Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use CodingQueen13/whisper-tiny-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodingQueen13/whisper-tiny-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="CodingQueen13/whisper-tiny-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("CodingQueen13/whisper-tiny-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("CodingQueen13/whisper-tiny-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from CodingQueen13/whisper-tiny-en: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/CodingQueen13/whisper-tiny-en/resolve/main/training_args.bin
- Command line
-
hf download hf://CodingQueen13/whisper-tiny-en/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CodingQueen13/whisper-tiny-en/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- a0cbcc00e4e1543ca51cda67f7eb64f113c6723799b20d73620ffe5646a5c883
- Size of remote file:
- 5.37 kB
- SHA256:
- dd7c2c826eba6dbe03239c62d95a14c5be3e8c74af2f4e9ef80d91e8471da7c8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.