Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Yoruba
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("jacccc/whisper-med-yo")
model = AutoModelForSpeechSeq2Seq.from_pretrained("jacccc/whisper-med-yo")Quick Links
Whisper Small Med Yo
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 1.1322
- Wer: 55.7814
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 |
|---|---|---|---|---|
| 0.026 | 6.8259 | 1000 | 0.8551 | 58.5043 |
| 0.0029 | 13.6519 | 2000 | 1.0240 | 56.1739 |
| 0.0003 | 20.4778 | 3000 | 1.1121 | 55.4286 |
| 0.0002 | 27.3038 | 4000 | 1.1322 | 55.7814 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for jacccc/whisper-med-yo
Base model
openai/whisper-mediumEvaluation results
- Wer on Common Voice 11.0self-reported55.781
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jacccc/whisper-med-yo")