model change

app.py

@@ -50,7 +50,7 @@ speed = 1.0
 fix_duration = None
 
 def load_model(exp_name, model_cls, model_cfg, ckpt_step):
-    checkpoint = torch.load(str(cached_path(f"hf://SWivid/F5-TTS/{exp_name}/model_{ckpt_step}.pt")), map_location=device)
+    checkpoint = torch.load(str(cached_path(f"hf://SWivid/{exp_name}/model_{ckpt_step}.pt")), map_location=device)
     vocab_char_map, vocab_size = get_tokenizer("Emilia_ZH_EN", "pinyin")
     model = CFM(
         transformer=model_cls(
@@ -85,7 +85,7 @@ E2TTS_ema_model, E2TTS_base_model = load_model("E2TTS_Base", UNetT, E2TTS_model_
 @spaces.GPU
 def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, progress = gr.Progress()):
     print(gen_text)
-    if model.predict(gen_text)['toxicity'] > 0.2: 
+    if model.predict(gen_text)['toxicity'] > 0.8: 
         print("Flagged for toxicity:", gen_text)
         raise gr.Error("Your text was flagged for toxicity, please try again with a different text.")
     gr.Info("Converting audio...")