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GPT_SoVITS/inference_webui.py

@@ -331,27 +331,29 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
         int(hps.data.sampling_rate * 0.3),
         dtype=np.float16 if is_half == True else np.float32,
     )
-    with torch.no_grad():
+    if not ref_free:
-        wav16k, sr = librosa.load(ref_wav_path, sr=16000)
+        with torch.no_grad():
-        if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
+            wav16k, sr = librosa.load(ref_wav_path, sr=16000)
-            raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
+            if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
-        wav16k = torch.from_numpy(wav16k)
+                raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
-        zero_wav_torch = torch.from_numpy(zero_wav)
+            wav16k = torch.from_numpy(wav16k)
-        if is_half == True:
+            zero_wav_torch = torch.from_numpy(zero_wav)
-            wav16k = wav16k.half().to(device)
+            if is_half == True:
-            zero_wav_torch = zero_wav_torch.half().to(device)
+                wav16k = wav16k.half().to(device)
-        else:
+                zero_wav_torch = zero_wav_torch.half().to(device)
-            wav16k = wav16k.to(device)
+            else:
-            zero_wav_torch = zero_wav_torch.to(device)
+                wav16k = wav16k.to(device)
-        wav16k = torch.cat([wav16k, zero_wav_torch])
+                zero_wav_torch = zero_wav_torch.to(device)
-        ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
+            wav16k = torch.cat([wav16k, zero_wav_torch])
-            "last_hidden_state"
+            ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
-        ].transpose(
+                "last_hidden_state"
-            1, 2
+            ].transpose(
-        )  # .float()
+                1, 2
-        codes = vq_model.extract_latent(ssl_content)
+            )  # .float()
-   
+            codes = vq_model.extract_latent(ssl_content)
-        prompt_semantic = codes[0, 0]
+            prompt_semantic = codes[0, 0]
+            prompt = prompt_semantic.unsqueeze(0).to(device)
+
     t1 = ttime()
 
     if (how_to_cut == i18n("凑四句一切")):
@@ -391,7 +393,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
 
         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(