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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
model-index:
- name: Whisper Large v2 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: 6.912473345757989
name: Wer
- type: wer
value: 6.89
name: WER
- type: wer_without_norm
value: 19.79
name: WER unnormalized
- type: cer
value: 1.88
name: CER
- type: mer
value: 6.84
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 Large v2 PL
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4222
- Wer: 6.9125
## 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: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1144 | 1.93 | 500 | 0.2016 | 7.4749 |
| 0.0441 | 3.86 | 1000 | 0.2193 | 7.3154 |
| 0.0099 | 5.79 | 1500 | 0.2983 | 7.0804 |
| 0.0048 | 7.72 | 2000 | 0.3514 | 7.0988 |
| 0.0017 | 9.65 | 2500 | 0.3614 | 7.0485 |
| 0.0014 | 11.58 | 3000 | 0.3814 | 7.1240 |
| 0.001 | 13.51 | 3500 | 0.3773 | 6.9931 |
| 0.0005 | 15.44 | 4000 | 0.4085 | 6.9662 |
| 0.0004 | 17.37 | 4500 | 0.4195 | 6.9192 |
| 0.0004 | 19.3 | 5000 | 0.4222 | 6.9125 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|