fix: SynthesizerTrn

GPT_SoVITS/module/models.py

@@ -1,6 +1,11 @@
+import warnings
+
+warnings.filterwarnings("ignore")
 import copy
 import math
-from typing import List
+import os
+import pdb
+
 import torch
 from torch import nn
 from torch.nn import functional as F
@@ -14,20 +19,22 @@ from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
 from module.commons import init_weights, get_padding
 from module.mrte_model import MRTE
 from module.quantize import ResidualVectorQuantizer
-from text import symbols
+# from text import symbols
+from text import symbols as symbols_v1
+from text import symbols2 as symbols_v2
 from torch.cuda.amp import autocast
 import contextlib
 
 
 class StochasticDurationPredictor(nn.Module):
     def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout,
-        n_flows=4,
-        gin_channels=0,
+            self,
+            in_channels,
+            filter_channels,
+            kernel_size,
+            p_dropout,
+            n_flows=4,
+            gin_channels=0,
     ):
         super().__init__()
         filter_channels = in_channels  # it needs to be removed from future version.
@@ -86,8 +93,8 @@ class StochasticDurationPredictor(nn.Module):
             h_w = self.post_convs(h_w, x_mask)
             h_w = self.post_proj(h_w) * x_mask
             e_q = (
-                torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype)
-                * x_mask
+                    torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype)
+                    * x_mask
             )
             z_q = e_q
             for flow in self.post_flows:
@@ -100,8 +107,8 @@ class StochasticDurationPredictor(nn.Module):
                 (F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]
             )
             logq = (
-                torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2])
-                - logdet_tot_q
+                    torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2])
+                    - logdet_tot_q
             )
 
             logdet_tot = 0
@@ -112,16 +119,16 @@ class StochasticDurationPredictor(nn.Module):
                 z, logdet = flow(z, x_mask, g=x, reverse=reverse)
                 logdet_tot = logdet_tot + logdet
             nll = (
-                torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2])
-                - logdet_tot
+                    torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2])
+                    - logdet_tot
             )
             return nll + logq  # [b]
         else:
             flows = list(reversed(self.flows))
             flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
             z = (
-                torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
-                * noise_scale
+                    torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
+                    * noise_scale
             )
             for flow in flows:
                 z = flow(z, x_mask, g=x, reverse=reverse)
@@ -132,7 +139,7 @@ class StochasticDurationPredictor(nn.Module):
 
 class DurationPredictor(nn.Module):
     def __init__(
-        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+            self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
     ):
         super().__init__()
 
@@ -175,15 +182,16 @@ class DurationPredictor(nn.Module):
 
 class TextEncoder(nn.Module):
     def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        latent_channels=192,
+            self,
+            out_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            latent_channels=192,
+            version="v1",
     ):
         super().__init__()
         self.out_channels = out_channels
@@ -194,6 +202,7 @@ class TextEncoder(nn.Module):
         self.kernel_size = kernel_size
         self.p_dropout = p_dropout
         self.latent_channels = latent_channels
+        self.version = version
 
         self.ssl_proj = nn.Conv1d(768, hidden_channels, 1)
 
@@ -209,6 +218,11 @@ class TextEncoder(nn.Module):
         self.encoder_text = attentions.Encoder(
             hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
         )
+
+        if self.version == "v1":
+            symbols = symbols_v1.symbols
+        else:
+            symbols = symbols_v2.symbols
         self.text_embedding = nn.Embedding(len(symbols), hidden_channels)
 
         self.mrte = MRTE()
@@ -224,13 +238,13 @@ class TextEncoder(nn.Module):
 
         self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
 
-    def forward(self, y, y_lengths, text, text_lengths, ge, test=None):
+    def forward(self, y, y_lengths, text, text_lengths, ge, speed=1, test=None):
         y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
             y.dtype
         )
 
         y = self.ssl_proj(y * y_mask) * y_mask
-     
+
         y = self.encoder_ssl(y * y_mask, y_mask)
 
         text_mask = torch.unsqueeze(
@@ -241,9 +255,10 @@ class TextEncoder(nn.Module):
         text = self.text_embedding(text).transpose(1, 2)
         text = self.encoder_text(text * text_mask, text_mask)
         y = self.mrte(y, y_mask, text, text_mask, ge)
-
         y = self.encoder2(y * y_mask, y_mask)
-
+        if (speed != 1):
+            y = F.interpolate(y, size=int(y.shape[-1] / speed) + 1, mode="linear")
+            y_mask = F.interpolate(y_mask, size=y.shape[-1], mode="nearest")
         stats = self.proj(y) * y_mask
         m, logs = torch.split(stats, self.out_channels, dim=1)
         return y, m, logs, y_mask
@@ -270,14 +285,14 @@ class TextEncoder(nn.Module):
 
