Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
hf-asr-leaderboard
whisper-event
Eval Results (legacy)
Instructions to use softcatala/whisper-medium-ca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use softcatala/whisper-medium-ca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="softcatala/whisper-medium-ca")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("softcatala/whisper-medium-ca") model = AutoModelForSpeechSeq2Seq.from_pretrained("softcatala/whisper-medium-ca") - Notebooks
- Google Colab
- Kaggle
Link
Browse files
README.md
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# openai/whisper-medium
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This is an automatic speech recognition model that also does punctuation and casing.
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 ca dataset.
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It achieves the following results on the evaluation set:
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# openai/whisper-medium
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This is an automatic speech recognition model that also does punctuation and casing. This model is for research only, **we do not recommend using this model on production environments**. See our [learnings](https://huggingface.co/softcatala/whisper-small-ca/blob/main/TRAINING.md) when training these models.
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 ca dataset.
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It achieves the following results on the evaluation set:
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