transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer vi dataset:google/fleurs base_model:openai/whisper-large-v2 license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-v2.vi2
  results: []
datasets:
- google/fleurs
language:
- vi
pipeline_tag: automatic-speech-recognition
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# whisper-large-v2.vi2

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3148
- Wer: 13.9211

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0284        | 2.66  | 500  | 0.2743          | 21.3778 |
| 0.0047        | 5.32  | 1000 | 0.2707          | 16.1299 |
| 0.0007        | 7.98  | 1500 | 0.2929          | 12.4846 |
| 0.0004        | 10.64 | 2000 | 0.3040          | 13.3418 |
| 0.0003        | 13.3  | 2500 | 0.3125          | 13.9983 |
| 0.0003        | 15.96 | 3000 | 0.3148          | 13.9211 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1