whisper-large-v2-sw / README.md
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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:
  • 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")