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AI 模型 97
“Transcripti”
MOSS-TTS-v1.5-GGUF
Self-contained GGUF packages of OpenMOSS-Team/MOSS-TTS-v1.5, for audio.cpp. Each file carries the 8B delay-pattern backbone, the MOSS-Audio-Tokenizer v1 codec (**both** encoder and decoder, because cloning needs the encoder), the tokenizer and the model spec — nothing else to download. q4_k is usable and clones as well as the others — but it is **not lossless on the words**. Across four takes of the same sentence at different seeds, one substituted an article ("the" for "this"); q8_0 was exact on all four. Two independent ASR models agree on that substitution, so it is the audio and not a transcription artifact. Four takes of one sentence is a small sample and the true rate is not pinned down by it. Treat q4_k as the option for when 9.3 GB versus 13.2 GB decides whether the model runs at all, and prefer q8_0 when it fits.
MERaLiON-3-3B-ASR
**MERaLiON-3-ASR** is the speech-recognition line in the MERaLiON-3 generation of Speech-Text Large Language Models, developed by I2R, A\*STAR, Singapore. It is purpose-built for Singapore- and Southeast-Asia-centric ASR, with broad coverage across regional languages, dialects, and natural conversational code-switching. - **Languages:** English (Global and Singapore), Mandarin, Malay, Tamil, Indonesian, Thai, Vietnamese - **Chinese dialects:** Cantonese, Hokkien - **Code-switching:** natural conversational English ↔ {Mandarin, Malay, Tamil, Vietnamese, Cantonese, Hokkien} mixtures, including Singlish **MERaLiON-3-ASR-API** is the hosted production endpoint, tuned for real-time and enterprise transcription workloads.
wav2vec2-large-robust-ft-libritts-voxpopuli
This checkpoint is a wav2vec2-large model that is useful for generating transcriptions with punctuation. It is intended for use in building transcriptions for TTS models, where punctuation is very important for prosody. This model was created by fine-tuning the `facebook/wav2vec2-large-robust-ft-libri-960h` checkpoint on the libritts and voxpopuli datasets with a new vocabulary that includes punctuation. The model gets a respectable WER of 4.45% on the librispeech validation set. The baseline, `facebook/wav2vec2-large-robust-ft-libri-960h`, got 4.3%.
CrisperWhisper
**CrisperWhisper** is an advanced variant of OpenAI's Whisper, designed for fast, precise, and verbatim speech recognition with accurate (**crisp**) word-level timestamps. Unlike the original Whisper, which tends to omit disfluencies and follows more of a intended transcription style, CrisperWhisper aims to transcribe every spoken word exactly as it is, including fillers, pauses, stutters and false starts. Checkout our repo for more details: https://github.com/nyrahealth/CrisperWhisper CrisperWhisper significantly outperforms Whisper Large v3, especially on datasets that have a more verbatim transcription style in the ground truth, such as AMI and TED-LIUM. CrisperWhisper demonstrates superior performance segmentation performance. This performance gap is especially pronounced around disfluencies and pauses. The following table uses the metrics as defined in the paper. For this table we used a collar of 50ms. Heads for each Model were selected using the method described in the How section and the result attaining the highest F1 Score was choosen for each model using varying number of heads.
granite-4.0-1b-speech
**Model Summary:** Granite-4.0-1b-speech is a compact and efficient speech-language model, specifically designed for multilingual automatic speech recognition (ASR) and bidirectional automatic speech translation (AST). The model was trained on a collection of public corpora comprising of diverse datasets for ASR and AST as well as synthetic datasets tailored to support Japanese ASR, keyword-biased ASR and speech translation. Granite-4.0-1b-speech was trained by modality aligning granite-4.0-1b-base to speech on publicly available open source corpora containing audio inputs and text targets. Compared to granite-speech-3.3-2b and granite-speech-3.3-8b, this model has the following additional capabilities and improvements: * Supports multilingual speech inputs in English, French, German, Spanish, Portuguese and Japanese, * Provides higher transcription accuracy for English ASR and faster inference through better encoder training and speculative decoding, * Has half the number of parameters of granite-speech-3.3-2b for running on resource-constrained devices, * Adds keyword list biasing capability for enhanced name and acronym recognition We evaluated granite-4.0-1b-speech alongside other speech-language models in the less than 8b parameter range as well as dedicated ASR and AST systems on standard benchmarks. The evaluation spanned multiple public benchmarks, with particular emphasis on English ASR tasks while also including multilingual ASR and AST for X-En and En-X translations.
