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app.py
CHANGED
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@@ -45,13 +45,13 @@ audio_examples = [
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df_init = pd.DataFrame(columns=['Task', 'Top 1', 'Top 2', 'Top 3', 'Top 4', 'Top 5'])
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transcription_df = gr.DataFrame(value=df_init, label="
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0, "dynamic"), max_rows=30, wrap=True, overflow_row_behaviour='paginate')
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# outputs = [gr.components.Textbox()]
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outputs = transcription_df
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df_init_live = pd.DataFrame(columns=['Task', 'Top 1', 'Top 2', 'Top 3', 'Top 4', 'Top 5'])
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transcription_df_live = gr.DataFrame(value=df_init_live, label="
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0, "dynamic"), max_rows=30, wrap=True, overflow_row_behaviour='paginate')
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outputs_live = transcription_df_live
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@@ -100,7 +100,15 @@ ID2CLASS = {
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}
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TASKS = ['GS', 'MTGInstrument', 'MTGGenre', 'MTGTop50', 'MTGMood', 'NSynthI', 'NSynthP', 'VocalSetS', 'VocalSetT','EMO',]
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Regression_TASKS = ['EMO']
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head_dir = './Prediction_Head/best-layer-MERT-v1-95M'
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for task in TASKS:
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@@ -159,7 +167,7 @@ def model_infernce(inputs):
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# task_output_texts = task_output_texts + f"TASK {task} output:\n" + "\n".join([str(ID2CLASS[task][str(sorted_idx[idx].item())])+f', probability: {sorted_prob[idx].item():.2%}' for idx in range(top_n_show)]) + '\n'
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# task_output_texts = task_output_texts + '----------------------\n'
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row_elements = [task]
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for idx in range(top_n_show):
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print(ID2CLASS[task])
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# print('id', str(sorted_idx[idx].item()))
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]
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df_init = pd.DataFrame(columns=['Task', 'Top 1', 'Top 2', 'Top 3', 'Top 4', 'Top 5'])
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transcription_df = gr.DataFrame(value=df_init, label="Model Results", row_count=(
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0, "dynamic"), max_rows=30, wrap=True, overflow_row_behaviour='paginate')
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# outputs = [gr.components.Textbox()]
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outputs = transcription_df
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df_init_live = pd.DataFrame(columns=['Task', 'Top 1', 'Top 2', 'Top 3', 'Top 4', 'Top 5'])
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transcription_df_live = gr.DataFrame(value=df_init_live, label="Model Results", row_count=(
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0, "dynamic"), max_rows=30, wrap=True, overflow_row_behaviour='paginate')
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outputs_live = transcription_df_live
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}
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#TASKS = ['GS', 'MTGInstrument', 'MTGGenre', 'MTGTop50', 'MTGMood', 'NSynthI', 'NSynthP', 'VocalSetS', 'VocalSetT','EMO',]
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TASKS = ['GS', 'MTGInstrument', 'MTGGenre', 'MTGMood', 'EMO']
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TASK_LABELS = {
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'GS': 'Scale',
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'MTGInstrument': 'Instruments',
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'MTGGenre': 'Genre',
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'MTGMood': 'Mood',
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'EMO','Emotion (Valence/Arousal'
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}
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Regression_TASKS = ['EMO']
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head_dir = './Prediction_Head/best-layer-MERT-v1-95M'
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for task in TASKS:
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# task_output_texts = task_output_texts + f"TASK {task} output:\n" + "\n".join([str(ID2CLASS[task][str(sorted_idx[idx].item())])+f', probability: {sorted_prob[idx].item():.2%}' for idx in range(top_n_show)]) + '\n'
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# task_output_texts = task_output_texts + '----------------------\n'
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row_elements = [TASK_LABELS[task]]
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for idx in range(top_n_show):
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print(ID2CLASS[task])
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# print('id', str(sorted_idx[idx].item()))
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