mask_ = self.wns1(fea, y_lengths1, ge)
+        return fea
+
+    def extract_latent(self, x):
+        ssl =  self.ssl_proj(x)
+        quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
+        return codes.transpose(0,1)