MCP Server2025
Runs a self-hosted meeting bot and transcription API that joins Google Meet, Teams and Zoom and streams speaker-attributed transcripts in real time. Compiles meetings into a git-backed Markdown workspace and runs sandboxed agents on your infrastructure; Apache-2.0 and air-gap capable.
AI Client2025
Turns web reading into an in-context language-learning experience by injecting context-aware translations, explanations, subtitle translation, and TTS directly into the browser. Supports selection translation, batch requests and configurable AI providers to balance cost and quality.
AI Dataset2025
Mohammad Javad Ranjbar Kalahroodi, Heshaam Faili +1University of Tehran, Institute for Research in Fundamental Sciences (IPM)
Provides a large-scale, multi-speaker Persian speech–text corpus constructed from audiobooks for TTS, ASR, and speaker research. Includes automated alignment and quality scoring, TTS-ready subsets (thousands of hours/1M+ segments) and metadata for speaker IDs and genders — suitable for multi-speaker synthesis and voice cloning research.
AI Audio2025
Provides text-to-speech synthesis and one-shot voice cloning via compact ONNX models and a larger KittenTTS 2 speech model. Offers CPU-first inference (25–506 MB models), 47 built-in voices, multilingual output, expression tags, streaming, and a Python SDK.
AI Client2025
Private, local-first desktop assistant for voice and text conversations that runs models on your hardware. Uses local model servers (Ollama or OpenAI-compatible endpoints), offers personal memory, dictation, TTS/STT, MCP integrations and automatic redaction; free for personal use.
AI Dataset2025
Yuhang Dai, Ziyu Zhang +14Audio, Speech and Language Processing Group (ASLP@NPU), Northwestern Polytechnical University, Beijing AISHELL Technology Co., Ltd. +3
Provides a 10,000-hour Sichuanese (Chuan-Yu) speech corpus with rich annotations (timestamps, speaker age/gender/emotion, SNR, DNSMOS) and unified metadata for ASR and TTS research; includes metadata.jsonl, evaluation benchmarks, and an LLM-assisted transcription pipeline.
AI Audio2025
Build and self-host production voice agents with a drag-and-drop workflow builder, real-time telephony integration, and pluggable LLM/STT/TTS backends. Docker-first with an optional managed cloud offering for teams that want faster onboarding.
AI Audio2025
Delivers multilingual, on-device text-to-speech via ONNX Runtime with prebuilt ONNX assets and cross-platform SDKs (Python, Node, mobile); targets low-latency, privacy-preserving TTS with ready demos and 31-language support in v3.
AI Audio2026
Generates low-latency, streaming text-to-speech entirely on CPUs (no GPU or cloud API required), using an ~100M-parameter model with voice cloning and multilingual support. Optimized for low resource use (2 CPU cores, ~200ms to first audio chunk) — suited for local, privacy-sensitive, or embedded TTS.
AI Deploy2026
Orchestrates low-latency, multi-stage pipelines for omni and multimodal models by running each stage with its own scheduler and using zero-copy shared memory for tensor transfer. Emphasizes per-stage bottleneck tuning and OpenAI-compatible streaming endpoints, suitable for TTS and multimodal serving.
AI Dataset2026
Provides open ASR and TTS speech data for 24 Sub‑Saharan African languages to train and evaluate speech models. Includes ~1,250 hours of transcribed ASR and ~235 hours of single‑speaker TTS with train/validation/test/unlabeled splits and mixed CC-BY licenses.
MCP Server2026
Extracts derived keys from running WeChat 4.x processes to decrypt SQLCipher 4 databases and .dat media files, and provides a real-time message monitor with a Web UI. Cross-platform (Windows/Linux/macOS) but requires process-memory or local-data access and is intended for decrypting your own WeChat data only.