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End-to-end speech recognition large model: 31 languages, dialects, accents, lyrics, hotwords, timestamps, speaker diarization. Trained on tens of millions of hours.
The main features of funaudiollm/fun-asr are: Language Model Development, Audio and Speech Models.
Projects with overlapping indexed features include: oxford-cs-deepnlp-2017/lectures — This repository is a deep learning for natural language processing course and curriculum. It provides educational… snakers4/silero-vad — Silero VAD is a voice activity detection model and deep learning speech classifier designed to distinguish human… ml-explore/mlx-examples — This repository provides a collection of reference implementations and code examples for training and deploying… andrewyng/aisuite — This project is a framework for managing generative AI services through a unified provider interface and adapter… dust-tt/dust — Custom AI agent platform to speed up your work. deepset-ai/haystack — Haystack is an orchestration framework designed for building complex search and generative AI pipelines. It functions…
This repository is a deep learning for natural language processing course and curriculum. It provides educational material and guides focused on neural network architectures used for processing natural language, speech signals, and text classification. The content includes instructional tutorials on sequence modeling and neural language modeling, covering the implementation of n-gram and recurrent neural networks. It also provides a framework for studying word embeddings to map linguistic meanings into numerical representations. The curriculum covers a broad range of capabilities, including
Silero VAD is a voice activity detection model and deep learning speech classifier designed to distinguish human speech from silence across diverse languages and noisy environments. It functions as a pre-trained neural network capable of identifying speech segments within both static audio recordings and real-time data streams. The project includes a language identification tool for classifying spoken languages and a framework for fine-tuning audio models. It provides utilities for optimizing detection thresholds using validation datasets and retraining the model with custom labeled audio to
This repository provides a collection of reference implementations and code examples for training and deploying machine learning models using the MLX framework. It serves as a practical guide for executing distributed training, fine-tuning large language models, converting model weights, and implementing multimodal generative workflows. The project distinguishes itself through specialized examples for local hardware execution, featuring weight quantization to reduce memory usage and low-rank adaptation for parameter-efficient fine-tuning. It also includes scripts for transforming external mod
This project is a framework for managing generative AI services through a unified provider interface and adapter layer. It provides a standardized API for calling multiple cloud-based and locally hosted models, translating provider-specific parameters and responses into a uniform format. The system includes an agent orchestrator designed for long-running tasks, featuring state persistence for resuming runs and execution tracing to monitor decision-making processes. It integrates the Model Context Protocol to connect models to external servers and filesystems and employs a policy-based executi