Category
Explore by categories
Provides an AI-driven English learning app suite (Enjoy) that focuses on speaking practice and pronunciation evaluation. Open-source repo backing a web app, browser extensions for YouTube/Netflix, and a local-first desktop/web client design; some scoring features require the project's paid Enjoy AI service.
Build, fine-tune, and deploy speech AI on NVIDIA GPUs: ASR, text-to-speech, and speech LLMs in one PyTorch stack. Ships pretrained Parakeet/Canary recognition and Magpie TTS checkpoints; broader LLM/multimodal training now lives in v2.7.0.
Provides a toolkit and codebase for building, training, and deploying speech and multimodal models — Automatic Speech Recognition, Text-to-Speech, and speech-aware LLMs — with modular neural components and pre-trained checkpoints for PyTorch. Supports streaming/low-latency inference, multi-language models, and optional compiled kernels for acceleration.
Extracts vocals and instrumentals from audio using an ensemble of models — MDX-Net/MDX23C, Demucs v3/v4, and the VR architecture. Runs locally via a Tkinter GUI with GPU acceleration across Nvidia, AMD, Intel, and Apple chips.
edge-tts is a Python module that enables the use of Microsoft Edge's online text-to-speech service directly from Python code or via command-line tools like edge-tts and edge-playback, without requiring Microsoft Edge, Windows, or an API key.
Multilingual sequence-to-sequence speech model and toolkit for speech recognition, speech-to-text translation, and language identification. Offers several model sizes (tiny → large/turbo) for different speed/accuracy trade-offs and ships with a CLI and Python API for offline transcription workflows.
Bundles ASR, voice activity detection, punctuation, and speaker diarization into one pipeline, with pretrained models like Paraformer and SenseVoice. SenseVoice runs ~17x realtime on CPU; also ships streaming ASR and an OpenAI-compatible API.
Create and run node-based generative AI workflows for images, video, 3D, and audio — reusable, shareable node graphs with custom nodes, live previews, and local/cloud runtime options. Open-source with Comfy Cloud and Hub for creators.
Reimplements OpenAI's Whisper speech-to-text on the CTranslate2 inference engine, running up to 4x faster at the same accuracy while using less memory. Adds a batched pipeline, 8-bit quantization, VAD filtering, and word-level timestamps.
Web UI to train and run retrieval-based voice conversion models from small datasets (≈10 minutes), featuring top-1 feature retrieval to avoid timbre leakage, model fusion, real-time conversion, vocal separation, and multi-hardware support.
Converts microphone or streamed audio to text with sub-second latency, pairing WebRTC/Silero voice-activity detection and wake-word activation with swappable local backends — faster-whisper by default, plus whisper.cpp, Moonshine, and sherpa-onnx.