MOSS-Transcribe-Diarize-HF
MOSS-Transcribe-Diarize 0.9B is an end-to-end audio understanding model for long-form multi-speaker transcription, diarization, timestamps, and acoustic event awareness. It supports transcription and diarization across **50+** languages, single-pass inference on audio recordings up to **90 minutes** long, and custom hotword prompting for domain-specific terms. Given an audio or video file, the model generates a compact speaker-aware transcript in one pass, including timestamps and anonymous speaker labels such as `[S01]`, `[S02]`, and beyond.
LightOnOCR-3-1B
**Same architecture as LightOnOCR-2-1B, new capabilities.** LightOnOCR-3-1B keeps the architecture of **LightOn's** previous LightOnOCR-2 models, so switching requires no change, and adds the new grounding, image description and chart extraction 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.
parakeet-tdt-0.6b-v2
> **🎉 NEW: Multilingual Parakeet TDT 0.6B V3 is now available!** > 🌍 **25 European Languages** | 🚀 **Enhanced Performance** | 🔗 **Try it here: nvidia/parakeet-tdt-0.6b-v3** `parakeet-tdt-0.6b-v2` is a 600-million-parameter automatic speech recognition (ASR) model designed for high-quality English transcription, featuring support for punctuation, capitalization, and accurate timestamp prediction. Try Demo here: https://huggingface.co/spaces/nvidia/parakeet-tdt-0.6b-v2 This XL variant of the FastConformer [1] architecture integrates the TDT [2] decoder and is trained with full attention, enabling efficient transcription of audio segments up to 24 minutes in a single pass. The model achieves an RTFx of 3380 on the HF-Open-ASR leaderboard with a batch size of 128. Note: *RTFx Performance may vary depending on dataset audio duration and batch size.*
cohere-transcribe-03-2026
Native speech-to-text in the **OpenASR** runtime — engineered for peak performance on CPU & GPU, **no Python at inference time**. RTF = real-time factor on the fixed 11s JFK clip (**lower is faster**); RAM peak measured per pack in an isolated subprocess. JFK ΔWER compares each quantized build's JFK transcript to this model's fp16 JFK transcript, so it measures quantization drift rather than absolute recognition accuracy. **q8_0** is the recommended default — near-reference quality at a fraction of the footprint. Cohere Transcribe 03-2026 is Cohere and Cohere Labs' open release of a 2B-parameter automatic speech recognition model. It is a dedicated audio-in, text-out architecture with a Conformer-based acoustic encoder and a lightweight Transformer decoder, trained from scratch for transcription. The upstream model card lists support for 14 languages across English, European, APAC, and MENA coverage and reports Apache-2.0 licensing. This OpenASR repo repackages the original `CohereLabs/cohere-transcribe-03-2026` weights as `.oasr` packs that run natively in the OpenASR runtime with no Python at inference time. For most users the q8_0 build is the recommended default; q4_k is for tighter memory budgets and fp16 is for verification or maximum fidelity.
SenseVoiceSmall-GGUF
GGUF build of **SenseVoiceSmall** (SAN-M encoder + CTC) for the zero-Python, CPU/edge **FunASR llama.cpp runtime** — multilingual ASR with language / emotion / event tags, **~20× real-time on CPU**. These are GGUF weights for the **FunASR llama.cpp runtime** — a whisper.cpp-style, single self-contained binary for CPU / edge. Grab a prebuilt binary, then fetch this model and run: The binary prints **transcription text** directly (no Python detok). `--ids` for raw ids / `--keep-tags` for the lang/emotion tags.