Complete demo

app.py

@@ -14,74 +14,306 @@ from espnet_model_zoo.downloader import ModelDownloader
 # tagen = 'kan-bayashi/ljspeech_vits' 
 # vocoder_tagen = "none" 
 
-speech2text_slurp = Speech2Text.from_pretrained(
-    asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
-    asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
-    # Decoding parameters are not included in the model file
-    lang_prompt_token="<|en|> <|ner|> <|SLURP|>",
-    prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
-    nbest=1
-)
 
-speech2text_fsc = Speech2Text.from_pretrained(
-    asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
-    asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
-    # Decoding parameters are not included in the model file
-    lang_prompt_token="<|en|> <|ic|> <|fsc|>",
-    prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
-    nbest=1
-)
 
-speech2text_grabo = Speech2Text.from_pretrained(
-    asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
-    asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
-    # Decoding parameters are not included in the model file
-    lang_prompt_token="<|nl|> <|scr|> <|grabo_scr|>",
-    prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
-    nbest=1
-)
+audio_class_str='0."dog", 1."rooster", 2."pig", 3."cow", 4."frog", 5."cat", 6."hen", 7."insects", 8."sheep", 9."crow", 10."rain", 11."sea waves", 12."crackling fire", 13."crickets", 14."chirping birds", 15."water drops", 16."wind", 17."pouring water", 18."toilet flush", 19."thunderstorm", 20."crying baby", 21."sneezing", 22."clapping", 23."breathing", 24."coughing", 25."footsteps", 26."laughing", 27."brushing teeth", 28."snoring", 29."drinking sipping", 30."door wood knock", 31."mouse click", 32."keyboard typing", 33."door wood creaks", 34."can opening", 35."washing machine", 36."vacuum cleaner", 37."clock alarm", 38."clock tick", 39."glass breaking", 40."helicopter", 41."chainsaw", 42."siren", 43."car horn", 44."engine", 45."train", 46."church bells", 47."airplane", 48."fireworks", 49."hand saw".'
+audio_class_arr=audio_class_str.split(", ")
+audio_class_arr=[k.split('"')[1] for k in audio_class_arr]
 
