matiows/whisper-tiny-pap-medical

Whisper Tiny Papiamento - Medical Domain Adaptation

This model is a medical domain fine-tuned version of sonnygeorge/whisper-tiny-pap specialized for healthcare and clinical Papiamento speech recognition.

Model Description

• Base model: sonnygeorge/whisper-tiny-pap (Papiamento Whisper model by Sonny George)
• Domain: Medical/Healthcare Papiamento
• Language: Papiamento (pap)
• Specialization: Clinical terminology, medical consultations, healthcare vocabulary
• Training: Fine-tuned on medical Papiamento audio data

Model Performance

This model builds upon Sonny George's excellent Papiamento Whisper foundation and adds:

• ✅ Enhanced medical terminology recognition
• ✅ Clinical context understanding
• ✅ Healthcare vocabulary optimization
• ✅ Single speaker adaptation for consistent medical speech patterns

Intended Uses

• Medical consultation transcription in Papiamento
• Clinical note generation from Papiamento audio
• Healthcare documentation automation
• Medical terminology recognition in Papiamento

Usage

from transformers import WhisperProcessor, WhisperForConditionalGeneration
import librosa

# Load model and processor
processor = WhisperProcessor.from_pretrained("your-username/whisper-tiny-pap-medical")
model = WhisperForConditionalGeneration.from_pretrained("your-username/whisper-tiny-pap-medical")

# Load medical audio
audio, sr = librosa.load("medical_consultation.m4a", sr=16000)

# Process and transcribe
inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
predicted_ids = model.generate(inputs["input_features"])
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]

print(transcription)  # Medical Papiamento transcription