diff --git a/api.py b/api.py
index b8d584e7..720ef529 100644
--- a/api.py
+++ b/api.py
@@ -30,7 +30,7 @@ endpoint: `/`
 
 使用执行参数指定的参考音频:
 GET:
-    `http://127.0.0.1:9880?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
+    `http://127.0.0.1:9880?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=中文`
 POST:
 ```json
 {
@@ -41,7 +41,7 @@ POST:
 
 手动指定当次推理所使用的参考音频:
 GET:
-    `http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
+    `http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=中文&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=中文`
 POST:
 ```json
 {
@@ -129,6 +129,7 @@ from text.cleaner import clean_text
 from module.mel_processing import spectrogram_torch
 from my_utils import load_audio
 import config as global_config
+from inference_webui import get_tts_wav
 
 g_config = global_config.Config()
 
@@ -316,82 +317,6 @@ dict_language = {
 }
 
 
-def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
-    t0 = ttime()
-    prompt_text = prompt_text.strip("\n")
-    prompt_language, text = prompt_language, text.strip("\n")
-    zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
-    with torch.no_grad():
-        wav16k, sr = librosa.load(ref_wav_path, sr=16000)
-        wav16k = torch.from_numpy(wav16k)
-        zero_wav_torch = torch.from_numpy(zero_wav)
-        if (is_half == True):
-            wav16k = wav16k.half().to(device)
-            zero_wav_torch = zero_wav_torch.half().to(device)
-        else:
-            wav16k = wav16k.to(device)
-            zero_wav_torch = zero_wav_torch.to(device)
-        wav16k = torch.cat([wav16k, zero_wav_torch])
-        ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2)  # .float()
-        codes = vq_model.extract_latent(ssl_content)
-        prompt_semantic = codes[0, 0]
-    t1 = ttime()
-    prompt_language = dict_language[prompt_language]
-    text_language = dict_language[text_language]
-    phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
-    phones1 = cleaned_text_to_sequence(phones1)
-    texts = text.split("\n")
-    audio_opt = []
-
-    for text in texts:
-        phones2, word2ph2, norm_text2 = clean_text(text, text_language)
-        phones2 = cleaned_text_to_sequence(phones2)
-        if (prompt_language == "zh"):
-            bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
-        else:
-            bert1 = torch.zeros((1024, len(phones1)), dtype=torch.float16 if is_half == True else torch.float32).to(
-                device)
-        if (text_language == "zh"):
-            bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
-        else:
-            bert2 = torch.zeros((1024, len(phones2))).to(bert1)
-        bert = torch.cat([bert1, bert2], 1)
-
-        all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
-        bert = bert.to(device).unsqueeze(0)
-        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
-        prompt = prompt_semantic.unsqueeze(0).to(device)
-        t2 = ttime()
-        with torch.no_grad():
-            # pred_semantic = t2s_model.model.infer(
-            pred_semantic, idx = t2s_model.model.infer_panel(
-                all_phoneme_ids,
-                all_phoneme_len,
-                prompt,
-                bert,
-                # prompt_phone_len=ph_offset,
-                top_k=config['inference']['top_k'],
-                early_stop_num=hz * max_sec)
-        t3 = ttime()
-        # print(pred_semantic.shape,idx)
-        pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)  # .unsqueeze(0)#mq要多unsqueeze一次
-        refer = get_spepc(hps, ref_wav_path)  # .to(device)
-        if (is_half == True):
-            refer = refer.half().to(device)
-        else:
-            refer = refer.to(device)
-        # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
-        audio = \
-            vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
-                            refer).detach().cpu().numpy()[
-                0, 0]  ###试试重建不带上prompt部分
-        audio_opt.append(audio)
-        audio_opt.append(zero_wav)
-        t4 = ttime()
-    print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
-    yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
-
-
 def handle_control(command):
     if command == "restart":
         os.execl(g_config.python_exec, g_config.python_exec, *sys.argv)