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AI 모델 149
“Transcript AI”
whisper-small-gguf
GGUF conversions of openai/whisper-small for use with transcribe.cpp. Ported from upstream commit 973afd2, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper small — 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.
smart-turn-v2
**Smart Turn v2** is an open‑source semantic Voice Activity Detection (VAD) model that tells you **_whether a speaker has finished their turn_** by analysing the raw waveform, not the transcript. Compared with v1 it is: The model outputs a single probability; values ≥ 0.5 indicate the speaker has completed their utterance. Please see the blog post and GitHub repo for more information on using the model, either standalone or with Pipecat.
MEL
**Model Name:** MEL (Modelo de Español Legal) **Model Type:** Encoder-only Transformer **Language:** Spanish **Domain:** Legal Texts **Paper:** Link to paper MEL is a transformer-based language model designed specifically for processing and understanding Spanish legal texts. Built upon **XLM-RoBERTa-large**, it is further pre-trained on a **large corpus of legal documents**, including the **Boletín Oficial del Estado (BOE), parliamentary transcripts, court rulings, and other legislative texts**. MEL significantly improves the performance of legal NLP tasks, such as **legal text classification** and **named entity recognition (NER)**. MEL is trained on a **curated corpus** of **5.52 million legal texts (~92.7GB)** sourced from: - **BOE (Boletín Oficial del Estado)** - **Parliamentary records** - **Court rulings** - **Legal statutes**
canary-qwen-2.5b
NVIDIA NeMo Canary-Qwen-2.5B is an English speech recognition model that achieves state-of-the art performance on multiple English speech benchmarks. With 2.5 billion parameters and running at 418 RTFx, Canary-Qwen-2.5B supports automatic speech-to-text recognition (ASR) in English with punctuation and capitalization (PnC). The model works in two modes: as a transcription tool (ASR mode) and as an LLM (LLM mode). In ASR mode, the model is only capable of transcribing the speech into text, but does not retain any LLM-specific skills such as reasoning. In LLM mode, the model retains all of the original LLM capabilities, which can be used to post-process the transcript, e.g. summarize it or answer questions about it. In LLM mode, the model does not "understand" the raw audio anymore - only its transcript. This model is ready for commercial use. Canary-Qwen-2.5B is released under the CC-BY-4.0 license. By using this model, you are agreeing to the terms and conditions of the license. For documentation, deployment guides, enterprise-ready APIs, and the latest open models—including Nemotron and other cutting-edge speech, translation, and generative AI—visit the NVIDIA Developer Portal at developer.nvidia.com. Join the community to access tools, support, and resources to accelerate your development with NVIDIA’s NeMo, Riva, NIM, and foundation models.
tada-tts-3b-ml-GGUF
TADA-3B-ML is a multilingual text-to-speech model built on Meta Llama 3.2 3B with a flow-matching (FM) speech decoder and custom Hume codec. Key property: **1:1 token alignment** — every text token maps to exactly one speech vector, eliminating transcript hallucination. Supports 10 languages (en, es, ja, zh, de, fr, it, pt, ko, ar). 24 kHz mono output. The Q4_K file uses a TADA-aware quantization policy (tail=14): large transformer block matrices are quantized, while the last 14 token-embedding rows and all `tada.*` flow-matching tensors are kept at F16. This preserves the timing and acoustic conditioning paths where quantization noise matters most. For near-lossless quality use `tada-tts-3b-ml-q8_0.gguf`; for the F16 reference quality use `tada-tts-3b-ml-f16.gguf`.
finbert-tone
`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens. - Corporate Reports 10-K & 10-Q: 2.5B tokens - Earnings Call Transcripts: 1.3B tokens - Analyst Reports: 1.1B tokens More technical details on `FinBERT`: Click Link This released `finbert-tone` model is the `FinBERT` model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using `FinBERT` for financial tone analysis, give it a try.
Qwen3-ASR-0.6B-gguf
GGUF conversions of Qwen/Qwen3-ASR-0.6B for use with transcribe.cpp. Ported from upstream commit 5eb1441, pinned 2026-04-19. Validated against the qwen_asr 0.0.6 reference at transcribe.cpp commit 3f61df7 on 2026-04-20. Offline multilingual speech-to-text. An 18-layer bidirectional audio encoder feeds a 28-layer Qwen3 causal LM with audio-token injection (fused audio+text sequence, no cross-attention). Auto-detects the audio's language across 30 languages and emits the transcript in that language. Takes a 16 kHz mono WAV and produces a transcript.
Voxtral-Mini-4B-Realtime-2602-gguf
GGUF conversions of mistralai/Voxtral-Mini-4B-Realtime-2602 for use with transcribe.cpp. Ported from upstream commit 2769294, pinned 2026-06-06. Validated against the Transformers reference at transcribe.cpp commit 483c122 on 2026-06-06. Streaming audio-LLM speech-to-text. A ~970M causal audio encoder (left-pad causal conv stem + 32-layer sliding-window RoPE transformer) feeds a 4-frame-group projector whose audio embeddings are added onto a ~3.4B Ministral decoder (26 layers, GQA 32/8, NEOX RoPE) with delay-token latency conditioning, emitting one text token per 80 ms audio slot (12.5 Hz). Takes a 16 kHz mono WAV and supports both incremental streaming (configurable latency/quality via --stream-chunk-ms and --stream-voxtral-delay) and offline transcription using an accuracy-first 2400 ms delay. Architecturally distinct from the offline Voxtral 2507 family — own arch, streaming frontend, causal encoder, additive audio fusion.
CrisperWhisper2.0_large
**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.`
LightOnOCR-3-0.8B
**Smallest model.** LightOnOCR-3-0.8B is the lightest model of **LightOn's** LightOnOCR-3 family, with the same layout and visual understanding features. LightOnOCR-3 is a new family of highly performant lightweight OCR models. Compared to the previous generation, they bring significant improvements in speed and transcription quality and introduce new visual understanding features: the models now output bounding box coordinates with labels for all visual elements of a document, short descriptions of images, and the numerical data of figures and charts. With these capabilities, they offer a ready-to-use, easier-to-maintain alternative to complex document understanding pipelines. The models come in three sizes. The 1B keeps the LightOnOCR-2-1B architecture, while the 0.8B and 4B adopt the Qwen3.5 vision-language architecture, which simplifies integration with existing tools and brings a significant speed-up. All models are released under the Apache 2.0 license for research and commercial use.