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---
language:
- br
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium Breton
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 br
type: mozilla-foundation/common_voice_11_0
config: br
split: test
args: br
metrics:
- name: Wer
type: wer
value: 41.611670718999655
---
<!-- 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 Breton
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8486
- Wer: 41.6117
## 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: 4e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0602 | 5.03 | 1000 | 0.7324 | 43.6957 |
| 0.0036 | 10.05 | 2000 | 0.8486 | 41.6117 |
| 0.001 | 15.08 | 3000 | 0.9033 | 42.0458 |
| 0.0004 | 20.1 | 4000 | 0.9351 | 41.6811 |
| 0.0003 | 25.13 | 5000 | 0.9468 | 41.7853 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|