Search Results for "raspberry-gpio-python" - Page 4
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Showing 83 open source projects for "raspberry-gpio-python"
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DeepSWE-Preview
State-of-the-art RL-trained coding agent for complex SWE tasks
DeepSWE-Preview is a 32.8B parameter open-source coding agent trained solely with reinforcement learning (RL) to perform complex software engineering (SWE) tasks. Built on top of Qwen3-32B, it achieves 59% accuracy on the SWE-Bench-Verified benchmark—currently the highest among open-weight models. The model navigates and edits large codebases using tools like a file editor, bash execution, and search, within the R2E-Gym environment. Its training emphasizes sparse reward signals, test-time... -
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mms-300m-1130-forced-aligner
CTC-based forced aligner for audio-text in 158 languages
... to the TorchAudio forced alignment API. Users can integrate it easily through the Python package ctc-forced-aligner, and it supports GPU acceleration via PyTorch. The alignment pipeline includes audio processing, emission generation, tokenization, and span detection, making it suitable for speech analysis, transcription syncing, and dataset creation. This model is especially useful for researchers and developers working with low-resource languages or building multilingual speech systems. -
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yolo-world-mirror
Mirror of Ultralytics YOLO-World model weights for object detection
... descriptions. These weights are compatible with Ultralytics’ tooling and documentation, making it easier for developers to deploy or fine-tune the model. The mirror allows users to work with YOLO-World models through a centralized platform without downloading from alternate sources. It enables flexible integration with Ultralytics’ Python API or CLI tools for real-time and high-performance object detection tasks. -
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segmentation-3.0
Speaker segmentation model for 10s audio chunks with powerset labels
segmentation-3.0 is a voice activity and speaker segmentation model from the pyannote.audio framework, designed to analyze 10-second mono audio sampled at 16kHz. It outputs a (num_frames, num_classes) matrix using a powerset encoding that includes non-speech, individual speakers, and overlapping speech for up to three speakers. Trained with pyannote.audio 3.0.0 on a rich blend of datasets—including AISHELL, DIHARD, VoxConverse, and more—it enables downstream tasks like voice activity... -
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MiniMax-M1
Open-weight, large-scale hybrid-attention reasoning model
MiniMax-M1 is the world’s first open-weight, large-scale hybrid-attention reasoning model designed for long-context and complex reasoning tasks. Powered by a hybrid Mixture-of-Experts (MoE) architecture combined with a lightning attention mechanism, it efficiently supports context lengths up to 1 million tokens—eight times larger than many contemporary models. MiniMax-M1 significantly reduces computational overhead at generation time, consuming only about 25% FLOPs compared to comparable... -
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OpenVLA 7B
Vision-language-action model for robot control via images and text
... supports real-world robotics tasks, with robust generalization to environments seen in pretraining. Its actions include delta values for position, orientation, and gripper status, and can be un-normalized based on robot-specific statistics. OpenVLA is MIT-licensed, fully open-source, and designed collaboratively by Stanford, Berkeley, Google DeepMind, and TRI. Deployment is facilitated via Python and Hugging Face tools, with flash attention support for efficient inference. -
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voice-activity-detection
Detects speech activity in audio using pyannote.audio 2.1 pipeline
The voice-activity-detection model by pyannote is a neural pipeline for detecting when speech occurs in audio recordings. Built on pyannote.audio 2.1, it identifies segments of active speech within any audio file, making it valuable for preprocessing tasks like transcription, diarization, or voice-controlled systems. The model was trained using datasets such as AMI, DIHARD, and VoxConverse, and it requires users to authenticate via Hugging Face for access. To use the model, users must accept... -
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chronos-t5-small
Time series forecasting model using T5 architecture with 46M params
... probabilistic forecasting by autoregressively sampling multiple future trajectories. The model is capable of generating full predictive distributions, making it well-suited for uncertainty-aware forecasting. It is compatible with the Chronos Python package and integrates easily into forecasting pipelines using PyTorch. Chronos models are open-source under Apache 2.0 and have been demonstrated to perform competitively in forecasting benchmarks.
