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")# pip install -U transformers accelerate # 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 flax_model.msgpack from openai/whisper-tiny: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/openai/whisper-tiny/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://openai/whisper-tiny/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/openai/whisper-tiny/resolve/main/flax_model.msgpack
151 MB
- Xet hash:
- b500837b05dd71dc5280233f6f184ee445f02fdcd942cd27b5a959e30b075a2d
- Size of remote file:
- 151 MB
- SHA256:
- a695f537d9f018eb43a9eb05bc22f4524351061bbf2890b2c01ac9b51b43ff4e
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