뉴스 및 전체 검색
뉴스, 도구, 모델, 가이드, 주제, 워크플로, 프롬프트를 함께 검색합니다.
AI 모델 149
“Transcript AI”
Geneformer
Geneformer is a foundational transformer model pretrained on a large-scale corpus of human single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology. Geneformer is a foundational transformer model pretrained on a large-scale corpus of single cell transcriptomes representing a broad range of human tissues. Geneformer V1 was originally pretrained in June 2021 on Genecorpus-30M, a corpus comprised of ~30 million human single cell transcriptomes. We excluded cells with high mutational burdens (e.g. malignant cells and immortalized cell lines) that could lead to substantial network rewiring without companion genome sequencing to facilitate interpretation. The current updated Geneformer V2 is pretrained on ~104 million human single cell transcriptomes (non-cancer) from Genecorpus-104M. The cancer continual learning V2 variant was continually pretrained on ~14 million cancer transcriptomes to yield a cancer domain-tuned model. Each single cell’s transcriptome is presented to the model as a rank value encoding where genes are ranked by their expression in that cell scaled by their expression across the entire Genecorpus (~30M for V1, ~104M for V2). The rank value encoding provides a nonparametric representation of that cell’s transcriptome and takes advantage of the many observations of each gene’s expression across the pretraining corpus to prioritize genes that distinguish cell state. Specifically, this method will deprioritize ubiquitously highly-expressed housekeeping genes by scaling them to a lower rank. Conversely, genes such as transcription factors that may be lowly expressed when they are expressed but highly distinguish cell state will move to a higher rank within the encoding. Furthermore, this rank-based approach may be more robust against technical artifacts that may systematically bias the absolute transcript counts value while the overall relative ranking of genes within each cell remains more stable.
whisper-large-v2-gguf
GGUF conversions of openai/whisper-large-v2 for use with transcribe.cpp. Ported from upstream commit ae46427, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper large-v2 — converted to GGUF for transcribe.cpp. Multilingual transcription, language detection, and speech translation (audio in any supported language → English text). Encoder-decoder transformer; 30-second windows with chunked long-form decoding.
moonshine-streaming-medium-gguf
Ported from upstream commit 57b8436, pinned 2026-05-06. Validated against the HF Transformers v5.7.0 reference at transcribe.cpp commit 0d312ce on 2026-05-06. English speech-to-text in both one-shot and streaming modes. An encoder-decoder ASR model designed for streaming use (ergodic encoder + sliding-window attention, 50 Hz time-domain frontend). Same family as moonshine-streaming-tiny and moonshine-streaming-small; deepest of the three (14 / 14 layers) and widest hidden dims (encoder 768 / decoder 640). Takes a 16 kHz mono WAV and produces a transcript. No translation, no multilingual capability, no timestamps. WER on the full LibriSpeech test-clean split (2,620 utterances). Figures without a commit were published before provenance was recorded.
whisper-base-gguf
Ported from upstream commit e37978b, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper base — converted to GGUF for transcribe.cpp. Multilingual transcription, language detection, and speech translation (audio in any supported language → English text). Encoder-decoder transformer; 30-second windows with chunked long-form decoding. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.
Voxtral-Small-24B-2507-gguf
GGUF conversions of mistralai/Voxtral-Small-24B-2507 for use with transcribe.cpp. Ported from upstream commit da5b424, pinned 2026-06-05. Validated against the Transformers reference at transcribe.cpp commit dac22fa on 2026-06-05. Offline audio-LLM speech-to-text and speech translation. A Whisper-large-v3 bidirectional audio encoder feeds a 4-frame-group projector (375 audio tokens per 30 s chunk) into a Mistral-Small-24B causal LM (40 layers, GQA 32/8, NEOX RoPE, SwiGLU) via audio-token injection. Takes a 16 kHz mono WAV and produces a transcript via greedy decoding. The larger sibling of Voxtral Mini 3B — same encoder, projector, frontend, and tokenizer, with a scaled-up decoder.
NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF
NVIDIA Nemotron 3 Nano Omni is a multimodal large language model that unifies video, audio, image, and text understanding to support enterprise-grade Q&A, summarization, transcription, and document intelligence workflows. It extends the Nemotron Nano family with integrated video+speech comprehension, Graphical User Interface (GUI), Optical Character Recognition (OCR), and speech transcription capabilities, enabling end-to-end processing of rich enterprise content such as meeting recordings, M&E assets, training videos, and complex business documents. NVIDIA Nemotron 3 Nano Omni was developed by NVIDIA as part of the Nemotron model family. This model was improved using Qwen3-VL-30B-A3B-Instruct, Qwen3.5-122B-A10B, Qwen3.5-397B-A17B, Qwen2.5-VL-72B-Instruct, and gpt-oss-120b. For more information, please see the Training Dataset section below. This model is designed for enterprise customers requiring multimodal understanding capabilities. Expected users include: - Customer service applications (e.g., Doordash video of drop-off at a given address via OCR, drive-thru order verification) - Media and Entertainment (M&E) — video and speech analysis, dense captions, video search and summarization - Document intelligence for AI assistants (contracts, SOW/MSA, scientific discovery, financial documents) - GUI automation for AI agentic applications (incident management, agentic search, browser agents, email agents)
CrisperWhisper2.0_medium
**The most accurate verbatim speech recognition you can run in production: controllable, multilingual, and timed to the word.** Most speech-to-text systems never actually decide whether to write down what was *said* or what was *meant*. They inherit that choice from their training data and apply it inconsistently. CrisperWhisper 2.0 makes it an explicit, controllable choice. One recording, two transcripts: > **Verbatim**, exactly what was said, in one consistent format: > `[um] so we we need to, to reschedule the th- thursday meeting to [uh] march third at nine thirty [laughter]` > > **Intended**, the clean version the speaker meant, with numbers, dates, > and emails formatted the way you'd write them: > `So we need to reschedule the Thursday meeting to March 3 at 9:30.`
Breeze-ASR-26
**BreezeASR-Taigi** is a Taiwanese Hokkien (Taigi / 台語) automatic speech recognition (ASR) model developed as part of the **Breeze Taigi** framework — a comprehensive framework centered on standardized benchmarks and evaluation methodologies for Taiwanese Hokkien speech technologies. Taiwanese Hokkien, also known as Taigi, is a critically important language with deep historical and cultural significance in Taiwan. While significant progress has been made in speech recognition for languages such as English and Mandarin Chinese, developing effective speech technologies for Taigi presents both opportunities and methodological challenges due to its complex phonological system, significant geographical accents, and diverse forms of Chinese character writing. BreezeASR-Taigi is built by fine-tuning the **Whisper** multilingual speech recognition framework on approximately **10,000 hours** of large-scale synthetic Taiwanese Hokkien speech data. The model transcribes spoken Taigi audio and outputs **Mandarin Chinese character** transcriptions, leveraging the substantial lexical overlap between Taigi and Mandarin for a pragmatic and reproducible evaluation framework.
LightOnOCR-3-4B-GGUF
> **LightOnOCR-3-4B**, developed by lightonai, is the largest and most accurate model in the LightOnOCR-3 family of end-to-end OCR vision-language models, built on the Qwen3.5-4B architecture and released under the Apache 2.0 license. Recommended for most OCR tasks, it serves as a powerful, single-model alternative to complex document understanding pipelines by combining high-quality text transcription (triggered by an empty prompt) with advanced visual understanding features. When used with the `grounding` prompt, the model outputs labeled bounding boxes for all document elements, generates short descriptions for images, and extracts numerical data from charts into structured HTML tables. Highly versatile and optimized for seamless integration with Transformers and vLLM, it excels at processing complex layouts—including tables, receipts, forms, multi-column documents, and math notation—and performs best when documents are preprocessed at 400 DPI with a target longest dimension of 2048px.
mimo-asr-GGUF
GGUF conversion of `XiaomiMiMo/MiMo-V2.5-ASR` for **CrispStrobe/CrispASR**. Pure C++ inference — no Python, no Transformers, runs on Apple Silicon (Metal), CPU, and CUDA. The runtime is functional end-to-end: greedy decode through the 36-layer Qwen2 LM, full prefill + step-decode KV-cached graphs, prompt construction matching the upstream `MimoAudio.asr_sft` reference exactly. JFK transcription test passes verbatim. The default `mimo-asr-q4_k.gguf` (re-uploaded May 2026, PLAN #60d) ships with per-LM-layer Q/K/V projections fused into a single `model.layers.{i}.attn.qkv.{weight,bias}` tensor pair, yielding ~1.7× faster per-step decode on M1 vs the prior unfused layout (3058 ms/step → 1806 ms/step on a contended-disk run; ~1.1-1.2× pure-compute on a quiet box). The CrispASR runtime auto-detects either layout: the F16 file above keeps working unchanged via the separate-Q/K/V fallback path. Re-upload of a fused F16 is queued behind disk-headroom availability.