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library_name: transformers
language:
- multilingual
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- multilingual
metrics:
- wer
model-index:
- name: Whisper-medium-Multilingual
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: combined_voice_dataset_en_ja
type: multilingual
args: 'config: en,ja, split: train,test'
metrics:
- name: Wer
type: wer
value: 14.18615639026695
---
<!-- 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-Multilingual
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the combined_voice_dataset_en_ja dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2195
- Wer: 14.1862
## 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: Use OptimizerNames.ADAMW_TORCH_FUSED 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 |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.4567 | 3.6364 | 1000 | 0.2337 | 28.9944 |
| 0.0403 | 7.2727 | 2000 | 0.2126 | 18.1510 |
| 0.0137 | 10.9091 | 3000 | 0.2142 | 17.1510 |
| 0.0013 | 14.5455 | 4000 | 0.2149 | 15.5248 |
| 0.0006 | 18.1818 | 5000 | 0.2195 | 14.1862 |
### Framework versions
- Transformers 5.15.1
- Pytorch 2.13.0+cu130
- Datasets 2.21.0
- Tokenizers 0.22.2
|