Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Danish
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use HollowVoice/whisper-tiny-da with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HollowVoice/whisper-tiny-da with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="HollowVoice/whisper-tiny-da")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("HollowVoice/whisper-tiny-da") model = AutoModelForSpeechSeq2Seq.from_pretrained("HollowVoice/whisper-tiny-da", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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language:
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license: apache-2.0
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tags:
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metrics:
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model-index:
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results:
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name: Automatic Speech Recognition
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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-tiny](https://huggingface.co/openai/whisper-tiny) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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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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metrics:
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- wer
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model-index:
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- name: whisper-tiny-da
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results:
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- task:
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name: Automatic Speech Recognition
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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-tiny-da
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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