Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use openai/whisper-tiny.en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-tiny.en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-tiny.en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from openai/whisper-tiny.en: direct link, hf CLI and curl.
- Browser
- Download file 2.13 MB
-
https://huggingface.co/openai/whisper-tiny.en/resolve/refs%2Fpr%2F13/tokenizer.json
- Command line
-
hf download hf://openai/whisper-tiny.en@refs/pr/13/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/openai/whisper-tiny.en/resolve/refs%2Fpr%2F13/tokenizer.json
2.13 MB
File too large to display, you can check the raw version instead.