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---
language:
- pl
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-medium
model-index:
- name: Whisper Small PL
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: pl
split: test
args: pl
metrics:
- type: wer
value: 8.85
name: WER
- type: wer_without_norm
value: 21.75
name: WER unnormalized
- type: cer
value: 2.63
name: CER
- type: mer
value: 8.76
name: MER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: facebook/voxpopuli
type: facebook/voxpopuli
config: pl
split: test
metrics:
- type: wer
value: 12.18
name: WER
- type: wer_without_norm
value: 32.17
name: WER unnormalized
- type: cer
value: 6.99
name: CER
- type: mer
value: 11.84
name: MER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: pl_pl
split: test
metrics:
- type: wer
value: 12.77
name: WER
- type: wer_without_norm
value: 32.37
name: WER unnormalized
- type: cer
value: 5.87
name: CER
- type: mer
value: 12.52
name: MER
---
<!-- 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. -->
# Whisper Small PL
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3739
- Wer: 8.5898
## 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: 32
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0474 | 1.1 | 1000 | 0.2561 | 9.4612 |
| 0.0119 | 3.09 | 2000 | 0.2901 | 8.9726 |
| 0.0045 | 5.08 | 3000 | 0.3151 | 8.8870 |
| 0.0007 | 7.07 | 4000 | 0.4218 | 8.6032 |
| 0.0005 | 9.07 | 5000 | 0.3739 | 8.5898 |
### Evaluation results
When tested on diffrent polish ASR datasets (splits: test), this model achieves the following results:
| Dataset | WER | WER unnormalized | CER | MER |
|:-----------------:|:-----:|:----------------:|:-----:|:-----:|
|common_voice_11_0 | 8.85 | 21.75 | 2.63 | 8.76 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|