Information

app.py

@@ -1,5 +1,6 @@
 import sys
 import os
+import time
 import torch
 import spaces
 # By using XTTS you agree to CPML license https://coqui.ai/cpml
@@ -30,6 +31,7 @@ tts = TTS(model_name, gpu=torch.cuda.is_available())
 tts.to(device_type)
 
 def predict(prompt, language, gender, audio_file_pth, mic_file_path, use_mic):
+    start = time.time()
     if len(prompt) < 2:
         gr.Warning("Please give a longer prompt text")
         return (
@@ -37,13 +39,13 @@ def predict(prompt, language, gender, audio_file_pth, mic_file_path, use_mic):
                 None,
                 None,
             )
-    if len(prompt) > 50000:
+    if 50000 < len(prompt):
         gr.Warning("Text length limited to 50,000 characters for this demo, please try shorter text")
         return (
-                None,
-                None,
-                None,
-            )
+            None,
+            None,
+            None,
+        )
 
     if use_mic:
         if mic_file_path is None:
@@ -80,12 +82,21 @@ def predict(prompt, language, gender, audio_file_pth, mic_file_path, use_mic):
         else:
             raise e
         
+    end = time.time()
+    secondes = int(end - start)
+    minutes = math.floor(secondes / 60)
+    secondes = secondes - (minutes * 60)
+    hours = math.floor(minutes / 60)
+    minutes = minutes - (hours * 60)
+    is_randomize_seed = False
+    information = ("Start again to get a different result. " if is_randomize_seed else "") + "The sound has been generated in " + ((str(hours) + " h, ") if hours != 0 else "") + ((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + str(secondes) + " sec."
+
     return (
         gr.make_waveform(
             audio="output.wav",
         ),
         "output.wav",
-        None,
+        information,
     )
 
 @spaces.GPU(duration=60)