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---
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title: Audio Emotion Analyzer
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emoji: 🎵
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colorFrom: blue
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colorTo: purple
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sdk: streamlit
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sdk_version: 1.31.0
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app_file: app.py
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pinned: false
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license: mit
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---
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# Audio Emotion Analyzer
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A Streamlit application that analyzes the emotional tone in speech audio files using a pre-trained Wav2Vec2 model.
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## Model
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This application uses the [superb/wav2vec2-base-superb-er](https://huggingface.co/superb/wav2vec2-base-superb-er) model from Hugging Face, which is a Wav2Vec2 model fine-tuned for speech emotion recognition.
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## Demo App
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[](https://share.streamlit.io/)
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## Features
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- Upload your own .wav audio files for emotion analysis
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- Select from existing .wav files in your current directory
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- Real-time emotion prediction
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- Visual feedback with emojis
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## Quick Use
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You can use this application in two ways:
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### Option 1: Run on Hugging Face Spaces
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Click the "Spaces" tab on the model page to access the hosted version of this app.
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### Option 2: Run Locally
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1. Clone this repository
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2. Install the required dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Download the pre-trained model:
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```bash
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python download_model.py
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```
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4. Run the Streamlit app:
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```bash
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streamlit run app.py
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```
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## Using Audio Files
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The application automatically scans for .wav files in:
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- The current directory where the app is running
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- Immediate subdirectories (one level deep)
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You can:
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1. Place .wav files in the same directory as the app
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2. Place .wav files in subdirectories
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3. Upload new .wav files directly through the interface
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## Supported Emotions
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The model can detect 7 different emotions:
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- Neutral 😐
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- Happy 😊
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- Sad 😢
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- Angry 😠
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- Fearful 😨
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- Disgusted 🤢
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- Surprised 😲
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## Technical Details
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This application uses:
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- [superb/wav2vec2-base-superb-er](https://huggingface.co/superb/wav2vec2-base-superb-er) pre-trained model
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- Wav2Vec2ForSequenceClassification for emotion classification
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- Wav2Vec2FeatureExtractor for audio feature extraction
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- Streamlit for the web interface
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## Limitations
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- The model works best with clear speech audio in English
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- Background noise may affect the accuracy of emotion detection
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- Short audio clips (1-5 seconds) tend to work better than longer recordings
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## Troubleshooting
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If you encounter issues with model loading, try:
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1. Running `python download_model.py` again to download the model files
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2. Ensuring you have a stable internet connection for the initial model download
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3. Checking that your audio files are in .wav format with a 16kHz sample rate
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4. Verifying that the model files (pytorch_model.bin, config.json, preprocessor_config.json) are in your current directory
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## Citation
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If you use this application or the underlying model in your work, please cite:
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```bibtex
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@misc{superb2021,
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author = {SUPERB Team},
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title = {SUPERB: Speech processing Universal PERformance Benchmark},
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year = {2021},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/s3prl/s3prl}},
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}
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```
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## License
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This project is licensed under the MIT License - see the LICENSE file for details. |
