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s2t-small-librispeech-asr

`s2t-small-librispeech-asr` is a Speech to Text Transformer (S2T) model trained for automatic speech recognition (ASR). The S2T model was proposed in this paper and released in this repository S2T is an end-to-end sequence-to-sequence transformer model. It is trained with standard autoregressive cross-entropy loss and generates the transcripts autoregressively. This model can be used for end-to-end speech recognition (ASR). See the model hub to look for other S2T checkpoints.

LightOnOCR-3-0.8B-GGUF

> LightOnOCR-3-0.8B, developed by lightonai, is the lightest and most efficient model in the LightOnOCR-3 family, designed for end-to-end Optical Character Recognition (OCR) and comprehensive document understanding. Built on the Qwen3.5 vision-language architecture, it streamlines complex document pipelines into a single model featuring two primary modes: a default transcription mode that outputs full-page Markdown text, and a "grounding" mode that returns bounding box coordinates and semantic labels for every visual element. Beyond standard text extraction, the model provides advanced visual understanding by automatically describing images and converting charts into structured HTML data tables. Capable of processing diverse formats—including tables, receipts, forms, multi-column layouts, and math notation—LightOnOCR-3-0.8B offers a fast, versatile, and Apache 2.0-licensed solution for both commercial and research document processing tasks.

kazakh-whisper-large-v3-turbo

Kazakh Whisper Large-v3 Turbo is a Whisper-based speech-to-text model fine-tuned specifically for Kazakh using a large-scale mixture of public speech datasets. The model is built on OpenAI's Whisper Large-v3 Turbo architecture and trained on over **841,000 speech-transcript pairs** representing approximately **1,500+ hours of Kazakh speech** collected from multiple public sources. The resulting checkpoint is a merged full Transformers model that can be used directly without LoRA adapters or custom loading code.

stt-2.6b-en-trfs

This is a model for streaming speech-to-text (STT, also known as automatic speech recognition, ASR). Unlike offline speech-to-text, where the model needs the entire audio to produce the transcript, our model starts to output the transcript as soon as a few seconds of audio become available. The model architecture is a Transformer that consumes audio tokenized by Mimi (see the Moshi paper) and outputs text tokens. The frame rate is 12.5 Hz and each audio frame is represented by 32 audio tokens. We release two models: - `kyutai/stt-1b-en_fr`, an English and French model with ~1B parameters, a 0.5 second delay, and a semantic VAD. - `kyutai/stt-2.6b-en`, an English-only model with ~2.6B parameters and a 2.5 second delay.

LFM2-2.6B-Transcript-GGUF

granite-speech-5.0-470m-turboctc

> 🎉 **NEW (October 21, 2026): Granite Speech 5.0 TurboCTC is now multilingual!** > 🌍 **7 Languages** (En, Fr, De, Es, Pt, It, Ja) > ❓ **Native punctuation & capitalization** > 🚀 **Better English WER (5.51% vs. 5.78%)** > ⏪ **Previous English-only release: revision 947f59a** **Model Summary:** Granite Speech 5.0 TurboCTC is a compact 470 million parameter multilingual ASR model for English, French, German, Spanish, Portuguese, Italian and Japanese with very high inference speed that is well suited for deployment on laptops, smartphones and other edge devices. The model consists of a conformer acoustic encoder with block self-attention, self-conditioning and temporal downsampling with an output layer corresponding to 16,384 multilingual BPE units. It was trained on approximately 153,000 hours of multilingual audio from public corpora using Connectionist Temporal Classification (CTC) and inference is done non-autoregressively with greedy decoding. The model outputs punctuated and capitalized transcripts. We evaluated granite-speech-5.0-470m-turboctc on standard short-form English ASR benchmarks from the Open ASR leaderboard. Despite adding six languages and native punctuation, the multilingual update improves the average English WER over the original English-only release (5.51% vs. 5.78%):

Whisper-Hindi2Hinglish-Apex

1. **Hinglish as a language**: Native ability to transcribe hindi into hinglish, making it easier to understand and process. 2. **Faster Inference**: The model achieves SOTA performance while being upto 8x faster. 3. **Performance Increase**: ~42% average performance increase versus pretrained model across benchmarking datasets. 4. Ranked #1 on Speech-To-Text Arena, beating competitors on transcription preference metric. ***Note***: - *To accurately measure Hinglish transcription performance, the original Hindi ground truth for each dataset was first transliterated to Hinglish. The WER scores below were calculated against this transliterated reference text.* - *To check our model's real-world performance against other SOTA models please head to our Speech-To-Text Arena arena space.* Flash-Attention 2 can be used to make the transcription fast. If your GPU supports Flash-Attention you can use it by, first installing Flash Attention:

title

On-device titles and descriptions: a short factual title and a one- to two-sentence description for any passage of text. Get a title and a one or two sentence description for any passage of text, on device. Title is fine-tuned on transcript clips, but it works on any prose. The register is deliberately plain, with no emoji, no hashtags and no clickbait, and a description identifies *this* passage rather than its topic.

parakeet-tdt-0.6b-v3

`parakeet-tdt-0.6b-v3` is a 600-million-parameter multilingual automatic speech recognition (ASR) model designed for high-throughput speech-to-text transcription. It extends the parakeet-tdt-0.6b-v2 model by expanding language support from English to 25 European languages. The model automatically detects the language of the audio and transcribes it without requiring additional prompting. It is part of a series of models that leverage the Granary [1, 2] multilingual corpus as their primary training dataset. **Supported Languages:** Bulgarian (**bg**), Croatian (**hr**), Czech (**cs**), Danish (**da**), Dutch (**nl**), English (**en**), Estonian (**et**), Finnish (**fi**), French (**fr**), German (**de**), Greek (**el**), Hungarian (**hu**), Italian (**it**), Latvian (**lv**), Lithuanian (**lt**), Maltese (**mt**), Polish (**pl**), Portuguese (**pt**), Romanian (**ro**), Slovak (**sk**), Slovenian (**sl**), Spanish (**es**), Swedish (**sv**), Russian (**ru**), Ukrainian (**uk**) `parakeet-tdt-0.6b-v3`'s key features are built on the foundation of its predecessor, parakeet-tdt-0.6b-v2, and include:

SenseVoiceSmall-gguf

GGUF conversions of FunAudioLLM/SenseVoiceSmall for use with transcribe.cpp. Ported from upstream commit 3eb3b4e, pinned 2026-05-06. Validated against the FunASR reference at transcribe.cpp commit f094d28 on 2026-05-06. Offline multilingual speech-to-text in Chinese, Cantonese, English, Japanese, and Korean. A SAN-M encoder with a single CTC head over a 25,055-token SentencePiece vocabulary. Takes a 16 kHz mono WAV (capped at 30 seconds per call, per upstream's direct-inference contract) and produces a transcript. Not a streaming model, no translation, no built-in long-form chunking. The same CTC head also emits language-ID, simple emotion labels, audio-event tags, and inverse-text-normalization control tags. These tags are hidden unless `--raw-tokens` is passed. ITN is on by default for readable casing, punctuation, and digits; pass `--no-itn` for upstream's spoken-form output.