Fine-tuning models in the field of automatic speech recognition (ASR) can significantly enhance their performance by adapting them to specific datasets. In this guide, we’ll dive into fine-tuning wav2vec2-large-xls-r-1b-common_voice-tr-ft, a model trained on the COMMON_VOICE – TR dataset.
Understanding the Model
This model is a fine-tuned version of facebook wav2vec2-xls-r-1b. Just like training a dog to respond to specific commands, fine-tuning allows our model to understand and interpret Turkish speech more precisely.
Evaluation Results
When assessed, this model achieved:
- Loss: 0.3015
- Word Error Rate (Wer): 0.2149
- Character Error Rate (Cer): 0.0503
Model Details
At this stage, further information regarding the model description, intended uses, and limitations is necessary to fully grasp its capabilities and boundaries. This information is pivotal when considering the model for practical applications.
Training Procedure
The training procedure is the beautiful architecture behind our fine-tuned model. Let’s break down the parameters used as if we were building a structure: the learning rate is the foundation, the batch sizes are like beams supporting our structure, and the optimizer is the architect ensuring everything is executed correctly.
Training Hyperparameters
- Learning Rate: 0.00005
- Train Batch Size: 8
- Eval Batch Size: 8
- Seed: 42
- Distributed Type: multi-GPU
- Number of Devices: 8
- Total Train Batch Size: 64
- Total Eval Batch Size: 64
- Optimizer: Adam (betas=(0.9,0.999), epsilon=1e-08)
- Learning Rate Scheduler Type: linear
- Scheduler Warmup Steps: 500
- Number of Epochs: 100.0
- Mixed Precision Training: Native AMP
Training Results
To view the detailed training metrics, please check the Training metrics.
Framework Versions
The following frameworks and versions were utilized during the training:
- Transformers: 4.13.0.dev0
- Pytorch: 1.9.0+cu111
- Datasets: 1.15.2.dev0
- Tokenizers: 0.10.3
Troubleshooting
If you encounter issues during your training process, try the following troubleshooting ideas:
- Ensure your dataset is properly formatted and accessible.
- Verify that your batch sizes are suitable for the GPU memory available.
- Check for any version mismatches in the libraries you are using.
- If you’re running out of memory, consider reducing the batch size or using mixed precision training.
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