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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
base_model: openai/whisper-small
model-index:
- name: Whisper Small PL
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: pl
split: test
metrics:
- type: wer
value: 14.57
name: WER
- type: wer_without_norm
value: 33.57
name: WER unnormalized
- type: cer
value: 4.02
name: CER
- type: mer
value: 14.37
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: 15.73
name: WER
- type: wer_without_norm
value: 34.51
name: WER unnormalized
- type: cer
value: 7.73
name: CER
- type: mer
value: 15.28
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: 16.79
name: WER
- type: wer_without_norm
value: 35.69
name: WER unnormalized
- type: cer
value: 4.99
name: CER
- type: mer
value: 16.55
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-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 and the FLEURS datasets.
It achieves the following results on the evaluation set:
- eval_loss: 0.3571
- eval_wer: 14.8004
- eval_runtime: 2233.4204
- eval_samples_per_second: 3.714
- eval_steps_per_second: 0.232
- epoch: 4.03
- step: 3000
## 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: 24
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- 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
### 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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