Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from openai/whisper-tiny: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/openai/whisper-tiny/resolve/refs%2Fpr%2F16/tf_model.h5
- Command line
-
hf download hf://openai/whisper-tiny@refs/pr/16/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/openai/whisper-tiny/resolve/refs%2Fpr%2F16/tf_model.h5
151 MB
- Xet hash:
- 97a60f793acdb492d345aac9bc7bc68b3158389ac2a317ad07212cc19a5228bd
- Size of remote file:
- 151 MB
- SHA256:
- 4144af98f9f31730fd565591248e046f56a6adbe1461d7306339769e09ec8ed0
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