metadata
language:
- sw
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper medium Sw2 - Kiazi Bora
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: sw
split: test
args: 'config: sw, split: test'
metrics:
- name: Wer
type: wer
value: 30.7
Model
- Name: Whisper Large-v2 Swahili
- Description: Whisper weights for speech-to-text task, fine-tuned and evaluated on normalized data.
- Dataset:
- Train and validation splits for Swahili subsets of Common Voice 11.0.
- Train, validation and test splits for Swahili subsets of Google Fleurs.
- Performance: 30.7 WER
Weights
- Date of release: 12.09.2022
- License: MIT
Usage
To use these weights in HuggingFace's transformers library, you can do the following:
from transformers import WhisperForConditionalGeneration
model = WhisperForConditionalGeneration.from_pretrained("hedronstone/whisper-large-v2-sw")