Emotion Diarization WavLM Large
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | Speech Emotion Diarization Paper |
| Framework | PyTorch (SpeechBrain) |
| Performance | 29.7% EDER on ZED test set |
What is emotion-diarization-wavlm-large?
This is a sophisticated speech emotion diarization model built on the WavLM large architecture using SpeechBrain. The model is designed to detect and temporally locate different emotional segments within speech recordings, essentially answering the question "which emotion appears when?" in continuous speech.
Implementation Details
The system combines a WavLM encoder with a frame-wise classifier for downstream processing. It operates on 16kHz sampled audio and includes automatic normalization for input processing. The model was trained on six prominent emotional datasets: ZaionEmotionDataset, IEMOCAP, RAVDESS, JL-corpus, ESD, and EMOV-DB.
- Automatic audio normalization and resampling
- Frame-wise emotion classification
- Support for multiple emotion categories including neutral, happy, and sad
- GPU-compatible inference
Core Capabilities
- Precise emotion boundary detection in speech
- Multiple emotion classification
- Temporal segmentation of emotional content
- Real-time processing support
- Automated preprocessing of audio inputs
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its ability to not just classify emotions but also precisely identify when emotional changes occur in speech, achieving a competitive 29.7% Emotion Diarization Error Rate (EDER). It's particularly notable for its training across six different emotional datasets, making it robust for real-world applications.
Q: What are the recommended use cases?
The model is ideal for applications requiring detailed emotional analysis of speech, such as call center monitoring, therapeutic applications, emotion-aware AI systems, and research in affective computing. It's particularly suited for scenarios where tracking emotional changes over time is crucial.
