Upload app.py

app.py

@@ -45,7 +45,7 @@ def Hindi(audio):
     transcriber = pipeline("automatic-speech-recognition", model="theainerd/Wav2Vec2-large-xlsr-hindi")
     sr, y = audio
     y = y.astype(np.float32)
-    y = np.max(np.abs(y))
+    y /= np.max(np.abs(y))
 
     text = transcriber({"sampling_rate":sr, "raw":y})["text"]
 
@@ -57,7 +57,7 @@ def Telegu(audio):
     transcriber = pipeline("automatic-speech-recognition", model="anuragshas/wav2vec2-large-xlsr-53-telugu")
     sr, y = audio
     y = y.astype(np.float32)
-    y = np.max(np.abs(y))
+    y /= np.max(np.abs(y))
 
     text = transcriber({"sampling_rate":sr, "raw":y})["text"]
 
@@ -68,7 +68,7 @@ def Tamil(audio):
     transcriber = pipeline("automatic-speech-recognition", model="Harveenchadha/vakyansh-wav2vec2-tamil-tam-250")
     sr, y = audio
     y = y.astype(np.float32)
-    y = np.max(np.abs(y))
+    y /= np.max(np.abs(y))
 
     text = transcriber({"sampling_rate":sr, "raw":y})["text"]
 
@@ -79,7 +79,7 @@ def Kannada(audio):
     transcriber = pipeline("automatic-speech-recognition", model="vasista22/whisper-kannada-medium")
     sr, y = audio
     y = y.astype(np.float32)
-    y = np.max(np.abs(y))
+    y /= np.max(np.abs(y))
 
     text = transcriber({"sampling_rate":sr, "raw":y})["text"]
 
@@ -142,8 +142,7 @@ demo = gr.Interface(
         )],
     "text",
     title = "Farmers-Helper-Bot",
-    description = "Ask your queries in your regional Language",
-    theme=gr.themes.Soft()
+    description = "Ask your queries in your regional Language"
 )
 
 demo.launch(share=True)