Update api.py

api.py

@@ -192,13 +192,18 @@ 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)  # 派蒙
+        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]
@@ -209,7 +214,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
     phones1 = cleaned_text_to_sequence(phones1)
     texts = text.split("\n")
     audio_opt = []
-    zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
+
     for text in texts:
         phones2, word2ph2, norm_text2 = clean_text(text, text_language)
         phones2 = cleaned_text_to_sequence(phones2)