Automatic Speech Recognition
Transformers
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use qmeeus/whisper-small-multilingual-spoken-ner-end2end 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 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")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("qmeeus/whisper-small-multilingual-spoken-ner-end2end") model = AutoModelForSpeechSeq2Seq.from_pretrained("qmeeus/whisper-small-multilingual-spoken-ner-end2end") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 8.94, | |
| "eval_loss": 0.3932703733444214, | |
| "eval_runtime": 186.6982, | |
| "eval_samples": 1000, | |
| "eval_samples_per_second": 5.356, | |
| "eval_steps_per_second": 0.67, | |
| "eval_wer": 0.14642407057340895 | |
| } |