Update app.py

app.py

@@ -29,9 +29,6 @@ def predict(text, speaker):
     if len(text.strip()) == 0:
         return (16000, np.zeros(0).astype(np.int16))
 
-    # text = getNews ()
-    # inputs = processor(text=text, return_tensors="pt")
-
     if Interest == "":
         inputs = processor(text=getNews(Interest), 
                            return_tensors="pt")
@@ -39,29 +36,11 @@ def predict(text, speaker):
         inputs = processor(text=getNews(text), 
                            return_tensors="pt")
 
-        
     # limit input length
     input_ids = inputs["input_ids"]
     input_ids = input_ids[..., :model.config.max_text_positions]
-
     
-    if speaker == "Surprise Me!":
-        # load one of the provided speaker embeddings at random
-        idx = np.random.randint(len(speaker_embeddings))
-        key = list(speaker_embeddings.keys())[idx]
-        speaker_embedding = np.load(speaker_embeddings[key])
-
-        # randomly shuffle the elements
-        np.random.shuffle(speaker_embedding)
-
-        # randomly flip half the values
-        x = (np.random.rand(512) >= 0.5) * 1.0
-        x[x == 0] = -1.0
-        speaker_embedding *= x
-
-        #speaker_embedding = np.random.rand(512).astype(np.float32) * 0.3 - 0.15
-    else:
-        speaker_embedding = np.load(speaker_embeddings[speaker[:3]])
+    speaker_embedding = np.load("spkemb/cmu_us_bdl_arctic-wav-arctic_a0009.npy")
 
     speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)