Update app.py

app.py

@@ -30,41 +30,48 @@ def get_user_models(hf_username, task):
         dataset = 'marsyas/gtzan'
       case "automatic-speech-recognition":
         dataset = 'PolyAI/minds14'
+      case "text-to-speech":
+        dataset = ""
       case _:
         print("Unsupported task")
 
     dataset_specific_models = []
-    
-    for model in user_model_ids:
-        meta = get_metadata(model)
-        if meta is None:
+
+    if dataset == "": 
+      return user_model_ids
+    else: 
+        for model in user_model_ids:
+          meta = get_metadata(model)
+          if meta is None:
+              continue
+          try:
+            if meta["datasets"] == [dataset]:
+                dataset_specific_models.append(model)
+          except: 
             continue
-        try:
-          if meta["datasets"] == [dataset]:
-              dataset_specific_models.append(model)
-        except: continue
-    return dataset_specific_models
+        return dataset_specific_models
+
 
 def calculate_best_result(user_models, task):
   """
   Calculate the best results of a unit for a given task
   :param user_model_ids: models of a user
   """
-  
+
   best_model = ""
-  
+
   if task == "audio-classification":
     best_result = -100
     larger_is_better = True
   elif task == "automatic-speech-recognition":
     best_result = 100
-    larger_is_better = False  
+    larger_is_better = False
 
   for model in user_models:
     meta = get_metadata(model)
     if meta is None:
-      continue 
-    metric = parse_metrics(model, task) 
+      continue
+    metric = parse_metrics(model, task)
 
     if larger_is_better:
      if metric > best_result:
@@ -76,7 +83,7 @@ def calculate_best_result(user_models, task):
         best_model = meta['model-index'][0]["name"]
 
   return best_result, best_model
-  
+
 
 def get_metadata(model_id):
   """
@@ -97,19 +104,19 @@ def extract_metric(model_card_content, task):
     :param model_card_content: model card content
     """
     accuracy_pattern = r"Accuracy: (\d+\.\d+)"
-    wer_pattern = r"Wer: (\d+\.\d+)" 
-    
+    wer_pattern = r"Wer: (\d+\.\d+)"
+
     if task == "audio-classification":
       pattern = accuracy_pattern
     elif task == "automatic-speech-recognition":
       pattern = wer_pattern
-    
+
     match = re.search(pattern, model_card_content)
     if match:
         metric = match.group(1)
         return float(metric)
     else:
-        return None    
+        return None
 
 
 def parse_metrics(model, task):
@@ -133,16 +140,16 @@ def certification(hf_username):
       },
   {
           "unit": "Unit 5: Automatic Speech Recognition",
-          "task": "automatic-speech-recognition",          
+          "task": "automatic-speech-recognition",
           "baseline_metric": 0.37,
           "best_result": 0,
           "best_model_id": "",
           "passed_": False
   },
   {
-          "unit": "Unit 6: TBD",
-          "task": "TBD",
-          "baseline_metric": 0.99,
+          "unit": "Unit 6: Text-to-Speech",
+          "task": "text-to-speech",
+          "baseline_metric": 0,
           "best_result": 0,
           "best_model_id": "",
           "passed_": False
@@ -155,7 +162,7 @@ def certification(hf_username):
           "best_model_id": "",
           "passed_": False
   },
-  ] 
+  ]
 
   for unit in results_certification:
     unit["passed"] = pass_emoji(unit["passed_"])
@@ -167,31 +174,39 @@ def certification(hf_username):
           best_result, best_model_id = calculate_best_result(user_ac_models, task = "audio-classification")
           unit["best_result"] = best_result
           unit["best_model_id"] = best_model_id
-          if unit["best_result"] >= unit["baseline_metric"]: 
+          if unit["best_result"] >= unit["baseline_metric"]:
             unit["passed_"] = True
             unit["passed"] = pass_emoji(unit["passed_"])
-        except: print("Either no relevant models found, or no metrics in the model card for audio classificaiton")      
+        except: print("Either no relevant models found, or no metrics in the model card for audio classificaiton")
       case "automatic-speech-recognition":
         try:
           user_asr_models = get_user_models(hf_username, task = "automatic-speech-recognition")
           best_result, best_model_id = calculate_best_result(user_asr_models, task = "automatic-speech-recognition")
           unit["best_result"] = best_result
           unit["best_model_id"] = best_model_id
-          if unit["best_result"] <= unit["baseline_metric"]: 
+          if unit["best_result"] <= unit["baseline_metric"]:
+            unit["passed_"] = True
+            unit["passed"] = pass_emoji(unit["passed_"])
+        except: print("Either no relevant models found, or no metrics in the model card for automatic speech recognition")
+      case "text-to-speech":
+        try:
+          user_tts_models = get_user_models(hf_username, task = "text-to-speech")
+          if user_tts_models: 
+            unit["best_result"] = 0
+            unit["best_model_id"] = user_tts_models[0]
             unit["passed_"] = True
             unit["passed"] = pass_emoji(unit["passed_"])
-        except: print("Either no relevant models found, or no metrics in the model card for automatic speech recognition")      
-      case "TBD":
+        except: print("Either no relevant models found, or no metrics in the model card for automatic speech recognition")
         print("Evaluation for this unit is work in progress")
       case _:
         print("Unknown task")
-    
+
   print(results_certification)
- 
+
   df = pd.DataFrame(results_certification)
   df = df[['passed', 'unit', 'task', 'baseline_metric', 'best_result', 'best_model_id']]
   return df
-
+    
 with gr.Blocks() as demo:
     gr.Markdown(f"""
     # 🏆 Check your progress in the Audio Course 🏆