# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("devkyle/whisper-small-no-dropout")
model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/whisper-small-no-dropout")Quick Links
whisper-small-akan
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2208
- Wer: 9.3196
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0726 | 10.0 | 250 | 0.8217 | 48.0395 |
| 0.0246 | 20.0 | 500 | 0.9650 | 44.0903 |
| 0.011 | 30.0 | 750 | 0.9165 | 40.6770 |
| 0.0022 | 40.0 | 1000 | 0.9419 | 39.9436 |
| 0.0009 | 50.0 | 1250 | 0.2120 | 9.7770 |
| 0.0002 | 60.0 | 1500 | 0.2179 | 9.0909 |
| 0.0001 | 70.0 | 1750 | 0.2201 | 9.3196 |
| 0.0001 | 80.0 | 2000 | 0.2208 | 9.3196 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for devkyle/whisper-small-no-dropout
Base model
openai/whisper-small
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/whisper-small-no-dropout")