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| from speechbrain.pretrained.interfaces import foreign_class | |
| import gradio as gr | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| # Loading the speechbrain emotion detection model | |
| learner = foreign_class( | |
| source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", | |
| # savedir="/home/harish3110/SeaWord/emotion/nbs/pretrained_models/CustomEncoderWav2vec2Classifier--8353113631630090076", | |
| pymodule_file="custom_interface.py", | |
| classname="CustomEncoderWav2vec2Classifier" | |
| ) | |
| # Building prediction function for gradio | |
| emotion_dict = { | |
| 'sad': 'Sad', | |
| 'hap': 'Happy', | |
| 'ang': 'Anger', | |
| 'neu': 'Neutral' | |
| } | |
| def predict_emotion(audio): | |
| out_prob, score, index, text_lab = learner.classify_file(audio.name) | |
| return emotion_dict[text_lab[0]] | |
| # Loading gradio interface | |
| inputs = gr.inputs.Audio(label="Input Audio", type="file") | |
| outputs = "text" | |
| title = "Emotion Detection" | |
| description = "Gradio demo for Emotion Detection. To use it, simply upload your audio, or click one of the examples to load them. Read more at the links below." | |
| gr.Interface(predict_emotion, inputs, outputs, title=title, description=description).launch() |