Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| from speechbrain.pretrained.interfaces import foreign_class | |
| import os | |
| # Function to get the list of audio files in the 'rec/' directory | |
| def get_audio_files_list(directory="rec"): | |
| try: | |
| return [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))] | |
| except FileNotFoundError: | |
| print("The 'rec' directory does not exist. Please make sure it is the correct path.") | |
| return [] | |
| # Loading the speechbrain emotion detection model | |
| learner = foreign_class( | |
| source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", | |
| pymodule_file="custom_interface.py", | |
| classname="CustomEncoderWav2vec2Classifier" | |
| ) | |
| # Building prediction function for Gradio | |
| emotion_dict = { | |
| 'sad': 'Sad', | |
| 'hap': 'Happy', | |
| 'ang': 'Anger', | |
| 'fea': 'Fear', | |
| 'sur': 'Surprised', | |
| 'neu': 'Neutral' | |
| } | |
| def selected_audio(audio_file): | |
| if audio_file is None: | |
| return None, "Please select an audio file." | |
| file_path = os.path.join("rec", audio_file) | |
| audio_data = gr.Audio(file=file_path) | |
| out_prob, score, index, text_lab = learner.classify_file(file_path) | |
| emotion = emotion_dict[text_lab[0]] | |
| return audio_data, emotion | |
| # Get the list of audio files for the dropdown | |
| audio_files_list = get_audio_files_list() | |
| # Define Gradio blocks | |
| with gr.Blocks() as blocks: | |
| gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>" + | |
| "Audio Emotion Detection" + | |
| "</h1>") | |
| with gr.Column(): | |
| input_audio_dropdown = gr.Dropdown(label="Select Audio", choices=audio_files_list) | |
| audio_ui = gr.Audio() | |
| output_text = gr.Textbox(label="Detected Emotion!") | |
| detect_btn = gr.Button("Detect Emotion") | |
| detect_btn.click(selected_audio, inputs=input_audio_dropdown, outputs=[audio_ui, output_text]) | |
| # Launch the Gradio blocks interface | |
| blocks.launch() |