update

README.md

@@ -124,7 +124,7 @@ from cosyvoice.utils.file_utils import load_wav
 import torchaudio
 
 ## cosyvoice2 usage
-cosyvoice2 = CosyVoice('pretrained_models/CosyVoice-300M-SFT', load_jit=True, load_onnx=False, load_trt=False)
+cosyvoice2 = CosyVoice('pretrained_models/CosyVoice-300M-SFT', load_jit=False, load_onnx=False, load_trt=False)
 # sft usage
 prompt_speech_16k = load_wav('zero_shot_prompt.wav', 16000)
 for i, j in enumerate(cosyvoice2.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', prompt_speech_16k, stream=True)):

cosyvoice/cli/model.py

@@ -287,8 +287,6 @@ class CosyVoice2Model:
     def load(self, llm_model, flow_model, hift_model):
         self.llm.load_state_dict(torch.load(llm_model, map_location=self.device), strict=True)
         self.llm.to(self.device).eval()
-        if self.fp16 is True:
-            self.llm.half()
         self.flow.load_state_dict(torch.load(flow_model, map_location=self.device), strict=True)
         self.flow.to(self.device).eval()
         self.flow.decoder.fp16 = False
@@ -319,8 +317,6 @@ class CosyVoice2Model:
         self.flow.decoder.fp16 = True
 
     def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
-        if self.fp16 is True:
-            llm_embedding = llm_embedding.half()
         with self.llm_context:
             for i in self.llm.inference(text=text.to(self.device),
                                         text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),

cosyvoice/flow/flow_matching.py

@@ -136,41 +136,26 @@ class ConditionalCFM(BASECFM):
                 'mask': mask.cpu().numpy(),
                 'mu': mu.cpu().numpy(),
                 't': t.cpu().numpy(),
-                'spk': spks.cpu().numpy(),
+                'spks': spks.cpu().numpy(),
-                'cond': cond.cpu().numpy(),
+                'cond': cond.cpu().numpy()
-                'mask_rand': torch.randn(1, 1, 1).numpy()
             }
             output = self.estimator.run(None, ort_inputs)[0]
             return torch.tensor(output, dtype=x.dtype, device=x.device)
         else:
-            if not x.is_contiguous():
-                x = x.contiguous()
-            if not mask.is_contiguous():
-                mask = mask.contiguous()
-            if not mu.is_contiguous():
-                mu = mu.contiguous()
-            if not t.is_contiguous():
-                t = t.contiguous()
-            if not spks.is_contiguous():
-                spks = spks.contiguous()
-            if not cond.is_contiguous():
-                cond = cond.contiguous()
             self.estimator.set_input_shape('x', (2, 80, x.size(2)))
             self.estimator.set_input_shape('mask', (2, 1, x.size(2)))
             self.estimator.set_input_shape('mu', (2, 80, x.size(2)))
             self.estimator.set_input_shape('t', (2,))
-            self.estimator.set_input_shape('spk', (2, 80))
+            self.estimator.set_input_shape('spks', (2, 80))
             self.estimator.set_input_shape('cond', (2, 80, x.size(2)))
-            self.estimator.set_input_shape('mask_rand', (1, 1, 1))
             # run trt engine
-            self.estimator.execute_v2([x.data_ptr(),
+            self.estimator.execute_v2([x.contiguous().data_ptr(),
-                                       mask.data_ptr(),
+                                        mask.contiguous().data_ptr(),
-                                       mu.data_ptr(),
+                                        mu.contiguous().data_ptr(),
-                                       t.data_ptr(),
+                                        t.contiguous().data_ptr(),
-                                       spks.data_ptr(),
+                                        spks.contiguous().data_ptr(),
-                                       cond.data_ptr(),
+                                        cond.contiguous().data_ptr(),
-                                       torch.randn(1, 1, 1).to(x.device).data_ptr(),
+                                        x.data_ptr()])
-                                       x.data_ptr()])
             return x
 
     def compute_loss(self, x1, mask, mu, spks=None, cond=None):
@@ -241,7 +226,7 @@ class CausalConditionalCFM(ConditionalCFM):
         """
 
         z = self.rand_noise[:, :, :mu.size(2)].to(mu.device) * temperature
-        if self.sp16 is True:
+        if self.fp16 is True:
             z = z.half()
         # fix prompt and overlap part mu and z
         t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)

requirements.txt

@@ -1,4 +1,5 @@
 --extra-index-url https://download.pytorch.org/whl/cu121
+conformer==0.3.2
 deepspeed==0.14.2; sys_platform == 'linux'
 diffusers==0.27.2
 gdown==5.1.0
@@ -16,8 +17,8 @@ modelscope==1.15.0
 networkx==3.1
 omegaconf==2.3.0
 onnx==1.16.0
-onnxruntime-gpu==1.16.0; sys_platform == 'linux'
+onnxruntime-gpu==1.18.0; sys_platform == 'linux'
-onnxruntime==1.16.0; sys_platform == 'darwin' or sys_platform == 'windows'
+onnxruntime==1.18.0; sys_platform == 'darwin' or sys_platform == 'windows'
 openai-whisper==20231117
 protobuf==4.25
 pydantic==2.7.0
@@ -25,8 +26,11 @@ rich==13.7.1
 soundfile==0.12.1
 tensorboard==2.14.0
 tensorrt-cu12==10.0.1
+tensorrt-cu12-bindings==10.0.1
+tensorrt-cu12-libs==10.0.1
 torch==2.3.1
 torchaudio==2.3.1
+transformers==4.40.1
 uvicorn==0.30.0
 wget==3.2
 fastapi==0.111.0