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AI 模型 2119
- MOSS-TTS-Local-Transformer-v1.5↗
OpenMOSS-Team
**MOSS-TTS-Local-Transformer-v1.5** is continued from MOSS-TTS-Local-Transformer-v1.0. It preserves the main 1.0 capabilities, including zero-shot voice cloning, long-form speech generation, token-level duration control, Pinyin/IPA pronunciation control, multilingual synthesis, and code-switching. For the full 1.0 feature walkthrough, input schema, and evaluation tables, please refer to the MOSS-TTS-Local-Transformer-v1.0 README. Compared with MOSS-TTS-Local-Transformer-v1.0, v1.5 focuses on the following improvements: - **Higher-fidelity stereo audio modeling**: v1.5 uses MOSS-Audio-Tokenizer-v2 as the audio tokenizer, supporting native 48 kHz stereo input and output for richer spatial detail and more natural perceived audio quality. Since the codec output is stereo, save the `[channels, samples]` tensor returned by `processor.decode(...)` directly. - **Stronger multilingual synthesis with language tags**: when the `language` field is omitted, v1.5 may improve some languages and regress slightly on others compared with 1.0. When the language is specified, v1.5 is stronger than 1.0 on almost all supported languages. Set the tag when building the user message, for example `processor.build_user_message(text=text_fr, language="French")`. - **More stable voice cloning**: v1.5 improves speaker similarity and reduces cloning variance, making repeated generations more consistent. - **Better long-reference, short-text cloning**: v1.5 handles scenarios where the reference audio is much longer than the target text more reliably than 1.0. - **More stable punctuation-following prosody**: v1.5 follows punctuation-driven pauses more closely, especially in long sentences. - **Explicit pause control**: v1.5 supports inline pause markers such as `"[pause 3.2s]"`. For example, `我今天学习了一首中国的古诗，它的名字是[pause 3.2s]静夜思！` inserts an explicit 3.2s pause before `静夜思`. MOSS-TTS Local Transformer v1.5 supports **31 languages**. It keeps the 20 languages supported by MOSS-TTS-Local-Transformer-v1.0 and extends multilingual continued training to additional languages including Cantonese, Dutch, Finnish, Hindi, Macedonian, Malay, Romanian, Swahili, Tagalog, Thai, and Vietnamese.
- Darwin-35B-ZTC-Vision↗
FINAL-Bench
Darwin-35B-ZTC-Vision is a vision decision model. It does not generate text. Given one or more images, a state and a typed question, it returns a calibrated probability for every option in one forward pass: yes or no (`noul`), one of several options (`choice`), or a level on an ordered scale (`score`). It handles up to four images per request, for example "which of the two photos has the higher quality" or "is there a defect on this part". It is a 35B mixture-of-experts model (about 3B active parameters per token) built on Darwin-35B-A3B-Opus with the Darwin ZTC decision readout. It is the vision member of the ZTC series; the text member is Darwin-27B-ZTC-v9. All 20 required vision benchmarks complete (the optional Box tasks are not supported), zero failed decisions. Merged by the maintainer on 2026-10-19 (PR #76). At submission it ranked first (Borda 91.00) on the published vision results of 2026-10-17 (16 complete models) with this model added, recomputed with the leaderboard's own Borda method; that table is below. More models have entered since: on the published results of 2026-10-19 (32 complete models) the same recomputation places it fifth with 81.80. The live vision leaderboard is authoritative.
- mailguard-jev-style-1.5b↗
JackKozmo29
**Local System One model for enterprise inbox triage. JEV-compatible typed decisions, no API key required.** > *Drop-in local alternative to TypeSafe Jev for inbox triage pipelines. > Returns the same typed judgment schema — swap the endpoint, keep the agent.* mailguard-jev-style-1.5b is a fine-tuned Qwen2.5-1.5B-Instruct optimized to produce **typed triage judgments** over enterprise email — matching the output contract of TypeSafe System One / Jev without requiring an API subscription.
- wav2vec2-xls-r-1b-portuguese↗
jonatasgrosman
Fine-tuned facebook/wav2vec2-xls-r-1b on Portuguese using the train and validation splits of Common Voice 8.0, CORAA, Multilingual TEDx, and Multilingual LibriSpeech. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the HuggingSound tool, and thanks to the GPU credits generously given by the OVHcloud :) Using the HuggingSound library:
- nemotron-3-diarization-coreml↗
altic-dev
Core ML export of the final `nvidia/Nemotron-3-Diarization` checkpoint (revision `98fcee8e866bb534eee85d1eb3817170700ae717`), for on-device speaker diarization on macOS 14+ / iOS 17+. Weights are stored as fp16 and widened to fp32 at load time (`constexpr_cast`), so every op computes in fp32. The package stays at fp16 size (~199 MB). Frame-level agreement with NVIDIA's NeMo reference, same streaming loop and post-processing, on real meeting recordings:
- VoiceTut-TTS-GGUF↗
mohammedaly22
**Egyptian Arabic & code-switching TTS for audio.cpp: up to 6.6× faster than the PyTorch package on the same GPU, no Python** These are standalone GGUF packages of VoiceTut-TTS, an OmniVoice fine-tune trained on ~380 hours of Egyptian podcast speech. It speaks natural Egyptian Arabic, switches between Arabic and English mid-sentence (`عندي meeting بكرة`), and clones voices zero-shot. VoiceTut has exactly the same architecture as OmniVoice, so it runs through audio.cpp's existing **`omnivoice`** family with every OmniVoice route: voice cloning, voice design, streaming, the long-text chunker, and the server.
- mlfoundations-dev_-_mistral_alpaca_sft_sample_6-gguf↗
RichardErkhov
- Hy4-preview-GGUF↗
AngelSlim
**Neither file runs on stock llama.cpp.** The `hyv4` architecture is not upstream. Apply the patches in `hy4-preview-patch/` **`Hy4-preview-Q4_K_M.gguf`** — a conventional Q4_K_M. Most tensors are Q4_K; `ffn_down_exps` gets Q6_K on 37 layers via llama.cpp's own logic. Use this unless you are memory-constrained. **`Hy4-preview-UD-IQ1_M.gguf`** - mixed precision with UD-IQ1_M strategy at ~2.44 bpw, roughly **half the size** for the same model. The routed-expert `gate`/`up` projections run at 1.75 bpw (IQ1_M) and 2.0625 bpw (IQ2_XXS).
- Silero-VAD-v5-MLX↗
aufklarer
MLX-compatible weights for Silero VAD v5, converted from the official JIT model. Silero VAD v5 is a lightweight (~309K params) voice activity detection model that processes 512-sample chunks (32ms @ 16kHz) with sub-millisecond latency. It outputs a speech probability between 0 and 1 for each chunk, with LSTM state carried across chunks for streaming operation. **Architecture:** STFT → 4×Conv1d+ReLU encoder → LSTM(128) → Conv1d decoder → sigmoid
- Qwen3.8-27B-swift-abliterated-nvfp4full-dflash2-NInfer-v3↗
kaushikvira
An **uncensored (abliterated), all-NVFP4** Qwen3.8-27B artifact for the NInfer engine, built end-to-end on a single RTX 5090: `d0xin`'s abliterated BF16 checkpoint → our own NVFP4 quantization pipeline → DFlash2 speculative drafter + indexed proposal head, packaged as a native **v3** `.ninfer` container. It is the uncensored counterpart to kaushikvira/Qwen3.8-27B-nvfp4full-dflash2-NInfer-v3 and replaced it as our production profile after a same-session A/B (below). This artifact exists only because of the following models and their authors — **all credit to them**: