metadata
base_model: openai/whisper-large-v3
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-large-v3-genbed-f-model
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: genbed
type: genbed
config: en
split: test
metrics:
- type: wer
value: 48.07
name: WER
whisper-large-v3-genbed-f-model
This model is a fine-tuned version of openai/whisper-large-v3 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5346
- Wer: 33.8051
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: 1.75e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 30000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.0784 | 0.6605 | 250 | 0.5140 | 48.6274 |
| 0.4405 | 1.3210 | 500 | 0.4665 | 40.7746 |
| 0.3641 | 1.9815 | 750 | 0.4253 | 37.1462 |
| 0.215 | 2.6420 | 1000 | 0.4413 | 35.1990 |
| 0.1871 | 3.3025 | 1250 | 0.4725 | 37.4548 |
| 0.1425 | 3.9630 | 1500 | 0.4407 | 34.2520 |
| 0.0918 | 4.6235 | 1750 | 0.4618 | 33.9860 |
| 0.0821 | 5.2840 | 2000 | 0.4980 | 33.8689 |
| 0.0665 | 5.9445 | 2250 | 0.5042 | 32.3367 |
| 0.048 | 6.6050 | 2500 | 0.4927 | 33.9860 |
| 0.0441 | 7.2655 | 2750 | 0.5449 | 32.0919 |
| 0.0387 | 7.9260 | 3000 | 0.5235 | 31.6876 |
| 0.0307 | 8.5865 | 3250 | 0.5227 | 31.7408 |
| 0.0282 | 9.2470 | 3500 | 0.5682 | 32.3792 |
| 0.0288 | 9.9075 | 3750 | 0.5346 | 33.8051 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1