---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- Hani89/medical_asr_recording_dataset
metrics:
- wer
model-index:
- name: Whisper Base - Shantanu
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: 'medical-speech-transcription-and-intent '
      type: Hani89/medical_asr_recording_dataset
      args: 'config: en, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 5.945355191256831
---

<!-- 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 Base - Shantanu

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the medical-speech-transcription-and-intent  dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1194
- Wer: 5.9454

## 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: Use OptimizerNames.ADAMW_TORCH 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.0544        | 3.0030  | 1000 | 0.1275          | 7.1403 |
| 0.007         | 6.0060  | 2000 | 0.1147          | 6.4044 |
| 0.0007        | 9.0090  | 3000 | 0.1183          | 5.9381 |
| 0.0004        | 12.0120 | 4000 | 0.1194          | 5.9454 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3