Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use model-man/whisper-tiny-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use model-man/whisper-tiny-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="model-man/whisper-tiny-dv")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("model-man/whisper-tiny-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("model-man/whisper-tiny-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from model-man/whisper-tiny-dv: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/model-man/whisper-tiny-dv/resolve/main/training_args.bin
- Command line
-
hf download hf://model-man/whisper-tiny-dv/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/model-man/whisper-tiny-dv/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- b670784c9c2e15f7bc4671c456b0372ac73ced5dd4434015304260ad99a89cc1
- Size of remote file:
- 4.09 kB
- SHA256:
- 97d9fd2ca6f928b6fc12562f7567e9455d52e325172745461084d22860d927f7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.