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Duplicate from harish3110/emotion_detection
Browse filesCo-authored-by: Harish Vadlamani <harish3110@users.noreply.huggingface.co>
- .gitattributes +27 -0
- README.md +13 -0
- app.py +32 -0
- requirements.txt +3 -0
.gitattributes
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README.md
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---
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title: Emotion Detection
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emoji: 🌖
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colorFrom: blue
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.0.19
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app_file: app.py
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pinned: false
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duplicated_from: harish3110/emotion_detection
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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from speechbrain.pretrained.interfaces import foreign_class
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import gradio as gr
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import warnings
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warnings.filterwarnings("ignore")
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# Loading the speechbrain emotion detection model
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learner = foreign_class(
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source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP",
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# savedir="/home/harish3110/SeaWord/emotion/nbs/pretrained_models/CustomEncoderWav2vec2Classifier--8353113631630090076",
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pymodule_file="custom_interface.py",
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classname="CustomEncoderWav2vec2Classifier"
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)
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# Building prediction function for gradio
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emotion_dict = {
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'sad': 'Sad',
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'hap': 'Happy',
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'ang': 'Anger',
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'neu': 'Neutral'
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}
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def predict_emotion(audio):
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out_prob, score, index, text_lab = learner.classify_file(audio.name)
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return emotion_dict[text_lab[0]]
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# Loading gradio interface
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inputs = gr.inputs.Audio(label="Input Audio", type="file")
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outputs = "text"
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title = "Emotion Detection"
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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."
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gr.Interface(predict_emotion, inputs, outputs, title=title, description=description).launch()
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requirements.txt
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speechbrain
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torchaudio
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transformers
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