whisper-small-finetuned-medical3

This model is a fine-tuned version of openai/whisper-small on the audiofolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.0671
• Wer: 3.125

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• 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: 80
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.51.3
• Pytorch 2.7.0+cu126
• Datasets 3.5.1
• Tokenizers 0.21.1

Usage

from transformers import pipeline
import gradio as gr

# Load the fine-tuned Whisper model for medical speech recognition
pipe = pipeline(model="Johnyquest7/whisper-small-finetuned-medical3", return_timestamps=True)

# Define transcription function
def transcribe(audio):
    text = pipe(audio)["text"]
    return text

# Create a Gradio interface
iface = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(sources=["upload", "microphone"], type="filepath"),
    outputs="text",
    title="Whisper Small Medical",
    description="Demo for medical speech recognition using a fine-tuned Whisper small model."
)

# Launch the interface
iface.launch()