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---
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
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