metadata
license: apache-2.0
tags:
- generated_from_trainer
- hf-asr-leaderboard
- whisper-event
metrics:
- wer
model-index:
- name: openai/whisper-medium
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 ca
type: mozilla-foundation/common_voice_11_0
args: 'config: ml, split: test'
metrics:
- name: Wer
type: wer
value: 8.282966640983934
openai/whisper-medium
This is an automatic speech recognition model that also does punctuation and casing. This model is for research only, we do not recommend using this model on production environments. See our learnings when training these models.
This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_11_0 ca dataset. It achieves the following results on the evaluation set:
- Loss: 0.2029
- Wer: 8.3235
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: 2
- eval_batch_size: 1
- 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: 20000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2652 | 0.1 | 2000 | 0.3469 | 15.3537 |
| 0.3273 | 0.2 | 4000 | 0.3151 | 14.1141 |
| 0.2696 | 0.3 | 6000 | 0.2955 | 13.2472 |
| 0.1725 | 0.4 | 8000 | 0.2787 | 11.6834 |
| 0.1741 | 0.5 | 10000 | 0.2648 | 11.0088 |
| 0.2037 | 0.6 | 12000 | 0.2470 | 10.1909 |
| 0.1586 | 0.7 | 14000 | 0.2333 | 9.4096 |
| 0.1548 | 0.8 | 16000 | 0.2184 | 8.9724 |
| 0.1799 | 1.08 | 18000 | 0.2064 | 8.2830 |
| 0.1165 | 1.18 | 20000 | 0.2029 | 8.3235 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.10.0+cu102
- Datasets 2.7.1
- Tokenizers 0.13.2