---
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- librispeech_asr
metrics:
- wer
model-index:
- name: SpeechGPT
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: librispeech_asr
      type: librispeech_asr
      config: clean
      split: None
      args: 'config: clean, split: train'
    metrics:
    - name: Wer
      type: wer
      value: 2.8092665855143033
---

<!-- 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. -->

# SpeechGPT

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0813
- Wer: 2.8093

## 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: 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
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0956        | 0.12  | 1000 | 0.1065          | 3.6519 |
| 0.1002        | 0.24  | 2000 | 0.0997          | 3.5453 |
| 0.0841        | 0.36  | 3000 | 0.0941          | 3.3057 |
| 0.0839        | 0.48  | 4000 | 0.0905          | 3.1783 |
| 0.0821        | 0.6   | 5000 | 0.0855          | 2.9595 |
| 0.0626        | 0.72  | 6000 | 0.0839          | 2.9310 |
| 0.0643        | 0.84  | 7000 | 0.0821          | 2.8112 |
| 0.0908        | 0.97  | 8000 | 0.0813          | 2.8093 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2