뉴스 및 전체 검색
뉴스, 도구, 모델, 가이드, 주제, 워크플로, 프롬프트를 함께 검색합니다.
AI 모델 97
“Transcripti”
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
canary-1b-v2
**``Canary-1b-v2``** is a powerful 1-billion parameter model built for high-quality speech transcription and translation across 25 European languages. It excels at both automatic speech recognition (ASR) and speech translation (AST), supporting: * **Speech Transcription (ASR) for 25 languages** * **Speech Translation (AST) from English → 24 languages** * **Speech Translation (AST) from 24 languages → English**
gemma-4-E4B-it-heretic
A decensored variant of google/gemma-4-E4B-it, produced with Heretic v1.4.0 (directional ablation / "abliteration"). Google's Gemma 4 E4B is an 8B-class multimodal MoE that takes text, image, video, and audio and generates text, with a 128K context and configurable thinking modes; refusal behaviour is suppressed here via targeted weight edits to the attention output and MLP down-projections rather than fine-tuning, so the model's multimodal perception and reasoning are left largely intact. **Who this is for:** developers who want Gemma 4's multimodal range — image, video, and audio input — without the alignment refusals, on hardware they already own. E4B is the sweet spot in the family: it runs on-device on a laptop, handles 128K context, supports thinking modes, and here answers directly instead of declining. Best for local document/UI/screenshot analysis, audio transcription and understanding, and multilingual assistant work over 140 languages. Weights only, at this model's native 8B size; add ~1 GB per 32K of context. Because this is a mixture-of-experts model, memory *bandwidth* matters far more than raw VRAM — a CPU with fast RAM often outruns a slow GPU. OOM? Drop one quant level. Headroom to spare? Go one up.
Qwen3-ASR-1.7B-hf
The Qwen3-ASR family includes **Qwen3-ASR-1.7B** and **Qwen3-ASR-0.6B**, which support language identification and ASR for 52 languages and dialects. Both leverage large-scale speech training data and the strong audio understanding capability of their foundation model, Qwen3-Omni. The 1.7B version achieves state-of-the-art performance among open-source ASR models and is competitive with the strongest proprietary commercial APIs. - **All-in-one:** Supports language identification and speech recognition for 30 languages and 22 Chinese dialects, including English accents from multiple countries and regions. - **Excellent and Fast:** High-quality and robust recognition under complex acoustic environments. Qwen3-ASR-0.6B reaches 2000× throughput at a concurrency of 128. Both models support streaming/offline unified inference with a single model and handle long audio. - **Forced Alignment:** Qwen3-ForcedAligner-0.6B supports timestamp prediction for arbitrary units within up to 5 minutes of speech in 11 languages, surpassing E2E-based forced-alignment models in accuracy. `apply_transcription_request` handles chat-template formatting for you and is the recommended entry point.
granite-speech-3.3-2b
**Model Summary:** Granite-speech-3.3-2b is a compact and efficient speech-language model, specifically designed for automatic speech recognition (ASR) and automatic speech translation (AST). Granite-speech-3.3-2b uses a two-pass design, unlike integrated models that combine speech and language into a single pass. Initial calls to granite-speech-3.3-2b will transcribe audio files into text. To process the transcribed text using the underlying Granite language model, users must make a second call as each step must be explicitly initiated. The model was trained on a collection of public corpora comprising diverse datasets for ASR and AST as well as synthetic datasets tailored to support the speech translation task. Granite-speech-3.3-2b was trained by modality aligning granite-3.3-2b-instruct (https://huggingface.co/ibm-granite/granite-3.3-2b-instruct) to speech on publicly available open source corpora containing audio inputs and text targets. Compared to the initial release, revision 3.3.2 * supports multilingual speech inputs in English, French, German, Spanish and Portuguese, * provides transcription accuracy improvements for English ASR by using a deeper acoustic encoder and additional training data.
whisper-tiny-gguf
Ported from upstream commit 169d4a4, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper tiny — 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.
Audio-Flamingo-GGUF
These self-contained GGUFs package the model tensors, tokenizer, configuration, and audio.cpp model spec for Audio Flamingo 3, Audio Flamingo Next, and Music Flamingo. AF3 and Next support transcription, captioning, and questions about speech, music, and environmental sounds. Music Flamingo specializes in music understanding; it produces text, not audio. Select a GGUF with `--model`. Use a question such as `Describe the instruments and tempo.` for general audio understanding. Requested timestamps or speaker descriptions are generated text, not structured alignment or diarization results. For server use, configure family `audio_flamingo`, task `asr`, and mode `offline`, then submit audio to `/v1/audio/transcriptions` with the instruction in `prompt`.
distil-large-v3
Distil-Whisper was proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling. This is the third and final installment of the Distil-Whisper English series. It the knowledge distilled version of OpenAI's Whisper large-v3, the latest and most performant Whisper model to date. Compared to previous Distil-Whisper models, the distillation procedure for distil-large-v3 has been adapted to give **superior long-form transcription accuracy** with OpenAI's **sequential long-form algorithm**.
moonshine-tiny
Moonshine Tiny is a 27M-parameter encoder-decoder speech-recognition model from Moonshine AI (formerly Useful Sensors), introduced in Moonshine: Speech Recognition for Live Transcription and Voice Commands. It transcribes English speech and is designed for fast on-device inference. This repository packages the model for LiteRT: a float32 model, an int8 model (dynamic-range-quantized decoder with a float32 encoder), and ahead-of-time compiled variants for a range of MediaTek and Qualcomm SoCs so the model can run on the device NPU. For an on-device Android demo that runs Moonshine (and other ASR models) with hardware acceleration, see the LiteRT speech recognition sample.
wav2vec2-base-korean
This model is a fine-tuned version of Facebook's wav2vec2-base model, adapted for Korean language recognition using the Zeroth-Korean dataset. The model has been trained to transcribe Korean speech into text, specifically utilizing the unique jamo characters of the Korean language. The model can be directly used for transcribing Korean speech to text without additional fine-tuning. It is particularly useful for applications requiring accurate Korean language recognition such as voice assistants, transcription services, and language learning tools. This model can be integrated into larger systems that require speech recognition capabilities, such as automated customer service, voice-controlled applications, and more.