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---
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: 16.15101446793939
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs ca
type: google/fleurs
args: 'config: ml, split: test'
metrics:
- name: Wer
type: wer
value: 20.4
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: projecte-aina/parlament_parla clean
type: projecte-aina/parlament_parla
args: 'config: ml, split: test'
metrics:
- name: Wer
type: wer
value: 21.14
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# openai/whisper-base
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](https://huggingface.co/softcatala/whisper-small-ca/blob/main/TRAINING.md) when training these models.
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_11_0 ca dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3608
- Wer: 16.1510
## 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: 40000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.4841 | 0.1 | 4000 | 0.5078 | 26.7974 |
| 0.3116 | 0.2 | 8000 | 0.4524 | 22.9455 |
| 0.3971 | 0.3 | 12000 | 0.4281 | 21.5427 |
| 0.2965 | 0.4 | 16000 | 0.4037 | 20.3082 |
| 0.2634 | 1.09 | 20000 | 0.3875 | 18.7980 |
| 0.2163 | 1.19 | 24000 | 0.3754 | 17.8170 |
| 0.3182 | 1.29 | 28000 | 0.3695 | 16.8587 |
| 0.2201 | 1.39 | 32000 | 0.3613 | 16.5785 |
| 0.155 | 2.08 | 36000 | 0.3633 | 16.3959 |
| 0.0904 | 2.18 | 40000 | 0.3608 | 16.1510 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.10.0+cu102
- Datasets 2.8.0
- Tokenizers 0.13.2
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