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---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: openai/whisper-medium
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: kk
split: test
args: kk
metrics:
- name: Wer
type: wer
value: 36.12903225806451
---
<!-- 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. -->
# openai/whisper-medium
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6413
- Wer: 36.1290
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0064 | 15.011 | 1000 | 0.5457 | 39.7097 |
| 0.0009 | 31.0094 | 2000 | 0.5771 | 38.3548 |
| 0.0 | 47.0078 | 3000 | 0.6180 | 36.4194 |
| 0.0 | 63.0062 | 4000 | 0.6349 | 36.0645 |
| 0.0 | 79.0046 | 5000 | 0.6413 | 36.1290 |
### Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.5.0+cu118
- Datasets 3.0.3.dev0
- Tokenizers 0.20.1
|