Add end-to-end version of MFA FastSpeech2, test=tts

paddlespeech/resource/pretrained_models.py

@@ -1668,7 +1668,7 @@ rhy_frontend_models = {
             'url':
             'https://paddlespeech.bj.bcebos.com/Rhy_e2e/rhy_e2e_pretrain.zip',
             'md5':
-            '2cc5a3fe9ced1e421f0a03929fb0d23c',
+            'd36566b835977ea05ffbd9c0210c8e3c',
         },
     },
 }

paddlespeech/t2s/exps/synthesize_e2e.py

@@ -241,7 +241,7 @@ def parse_args():
         type=str,
         help="text to synthesize, a 'utt_id sentence' pair per line.")
     parser.add_argument("--output_dir", type=str, help="output dir.")
-    parse.add_argument(
+    parser.add_argument(
         "--use_rhy", action="store_true", help="run rhythm frontend or not")
 
     args = parser.parse_args()

paddlespeech/t2s/frontend/rhy_prediction/rhy_predictor.py

@@ -37,7 +37,7 @@ class Rhy_predictor():
             model_dir: os.PathLike=MODEL_HOME, ):
         uncompress_path = download_and_decompress(
             rhy_frontend_models['rhy_e2e'][model_version], model_dir)
-        with open(os.path.join(uncompress_path, 'default.yaml')) as f:
+        with open(os.path.join(uncompress_path, 'rhy_default.yaml')) as f:
             config = CfgNode(yaml.safe_load(f))
         self.punc_list = []
         with open(os.path.join(uncompress_path, 'rhy_token'), 'r') as f:
@@ -45,11 +45,11 @@ class Rhy_predictor():
                 self.punc_list.append(line.strip())
         self.punc_list = [0] + self.punc_list
         self.make_rhy_dict()
-        self.model = DefinedClassifier[config["model_type"]](**config["model"])
+        self.model = DefinedClassifier["ErnieLinear"](**config["model"])
         pretrained_token = config['data_params']['pretrained_token']
         self.tokenizer = ErnieTokenizer.from_pretrained(pretrained_token)
         state_dict = paddle.load(
-            os.path.join(uncompress_path, 'snapshot_iter_153000.pdz'))
+            os.path.join(uncompress_path, 'snapshot_iter_2600.pdz'))
         self.model.set_state_dict(state_dict["main_params"])
         self.model.eval()