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---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- whisper-event
- generated_from_trainer
datasets:
- OUTCOMESAI/medical_speech_corpus
metrics:
- wer
model-index:
- name: Whisper Small Medical
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: OUTCOMESAI/medical_speech_corpus zh-en
type: OUTCOMESAI/medical_speech_corpus
metrics:
- name: Wer
type: wer
value: 44.25531914893617
---
<!-- 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 Small Medical
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the OUTCOMESAI/medical_speech_corpus zh-en dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6201
- Wer: 44.2553
## 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: 5e-07
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Use 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: 50
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 7.4337 | 25.0 | 50 | 0.6201 | 44.2553 |
| 5.7447 | 50.0 | 100 | 0.6113 | 51.2340 |
### Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 3.1.1.dev0
- Tokenizers 0.21.0
|