Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Swahili
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("mn720/inctraining5")
model = AutoModelForSpeechSeq2Seq.from_pretrained("mn720/inctraining5")Quick Links
Incremental Swahili Luganda
This model is a fine-tuned version of openai/whisper-small on the Mix data dataset. It achieves the following results on the evaluation set:
- Loss: 0.3450
- Wer: 30.7579
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1454 | 0.1129 | 500 | 0.3666 | 32.6860 |
| 0.1537 | 0.2258 | 1000 | 0.3721 | 32.9290 |
| 0.1471 | 0.3388 | 1500 | 0.3665 | 32.9660 |
| 0.1397 | 0.4517 | 2000 | 0.3626 | 32.0067 |
| 0.1501 | 0.5646 | 2500 | 0.3562 | 32.2413 |
| 0.1381 | 0.6775 | 3000 | 0.3510 | 30.8636 |
| 0.14 | 0.7904 | 3500 | 0.3476 | 30.9122 |
| 0.135 | 0.9033 | 4000 | 0.3450 | 30.7579 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for mn720/inctraining5
Base model
openai/whisper-smallEvaluation results
- Wer on Mix datavalidation set self-reported30.758
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mn720/inctraining5")