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:
- 0e55c95d050f088a39ed96738ec2ac0fe4961cb82e21266d837db97d0d11f12a
- Size of remote file:
- 134 Bytes
- SHA256:
- fc0d9154656874521265c79e7a37a4b230ebadec35ba861fb55b8c5ce8a456d4
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