Automatic Speech Recognition
Transformers
PyTorch
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results
Instructions to use openai/whisper-large-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-large-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Add Vaani-Benchmark-V1.0 Hindi WER eval results
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by PranavdbhatArtpark - opened
.eval_results/ARTPARK-IISc-Vaani-Benchmark-V1-0.yaml
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- dataset:
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id: ARTPARK-IISc/Vaani-Benchmark-V1.0
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task_id: Hindi_WER
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date: '2026-06-26'
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value: 26.8
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