Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Italian
whisper
Generated from Trainer
whisper-event
Eval Results (legacy)
Instructions to use luigisaetta/whispermedium2plus-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luigisaetta/whispermedium2plus-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="luigisaetta/whispermedium2plus-it")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("luigisaetta/whispermedium2plus-it") model = AutoModelForSpeechSeq2Seq.from_pretrained("luigisaetta/whispermedium2plus-it") - Notebooks
- Google Colab
- Kaggle
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README.md
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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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#
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_11_0 dataset.
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It achieves the following results on the evaluation set:
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metrics:
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model-index:
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- name: luigisaetta/whispermedium2plus-it
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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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# luigisaetta/whisper-medium
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_11_0 dataset.
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It achieves the following results on the evaluation set:
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