Whisper Small Yoruba - Bola Ologundudu
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 1.2225
- Wer: 70.6135
Model description
from transformers import pipeline import torch
modelName="ajibs75/whisper-small-yoruba" device = 0 if torch.cuda.is_available() else "cpu" pipe = pipeline(task="automatic-speech-recognition",model=modelName,chunk_length_s=30,device=device,) pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="yo", task="transcribe")
audio = "sample.mp3" text = pipe(audio) transacribed_audio = text["text"] print(transacribed_audio)
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: 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.066 | 7.6923 | 1000 | 0.8962 | 74.0141 |
| 0.004 | 15.3846 | 2000 | 1.1411 | 71.6613 |
| 0.0004 | 23.0769 | 3000 | 1.1959 | 70.6516 |
| 0.0003 | 30.7692 | 4000 | 1.2225 | 70.6135 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
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Base model
openai/whisper-small