feat(dvae): expose coef for customize (#405)

and unify coef of dvae & decoder

ChatTTS/core.py

@@ -51,12 +51,15 @@ def load_models(
         self,
         source: Literal['huggingface', 'local', 'custom']='local',
         force_redownload=False,
-        custom_path='<LOCAL_PATH>',
-        **kwargs,
+        compile: bool = True,
+        custom_path: Optional[torch.serialization.FILE_LIKE]=None,
+        device: Optional[torch.device] = None,
+        coef: Optional[torch.Tensor] = None,
     ):
         if source == 'local':
+            torch.load
             download_path = os.getcwd()
-            if not check_all_assets(update=True):
+            if not check_all_assets(update=True) or force_redownload:
                 with tempfile.TemporaryDirectory() as tmp:
                     download_all_assets(tmpdir=tmp)
                 if not check_all_assets(update=False):
@@ -77,7 +80,10 @@ def load_models(
             self.logger.log(logging.INFO, f'Load from local: {custom_path}')
             download_path = custom_path
 
-        return self._load(**{k: os.path.join(download_path, v) for k, v in OmegaConf.load(os.path.join(download_path, 'config', 'path.yaml')).items()}, **kwargs)
+        return self._load(
+            device=device, compile=compile, coef=coef,
+            **{k: os.path.join(download_path, v) for k, v in OmegaConf.load(os.path.join(download_path, 'config', 'path.yaml')).items()},
+        )
 
     def _load(
         self, 
@@ -92,6 +98,7 @@ def _load(
         tokenizer_path: str = None,
         device: Optional[torch.device] = None,
         compile: bool = True,
+        coef: Optional[str] = None
     ):
         if device is None:
             device = select_device(4096)
@@ -110,7 +117,8 @@ def _load(
 
         if dvae_config_path:
             cfg = OmegaConf.load(dvae_config_path)
-            dvae = DVAE(**cfg).to(device).eval()
+            dvae = DVAE(**cfg, coef=coef).to(device).eval()
+            coef = str(dvae)
             assert dvae_ckpt_path, 'dvae_ckpt_path should not be None'
             dvae.load_state_dict(torch.load(dvae_ckpt_path))
             self.pretrain_models['dvae'] = dvae
@@ -134,7 +142,8 @@ def _load(
 
         if decoder_config_path:
             cfg = OmegaConf.load(decoder_config_path)
-            decoder = DVAE(**cfg).to(device).eval()
+            decoder = DVAE(**cfg, coef=coef).to(device).eval()
+            coef = str(decoder)
             assert decoder_ckpt_path, 'decoder_ckpt_path should not be None'
             decoder.load_state_dict(torch.load(decoder_ckpt_path, map_location='cpu'))
             self.pretrain_models['decoder'] = decoder
@@ -145,7 +154,9 @@ def _load(
             tokenizer.padding_side = 'left'
             self.pretrain_models['tokenizer'] = tokenizer
             self.logger.log(logging.INFO, 'tokenizer loaded.')
-
+
+        self.coef = coef
+
         return self.check_model()
 
     def _infer(

ChatTTS/model/dvae.py

@@ -1,6 +1,8 @@
 import math
-from typing import List
+from typing import List, Optional
 
+import pybase16384 as b14
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -74,7 +76,7 @@ def __init__(self,
 
     def _embed(self, x: torch.Tensor):
         if self.transpose:
-            x.transpose_(1, 2)
+            x = x.transpose(1, 2)
         """
         x = rearrange(
             x, "b t (g r) -> g b t r", g = self.G, r = self.R,
@@ -84,9 +86,9 @@ def _embed(self, x: torch.Tensor):
         feat = self.quantizer.get_output_from_indices(x)
         return feat.transpose_(1,2) if self.transpose else feat
 
-    def forward(self, x,):
+    def forward(self, x):
         if self.transpose:
-            x.transpose_(1,2)
+            x = x.transpose(1, 2)
         feat, ind = self.quantizer(x)
         """
         ind = rearrange(
@@ -127,7 +129,7 @@ def __init__(self, idim: int, odim: int,
             for _ in range(n_layer)])
         self.conv_out = nn.Conv1d(hidden, odim, kernel_size=1, bias=False)
 
-    def forward(self, input, conditioning=None):
+    def forward(self, input: torch.Tensor, conditioning=None) -> torch.Tensor:
         # B, T, C
         x = input.transpose_(1, 2)
         y = self.conv_in(x)
@@ -142,17 +144,24 @@ def forward(self, input, conditioning=None):
 
 class DVAE(nn.Module):
     def __init__(
-        self, decoder_config, vq_config, dim=512
+        self, decoder_config, vq_config, dim=512, coef: Optional[str] = None,
     ):
         super().__init__()
-        self.register_buffer('coef', torch.randn(1, 100, 1))
+        if coef is None:
+            coef = torch.rand(100)
+        else:
+            coef = torch.from_numpy(np.frombuffer(b14.decode_from_string(coef), dtype=np.float32))
+        self.register_buffer('coef', coef.unsqueeze(0).unsqueeze_(2))
 
         self.decoder = DVAEDecoder(**decoder_config)
         self.out_conv = nn.Conv1d(dim, 100, 3, 1, 1, bias=False)
         if vq_config is not None:
             self.vq_layer = GFSQ(**vq_config)
         else:
             self.vq_layer = None
+
+    def __repr__(self) -> str:
+        return b14.encode_to_string(self.coef.cpu().numpy().astype(np.float32).tobytes())
 
     def forward(self, inp: torch.Tensor) -> torch.Tensor:
 

requirements.txt

@@ -9,6 +9,7 @@ vocos
 IPython
 gradio
 python-dotenv
+pybase16384
 pynini==2.1.5; sys_platform == 'linux'
 WeTextProcessing; sys_platform == 'linux'
 nemo_text_processing; sys_platform == 'linux'

setup.py

@@ -6,12 +6,12 @@
       install_requires=['omegaconf>=2.3.0',
                         'numpy<2.0.0',
                         'numba',
+                        'pybase16384',
                 'torch>=2.1.0',
                 'tqdm',
                 'vector_quantize_pytorch',
                 'transformers>=4.41.1',
                 'vocos',
-                'IPython',
                 ],  # 定义依赖哪些模块
       packages=find_packages(),  # 系统自动从当前目录开始找包
       )