If you’re embarking on a journey to enhance speech recognition capabilities for the Sorani language, you’re in the right place! In this article, we’ll delve into the process of fine-tuning the Whisper-Small model, leveraging the Common Voice v15 dataset. We’ll break down the necessary steps and provide troubleshooting tips, ensuring a smooth ride on your AI adventure.

Overview of the Whisper-Small Model

The Whisper-Small model is an efficient audio processing tool from OpenAI, designed to transcribe and understand speech within various languages. Our specific version, tailored for Sorani, utilizes the Common Voice v15 dataset for finely-tuned results.

Getting Started with Fine-Tuning

To fine-tune a model, think of it as teaching a child how to recognize different animals. You show them a cat multiple times, explaining the features that define a cat, and over time, they learn to identify a cat in a crowd. Similarly, we guide the Whisper-Small model through the training dataset, adjusting its parameters until it can accurately interpret Sorani speech.

Training Hyperparameters

Here are the hyperparameters used during this training process:

• Learning Rate: 1e-05
• Training Batch Size: 56
• Evaluation Batch Size: 32
• Random Seed: 42
• Distributed Type: Multi-GPU
• Optimizer: Adam (Betas=(0.9,0.999), Epsilon=1e-08)
• Learning Rate Scheduler: Linear
• Warmup Steps: 500
• Training Steps: 5000

Training Results

Throughout the training process, the model’s performance was meticulously tracked. Here’s a summary of the training results:

 Epoch   Step   Validation Loss   WER
 0.55    1000   0.1405           0.2743  
 1.09    2000   0.0858           0.1772  
 1.64    3000   0.0585           0.1151  
 2.19    4000   0.0408           0.0789  
 2.74    5000   0.0334           0.0613  

As we can see, after 5000 training steps, the model achieved a loss of 0.0334 and a Word Error Rate (WER) of 0.0613, reflecting its improved accuracy in speech recognition.

Troubleshooting Common Issues

Even with a solid training framework, you might encounter some common issues while working with the Whisper-Small model. Here are a few troubleshooting tips:

• Model Not Converging: If the loss is not decreasing, try adjusting the learning rate or increasing the training steps.
• High WER: If the word error rate is higher than expected, check your dataset for clarity and ensure it is well-labeled.
• System Overload: Monitor your GPU usage. If your system is running out of memory, consider reducing the batch size.
• Incompatible Versions: Ensure that you are using compatible versions of libraries, such as Transformers 4.34.0.dev0 and Pytorch 2.0.1+cu117.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

The Takeaway

Fine-tuning the Whisper-Small model can be a rewarding venture, enhancing the capabilities of AI in understanding the Sorani language. As your model transforms from basic understanding to nuanced recognition, remember that patience and careful tuning are key!

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.