Update app.py

app.py

@@ -50,26 +50,31 @@ def load_models():
     return model_dict
 
 def translation(source, target, text):
-    if len(model_dict) == 2:
-        model_name = 'nllb-distilled-1.3B'
-
-    start_time = time.time()
-    source = flores_codes[source]
-    target = flores_codes[target]
-
-    model = model_dict[model_name + '_model']
-    tokenizer = model_dict[model_name + '_tokenizer']
-
-    translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=source, tgt_lang=target)
-    output = translator(text, max_length=400)
-
-    end_time = time.time()
-
-    output = output[0]['translation_text']
-    result = {'inference_time': end_time - start_time,
-              'source': source,
-              'target': target,
-              'result': output}
+    try:
+        print("Début de la traduction")
+        if len(model_dict) == 2:
+            model_name = 'nllb-distilled-1.3B'
+            
+        start_time = time.time()
+        source = flores_codes[source]
+        target = flores_codes[target]
+        
+        model = model_dict[model_name + '_model']
+        tokenizer = model_dict[model_name + '_tokenizer']
+        
+        translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=source, tgt_lang=target)
+        output = translator(text, max_length=400)
+        
+        end_time = time.time()
+        
+        output = output[0]['translation_text']
+        result = {'inference_time': end_time - start_time,
+                  'source': source,
+                  'target': target,
+                  'result': output}
+        print("Fin de la transcription")
+    except Exception as e:
+        print(f"Erreur lors de la transcription : {e}")     
     return result
 
 
@@ -77,8 +82,13 @@ def transcribe(inputs, task, source, target):
     if inputs is None:
         raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
 
-    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-    translated_text = translation(source, target, text)
+    try:
+        print("Début de la transcription")
+        text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
+        translated_text = translation(source, target, text)
+         print("Fin de la transcription")
+    except Exception as e:
+        print(f"Erreur lors de la transcription : {e}")
     return  text, translated_text