Automatic Speech Recognition
Transformers
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use qmeeus/whisper-small-multilingual-spoken-ner-end2end-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qmeeus/whisper-small-multilingual-spoken-ner-end2end-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="qmeeus/whisper-small-multilingual-spoken-ner-end2end-v2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("qmeeus/whisper-small-multilingual-spoken-ner-end2end-v2") model = AutoModelForSpeechSeq2Seq.from_pretrained("qmeeus/whisper-small-multilingual-spoken-ner-end2end-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 423075ab02c0d2639bc264b690d5405cd58aec5bdc1a88ca750e5e6261162e5d
- Size of remote file:
- 967 MB
- SHA256:
- efbd98c6f6cf6c459916b90960c4c80f64fae0fed7b1f4a25e7cbba2f4687328
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