Search public pages, research tools, and SAVRN solutions.
Organization
onnx-community
Kokoro is a frontier TTS model for its size of 82 million parameters (text in/audio out). First, install the kokoro-js library from NPM using: You can then generate speech as follows: Optionally, save the audio to a file: The model is resilient to quantization, enabling efficient high-quality speech synthesis at a fraction of the original model size.
Kokoro is a frontier TTS model for its size of 82 million parameters (text in/audio out). First, install the kokoro-js library from NPM using: You can then generate speech as follows: Optionally, save the audio to a file: The model is resilient to quantization, enabling efficient high-quality speech synthesis at a fraction of the original model size.
https://huggingface.co/microsoft/Florence-2-base-ft with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Example: Perform image captioning with onnx-community/Florence-2-base-ft. We also released an online demo, which you can try yourself: https://huggingface.co/spaces/Xenova/florence2-webgpu Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).
If you haven't already, you can install the Transformers.js JavaScript library from NPM using
This is an ONNX version of UX4567/Text-Summarizer-t5-small. It was automatically converted and uploaded using this Hugging Face Space. See the pipeline documentation for summarization: https://huggingface.co/docs/transformers.js/api/pipelines#modulepipelines.SummarizationPipeline This model is a fine-tuned version of T5 designed for abstractive text summarization. It condenses long articles, documents, or paragraphs into short, accurate, and context-aware summaries. You can load and test the model using the Hugging Face transformers pipeline or direct model classes: from transformers import pipeline summarizer = pipeline("text-generation", model="UX4567/Text-Summarizer-t5-small") text = """…