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
Ctrl+K
- checkpoint-1000
- checkpoint-1500
- checkpoint-2000
- checkpoint-2500
- checkpoint-3000
- checkpoint-3500
- checkpoint-4000
- checkpoint-4500
- checkpoint-500
- checkpoint-5000
- predictions
- 1.52 kB
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- 35.1 kB
- 501 Bytes
- 2.9 kB
- 352 Bytes
- 3.84 kB
- 494 kB
- 967 MB xet
- 52.7 kB
- 339 Bytes
- 2.19 kB
- 2.48 MB
- 286 kB
- 170 Bytes
- 10.5 kB
- 4.98 kB xet
- 836 kB