Dev gzf (#1474)

* qwenaudio qwenaudiochat

* qwenaudio qwenaudiochat

* whisper

* whisper

* llm

* llm

* llm

* llm

* llm

* llm

* llm

* llm

* export onnx

* export onnx

* export onnx

* dingding

* dingding

* llm

* doc

* onnx

* onnx

* onnx

* onnx

* onnx

* onnx

* v1.0.15

* qwenaudio

README.md

@@ -227,6 +227,21 @@ model = AutoModel(model="paraformer", device="cpu")
 res = model.export(quantize=False)
 ```
 
+### Text ONNX
+```python
+# pip3 install -U funasr-onnx
+from funasr_onnx import Paraformer
+model_dir = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+model = Paraformer(model_dir, batch_size=1, quantize=True)
+
+wav_path = ['~/.cache/modelscope/hub/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']
+
+result = model(wav_path)
+print(result)
+```
+
+More examples ref to [demo](runtime/python/onnxruntime)
+
 ## Deployment Service
 FunASR supports deploying pre-trained or further fine-tuned models for service. Currently, it supports the following types of service deployment:
 - File transcription service, Mandarin, CPU version, done

README_zh.md

@@ -216,7 +216,7 @@ print(res)
 funasr-export ++model=paraformer ++quantize=false
 ```
 
-### 从python指令导出
+### 从Python导出
 ```python
 from funasr import AutoModel
 
@@ -225,6 +225,20 @@ model = AutoModel(model="paraformer")
 res = model.export(quantize=False)
 ```
 
+### 测试ONNX
+```python
+# pip3 install -U funasr-onnx
+from funasr_onnx import Paraformer
+model_dir = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+model = Paraformer(model_dir, batch_size=1, quantize=True)
+
+wav_path = ['~/.cache/modelscope/hub/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']
+
+result = model(wav_path)
+print(result)
+```
+
+更多例子请参考 [样例](runtime/python/onnxruntime)
 
 <a name="服务部署"></a>
 ## 服务部署

funasr/download/download_from_hub.py

@@ -48,9 +48,10 @@ def download_from_ms(**kwargs):
             if "file_path_metas" in conf_json:
                 add_file_root_path(model_or_path, conf_json["file_path_metas"], cfg)
             cfg.update(kwargs)
-            config = OmegaConf.load(cfg["config"])
-            kwargs = OmegaConf.merge(config, cfg)
-        kwargs["model"] = config["model"]
+            if "config" in cfg:
+                config = OmegaConf.load(cfg["config"])
+                kwargs = OmegaConf.merge(config, cfg)
+                kwargs["model"] = config["model"]
     elif os.path.exists(os.path.join(model_or_path, "config.yaml")) and os.path.exists(os.path.join(model_or_path, "model.pt")):
         config = OmegaConf.load(os.path.join(model_or_path, "config.yaml"))
         kwargs = OmegaConf.merge(config, kwargs)

funasr/version.txt

@@ -1 +1 @@
-1.0.14
+1.0.15

runtime/python/onnxruntime/funasr_onnx/punc_bin.py

@@ -56,9 +56,9 @@ class CT_Transformer():
                       "\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple"
 
             model = AutoModel(model=model_dir)
-            model_dir = model.export(type="onnx", quantize=quantize)
+            model_dir = model.export(quantize=quantize)
             
-        config_file = os.path.join(model_dir, 'punc.yaml')
+        config_file = os.path.join(model_dir, 'confi.yaml')
         config = read_yaml(config_file)
         token_list = os.path.join(model_dir, 'tokens.json')
         with open(token_list, 'r', encoding='utf-8') as f:
@@ -67,7 +67,7 @@ class CT_Transformer():
         self.converter = TokenIDConverter(token_list)
         self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
         self.batch_size = 1
-        self.punc_list = config['punc_list']
+        self.punc_list = config["model_conf"]['punc_list']
         self.period = 0
         for i in range(len(self.punc_list)):
             if self.punc_list[i] == ",":
@@ -76,9 +76,9 @@ class CT_Transformer():
                 self.punc_list[i] = "？"
             elif self.punc_list[i] == "。":
                 self.period = i
-        if "seg_jieba" in config:
+        self.jieba_usr_dict_path = os.path.join(model_dir, 'jieba_usr_dict')
+        if os.path.exists(self.jieba_usr_dict_path):
             self.seg_jieba = True
-            self.jieba_usr_dict_path = os.path.join(model_dir, 'jieba_usr_dict')
             self.code_mix_split_words_jieba = code_mix_split_words_jieba(self.jieba_usr_dict_path)
         else:
             self.seg_jieba = False
@@ -175,7 +175,7 @@ class CT_Transformer_VadRealtime(CT_Transformer):
                  intra_op_num_threads: int = 4,
                  cache_dir: str = None
                  ):
-        super(CT_Transformer_VadRealtime, self).__init__(model_dir, batch_size, device_id, quantize, intra_op_num_threads, cache_dir=cache_dir)
+        super().__init__(model_dir, batch_size, device_id, quantize, intra_op_num_threads, cache_dir=cache_dir)
 
     def __call__(self, text: str, param_dict: map, split_size=20):
         cache_key = "cache"

runtime/python/onnxruntime/setup.py

@@ -13,7 +13,7 @@ def get_readme():
 
 
 MODULE_NAME = 'funasr_onnx'
-VERSION_NUM = '0.2.5'
+VERSION_NUM = '0.3.0'
 
 setuptools.setup(
     name=MODULE_NAME,