Instructions to use arampacha/whisper-large-uk-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arampacha/whisper-large-uk-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arampacha/whisper-large-uk-2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("arampacha/whisper-large-uk-2") model = AutoModelForSpeechSeq2Seq.from_pretrained("arampacha/whisper-large-uk-2") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
Browse filesThis pull request aims to enrich the metadata of your model by adding [`openai/whisper-large-v2`](https://huggingface.co/openai/whisper-large-v2) as a `base_model` field, situated in the `YAML` block of your model's `README.md`.
How did we find this information? We performed a regular expression match on your `README.md` file to determine the connection.
**Why add this?** Enhancing your model's metadata in this way:
- **Boosts Discoverability** - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub.
- **Highlights Impact** - It showcases the contributions and influences different models have within the community.
For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at [librarian-bots/base_model_explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer).
This PR comes courtesy of [Librarian Bot](https://huggingface.co/librarian-bot). If you have any feedback, queries, or need assistance, please don't hesitate to reach out to [@davanstrien](https://huggingface.co/davanstrien). Your input is invaluable to us!
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datasets:
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- mozilla-foundation/common_voice_11_0
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- google/fleurs
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model-index:
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- name: whisper-large-uk
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Common Voice 11.0
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type: mozilla-foundation/common_voice_11_0
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split: test
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args: uk
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metrics:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Fleurs
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type: google/fleurs
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split: test
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args: uk_ua
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metrics:
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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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datasets:
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- mozilla-foundation/common_voice_11_0
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- google/fleurs
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base_model: openai/whisper-large-v2
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model-index:
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- name: whisper-large-uk
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results:
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- task:
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type: automatic-speech-recognition
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name: Automatic Speech Recognition
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dataset:
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name: Common Voice 11.0
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type: mozilla-foundation/common_voice_11_0
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split: test
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args: uk
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metrics:
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- type: wer
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value: 10.02262314404669
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name: Wer
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- task:
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type: automatic-speech-recognition
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name: Automatic Speech Recognition
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dataset:
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name: Fleurs
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type: google/fleurs
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split: test
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args: uk_ua
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metrics:
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- type: wer
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value: 7.564370215727209
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name: 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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