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-q5_0.bin from NbAiLabBeta/nb-whisper-medium-verbatim: direct link, hf CLI and curl.
- Browser
- Download file 539 MB
-
https://huggingface.co/NbAiLabBeta/nb-whisper-medium-verbatim/resolve/main/ggml-model-q5_0.bin
- Command line
-
hf download hf://NbAiLabBeta/nb-whisper-medium-verbatim/ggml-model-q5_0.bin
-
curl -L -o ggml-model-q5_0.bin https://huggingface.co/NbAiLabBeta/nb-whisper-medium-verbatim/resolve/main/ggml-model-q5_0.bin
539 MB
- Xet hash:
- bfcd6743e6a0f05b29a64362ec8f5c3fc37eb40e84108f78588a829eaa38a29d
- Size of remote file:
- 539 MB
- SHA256:
- 600aff00e417bf070364da36d6d4a6ed5f28a3deac11c5919b62733ee17cecd1
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