Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-tiny-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-tiny-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-tiny-verbatim")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-tiny-verbatim") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-tiny-verbatim") - Notebooks
- Google Colab
- Kaggle
File size: 557 Bytes
5a2550f | 1 2 3 4 5 6 7 8 9 10 11 | {
"template_url": "https://raw.githubusercontent.com/NbAiLab/nb-whisper/main/template.md",
"replacements": {
"#Finetuned#": "# Finetuned Verbatim model. \n\nThis model is trained 200 additional steps on top of the model below. This makes it outputting only text in lowercase and without punctation. It is also considerably more verbatim, and will not make any attempt at correcting grammatical errors in the text",
"#Size#": "Tiny Verbatim",
"#size#": "tiny",
"#model_name#": "NbAiLabBeta/nb-whisper-tiny-verbatim"
}
}
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