 class ResidualCouplingBlock(nn.Module):
     def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
+            self,
+            channels,
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            n_flows=4,
+            gin_channels=0,
     ):
         super().__init__()
         self.channels = channels
@@ -315,14 +330,14 @@ class ResidualCouplingBlock(nn.Module):
 
 class PosteriorEncoder(nn.Module):
     def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
+            self,
+            in_channels,
+            out_channels,
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=0,
     ):
         super().__init__()
         self.in_channels = in_channels
@@ -359,14 +374,14 @@ class PosteriorEncoder(nn.Module):
 
 class WNEncoder(nn.Module):
     def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
+            self,
+            in_channels,
+            out_channels,
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=0,
     ):
         super().__init__()
         self.in_channels = in_channels
@@ -401,15 +416,15 @@ class WNEncoder(nn.Module):
 
 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,
+            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)
@@ -424,7 +439,7 @@ class Generator(torch.nn.Module):
             self.ups.append(
                 weight_norm(
                     ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** i),
                         upsample_initial_channel // (2 ** (i + 1)),
                         k,
                         u,
@@ -437,7 +452,7 @@ class Generator(torch.nn.Module):
         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)
+                    zip(resblock_kernel_sizes, resblock_dilation_sizes)
             ):
                 self.resblocks.append(resblock(ch, k, d))
 
@@ -679,9 +694,9 @@ class Quantizer_module(torch.nn.Module):
 
     def forward(self, x):
         d = (
-            torch.sum(x**2, 1, keepdim=True)
-            + torch.sum(self.embedding.weight**2, 1)
-            - 2 * torch.matmul(x, self.embedding.weight.T)
+                torch.sum(x ** 2, 1, keepdim=True)
+                + torch.sum(self.embedding.weight ** 2, 1)
+                - 2 * torch.matmul(x, self.embedding.weight.T)
         )
         min_indicies = torch.argmin(d, 1)
         z_q = self.embedding(min_indicies)
@@ -736,16 +751,16 @@ class Quantizer(torch.nn.Module):
 
 class CodePredictor(nn.Module):
     def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        n_q=8,
-        dims=1024,
-        ssl_dim=768,
+            self,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            n_q=8,
+            dims=1024,
+            ssl_dim=768,
     ):
         super().__init__()
         self.hidden_channels = hidden_channels
@@ -804,28 +819,29 @@ class SynthesizerTrn(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,
-        n_speakers=0,
-        gin_channels=0,
-        use_sdp=True,
-        semantic_frame_rate=None,
-        freeze_quantizer=None,
-        **kwargs
+            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="v1",
+            **kwargs
     ):
         super().__init__()
         self.spec_channels = spec_channels
@@ -845,6 +861,7 @@ class SynthesizerTrn(nn.Module):
         self.segment_size = segment_size
         self.n_speakers = n_speakers
         self.gin_channels = gin_channels
+        self.version = version
 
         self.use_sdp = use_sdp
         self.enc_p = TextEncoder(
@@ -855,6 +872,7 @@ class SynthesizerTrn(nn.Module):
             n_layers,
             kernel_size,
             p_dropout,
+            version=version,
         )
         self.dec = Generator(
             inter_channels,
@@ -879,9 +897,11 @@ class SynthesizerTrn(nn.Module):
             inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
         )
 
-        self.ref_enc = modules.MelStyleEncoder(
-            spec_channels, style_vector_dim=gin_channels
-        )
+        # self.version=os.environ.get("version","v1")
+        if (self.version == "v1"):
+            self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)
+        else:
+            self.ref_enc = modules.MelStyleEncoder(704, style_vector_dim=gin_channels)
 
         ssl_dim = 768
         assert semantic_frame_rate in ["25hz", "50hz"]
@@ -893,20 +913,15 @@ class SynthesizerTrn(nn.Module):
 
         self.quantizer = ResidualVectorQuantizer(dimension=ssl_dim, n_q=1, bins=1024)
         self.freeze_quantizer = freeze_quantizer
-        # if freeze_quantizer:
-        #     self.ssl_proj.requires_grad_(False)
-        #     self.quantizer.requires_grad_(False)
-            #self.quantizer.eval()
-            # self.enc_p.text_embedding.requires_grad_(False)
-            # self.enc_p.encoder_text.requires_grad_(False)
-            # self.enc_p.mrte.requires_grad_(False)
 
