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--- |
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title: Whisper V3 Playground |
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description: Translate audio snippets into text on a Streamlit playground. |
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version: EN |
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--- |
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## Try out this model on [VESSL Hub](https://vessl.ai/hub). |
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This example runs a general-purpose speech recognition model, [Whisper V3](https://github.com/openai/whisper). It is trained on a 680k hours of diverse labelled audio. Whisper is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. It can generalize to many domains without additional fine-tuning. |
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<img |
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className="rounded-md" |
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src="/images/whisper-results.png" |
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/> |
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## Running the model |
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You can run the model with our quick command. |
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```sh |
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vessl run create -f whisper.yaml |
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``` |
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If you open log pages, you can see the result of inference for first 5 data in [Librispeech_asr dataset](https://www.openslr.org/12). |
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Here's a rundown of the `whisper.yaml` file. |
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```yaml |
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name: whisper-v3 |
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description: A template Run for inference of whisper v3 on librispeech_asr test set |
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resources: |
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cluster: vessl-gcp-oregon |
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preset: v1.l4-1.mem-42 |
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image: quay.io/vessl-ai/hub:torch2.1.0-cuda12.2-202312070053 |
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import: |
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/model/: hf://huggingface.co/VESSL/Whisper-large-v3 |
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/dataset/: hf://huggingface.co/datasets/VESSL/librispeech_asr_clean_test |
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/code/: |
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git: |
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url: https://github.com/vessl-ai/hub-model |
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ref: main |
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run: |
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- command: |- |
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pip install -r requirements.txt |
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python inference.py |
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workdir: /code/whisper-v3 |
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``` |
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