refactor the cli/st, test=st

paddlespeech/cli/st/infer.py

@@ -40,11 +40,11 @@ __all__ = ["STExecutor"]
 pretrained_models = {
     "fat_st_ted-en-zh": {
         "url":
-        "https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/fat_st_ted-en-zh.tar.gz",
+        "https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/st1_transformer_mtl_noam_ted-en-zh_ckpt_0.1.1.model.tar.gz",
         "md5":
-        "fa0a7425b91b4f8d259c70b2aca5ae67",
+        "d62063f35a16d91210a71081bd2dd557",
         "cfg_path":
-        "conf/transformer_mtl_noam.yaml",
+        "model.yaml",
         "ckpt_path":
         "exp/transformer_mtl_noam/checkpoints/fat_st_ted-en-zh.pdparams",
     }
@@ -170,24 +170,19 @@ class STExecutor(BaseExecutor):
         #Init body.
         self.config = CfgNode(new_allowed=True)
         self.config.merge_from_file(self.cfg_path)
-        self.config.decoding.decoding_method = "fullsentence"
+        self.config.decode.decoding_method = "fullsentence"
 
         with UpdateConfig(self.config):
-            self.config.collator.vocab_filepath = os.path.join(
+            self.config.cmvn_path = os.path.join(
-                res_path, self.config.collator.vocab_filepath)
+                res_path, self.config.cmvn_path)
-            self.config.collator.cmvn_path = os.path.join(
+            self.config.spm_model_prefix = os.path.join(
-                res_path, self.config.collator.cmvn_path)
+                res_path, self.config.spm_model_prefix)
-            self.config.collator.spm_model_prefix = os.path.join(
-                res_path, self.config.collator.spm_model_prefix)
             self.text_feature = TextFeaturizer(
-                unit_type=self.config.collator.unit_type,
+                unit_type=self.config.unit_type,
-                vocab=self.config.collator.vocab_filepath,
+                vocab=self.config.vocab_filepath,
-                spm_model_prefix=self.config.collator.spm_model_prefix)
+                spm_model_prefix=self.config.spm_model_prefix)
-            self.config.model.input_dim = self.config.collator.feat_dim
+
-            self.config.model.output_dim = self.text_feature.vocab_size
+        model_conf = self.config
-
-        model_conf = self.config.model
-        logger.info(model_conf)
         model_name = model_type[:model_type.rindex(
             '_')]  # model_type: {model_name}_{dataset}
         model_class = dynamic_import(model_name, model_alias)
@@ -218,7 +213,7 @@ class STExecutor(BaseExecutor):
         logger.info("Preprocess audio_file:" + audio_file)
 
         if "fat_st" in model_type:
-            cmvn = self.config.collator.cmvn_path
+            cmvn = self.config.cmvn_path
             utt_name = "_tmp"
 
             # Get the object for feature extraction
@@ -284,7 +279,7 @@ class STExecutor(BaseExecutor):
         """
             Model inference and result stored in self.output.
         """
-        cfg = self.config.decoding
+        cfg = self.config.decode
         audio = self._inputs["audio"]
         audio_len = self._inputs["audio_len"]
         if model_type == "fat_st_ted":

utils/generate_infer_yaml.py

@@ -67,6 +67,9 @@ def merge_configs(
     config = load(conf_path)
     decode_config = load(decode_path)
     vocab_list = load_dict(vocab_path)
+
+    # If use the kaldi feature, do not load the cmvn file
+    if cmvn_path.split(".")[-1] == 'json':
         cmvn_stats = load_json(cmvn_path)
         if os.path.exists(preprocess_path):
             preprocess_config =  load(preprocess_path)
@@ -81,7 +84,9 @@ def merge_configs(
             cmvn_stats = load_cmvn_from_json(cmvn_stats)
             config.mean_std_filepath = [{"cmvn_stats":cmvn_stats}]
             config.augmentation_config = ''
-
+    # the cmvn file is end with .ark
+    else:
+        config.cmvn_path = cmvn_path
     # Updata the config
     config.vocab_filepath = vocab_list
     config.input_dim = config.feat_dim