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Bhaveen/
Medical-Speech-Transcription-Whisper-Small-Fine-Tuned

Name: Medical-Speech-Transcription-Whisper-Small-Fine-Tuned
Availability: InStock

automatic-speech-recognition

generated_from_trainer

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Whisper Small Fine Tuned

This model is a fine-tuned version of openai/whisper-small on the 500 samples of doctor-patient conversations. It achieves the following results on the evaluation set:

Loss: 0.4537
Wer: 10.6667

Intended uses & limitations

Model was trained for Educational Purposes

How to Use

You can use this fine-tuned Whisper model for transcribing short medical conversations using Hugging Face's transformers and datasets libraries.

python

from transformers import pipeline

# Load the fine-tuned model from Hugging Face
pipe = pipeline(
    task="automatic-speech-recognition",
    model="Bhaveen/Medical-Speech-Transcription-Whisper-Small-Fine-Tuned",
    device=0  # set to -1 for CPU, or 0 for GPU if available
)

# Run inference on an audio file
result = pipe("path_to_audio.wav")  # Replace with your actual audio file path
print(result["text"])

Requirements

Install dependencies if you haven't already:

pip install transformers datasets torchaudio

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-06
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: 200
num_epochs: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0384	1.0	25	0.4705	12.2222
0.0375	2.0	50	0.4689	12.0
0.0332	3.0	75	0.4666	11.0370
0.0293	4.0	100	0.4636	11.1111
0.0242	5.0	125	0.4612	11.0370
0.0197	6.0	150	0.4586	10.9630
0.0152	7.0	175	0.4555	10.7407
0.0116	8.0	200	0.4537	10.6667

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0