     def forward(self, ssl, y, y_lengths, text, text_lengths):
         y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
             y.dtype
         )
-        ge = self.ref_enc(y * y_mask, y_mask)
-
+        if (self.version == "v1"):
+            ge = self.ref_enc(y * y_mask, y_mask)
+        else:
+            ge = self.ref_enc(y[:, :704] * y_mask, y_mask)
         with autocast(enabled=False):
             maybe_no_grad = torch.no_grad() if self.freeze_quantizer else contextlib.nullcontext()
             with maybe_no_grad:
@@ -947,7 +962,10 @@ class SynthesizerTrn(nn.Module):
         y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
             y.dtype
         )
-        ge = self.ref_enc(y * y_mask, y_mask)
+        if (self.version == "v1"):
+            ge = self.ref_enc(y * y_mask, y_mask)
+        else:
+            ge = self.ref_enc(y[:, :704] * y_mask, y_mask)
 
         ssl = self.ssl_proj(ssl)
         quantized, codes, commit_loss, _ = self.quantizer(ssl, layers=[0])
@@ -967,14 +985,28 @@ class SynthesizerTrn(nn.Module):
         return o, y_mask, (z, z_p, m_p, logs_p)
 
     @torch.no_grad()
-    def decode(self, codes, text, refer, noise_scale=0.5):
-        ge = None
-        if refer is not None:
-            refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
-            refer_mask = torch.unsqueeze(
-                commons.sequence_mask(refer_lengths, refer.size(2)), 1
-            ).to(refer.dtype)
-            ge = self.ref_enc(refer * refer_mask, refer_mask)
+    def decode(self, codes, text, refer, noise_scale=0.5, speed=1):
+        def get_ge(refer):
+            ge = None
+            if refer is not None:
+                refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
+                refer_mask = torch.unsqueeze(
+                    commons.sequence_mask(refer_lengths, refer.size(2)), 1
+                ).to(refer.dtype)
+                if (self.version == "v1"):
+                    ge = self.ref_enc(refer * refer_mask, refer_mask)
+                else:
+                    ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
+            return ge
+
+        if (type(refer) == list):
+            ges = []
+            for _refer in refer:
+                ge = get_ge(_refer)
+                ges.append(ge)
+            ge = torch.stack(ges, 0).mean(0)
+        else:
+            ge = get_ge(refer)
 
         y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
         text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
@@ -984,9 +1016,8 @@ class SynthesizerTrn(nn.Module):
             quantized = F.interpolate(
                 quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
             )
-
         x, m_p, logs_p, y_mask = self.enc_p(
-            quantized, y_lengths, text, text_lengths, ge
+            quantized, y_lengths, text, text_lengths, ge, speed
         )
         z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
 
@@ -994,57 +1025,8 @@ class SynthesizerTrn(nn.Module):
 
         o = self.dec((z * y_mask)[:, :, :], g=ge)
         return o
-    
-    
-    @torch.no_grad()
-    def batched_decode(self, codes, y_lengths, text, text_lengths, refer, noise_scale=0.5):
-        ge = None
-        if refer is not None:
-            refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
-            refer_mask = torch.unsqueeze(
-                commons.sequence_mask(refer_lengths, refer.size(2)), 1
-            ).to(refer.dtype)
-            ge = self.ref_enc(refer * refer_mask, refer_mask)
-
-        # y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, codes.size(2)), 1).to(
-        #     codes.dtype
-        # )
-        y_lengths = (y_lengths * 2).long().to(codes.device)
-        text_lengths = text_lengths.long().to(text.device)
-        # y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
-        # text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
-
-        # 假设padding之后再decode没有问题, 影响未知，但听起来好像没问题？
-        quantized = self.quantizer.decode(codes)
-        if self.semantic_frame_rate == "25hz":
-            quantized = F.interpolate(
-                quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
-            )
-
-        x, m_p, logs_p, y_mask = self.enc_p(
-            quantized, y_lengths, text, text_lengths, ge
-        )
-        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)
-        z_masked = (z * y_mask)[:, :, :]
-        
-        # 串行。把padding部分去掉再decode
-        o_list:List[torch.Tensor] = []
-        for i in range(z_masked.shape[0]):
-            z_slice = z_masked[i, :, :y_lengths[i]].unsqueeze(0)
-            o = self.dec(z_slice, g=ge)[0, 0, :].detach()
-            o_list.append(o)
-        
-        # 并行（会有问题）。先decode，再把padding的部分去掉
-        # o = self.dec(z_masked, g=ge)
-        # upsample_rate = int(math.prod(self.upsample_rates))
-        # o_lengths = y_lengths*upsample_rate
-        # o_list = [o[i, 0, :idx].detach() for i, idx in enumerate(o_lengths)]
-
-        return o_list
 
     def extract_latent(self, x):
         ssl = self.ssl_proj(x)
         quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
-        return codes.transpose(0, 1)
+        return codes.transpose(0, 1)