AI Audio2026
An end-to-end 11B full-duplex speech model for real-time conversational AI that jointly performs streaming speech understanding and generation, enabling ~450 ms turn-taking, barge‑in and live tool calling in a single unified architecture; research use only.
Retrieves short speech segments from MEG recordings with a compact interpretable neural decoder trained against wav2vec 2.0 embeddings, and maps decoder weights to cortical source space to reveal which acoustic and linguistic features drive retrieval.
Supervises audio reasoning by generating per-sample, audio-grounded rubrics that evolve with model rollouts and serve as reinforcement-learning rewards, improving perception and adaptive multi-step reasoning while avoiding reward saturation.
Generates multi‑speaker speech and environmental audio from textual instructions or a reference clip, supporting zero‑shot voice cloning and detailed scene/specification control. Combines a cleaned, captioned dataset with a VAE-based multimodal generator, reward-conditioned quality control, and staged training to improve expressiveness and multi-audio modeling.
AI Dataset2026
100-hour, single-narrator Egyptian Arabic speech corpus with 15,653 aligned clips at 24 kHz for TTS and ASR fine-tuning; studio-consistent audio, machine-generated undiacritized transcripts, CC BY-NC 4.0 (research/non-commercial use).
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 Audio2026
Generates and edits speech from natural-language instructions plus optional reference audio, supporting zero-shot TTS, content/acoustic/paralinguistic edits, enhancement, and source separation. Open-source 1.5B-parameter base model with a 4-step distilled AuK‑Flash for faster inference.
AI Audio2026
Zero-shot multilingual text-to-speech checkpoint for speech generation and voice cloning with a compact footprint. Features an ~170M-parameter main model plus a bundled ~120M-parameter codec decoder, with primary support for Chinese and English; other languages show more variable quality and long/noisy references reduce fidelity.
AI Model2026
Open-weights LLM fine-tuned for phone-based voice agents that prioritizes low latency and reliable tool/function calling. Based on NVIDIA Nemotron 3 Nano (30B total, 3.5B active), supports very long contexts (262,144 tokens) and recommends temperature=0 with thinking disabled for deployment.
AI Video Papers2026
Generates enterable omnimodal world-model rollouts that follow continuous 6-DoF camera control while jointly producing 720p video, environmental sound, music and speech. Uses dataset-level motion calibration, a specialized data engine, progressive training and autoregressive post-training to support long-horizon first- and third-person interaction.
AI Audio2026
Generates low-latency, instruction-driven English and Chinese speech for voice cloning, voice design, and directed performances; supports real-time streaming, reference-free voice creation, and reference-guided cloning. Open-weight PyTorch model released under a research/non-commercial license with GPU recommendations.
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.