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WernL/whisper-afrikaans-whisper_training_1756041390

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

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whisper-afrikaans-whispertraining1756041390

This is a LoRA (Low-Rank Adaptation) adapter for openai/whisper-large-v3 fine-tuned on Afrikaans speech data.

Model Details

—Language: Afrikaans (af)
—Base Model: openai/whisper-large-v3
—Training Method: LoRA (Low-Rank Adaptation)
—Training Steps: 1000
—Hardware: gpu-t4
—Training Time: N/A hours
—LoRA Rank: 8
—LoRA Alpha: 32

Usage

This model requires the peft library to load the LoRA adapter weights:

python

from transformers import WhisperProcessor, WhisperForConditionalGeneration
from peft import PeftModel
import torch

# Load base model and processor
base_model_name = "openai/whisper-large-v3"
processor = WhisperProcessor.from_pretrained(base_model_name)
base_model = WhisperForConditionalGeneration.from_pretrained(base_model_name)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "WernL/whisper-afrikaans-whisper_training_1756041390")

# Load audio
import librosa
audio, sr = librosa.load("path_to_audio.wav", sr=16000)

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

Alternative: Direct Loading (if supported)

python

from transformers import pipeline

# This may work if the adapter is properly configured
pipe = pipeline("automatic-speech-recognition", model="WernL/whisper-afrikaans-whisper_training_1756041390")
result = pipe("path_to_audio.wav")
print(result["text"])

Training Configuration

—Dataset: commonvoiceaf_v1
—Batch Size: 16
—Learning Rate: 1e-05
—Max Steps: 1000

Performance

Final training metrics:

—WER: 0.094
—Loss: 0.213

This model was trained using the Whisper Training App.