Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Apocalypse-19/whisper-tiny-en")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Apocalypse-19/whisper-tiny-en")Quick Links
whisper-tiny-en
This model is a fine-tuned version of openai/whisper-tiny on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set:
- Loss: 0.7823
- Wer Ortho: 0.3492
- Wer: 0.3430
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1500
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.0016 | 17.86 | 500 | 0.7168 | 0.3504 | 0.3394 |
| 0.0004 | 35.71 | 1000 | 0.7683 | 0.3516 | 0.3442 |
| 0.0003 | 53.57 | 1500 | 0.7823 | 0.3492 | 0.3430 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Downloads last month
- 6
Model tree for Apocalypse-19/whisper-tiny-en
Base model
openai/whisper-tinyDataset used to train Apocalypse-19/whisper-tiny-en
Evaluation results
- Wer on PolyAI/minds14self-reported0.343
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Apocalypse-19/whisper-tiny-en")