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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- audiofolder
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metrics:
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- wer
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model-index:
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results:
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name: Automatic Speech Recognition
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metrics:
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type: wer
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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#
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This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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---
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license: apache-2.0
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tags:
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- whisper-event
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- generated_from_trainer
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datasets:
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- audiofolder
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metrics:
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- wer
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model-index:
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- name: "Whisper Large S\xE1mi"
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results:
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- task:
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name: Automatic Speech Recognition
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metrics:
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- name: Wer
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type: wer
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value: 24.914285714285715
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Whisper Large Sámi
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This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5559
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- Wer: 24.9143
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## Model description
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