 def inference(wav,data):
+#   import pdb;pdb.set_trace()
   with torch.no_grad():
+      speech, rate = soundfile.read(wav)
+      if len(speech.shape)==2:
+          speech=speech[:,0] 
       if data == "english_slurp":
-          speech, rate = soundfile.read(wav.name)
-          nbests = speech2text_slurp(speech)
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token="<|en|> <|ner|> <|SLURP|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            beam_size=20,
+            ctc_weight=0.0,
+            penalty=0.1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
           text, *_ = nbests[0]
-          # intent=text.split(" ")[0]
-          # scenario=intent.split("_")[0]
-          # action=intent.split("_")[1]
-          # text="{scenario: "+scenario+", action: "+action+"}"
+          text=text.split("|>")[-1]
+          intent=text.split(" ")[0].replace("in:","")
+          scenario=intent.split("_")[0]
+          action=intent.split("_")[1]
+          ner_text=text.split(" SEP ")[1:-1]
+          text="INTENT: {scenario: "+scenario+", action: "+action+"}\n"
+          text=text+"NAMED ENTITIES: {"
+          for k in ner_text:
+            slot_name=k.split(" FILL ")[0].replace("sl:","")
+            slot_val=k.split(" FILL ")[1]
+            text=text+" "+slot_name+" : "+slot_val+","
+          text=text+"}"
       elif data == "english_fsc":
-          print(wav.name)
-          speech, rate = soundfile.read(wav.name)
-          print(speech.shape)
-          if len(speech.shape)==2:
-            speech=speech[:,0]
-            # soundfile.write("store_file.wav", speech, rate, subtype='FLOAT')
-          print(speech.shape)
-          nbests = speech2text_fsc(speech)
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token="<|en|> <|ic|> <|fsc|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            nbest=1
+          )
+          nbests = speech2text(speech)
           text, *_ = nbests[0]
-          # intent=text.split(" ")[0]
-          # action=intent.split("_")[0]
-          # objects=intent.split("_")[1]
-          # location=intent.split("_")[2]
-          # text="{action: "+action+", object: "+objects+", location: "+location+"}"
-      # elif data == "english_snips":
-      #     print(wav.name)
-      #     speech, rate = soundfile.read(wav.name)
-      #     nbests = speech2text_snips(speech)
-      #     text, *_ = nbests[0]
-      elif data == "dutch":
-          print(wav.name)
-          speech, rate = soundfile.read(wav.name)
-          nbests = speech2text_grabo(speech)
+          text=text.split("|>")[-1]
+          intent=text.split(" ")[0].replace("in:","")
+          action=intent.split("_")[0]
+          objects=intent.split("_")[1]
+          location=intent.split("_")[2]
+          text="INTENT: {action: "+action+", object: "+objects+", location: "+location+"}"
+      elif data == "english_snips":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token="<|en|> <|ic|> <|SNIPS|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            nbest=1
+          )
+          nbests = speech2text(speech)
           text, *_ = nbests[0]
-          # intent=text.split(" ")[0]
-          # action=intent.split("_")[0]
-          # objects=intent.split("_")[1]
-          # location=intent.split("_")[2]
-          # text="{action: "+action+", object: "+objects+", location: "+location+"}"
-
+          text=text.split("|>")[-1]
+          intent=text.split(" ")[0].replace("in:","")
+          text="INTENT: "+intent
+      elif data == "dutch_scr":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token="<|nl|> <|scr|> <|grabo_scr|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=20,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+          text, *_ = nbests[0]
+          text=text.split("|>")[-1]
+          intent=text.split(" ")[0]
+          text="SPEECH COMMAND: "+intent
+      elif data == "english_scr":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token="<|en|> <|scr|> <|google_scr|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+          text, *_ = nbests[0]
+          text=text.split("|>")[-1]
+          intent=text.split(" ")[0].replace("command:","")
+          text="SPEECH COMMAND: "+intent
+      elif data == "lithuanian_scr":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token= "<|lt|> <|scr|> <|lt_scr|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+          text, *_ = nbests[0]
+          text=text.split("|>")[-1]
+          intent=text
+          text="SPEECH COMMAND: "+intent
+      elif data == "arabic_scr":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token= "<|ar|> <|scr|> <|ar_scr|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+          text, *_ = nbests[0]
+          text=text.split("|>")[-1]
+          intent=text.split(" ")[0].replace("command:","")
+          text="SPEECH COMMAND: "+intent
+      elif data == "lid_voxforge":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lid_prompt=True,
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+        #   import pdb;pdb.set_trace()
+          lang=speech2text.converter.tokenizer.tokenizer.convert_ids_to_tokens(nbests[0][2][0]).replace("|>","").replace("<|","")
+          text="LANG: "+lang
+      elif data == "fake_speech_detection_asvspoof":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token="<|en|> <|fsd|> <|asvspoof|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+          text, *_ = nbests[0]
+          text=text.split("|>")[-1]
+          intent=text.split(" ")[0].replace("class:","")
+          text="SPEECH CLASS: "+intent
+      elif data == "emotion_rec_iemocap":
+          replace_dict={}
+          replace_dict["em:neu"]="Neutral"
+          replace_dict["em:ang"]="Angry"
+          replace_dict["em:sad"]="Sad"
+          replace_dict["em:hap"]="Happy"
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token="<|en|> <|er|> <|iemocap|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+          text, *_ = nbests[0]
+          text=text.split("|>")[-1]
+          intent=replace_dict[text.split(" ")[0]]
+          text="EMOTION: "+intent
+      elif data == "accent_classify_accentdb":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token="<|en|> <|accent_rec|> <|accentdb|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+          text, *_ = nbests[0]
+          text=text.split("|>")[-1]
+          intent=text.split(" ")[0].replace("accent:","")
+          text="ACCENT: "+intent
+      elif data == "sarcasm_mustard":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token="<|en|> <|scd|> <|mustard|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+          text, *_ = nbests[0]
+          text=text.split("|>")[-1]
+          intent=text.split(" ")[0].replace("class:","")
+          text="SARCASM CLASS: "+intent
+      elif data == "sarcasm_mustard_plus":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token="<|en|> <|scd|> <|mustard_plus_plus|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+          text, *_ = nbests[0]
+          text=text.split("|>")[-1]
+          intent=text.split(" ")[0].replace("class:","")
+          text="SARCASM CLASS: "+intent
+      elif data == "gender_voxceleb1":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token="<|en|> <|gid|> <|voxceleb|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+          text, *_ = nbests[0]
+          text=text.split("|>")[-1]
+          intent=text.split(" ")[0].replace("gender:f","female").replace("gender:m","male")
+          text="GENDER: "+intent
+      elif data == "audio_classification_esc50":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token="<|audio|> <|auc|> <|esc50|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+          text, *_ = nbests[0]
+          text=text.split("|>")[-1]
+          intent=text.split(" ")[0].replace("audio_class:","")
+          text="AUDIO EVENT CLASS: "+audio_class_arr[int(intent)]
+      elif data == "semantic_parsing_stop":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lang_prompt_token="<|en|> <|sp|> <|STOP|>",
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=20,
+            penalty=0.1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+          text, *_ = nbests[0]
+          text=text.split("|>")[-1].replace("_STOP","")
+          text="SEMANTIC PARSE SEQUENCE: "+text
+      elif data == "vad_freesound":
+          speech2text = Speech2Text.from_pretrained(
+            asr_train_config="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml",
+            asr_model_file="UniverSLU-17-Task-Specifier/exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth",
+            # Decoding parameters are not included in the model file
+            lid_prompt=True,
+            prompt_token_file="UniverSLU-17-Task-Specifier/add_tokens-Copy1.txt",
+            ctc_weight=0.0,
+            beam_size=1,
+            nbest=1
+          )
+          nbests = speech2text(speech)
+          lang=speech2text.converter.tokenizer.tokenizer.convert_ids_to_tokens(nbests[0][2][0])
+          if lang=="<|nospeech|>":
+            text="VAD: no speech"
+          else:
+             text="VAD: speech" 
       # if lang == "chinese":
       #     wav = text2speechch(text)["wav"]
       #     scipy.io.wavfile.write("out.wav",text2speechch.fs , wav.view(-1).cpu().numpy())
@@ -91,19 +323,18 @@ def inference(wav,data):
   return  text
 
