# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("hiwden00/fs-w-he-base-en")
model = AutoModelForSpeechSeq2Seq.from_pretrained("hiwden00/fs-w-he-base-en")Quick Links
fs-w-he-base-en
This model is a fine-tuned version of openai/whisper-base.en on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0616
- Wer: 134.8528
- Cer: 128.2635
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: 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: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 5.5657 | 4.5872 | 500 | 5.7291 | 440.3609 | 462.4528 |
| 1.368 | 9.1743 | 1000 | 1.8578 | 127.8727 | 122.6554 |
| 0.5271 | 13.7615 | 1500 | 1.0031 | 101.1871 | 87.2552 |
| 0.3551 | 18.3486 | 2000 | 0.7689 | 107.6448 | 90.9653 |
| 0.2646 | 22.9358 | 2500 | 0.7395 | 197.5309 | 237.3841 |
| 0.1357 | 27.5229 | 3000 | 0.7802 | 124.2165 | 120.3023 |
| 0.0493 | 32.1101 | 3500 | 0.8682 | 150.9497 | 148.3167 |
| 0.0112 | 36.6972 | 4000 | 0.9626 | 140.2659 | 152.1384 |
| 0.001 | 41.2844 | 4500 | 1.0325 | 135.9924 | 125.2662 |
| 0.0004 | 45.8716 | 5000 | 1.0616 | 134.8528 | 128.2635 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
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Model tree for hiwden00/fs-w-he-base-en
Base model
openai/whisper-base.en
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hiwden00/fs-w-he-base-en")