If you’ve ever wanted to build a highly efficient automatic speech recognition (ASR) model for the Hindi language, you’re in the right place! We’re going to explore how to fine-tune the Whisper Small model using the Common Voice 11.0 dataset and optimize it for accuracy. This model card provides a glimpse of the training process, hyperparameters, and results. Let’s dive in!
Understanding the Whisper Model
The Whisper Small model is like an eloquent linguist, capable of deciphering spoken words into text. Imagine it as a friendly librarian who can memorize countless books and instantly retrieve the content upon your request, but instead of books, it reads the sounds and sentences from audio files.
Model Details
We are utilizing a fine-tuned version of the OpenAI Whisper Small model specifically tailored for Hindi using the Mozilla Foundation’s Common Voice 11.0 dataset. Here are the noteworthy achievements of our model:
- Loss: 0.6357
- Word Error Rate (WER): 18.7986
Training Hyperparameters
To ensure our model learns effectively, we used the following hyperparameters during training:
- Learning Rate: 1e-05
- Training Batch Size: 64
- Evaluation Batch Size: 8
- Seed: 42
- Optimizer: Adam (betas=(0.9,0.999), epsilon=1e-08)
- Learning Rate Scheduler: Linear
- Warmup Steps: 500
- Training Steps: 5000
- Mixed Precision Training: Native AMP
Training Results
The model’s evolution was recorded through various training epochs, revealing a continuous improvement in performance:
Epoch | Step | Validation Loss | WER
------|--------|-----------------|-------
14.01 | 1000 | 0.4715 | 19.1786
28.01 | 2000 | 0.5589 | 18.5377
43.01 | 3000 | 0.6008 | 18.5903
57.01 | 4000 | 0.6234 | 18.7735
72.01 | 5000 | 0.6357 | 18.7986 Just like a marathon runner who gradually improves their timing with every lap, our model displayed decreasing WER and validation loss over time, indicating effective learning.
Troubleshooting Tips
If you encounter issues while implementing the model or training it, consider the following troubleshooting ideas:
- Check the dataset format: Ensure that your dataset follows the expected input format for the Whisper model.
- Learning rate adjustments: If the model isn’t converging, experiment with different learning rates.
- Batch size variations: Larger batch sizes can help the model generalize better, while smaller sizes may lead to better convergence.
- Hardware limitations: Ensure your machine has enough resources (like RAM and GPU power) to handle the training procedure.
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Conclusion
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.