Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Sangramsing/whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sangramsing/whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sangramsing/whisper-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sangramsing/whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sangramsing/whisper-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Sangramsing/whisper-large: direct link, hf CLI and curl.
- Browser
- Download file 135 Bytes
-
https://huggingface.co/Sangramsing/whisper-large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Sangramsing/whisper-large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Sangramsing/whisper-large/resolve/main/flax_model.msgpack
135 Bytes
- Xet hash:
- 56e070e892d2ed007423a28897d5a911587396840e0227fa798047bdc35f8595
- Size of remote file:
- 135 Bytes
- SHA256:
- deb9c6a994a4de11f09f2cd07cccda7e8faa45867ea31190f4ac45e21166dea8
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