Instructions to use lgris/sew-tiny-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lgris/sew-tiny-pt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lgris/sew-tiny-pt")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("lgris/sew-tiny-pt") model = AutoModel.from_pretrained("lgris/sew-tiny-pt") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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# SEW-tiny
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[SEW by ASAPP Research](https://github.com/asappresearch/sew)
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The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...
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license: apache-2.0
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# SEW-tiny-pt
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This is a pretrained version of [SEW tiny by ASAPP Research](https://github.com/asappresearch/sew) trained over Brazilian Portuguese audio.
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The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...
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