transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer base_model:openai/whisper-large-v2 license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: openai/whisper-large-v2
model-index:
- name: whisper-large-ver1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# whisper-large-ver1

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Cer: 10.8895
- Loss: 0.4810

## 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Cer     | Validation Loss |
|:-------------:|:-----:|:----:|:-------:|:---------------:|
| 0.024         | 5.6   | 1000 | 11.4526 | 0.3606          |
| 0.0038        | 11.2  | 2000 | 10.7559 | 0.4166          |
| 0.0009        | 16.81 | 3000 | 10.8609 | 0.4669          |
| 0.0007        | 22.45 | 4000 | 10.8895 | 0.4810          |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
