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Hey folks, maybe a really dumb question from an inexperienced PhD student. I recently submitted to ICLR and spotted a bunch of papers that I was already familiar with because I had seen them on arXiv before. Doesn't this break the double-blind review process? I mean, on the one hand, people pre-publishing their work want to establish priority as quickly as possible, but on the other hand, they still want their papers to be treated as valid submissions in a double-blind review system. I generally avoid pre-publishing my work for this reason, although this can cause some trouble when it comes to
I've been learning how language models work, especially next-token prediction, and came across this paper on Next-Concept Modeling (NCM). The interesting part is how it generates text. Instead of predicting tokens one by one, NCM learns a hierarchy of discrete codes. It generates the coarse level first, then predicts the codes at each finer level in parallel. A decoder finally converts those representations into text. The researchers report up to 31x faster generation compared with diffusion language model baselines on specific benchmarks. I'm still learning about the architecture, especially
TL;DR: When Coqui AI shut down, they left XTTS v2 without official training code for the DVAE audio tokenizer and Latent HiFi-GAN vocoder, and fine-tuning new scripts (Arabic, Indic, etc.) broke on fixed vocabularies. I built Open-XTTS-Trainer — an open-source, modular 3-component training suite under MPL-2.0 with verified GPU loss convergence on real human speech and a 1-click Colab demo. Why I Built This XTTS v2 is one of the best local zero-shot TTS models we have, but training/adapting it has been painful since Coqui closed: Missing Component Recipes: Only GPT fine-tuning was commonly avai
A while back I posted SteerTTS v1, a small English TTS you steer with numbers (https://huggingface.co/WhissleAI/steer-tts). Today I am releasing the edge variant: https://huggingface.co/WhissleAI/steer-tts-edge Size: everything an app has to ship (weights, voices, text front end and Python runtime) totals 11.84 MB. How: Acoustic model (9.25M params, attention + bidirectional Mamba-3) is quantization-aware trained to int4 6.2M-param Vocos-style vocoder is distilled from charactr/vocos-mel-24khz, also int4 QAT Text front end is pure numpy, with no NLTK, g2p_en, torchaudio or num2words at runtime
