Allow user to select a model from dropdown

app.py

@@ -2,22 +2,55 @@ import gradio as gr
 
 from transformers import pipeline
 
-#MODEL_NAME="ctaguchi/wav2vec2-large-xlsr-japlmthufielta-ipa1000-ns"
-MODEL_NAME="ginic/wav2vec-large-xlsr-en-ipa"
-
-pipe = pipeline(task="automatic-speech-recognition", model=MODEL_NAME)
-
-def predict(audio_in):
-    return pipe(audio_in)["text"]
-
+DEFAULT_MODEL = "ginic/data_seed_4_wav2vec2-large-xlsr-buckeye-ipa"
+
+VALID_MODELS = [
+    "ctaguchi/wav2vec2-large-xlsr-japlmthufielta-ipa-plus-2000",
+    "ginic/hyperparam_tuning_1_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/data_seed_1_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/data_seed_2_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/data_seed_3_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/data_seed_4_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/gender_split_30_female_1_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/gender_split_30_female_2_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/gender_split_30_female_3_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/gender_split_30_female_4_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/gender_split_30_female_5_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/gender_split_70_female_1_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/gender_split_70_female_2_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/gender_split_70_female_3_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/gender_split_70_female_4_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/gender_split_70_female_5_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/vary_individuals_old_only_1_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/vary_individuals_old_only_2_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/vary_individuals_old_only_3_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/vary_individuals_young_only_1_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/vary_individuals_young_only_2_wav2vec2-large-xlsr-buckeye-ipa",
+    "ginic/vary_individuals_young_only_3_wav2vec2-large-xlsr-buckeye-ipa"
+]
+
+def load_model_and_predict(model_name, audio_in, model_state):
+    if model_state["model_name"] != model_name:
+        model_state = {"loaded_model":pipeline(task="automatic-speech-recognition", model=model_name), 
+                       "model_name": model_name}
+    
+    return model_state["loaded_model"](audio_in)["text"], model_state
 
 def launch_demo():
-    with gr.Blocks() as demo:
-        gr.Markdown(f"""
-        # Automatic International Phonetic Alphabet Transcription
-        This demo allows you to experiment with producing phonetic transcriptions of uploaded or recorded audio using the model '{MODEL_NAME}'.
-        """)
-        gr.Interface(fn=predict, inputs=gr.Audio(type="filepath"), outputs="text", allow_flagging="never")
+    initial_model = {"loaded_model":pipeline(task="automatic-speech-recognition", model=DEFAULT_MODEL), 
+                    "model_name": DEFAULT_MODEL}
+    demo= gr.Interface(
+        fn=load_model_and_predict, 
+        inputs= [
+            gr.Dropdown(VALID_MODELS, value=DEFAULT_MODEL, label="IPA transcription ASR model", info="Select the model to use for prediction."),
+            gr.Audio(type="filepath"),
+            gr.State(value=initial_model) #Store the name of the currently loaded model
+        ],  
+        outputs=["text", gr.State()],
+        allow_flagging="never",
+        title="Automatic International Phonetic Alphabet Transcription",
+        description="This demo allows you to experiment with producing phonetic transcriptions of uploaded or recorded audio using a selected automatic speech recognition (ASR) model."
+    )
 
     demo.launch()