metadata
license: apache-2.0
language:
- sw
tags:
- hf-asr-leaderboard
- generated-from-trainer
- normalized transcriptions
datasets:
- mozilla_foundation/common_voice_11_0
metrics:
- WER
Model
- Name: Whisper Large-v2 Swahili
- Description: Fine-tuned Whisper weights for speech-to-text task.
- 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: 19.887087 WER
Weights
- Date of release: 12.09.2022
- Size:
- 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")