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| from speechbrain.pretrained.interfaces import foreign_class | |
| import gradio as gr | |
| import os | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| # 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 predict_emotion(selected_audio): | |
| file_path = os.path.join("rec", selected_audio) | |
| out_prob, score, index, text_lab = learner.classify_file(file_path) | |
| emotion = emotion_dict[text_lab[0]] | |
| return emotion, file_path # Return both emotion and file path | |
| # Get the list of audio files for the dropdown | |
| audio_files_list = get_audio_files_list() | |
| # Loading Gradio interface | |
| inputs = gr.Dropdown(label="Select Audio", choices=audio_files_list) | |
| outputs = [gr.outputs.Textbox(label="Predicted Emotion"), gr.outputs.Audio(label="Play Audio")] | |
| title = "ML Speech Emotion Detection" | |
| description = "Speechbrain powered wav2vec 2.0 pretrained model on IEMOCAP dataset using Gradio." | |
| interface = gr.Interface(fn=predict_emotion, inputs=inputs, outputs=outputs, title=title, description=description) | |
| interface.launch() |