Luganda Whisper ASR
Collection
Luganda Speech To Text/ Automatic Speech Recognition • 4 items • Updated • 1
Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Ganda
whisper
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("allandclive/whisper-tiny-luganda-v2")
model = AutoModelForSpeechSeq2Seq.from_pretrained("allandclive/whisper-tiny-luganda-v2")Quick Links
whisper-tiny-luganda-v2
This model is a fine-tuned version of openai/whisper-tiny on the None dataset.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 21000
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for allandclive/whisper-tiny-luganda-v2
Base model
openai/whisper-tiny
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="allandclive/whisper-tiny-luganda-v2")