Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use FrancescoBonzi/whisper-tiny-librispeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FrancescoBonzi/whisper-tiny-librispeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="FrancescoBonzi/whisper-tiny-librispeech")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("FrancescoBonzi/whisper-tiny-librispeech") model = AutoModelForSpeechSeq2Seq.from_pretrained("FrancescoBonzi/whisper-tiny-librispeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from FrancescoBonzi/whisper-tiny-librispeech: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/FrancescoBonzi/whisper-tiny-librispeech/resolve/main/training_args.bin
- Command line
-
hf download hf://FrancescoBonzi/whisper-tiny-librispeech/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/FrancescoBonzi/whisper-tiny-librispeech/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- b7985bca2b9e936073e32ec8d399a52a0df53aa48dc905ca28c660b0d4de01c5
- Size of remote file:
- 5.05 kB
- SHA256:
- abcdbf81d51ffe6b34ad3026be44739d99da9418f205d975e0853b541759287d
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