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
Commit History
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add timestamp tokens (#33)
- add timestamp tokens (e12501d8fd1f9cca96b62761331e6c106f1eeacb)e8ff9f9
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Changes for fast tokenizer (#5)
- Add tokenizer.json (dcca07232bfb1028e499333730f868b87fd3d043) - Update unknown token (8852c40b30c9b7b981faf4fa77167fd862fd5fdb) - Move <|endoftext|> from added_tokens.json to vocab.json (3e9581879a6134abfb58f5788096027dd1756a63) Co-authored-by: Jonatan Kłosko <jonatanklosko@users.noreply.huggingface.co>a179016
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Update `return_mask` param
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Upload processor
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Upload tokenizer
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Upload tokenizer
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Upload processor
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Upload processor
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