diff --git a/paddlespeech/t2s/exps/syn_utils.py b/paddlespeech/t2s/exps/syn_utils.py
index cabea98972e147955317abf76aee069739eed49f..77abf97d9227f0a5df042a1a831d6cdc902bc499 100644
--- a/paddlespeech/t2s/exps/syn_utils.py
+++ b/paddlespeech/t2s/exps/syn_utils.py
@@ -29,6 +29,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.t2s.datasets.data_table import DataTable
 from paddlespeech.t2s.frontend import English
+from paddlespeech.t2s.frontend.mix_frontend import MixFrontend
 from paddlespeech.t2s.frontend.zh_frontend import Frontend
 from paddlespeech.t2s.modules.normalizer import ZScore
 from paddlespeech.utils.dynamic_import import dynamic_import
@@ -98,6 +99,8 @@ def get_sentences(text_file: Optional[os.PathLike], lang: str='zh'):
                 sentence = "".join(items[1:])
             elif lang == 'en':
                 sentence = " ".join(items[1:])
+            elif lang == 'mix':
+                sentence = " ".join(items[1:])
             sentences.append((utt_id, sentence))
     return sentences
 
@@ -111,7 +114,8 @@ def get_test_dataset(test_metadata: List[Dict[str, Any]],
     am_dataset = am[am.rindex('_') + 1:]
     if am_name == 'fastspeech2':
         fields = ["utt_id", "text"]
-        if am_dataset in {"aishell3", "vctk"} and speaker_dict is not None:
+        if am_dataset in {"aishell3", "vctk",
+                          "mix"} and speaker_dict is not None:
             print("multiple speaker fastspeech2!")
             fields += ["spk_id"]
         elif voice_cloning:
@@ -140,6 +144,10 @@ def get_frontend(lang: str='zh',
             phone_vocab_path=phones_dict, tone_vocab_path=tones_dict)
     elif lang == 'en':
         frontend = English(phone_vocab_path=phones_dict)
+    elif lang == 'mix':
+        frontend = MixFrontend(
+            phone_vocab_path=phones_dict, tone_vocab_path=tones_dict)
+
     else:
         print("wrong lang!")
     print("frontend done!")
@@ -341,8 +349,12 @@ def get_am_output(
         input_ids = frontend.get_input_ids(
             input, merge_sentences=merge_sentences)
         phone_ids = input_ids["phone_ids"]
+    elif lang == 'mix':
+        input_ids = frontend.get_input_ids(
+            input, merge_sentences=merge_sentences)
+        phone_ids = input_ids["phone_ids"]
     else:
-        print("lang should in {'zh', 'en'}!")
+        print("lang should in {'zh', 'en', 'mix'}!")
 
     if get_tone_ids:
         tone_ids = input_ids["tone_ids"]
diff --git a/paddlespeech/t2s/exps/synthesize_e2e.py b/paddlespeech/t2s/exps/synthesize_e2e.py
index 28657eb27257f69f93589f65bcfdcfdd95ee52e8..ef9543296201f4d5b1dc4c5e59ae9a28cf956f27 100644
--- a/paddlespeech/t2s/exps/synthesize_e2e.py
+++ b/paddlespeech/t2s/exps/synthesize_e2e.py
@@ -113,8 +113,12 @@ def evaluate(args):
                 input_ids = frontend.get_input_ids(
                     sentence, merge_sentences=merge_sentences)
                 phone_ids = input_ids["phone_ids"]
+            elif args.lang == 'mix':
+                input_ids = frontend.get_input_ids(
+                    sentence, merge_sentences=merge_sentences)
+                phone_ids = input_ids["phone_ids"]
             else:
-                print("lang should in {'zh', 'en'}!")
+                print("lang should in {'zh', 'en', 'mix'}!")
             with paddle.no_grad():
                 flags = 0
                 for i in range(len(phone_ids)):
@@ -122,7 +126,7 @@ def evaluate(args):
                     # acoustic model
                     if am_name == 'fastspeech2':
                         # multi speaker
-                        if am_dataset in {"aishell3", "vctk"}:
+                        if am_dataset in {"aishell3", "vctk", "mix"}:
                             spk_id = paddle.to_tensor(args.spk_id)
                             mel = am_inference(part_phone_ids, spk_id)
                         else:
@@ -170,7 +174,7 @@ def parse_args():
         choices=[
             'speedyspeech_csmsc', 'speedyspeech_aishell3', 'fastspeech2_csmsc',
             'fastspeech2_ljspeech', 'fastspeech2_aishell3', 'fastspeech2_vctk',
-            'tacotron2_csmsc', 'tacotron2_ljspeech'
+            'tacotron2_csmsc', 'tacotron2_ljspeech', 'fastspeech2_mix'
         ],
         help='Choose acoustic model type of tts task.')
     parser.add_argument(
@@ -231,7 +235,7 @@ def parse_args():
         '--lang',
         type=str,
         default='zh',
-        help='Choose model language. zh or en')
+        help='Choose model language. zh or en or mix')
 
