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---
language:
- et
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small et - Common Voice+FLEURS
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0, FLEURS
type: mozilla-foundation/common_voice_11_0
config: et
split: train
metrics:
- name: Wer
type: wer
value: 42.48444526581655
---
<!-- 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 et - Common Voice+FLEURS
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the Common Voice 11.0, FLEURS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8754
- Wer: 42.4844
## 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: 64
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0094 | 10.0 | 1000 | 0.7125 | 43.4085 |
| 0.0024 | 20.01 | 2000 | 0.7960 | 42.1795 |
| 0.0012 | 30.01 | 3000 | 0.8237 | 41.8961 |
| 0.0006 | 40.02 | 4000 | 0.8627 | 41.7853 |
| 0.0004 | 51.0 | 5000 | 0.8754 | 42.4844 |
### 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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