Showing 4 open source projects for "coffee-bytes"

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    ElevenLabs Python

    ElevenLabs Python

    The official Python SDK for the ElevenLabs API

    ...The SDK is designed for quick setup: after installing the package and setting an API key, you can generate speech in multiple languages and play or process the resulting audio bytes. It includes helper utilities (like play and stream) so you can either play audio locally or integrate it into your own playback or networking pipeline.
    Downloads: 8 This Week
    Last Update:
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  • 2
    DocArray

    DocArray

    The data structure for multimodal data

    ...Data science powerhouse: greatly accelerate data scientists’ work on embedding, k-NN matching, querying, visualizing, evaluating via Torch/TensorFlow/ONNX/PaddlePaddle on CPU/GPU. Data in transit: optimized for network communication, ready-to-wire at anytime with fast and compressed serialization in Protobuf, bytes, base64, JSON, CSV, DataFrame. Perfect for streaming and out-of-memory data. One-stop k-NN: Unified and consistent API for mainstream vector databases.
    Downloads: 2 This Week
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  • 3
    Grounded-Segment-Anything

    Grounded-Segment-Anything

    Marrying Grounding DINO with Segment Anything & Stable Diffusion

    ...The core idea behind the project is to pair Grounding DINO — a zero-shot object detector that can locate objects described by natural language — with Segment Anything Model (SAM), which can produce detailed masks for objects once they are localized. This fusion lets users provide arbitrary text descriptions (e.g., “a cat, a bicycle, or a coffee mug”), have the detection model find relevant bounding boxes, and then use SAM to generate precise segmentation masks that isolate each object in the scene.
    Downloads: 0 This Week
    Last Update:
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  • 4
    minbpe

    minbpe

    Minimal, clean code for the Byte Pair Encoding (BPE) algorithm

    minbpe is a minimal, clean implementation of byte-level Byte Pair Encoding (BPE), the tokenization approach widely used in modern language models. It operates on UTF-8 encoded bytes rather than Unicode characters, which makes it robust to arbitrary text inputs and avoids needing a language-specific character vocabulary. The repository is structured as a teaching-oriented implementation that shows how to train a tokenizer by learning merge rules, then apply those merges to encode text into token IDs and decode tokens back into text. ...
    Downloads: 0 This Week
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