     parser.add_argument(
         "--inference_dir",
diff --git a/paddlespeech/t2s/frontend/mix_frontend.py b/paddlespeech/t2s/frontend/mix_frontend.py
new file mode 100644
index 0000000000000000000000000000000000000000..6386c871e138c745843f2e75a135fde7fdfd5ea2
--- /dev/null
+++ b/paddlespeech/t2s/frontend/mix_frontend.py
@@ -0,0 +1,179 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 re
+from typing import Dict
+from typing import List
+
+import paddle
+
+from paddlespeech.t2s.frontend import English
+from paddlespeech.t2s.frontend.zh_frontend import Frontend
+
+
+class MixFrontend():
+    def __init__(self,
+                 g2p_model="pypinyin",
+                 phone_vocab_path=None,
+                 tone_vocab_path=None):
+
+        self.zh_frontend = Frontend(
+            phone_vocab_path=phone_vocab_path, tone_vocab_path=tone_vocab_path)
+        self.en_frontend = English(phone_vocab_path=phone_vocab_path)
+        self.SENTENCE_SPLITOR = re.compile(r'([：、，；。？！,;?!][”’]?)')
+        self.sp_id = self.zh_frontend.vocab_phones["sp"]
+        self.sp_id_tensor = paddle.to_tensor([self.sp_id])
+
+    def is_chinese(self, char):
+        if char >= '\u4e00' and char <= '\u9fa5':
+            return True
+        else:
+            return False
+
+    def is_alphabet(self, char):
+        if (char >= '\u0041' and char <= '\u005a') or (char >= '\u0061' and
+                                                       char <= '\u007a'):
+            return True
+        else:
+            return False
+
+    def is_number(self, char):
+        if char >= '\u0030' and char <= '\u0039':
+            return True
+        else:
+            return False
+
+    def is_other(self, char):
+        if not (self.is_chinese(char) or self.is_number(char) or
+                self.is_alphabet(char)):
+            return True
+        else:
+            return False
+
+    def _split(self, text: str) -> List[str]:
+        text = re.sub(r'[《》【】<=>{}()（）#&@“”^_|…\\]', '', text)
+        text = self.SENTENCE_SPLITOR.sub(r'\1\n', text)
+        text = text.strip()
+        sentences = [sentence.strip() for sentence in re.split(r'\n+', text)]
+        return sentences
+
+    def _distinguish(self, text: str) -> List[str]:
+        # sentence --> [ch_part, en_part, ch_part, ...]
+
+        segments = []
+        types = []
+
+        flag = 0
+        temp_seg = ""
+        temp_lang = ""
+
+        # Determine the type of each character. type: blank, chinese, alphabet, number, unk.
+        for ch in text:
+            if self.is_chinese(ch):
+                types.append("zh")
+            elif self.is_alphabet(ch):
+                types.append("en")
+            elif ch == " ":
+                types.append("blank")
+            elif self.is_number(ch):
+                types.append("num")
+            else:
+                types.append("unk")
+
+        assert len(types) == len(text)
+
+        for i in range(len(types)):
+
+            # find the first char of the seg
+            if flag == 0:
+                if types[i] != "unk" and types[i] != "blank":
+                    temp_seg += text[i]
+                    temp_lang = types[i]
+                    flag = 1
+
+            else:
+                if types[i] == temp_lang or types[i] == "num":
+                    temp_seg += text[i]
+
+                elif temp_lang == "num" and types[i] != "unk":
+                    temp_seg += text[i]
+                    if types[i] == "zh" or types[i] == "en":
+                        temp_lang = types[i]
+
+                elif temp_lang == "en" and types[i] == "blank":
+                    temp_seg += text[i]
+
+                elif types[i] == "unk":
+                    pass
+
+                else:
+                    segments.append((temp_seg, temp_lang))
+
+                    if types[i] != "unk" and types[i] != "blank":
+                        temp_seg = text[i]
+                        temp_lang = types[i]
+                        flag = 1
+                    else:
+                        flag = 0
+                        temp_seg = ""
+                        temp_lang = ""
+
+        segments.append((temp_seg, temp_lang))
+
+        return segments
+
+    def get_input_ids(self,
+                      sentence: str,
+                      merge_sentences: bool=True,
+                      get_tone_ids: bool=False,
+                      add_sp: bool=True) -> Dict[str, List[paddle.Tensor]]:
+
+        sentences = self._split(sentence)
+        phones_list = []
+        result = {}
+
+        for text in sentences:
+            phones_seg = []
+            segments = self._distinguish(text)
+            for seg in segments:
+                content = seg[0]
+                lang = seg[1]
+                if lang == "zh":
+                    input_ids = self.zh_frontend.get_input_ids(
+                        content,
+                        merge_sentences=True,
+                        get_tone_ids=get_tone_ids)
+
+                elif lang == "en":
+                    input_ids = self.en_frontend.get_input_ids(
+                        content, merge_sentences=True)
+
+                phones_seg.append(input_ids["phone_ids"][0])
+                if add_sp:
+                    phones_seg.append(self.sp_id_tensor)
+
+            phones = paddle.concat(phones_seg)
+            phones_list.append(phones)
+
+        if merge_sentences:
+            merge_list = paddle.concat(phones_list)
+            # rm the last 'sp' to avoid the noise at the end
+            # cause in the training data, no 'sp' in the end
+            if merge_list[-1] == self.sp_id_tensor:
+                merge_list = merge_list[:-1]
+            phones_list = []
+            phones_list.append(merge_list)
+
+        result["phone_ids"] = phones_list
+
+        return result