direct input

app.py

@@ -45,15 +45,8 @@ with open('./README.md', 'r') as f:
 df_init = pd.DataFrame(columns=['Task', 'Top 1', 'Top 2', 'Top 3', 'Top 4', 'Top 5'])
 transcription_df = gr.DataFrame(value=df_init, label="Output Dataframe", row_count=(
     0, "dynamic"), max_rows=30, wrap=True, overflow_row_behaviour='paginate')
-# outputs = [gr.components.Textbox()]
 outputs = transcription_df
 
-df_init_live = pd.DataFrame(columns=['Task', 'Top 1', 'Top 2', 'Top 3', 'Top 4', 'Top 5'])
-transcription_df_live = gr.DataFrame(value=df_init_live, label="Output Dataframe", row_count=(
-    0, "dynamic"), max_rows=30, wrap=True, overflow_row_behaviour='paginate')
-outputs_live = transcription_df_live
-
-# Load the model and the corresponding preprocessor config
 # model = AutoModel.from_pretrained("m-a-p/MERT-v0-public", trust_remote_code=True)
 # processor = Wav2Vec2FeatureExtractor.from_pretrained("m-a-p/MERT-v0-public",trust_remote_code=True)
 model = modeling_MERT.MERTModel.from_pretrained("./MERT-v1-95M")
@@ -112,7 +105,6 @@ for task in TASKS:
 
 model.to(device)
 
-
 def model_inference(inputs):
     waveform, sample_rate = torchaudio.load(inputs)
 
@@ -176,23 +168,18 @@ def model_inference(inputs):
     df = pd.DataFrame(df_objects, columns=['Task', 'Top 1', 'Top 2', 'Top 3', 'Top 4', 'Top 5'])
     return df
     
-def convert_audio(inputs, microphone):
-    if (microphone is not None):
-        inputs = microphone
-    df = model_inference(inputs)    
-    return df
-
-demo = gr.Blocks()
-with demo:
-    gr.Interface(
-        fn=convert_audio,
-        inputs=gr.Audio(source="microphone"),
-        outputs=outputs,
-        allow_flagging="never",
-        title=title,
-        description=description,
-        article=article,
-    )
+def convert_audio(inputs):
+    return model_inference(inputs)    
+
+demo =  gr.Interface(
+    fn=convert_audio,
+    inputs=gr.Audio(source="microphone"),
+    outputs=outputs,
+    allow_flagging="never",
+    title=title,
+    description=description,
+    article=article,
+)
 
 # demo.queue(concurrency_count=1, max_size=5)
 demo.launch()