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AI 모델 535
- whisper-small-uzbek↗
navai-uz
- whisper-large-v2-taiwanese-hakka-v1↗
formospeech
- gemini_whisper_ipa↗
thunderboltc
- lite-whisper-large-v3-turbo-fast↗
efficient-speech
- Voxtral-Small-24B-2507-gguf↗
handy-computer
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.
- tarteel-ai-onnx-whisper-base-ar-quran↗
eventhorizon0
- kotoba-whisper-v2.0-faster↗
kotoba-tech
This repository contains the conversion of kotoba-tech/kotoba-whisper-v2.0 to the CTranslate2 model format. We measure the inference speed of different kotoba-whisper-v2.0 implementations with four different Japanese speech audio on MacBook Pro with the following spec: - Apple M2 Pro - 32GB - 14-inch, 2023 - OS Sonoma Version 14.4.1 (23E224) Also, currently whisper.cpp and faster-whisper support the sequential long-form decoding, and only Huggingface pipeline supports the chunked long-form decoding, which we empirically found better than the sequnential long-form decoding.
- CrisperWhisper2.0_medium↗
nyralabs
**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↗
MediaTek-Research
**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.
- whispernow-lfm2.5-1.2b-GGUF↗
mradermacher