mozilla-foundation/common_voice_13_0
Updated • 2.38k • 3
How to use Skier8402/whisper-small-tiny with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="Skier8402/whisper-small-tiny") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Skier8402/whisper-small-tiny")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Skier8402/whisper-small-tiny")This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 13 dataset using the swahili only.
More information needed.
The model was trained without enough noise added as a form of data augmentation. Do not use this production. I recommend using a larger version of whisper with more hyperparameter tuning especially the learning rate, momentum, weight decay and adjusting the batch size.
I followed the tutorial here. Very minimum edits to the code were done following this tutorial.
The following hyperparameters were used during training:
Base model
openai/whisper-tiny