add cosyvoice code

cosyvoice/cli/model.py

@@ -0,0 +1,59 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+
+class CosyVoiceModel:
+
+    def __init__(self,
+                 llm: torch.nn.Module,
+                 flow: torch.nn.Module,
+                 hift: torch.nn.Module):
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.llm = llm
+        self.flow = flow
+        self.hift = hift
+
+    def load(self, llm_model, flow_model, hift_model):
+        self.llm.load_state_dict(torch.load(llm_model, map_location=self.device))
+        self.llm.to(self.device).eval()
+        self.flow.load_state_dict(torch.load(flow_model, map_location=self.device))
+        self.flow.to(self.device).eval()
+        self.hift.load_state_dict(torch.load(hift_model, map_location=self.device))
+        self.hift.to(self.device).eval()
+
+    def inference(self, text, text_len, flow_embedding, llm_embedding=torch.zeros(0, 192),
+                  prompt_text=torch.zeros(1, 0, dtype=torch.int32), prompt_text_len=torch.zeros(1, dtype=torch.int32),
+                  llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), llm_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
+                  flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), flow_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
+                  prompt_speech_feat=torch.zeros(1, 0, 80), prompt_speech_feat_len=torch.zeros(1, dtype=torch.int32)):
+        tts_speech_token = self.llm.inference(text=text.to(self.device),
+                                              text_len=text_len.to(self.device),
+                                              prompt_text=prompt_text.to(self.device),
+                                              prompt_text_len=prompt_text_len.to(self.device),
+                                              prompt_speech_token=llm_prompt_speech_token.to(self.device),
+                                              prompt_speech_token_len=llm_prompt_speech_token_len.to(self.device),
+                                              embedding=llm_embedding.to(self.device),
+                                              beam_size=1,
+                                              sampling=25,
+                                              max_token_text_ratio=30,
+                                              min_token_text_ratio=3)
+        tts_mel = self.flow.inference(token=tts_speech_token,
+                                      token_len=torch.tensor([tts_speech_token.size(1)], dtype=torch.int32).to(self.device),
+                                      prompt_token=flow_prompt_speech_token.to(self.device),
+                                      prompt_token_len=flow_prompt_speech_token_len.to(self.device),
+                                      prompt_feat=prompt_speech_feat.to(self.device),
+                                      prompt_feat_len=prompt_speech_feat_len.to(self.device),
+                                      embedding=flow_embedding.to(self.device))
+        tts_speech = self.hift.inference(mel=tts_mel).cpu()
+        return {'tts_speech': tts_speech}