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---
language:
- pl
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Medium 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: 8.68718413673836
name: Wer
- type: wer
value: 8.71
name: WER
- type: wer_without_norm
value: 22.0
name: WER unnormalized
- type: cer
value: 2.41
name: CER
- type: mer
value: 8.65
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: 11.99
name: WER
---
<!-- 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 Medium PL
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 and the FLEURS datasets.
It achieves the following results on the evaluation set:
- Loss: 0.3947
- Wer: 8.6872
## 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: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0805 | 0.48 | 500 | 0.2556 | 10.4888 |
| 0.0685 | 0.96 | 1000 | 0.2462 | 10.7608 |
| 0.0356 | 1.45 | 1500 | 0.2561 | 9.6728 |
| 0.0337 | 1.93 | 2000 | 0.2327 | 9.6459 |
| 0.017 | 2.41 | 2500 | 0.2444 | 9.9464 |
| 0.0179 | 2.9 | 3000 | 0.2554 | 9.6476 |
| 0.0056 | 3.38 | 3500 | 0.3001 | 9.3638 |
| 0.007 | 3.86 | 4000 | 0.2809 | 9.2245 |
| 0.0033 | 4.34 | 4500 | 0.3235 | 9.3437 |
| 0.0024 | 4.83 | 5000 | 0.3148 | 9.0633 |
| 0.0008 | 5.31 | 5500 | 0.3416 | 9.0112 |
| 0.0011 | 5.79 | 6000 | 0.3876 | 9.1858 |
| 0.0004 | 6.27 | 6500 | 0.3745 | 8.7292 |
| 0.0003 | 6.76 | 7000 | 0.3704 | 9.0314 |
| 0.0003 | 7.24 | 7500 | 0.3929 | 8.6553 |
| 0.0002 | 7.72 | 8000 | 0.3947 | 8.6872 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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