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
File size: 501 Bytes
26d3939 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"epoch": 1.42,
"eval_combined_wer": 0.1491414618777568,
"eval_f1_score": 0.7163398692810456,
"eval_label_f1": 0.8200435729847495,
"eval_loss": 0.2754737436771393,
"eval_runtime": 117.0137,
"eval_samples": 1000,
"eval_samples_per_second": 8.546,
"eval_steps_per_second": 0.137,
"eval_wer": 0.08582479210984335,
"train_loss": 0.20275388622283935,
"train_runtime": 28462.3036,
"train_samples_per_second": 22.486,
"train_steps_per_second": 0.176
} |