transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer en base_model:openai/whisper-tiny license:apache-2.0 endpoints_compatible region:us
---
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper_tiny-drl
  results: []
---

<!-- 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_tiny-drl

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
- Wer: 0.0

## 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: 100
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 2.572         | 25.0  | 100  | 1.8923          | 0.0 |
| 0.2239        | 50.0  | 200  | 0.1132          | 0.0 |
| 0.0035        | 75.0  | 300  | 0.0022          | 0.0 |
| 0.0009        | 100.0 | 400  | 0.0008          | 0.0 |
| 0.0006        | 125.0 | 500  | 0.0005          | 0.0 |
| 0.0004        | 150.0 | 600  | 0.0004          | 0.0 |
| 0.0003        | 175.0 | 700  | 0.0003          | 0.0 |
| 0.0003        | 200.0 | 800  | 0.0003          | 0.0 |
| 0.0003        | 225.0 | 900  | 0.0002          | 0.0 |
| 0.0003        | 250.0 | 1000 | 0.0002          | 0.0 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
