metadata
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-medium
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- medical_speech_transcription
metrics:
- wer
model-index:
- name: whisper_fine_tune_Santhosh
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Medical Speech, Transcription, and Intent
type: medical_speech_transcription
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 4.31026474686484
whisper_fine_tune_Santhosh
This model is a fine-tuned version of openai/whisper-medium on the Medical Speech, Transcription, and Intent dataset. It achieves the following results on the evaluation set:
- Loss: 0.0725
- Wer: 4.3103
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: 100
- training_steps: 600
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5679 | 0.2825 | 100 | 0.2478 | 13.0980 |
| 0.1266 | 0.5650 | 200 | 0.1474 | 8.1003 |
| 0.0872 | 0.8475 | 300 | 0.1034 | 5.9266 |
| 0.0399 | 1.1299 | 400 | 0.0865 | 5.3507 |
| 0.0229 | 1.4124 | 500 | 0.0771 | 4.1709 |
| 0.0259 | 1.6949 | 600 | 0.0725 | 4.3103 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.1.dev0
- Tokenizers 0.19.1