Whisper Small Amharic

This model is a fine-tuned version of openai/whisper-small on the Common Voice 17.0 and surafelabebe/fleurs_am (a subset of google/fleurs) datasets. It achieves the following results on the evaluation set:

• Loss: 0.4352
• Wer: 50.9657

Model description

The model was trained for 10 hours on T4 GPU. Training results indicate potential overfitting. Future improvements will focus on mitigating this by incorporating a larger dataset, extended training epochs, and dropout regularization.

Usage

from transformers import pipeline

pipe = pipeline(model="surafelabebe/whisper-small-am")

text = pipe("sample.wav")["text"]  # change to "your audio file name"

print(text)

Training procedure

The fine-tuning process followed a similar procedure to that described in this blog post.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 4000
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.49.0
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0