Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Northern Sami
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use NbAiLab/whisper-large-sme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/whisper-large-sme with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/whisper-large-sme")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/whisper-large-sme") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/whisper-large-sme") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
all_results.json
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{
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"epoch": 1.0,
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"eval_loss": 0.5558891892433167,
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"eval_runtime": 163.4842,
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"eval_samples_per_second": 0.612,
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"eval_steps_per_second": 0.104,
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"eval_wer": 24.914285714285715,
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"train_loss": 3.482638488374606e-09,
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"train_runtime": 45.4914,
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"train_samples_per_second": 15827.168,
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"train_steps_per_second": 1318.931
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}
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eval_results.json
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{
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"epoch": 1.0,
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"eval_loss": 0.5558891892433167,
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"eval_runtime": 163.4842,
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"eval_samples_per_second": 0.612,
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"eval_steps_per_second": 0.104,
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"eval_wer": 24.914285714285715
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}
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runs/Dec15_11-07-38_dante/events.out.tfevents.1671103034.dante.3886445.2
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version https://git-lfs.github.com/spec/v1
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oid sha256:9212c2044ec4afb3848f55a3db0a8097c9cb2fc871fdc0ed872f07181bfff45d
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size 364
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train_results.json
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{
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"epoch": 1.0,
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"train_loss": 3.482638488374606e-09,
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"train_runtime": 45.4914,
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"train_samples_per_second": 15827.168,
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"train_steps_per_second": 1318.931
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}
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trainer_state.json
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