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:
- 18c3845e493e5ed2ec8e5f268b26188a39a8e9606af00e60da8fc6fd36ffa4bb
- Size of remote file:
- 1.23 GB
- SHA256:
- 75bfd99d9cf5a66585b2b3a22600ef25cf62a0d8572cd591e5eaad2c20068c29
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