add cosyvoice code

2026-02-04 17:39:25 +08:00 · 2024-07-04 21:15:12 +08:00
parent 06984ac149
commit 076829ab84
64 changed files with 8428 additions and 18 deletions
--- a/cosyvoice/cli/cosyvoice.py
+++ b/cosyvoice/cli/cosyvoice.py
@@ -0,0 +1,83 @@
+# 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 os
+import torch
+from hyperpyyaml import load_hyperpyyaml
+from modelscope import snapshot_download
+from cosyvoice.cli.frontend import CosyVoiceFrontEnd
+from cosyvoice.cli.model import CosyVoiceModel
+
+class CosyVoice:
+
+    def __init__(self, model_dir):
+        instruct = True if '-Instruct' in model_dir else False
+        self.model_dir = model_dir
+        if not os.path.exists(model_dir):
+            model_dir = snapshot_download(model_dir)
+        with open('{}/cosyvoice.yaml'.format(model_dir), 'r') as f:
+            configs = load_hyperpyyaml(f)
+        self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],
+                                          configs['feat_extractor'],
+                                          '{}/campplus.onnx'.format(model_dir),
+                                          '{}/speech_tokenizer_v1.onnx'.format(model_dir),
+                                          '{}/spk2info.pt'.format(model_dir),
+                                          instruct,
+                                          configs['allowed_special'])
+        self.model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'])
+        self.model.load('{}/llm.pt'.format(model_dir),
+                        '{}/flow.pt'.format(model_dir),
+                        '{}/hift.pt'.format(model_dir))
+        del configs
+
+    def list_avaliable_spks(self):
+        spks = list(self.frontend.spk2info.keys())
+        return spks
+
+    def inference_sft(self, tts_text, spk_id):
+        tts_speeches = []
+        for i in self.frontend.text_normalize(tts_text, split=True):
+            model_input = self.frontend.frontend_sft(i, spk_id)
+            model_output = self.model.inference(**model_input)
+            tts_speeches.append(model_output['tts_speech'])
+        return {'tts_speech': torch.concat(tts_speeches, dim=1)}
+
+    def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k):
+        prompt_text = self.frontend.text_normalize(prompt_text, split=False)
+        tts_speeches = []
+        for i in self.frontend.text_normalize(tts_text, split=True):
+            model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k)
+            model_output = self.model.inference(**model_input)
+            tts_speeches.append(model_output['tts_speech'])
+        return {'tts_speech': torch.concat(tts_speeches, dim=1)}
+
+    def inference_cross_lingual(self, tts_text, prompt_speech_16k):
+        if self.frontend.instruct is True:
+            raise ValueError('{} do not support cross_lingual inference'.format(self.model_dir))
+        tts_speeches = []
+        for i in self.frontend.text_normalize(tts_text, split=True):
+            model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k)
+            model_output = self.model.inference(**model_input)
+            tts_speeches.append(model_output['tts_speech'])
+        return {'tts_speech': torch.concat(tts_speeches, dim=1)}
+
+    def inference_instruct(self, tts_text, spk_id, instruct_text):
+        if self.frontend.instruct is False:
+            raise ValueError('{} do not support instruct inference'.format(self.model_dir))
+        instruct_text = self.frontend.text_normalize(instruct_text, split=False)
+        tts_speeches = []
+        for i in self.frontend.text_normalize(tts_text, split=True):
+            model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
+            model_output = self.model.inference(**model_input)
+            tts_speeches.append(model_output['tts_speech'])
+        return {'tts_speech': torch.concat(tts_speeches, dim=1)}