Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-medium-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-medium-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-medium-verbatim")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-medium-verbatim") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-medium-verbatim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download ggml-model.bin from NbAiLabBeta/nb-whisper-medium-verbatim: direct link, hf CLI and curl.
- Browser
- Download file 1.53 GB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-medium-verbatim/resolve/main/ggml-model.bin
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-medium-verbatim/ggml-model.bin
-
curl -L -o ggml-model.bin https://huggingface.co/NbAiLabBeta/nb-whisper-medium-verbatim/resolve/main/ggml-model.bin
1.53 GB
- Xet hash:
- 4604e74301c2991f5055d3ec17c7e3aa1187fc37a1ef0b4bc6f44dee2a1cdae0
- Size of remote file:
- 1.53 GB
- SHA256:
- 5d6c56c7ccaeee20b8ef65f0188ba9f6a06c2f0d35797b86b851e77adc451770
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