Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Sangramsing/whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sangramsing/whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sangramsing/whisper-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sangramsing/whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sangramsing/whisper-tiny") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4b52ca8981038b6cb4fbec1f579ab308aa0a1de35c603766f2b189bf9c575bb
- Size of remote file:
- 134 Bytes
- SHA256:
- a0a9f1da36f181e54a0086de79b81976022c14d9ca36a38fce7b6b84f112dfc6
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