AI Audio2026
Desktop app for local voice cloning, real-time dictation, and end-to-end video dubbing using zero-shot TTS across 600+ languages; features multi-engine TTS/ASR, speaker diarization, vocal isolation, batch pipelines, and invisible audio watermarking — all run fully offline.
AI Client2026
A local-first web and desktop dashboard for Hermes Agent that runs streamed agent chats, manages profiles/providers/models/credentials, schedules cron jobs, and inspects files and terminals across local, Docker, SSH and Singularity backends.
AI Audio2026
Converts long-form multi-speaker audio/video into a compact, speaker-aware transcript with timestamps and anonymous speaker labels in one pass. Combines ASR and diarization in a single model, supports custom prompts/hotwords, and targets meetings, podcasts, interviews and long recordings.
AI Model2026
Adds discrete audio tokens and an audio encoder to a 30B MoE text LLM so a single model can perform ASR, speech translation, TTS, text-to-audio and speech-to-speech while preserving text reasoning and long-context capabilities; supports thinking/instruct modes and up to 1M-token context.
AI Audio2026
Transcribes Arabic speech to text using a CohereLabs-trained ASR model compatible with the Hugging Face Transformers pipeline. Provides safetensors weights, endpoint compatibility and a DOI-tagged release; suitable for Arabic transcription workflows but may require adaptation for diverse dialects or noisy audio.
AI Audio2026
Multilingual, real-time ASR for edge CPUs that uses heterogeneous quantization to reduce model size (4.62→1.58 GB) and lower inference latency. Trades some accuracy for 1.6–2.3× faster inference vs. Whisper.cpp and real-time capability on a few CPU threads, making it suitable for memory- and compute-constrained on-device transcription.
AI Model2026
Converts raw ASR transcripts into clean written text: adds punctuation and capitalization, expands spoken numbers/dates/times/currencies/emails, removes fillers and resolves self-corrections. Fine-tuned from Qwen3-0.6B (≈0.6B params), 94.8% token accuracy on a 7,519-case English test set; designed for CPU/edge deployment and deterministic post-processing.
AI Dataset2026
Provides 22.7 hours of read Amharic speech (7,405 clips, 320 speakers) for ASR, collected via a crowdsourced Telegram bot and peer-validated; speaker- and prompt-disjoint train/validation/test splits, 16 kHz audio under CC BY 4.0.