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("mory91/whisper-tiny-en")
model = AutoModelForSpeechSeq2Seq.from_pretrained("mory91/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: 0.6966
- Wer Ortho: 0.2806
- Wer: 0.2828
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: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 2000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.0008 | 17.24 | 500 | 0.5855 | 0.2740 | 0.2758 |
| 0.0002 | 34.48 | 1000 | 0.6411 | 0.2780 | 0.2809 |
| 0.0001 | 51.72 | 1500 | 0.6713 | 0.2793 | 0.2815 |
| 0.0001 | 68.97 | 2000 | 0.6966 | 0.2806 | 0.2828 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for mory91/whisper-tiny-en
Base model
openai/whisper-tinyDataset used to train mory91/whisper-tiny-en
Evaluation results
- Wer on PolyAI/minds14self-reported0.283
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mory91/whisper-tiny-en")