뉴스 및 전체 검색

Fun-ASR-Nano-2512

This model is part of the **FunASR** ecosystem — one industrial-grade open-source toolkit for **ASR · VAD · punctuation · speaker diarization · emotion / event · LLM-ASR**. A Star really helps the project (and keeps you updated): > ⚡ **CPU / edge — no GPU, no Python:** run Fun-ASR-Nano as a single self-contained binary via **llama.cpp / GGUF** (like whisper.cpp), with built-in VAD. Prebuilt binaries + one-command model download → Fun-ASR-Nano-GGUF · runtime · guide Fun-ASR is an end-to-end speech recognition large model launched by Tongyi Lab. It is trained on tens of millions of hours of real speech data, possessing powerful contextual understanding capabilities and industry adaptability. It supports low-latency real-time transcription and covers 31 languages. It excels in vertical domains such as education and finance, accurately recognizing professional terminology and industry expressions, effectively addressing challenges like "hallucination" generation and language confusion, achieving "clear hearing, understanding meaning, and accurate writing."

canary-180m-flash-gguf

GGUF conversions of nvidia/canary-180m-flash for use with transcribe.cpp. Ported from upstream commit b12ab41, pinned 2026-05-08. Validated against the NeMo reference at transcribe.cpp commit db53eda on 2026-05-08. Offline multilingual speech-to-text and translation. A multitask AED with a 17-layer FastConformer encoder and a 4-layer Transformer decoder. Supports automatic speech recognition in English, German, Spanish, and French, and bidirectional EN↔{DE, ES, FR} translation. Takes a 16 kHz mono WAV and produces a transcript. Not a streaming model; word/segment timestamps are upstream-experimental and not exposed in the v1 port.

VibeVoice-ASR-HF

**VibeVoice-ASR** is a unified speech-to-text model designed to handle **60-minute long-form audio** in a single pass, generating structured transcriptions containing **Who (Speaker), When (Timestamps), and What (Content)**, with support for **Customized Hotwords** and over **50 languages**. ➡️ **Demo:** VibeVoice-ASR-Demo ➡️ **Report:** VibeVoice-ASR Technical Report - **🕒 60-minute Single-Pass Processing**: Unlike conventional ASR models that slice audio into short chunks (often losing global context), VibeVoice ASR accepts up to **60 minutes** of continuous audio input within 64K token length. This ensures consistent speaker tracking and semantic coherence across the entire hour.

whisper-large-v3-turbo-gguf

GGUF conversions of openai/whisper-large-v3-turbo for use with transcribe.cpp. Ported from upstream commit 41f01f3, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper large-v3-turbo — converted to GGUF for transcribe.cpp. Multilingual transcription and language detection; unlike the full large-v3 model, this turbo variant does not support speech translation. The v3 family adds Cantonese (yue) and uses a 128-bin mel input. Encoder-decoder transformer; 30-second windows with chunked long-form decoding.

moonshine-streaming-small-gguf

Ported from upstream commit 2c03650, 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; deeper encoder/decoder (10 / 10 layers) and wider hidden dims (encoder 620 / decoder 512). 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.

granite-speech-4.1-2b

**Model Summary:** Granite Speech 4.1 2B is a compact and efficient speech-language model, specifically designed for multilingual automatic speech recognition (ASR) and bidirectional automatic speech translation (AST) for English, French, German, Spanish, Portuguese and Japanese. The model was trained on 174,000 hours of audio from public corpora for ASR and AST as well as synthetic datasets tailored to support Japanese ASR, keyword-biased ASR and speech translation. Granite Speech 4.1 2B was trained by modality aligning an intermediate checkpoint of granite-4.0-1b-base to speech on publicly available open source corpora containing audio inputs and text targets. Compared to its predecessor granite-4.0-1b-speech, this model has the same parameter count (the new naming convention reflects actual instead of base LLM size) and provides additional capabilities and improvements: * Higher transcription accuracy for multilingual ASR due to a novel dual-head CTC encoder with both graphemic and BPE outputs and frame importance sampling to focus on informative parts of the audio * Punctuation and truecasing for ASR and AST in all languages (including German noun capitalization) with a simple prompt change * Better keyword list biasing capability for enhanced recognition of names, acronyms and technical jargon Two additional model variants explore different capabilities and inference optimization: * granite-speech-4.1-2b-plus adds speaker-attributed ASR and word-level time stamps * granite-speech-4.1-2b-nar introduces a novel non-autoregressive architecture for higher throughput

LiquidAI.LFM2-2.6B-Transcript-GGUF

canary-qwen-2.5b-gguf

GGUF conversions of nvidia/canary-qwen-2.5b for use with transcribe.cpp. Ported from upstream commit b1469e1, pinned 2026-05-15. Validated against the NeMo SALM 2.7.3 reference at transcribe.cpp commit 6f6c699 on 2026-05-16. Offline English speech-to-text. NeMo SALM (Speech-Augmented Language Model): a FastConformer audio encoder (32 layers, `d_model=1024`) feeds audio embeddings into a Qwen3-1.7B causal LM (28 layers, `hidden_size=2048`) via audio-token injection at a sentinel position in the prompt. English only. Takes a 16 kHz mono WAV and produces a transcript via greedy decoding.

muscriptor-gguf

GGUF conversions of the MuScriptor multi-instrument music transcription weights by **Mirelo** and **Kyutai**, converted for use with `muscriptor.cpp`, a C++/ggml port of the reference implementation, and with NeuralNote. These are format conversions: safetensors → GGUF, with the bulk weights cast to float16. Nothing was retrained or fine-tuned. All credit for the model belongs to Mirelo and Kyutai — see `NOTICE.md`. `medium` is the default. `small` is the one to reach for on a machine without a usable GPU; `large` is worth it only with one.

Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8

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. Governing Terms: Use of this model is governed by the NVIDIA Open Model Agreement