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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2de871076c9518c6c050c1fa9f1cab0df39b79aa5c8178649068b331c3af028f
- Size of remote file:
- 134 Bytes
- SHA256:
- 1c6e73fd661f5c07e36eb91c850ab64e7077f74096794ad9b3603fa3b4b5e9b7
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