Urdu Audio Emotions Model
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Base Model | facebook/wav2vec2-large-xlsr-53 |
| Final Accuracy | 97.5% |
| Training Epochs | 50 |
What is urdu-audio-emotions?
The urdu-audio-emotions model is a specialized speech emotion recognition system fine-tuned on the wav2vec2-large-xlsr-53 architecture for Urdu language audio processing. It's designed to classify audio inputs into four distinct emotional categories: angry, happy, neutral, and sad, making it a valuable tool for Urdu speech analysis.
Implementation Details
The model leverages PyTorch and Transformers frameworks, utilizing native AMP for mixed-precision training. It was trained with the Adam optimizer using a learning rate of 5e-05 and a batch size of 32. The training process spanned 50 epochs, demonstrating consistent improvement from an initial accuracy of 22.5% to a final accuracy of 97.5%.
- Mixed-precision training with Native AMP
- Linear learning rate scheduler
- Batch size: 32 for both training and evaluation
- 50 epochs of training with remarkable convergence
Core Capabilities
- Accurate emotion classification in Urdu speech
- Support for four emotional states
- High accuracy rate of 97.5%
- Efficient processing of audio inputs
Frequently Asked Questions
Q: What makes this model unique?
This model addresses the specific challenge of emotion recognition in Urdu speech, providing highly accurate results with 97.5% accuracy. It's built on the robust wav2vec2 architecture and specifically optimized for Urdu language processing.
Q: What are the recommended use cases?
The model is ideal for Urdu speech analysis applications, including sentiment analysis in call centers, emotional content analysis in media, and research applications requiring Urdu speech emotion detection. It's particularly useful where accurate emotion classification in Urdu audio is crucial.