 title = "UniverSLU"
-description = "Gradio demo for UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions. To use it, simply record your audio or click one of the examples to load them. Read more at the links below."
+description = "Gradio demo for UniverSLU Task Specifier (https://huggingface.co/espnet/UniverSLU-17-Task-Specifier). UniverSLU-17 Task Specifier is a Multi-task Spoken Language Understanding model from CMU WAVLab. It adapts Whisper to additional tasks using single-token task specifiers. To use it, simply record your audio or click one of the examples to load them. More details about the SLU tasks that the model is trained on and it's performance on these tasks can be found in our paper: https://aclanthology.org/2024.naacl-long.151/"
 article = "<p style='text-align: center'><a href='https://github.com/espnet/espnet' target='_blank'>Github Repo</a></p>"
 
-examples=[['audio_slurp.flac',"english_slurp"],['audio_fsc.wav',"english_fsc"],['audio_grabo.wav',"dutch"]]
+examples=[['audio_slurp_ner.flac',"english_slurp"],['audio_fsc.wav',"english_fsc"],['audio_grabo.wav',"dutch_scr"],['audio_english_scr.wav',"english_scr"],['audio_lt_scr.wav',"lithuanian_scr"],['audio_ar_scr.wav',"arabic_scr"],['audio_snips.wav',"english_snips"],['audio_lid.wav',"lid_voxforge"],['audio_fsd.wav',"fake_speech_detection_asvspoof"],['audio_er.wav',"emotion_rec_iemocap"],['audio_acc.wav',"accent_classify_accentdb"],['audio_mustard.wav',"sarcasm_mustard"],['audio_mustard_plus.wav',"sarcasm_mustard_plus"],['audio_voxceleb1.wav',"gender_voxceleb1"],['audio_esc50.wav',"audio_classification_esc50"],['audio_stop.wav',"semantic_parsing_stop"],['audio_freesound.wav',"vad_freesound"]]
 
 # gr.inputs.Textbox(label="input text",lines=10),gr.inputs.Radio(choices=["english"], type="value", default="english", label="language")
 gr.Interface(
     inference, 
-    [gr.Audio(label="input audio",source = "microphone", type="file"),gr.Radio(choices=["english_slurp","english_fsc","dutch_scd"], type="value", default="english_fsc", label="Task")], 
-    gr.Textbox(type="str", label="Output"),
+    [gr.Audio(label="input audio",sources=["microphone"],type="filepath"),gr.Radio(choices=["english_slurp","english_fsc","dutch_scr","english_scr","lithuanian_scr","arabic_scr","english_snips","lid_voxforge","fake_speech_detection_asvspoof","emotion_rec_iemocap","accent_classify_accentdb","sarcasm_mustard","sarcasm_mustard_plus","gender_voxceleb1","audio_classification_esc50","semantic_parsing_stop","vad_freesound"], type="value", label="Task")], 
+    gr.Textbox(type="text", label="Output"),
     title=title,
     description=description,
     article=article,
-    enable_queue=True,
     examples=examples
-    ).launch(debug=True)
+    ).launch(debug=True)