metadata
library_name: transformers
language:
- da
license: apache-2.0
base_model: openai/whisper-large
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- alexandrainst/ftspeech
metrics:
- wer
model-index:
- name: Whisper small FTSpeech - Julie
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: ftspeech
type: alexandrainst/ftspeech
args: 'split: test'
metrics:
- name: Wer
type: wer
value: 19.463820660777202
Whisper small FTSpeech - Julie
This model is a fine-tuned version of openai/whisper-large on the ftspeech dataset. It achieves the following results on the evaluation set:
- Loss: 0.2781
- Wer: 19.4638
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4214 | 0.0080 | 500 | 0.4317 | 26.8590 |
| 0.3568 | 0.0161 | 1000 | 0.3763 | 24.5151 |
| 0.3443 | 0.0241 | 1500 | 0.3443 | 23.0618 |
| 0.3218 | 0.0321 | 2000 | 0.3275 | 22.0048 |
| 0.2851 | 0.0402 | 2500 | 0.3139 | 21.2409 |
| 0.2638 | 0.0482 | 3000 | 0.3021 | 20.4187 |
| 0.2515 | 0.0562 | 3500 | 0.2943 | 20.2420 |
| 0.2692 | 0.0643 | 4000 | 0.2864 | 19.9020 |
| 0.2503 | 0.0723 | 4500 | 0.2806 | 19.6671 |
| 0.2396 | 0.0803 | 5000 | 0.2781 | 19.4638 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.21.0