Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Sangramsing/whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sangramsing/whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sangramsing/whisper-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sangramsing/whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sangramsing/whisper-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Sangramsing/whisper-base: direct link, hf CLI and curl.
- Browser
- Download file 134 Bytes
-
https://huggingface.co/Sangramsing/whisper-base/resolve/main/tf_model.h5
- Command line
-
hf download hf://Sangramsing/whisper-base/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Sangramsing/whisper-base/resolve/main/tf_model.h5
134 Bytes
- Xet hash:
- c2e5854d25c3e8e674c7af72b72e5348d066d26a29375bc1ef9d190bde583308
- Size of remote file:
- 134 Bytes
- SHA256:
- 34ba2336db5317a0ea94b1eecbb6bbc2baa92d67633b0fe100a2cbf401c9a6d3
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