Open-Weight Models

Model

wavlm-base-plus-sv

losCrossos

The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. - 60,000 hours of Libri-Light - 10,000 hours of GigaSpeech - 24,000 hours of VoxPopuli Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong…

This repository exports the checkpoint used as “Ours 10ms” in the historical LibriSpeech → TIMIT segmentation comparison. It includes the full trained WavLM backbone, projection, FiLM module, recognition heads, labels, and an explicitly frozen encoder adapter for PRiSM representation probing. The final checkpoint is epoch 3 / step 450 of a Libri100 alignment-CE refinement initialized from a Libri960 Subphonetic OTTC curriculum. It is not the later Libri960 recognition-PER-best epoch-17 model, and it is not an OTTC-only final stage. 中文说明：这是此前 segmentation 主表中的 S3T2 + FiLM 版本。训练链包括 Libri960 阶段和后续 Libri100 CE 校准；Libri100 是 Libri960 的子集。上传时保留 完整表示网络，供 PRiSM 冻结编码器、训练新的下游预测头。原始识别 logits 不是 PRiSM…

Model · Zero-shot classification

wazn-2b-v0.1

Numidlabs

returns a probability distribution over a set of labels you supply, each with an optional definition and few-shot examples. Nothing is generated. Experimental. For trying the model out, not for production. TODO: known weak spots, training data summary, evaluation results. The head weights are released under Apache-2.0. The backbone keeps its own license; see Qwen/Qwen3.5-2B-Base.

Model

weather-rescue-brazil-reader

Gabriel Bueno

A small open model that reads one printed table row out of a scanned nineteenth-century meteorological table and returns its cells as text. It is not a chatbot, not a general OCR model, and not a climate model. It reads a strip of paper and writes back numbers. Trained and evaluated entirely on Brazilian historical records, but the interesting result is that it reads layouts it has never seen — and usually with no labelled rows at all. Of the 18 layouts this project describes, two ever needed hand-labelling; the other fifteen were added by writing a JSON description of the columns and running the pipeline. docs/ADDING-A-PUBLICATION.md is the path and the evidence. Base: Qwen/Qwen3.5-2B…

Sculpt real ECMWF ERA5 atmospheric state into publish-ready video, GIF, and sound. Not animation. Not simulation. Real observed weather, rendered through 4 tuned presets. - Global 0.25 deg x 7 days (5 variables: t2m, u10, v10, tp, msl) - China region 0.25 deg x 30 days (5 variables) - 4 presets: temperature waves / wind streamlines / rain curtain / pressure vortex - Weather sonification (WAV) - "hear" the atmosphere - Pre-downloaded, zstd-compressed, SHA-256 checksummed https://roblitz-spec.github.io/weather-sculpt/ - Full dataset + commercial license: https://robloxer31.gumroad.com/l/Weather-Sculpt ECMWF ERA5 reanalysis (CC-BY 4.0). The same dataset used by meteorologists worldwide.

Model · Object detection

web-sdk-pp-rotated-detection

Chen Mohan

由chenmohan维护，非Paddle官方账号。PP-YOLOE-R-s 1024 FP32，DOTA15类单帧遥感旋转框检测，33,161,415字节。 Apache-2.0采用依据为固定官方项目声明与模型表，未发现独立权重许可文本；完整来源、转换和边界见模型卡及LICENSE/NOTICE。 Maintained by chenmohan, not the official Paddle account. Single-image DOTA15 rotated detection, PP-YOLOE-R-s 1024 FP32. Apache-2.0 is adopted from the pinned project license and model table; no separate weight-specific license text was found. See the cards for provenance, conversion and desktop verification scope.

This model is a fine-tuned version of JetLM/SDAR-8B-Chat-b8 on the sdarwebshopbs4eighthreason dataset. The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 1 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 4 - totaltrainbatchsize: 4 - totalevalbatchsize: 32 - lrschedulertype: constantwithwarmup - lrschedulerwarmupratio: 0.03 - numepochs: 1.0 - Transformers 4.52.4 - Pytorch 2.9.1+cu129 - Datasets 3.6.0 - Tokenizers 0.21.1

Model · Feature extraction

whisper-cluster

Keisuke Miyamoto

This repository contains a 500-cluster K-means codebook for Whisper large-v3-turbo encoder representations, trained on Japanese audio from Lambda Audio Corpus. Each 1,280-dimensional audio frame can be assigned to its nearest cluster center to produce a discrete unit ID from 0 to 499. These IDs can be used as targets for speech representation learning. They are learned acoustic units, not text tokens or labeled phonemes. The codebook was trained on 18,000,004 sampled frames from 199.89 hours of accepted audio. The repository includes the cluster centers, training features, and metadata. Original audio and transcripts are not included. The files are stored under codebook/. Only centroids.npy…

Model · Speech recognition

whisper-heb-v8-multi

Voicenter

whisper-heb-v7-multi (Hebrew / English / Arabic / Russian / Yiddish with built-in language ID) plus speech-free negatives: the model has learned to answer with nothing on line noise, hold music, tones and silence instead of inventing words. Same architecture (Whisper large-v3-turbo, 809M), same languages, same usage; drop-in replacement for v7. On telephony audio Whisper fine-tunes hallucinate short phrases when there is no speech. v7 produced a transcript for 98% of real line-noise clips (27% of them "תודה רבה") and 46% of synthetic noise. v8 was warm-started from v7 and trained 5,000 steps on the v7 corpus plus 83,920 speech-free rows (real Voicenter line noise ×4, synthetic…

Hindi audio in → Roman-script Hinglish captions out. 100% in-browser, no server. This is a browser-fixed repack of (fine-tune of openai/whisper-tiny for Hindi → Hinglish). Original model and training credit belong to the original author — this repo only fixes the two browser blockers and trims the package for web use. Young India reads Hindi in Roman script (WhatsApp/Instagram culture) — they speak Hindi but don't read Devanagari. This model outputs captions the ~102 MB total — small enough to run on-device via transformers.js, including mobile browsers. 1. tokenizer.json — BPE merges were stored as lists (["a", "b"]); converted to strings ("a b"), which is what the browser tokenizer path…

Model · Speech recognition

whisper-large-v3-turbo-anv-sot-ct2

Gyori Lab for Computational Biomedicine

A CTranslate2 conversion of a Whisper large-v3-turbo fine-tune for Sesotho (Southern Sotho) trained by DSFSI on the African Next Voices corpus. The weights are unchanged apart from the format conversion and int8 quantization, for use with All credit for the model goes to the original authors. Please cite and refer to the source model and the African Next Voices dataset. Whisper has no Sesotho language token. The fine-tune decodes under the English token (en), which Whisper's language detection narrowly picks for it, so pass it explicitly to avoid detection drifting to another language. Turning off conditiononprevioustext avoids repetition loops on long audio. Converted from source revision…

Model · Speech recognition

whisper-large-v3-turbo-anv-sot-mlx

Gyori Lab for Computational Biomedicine

An MLX conversion of a Whisper large-v3-turbo fine-tune for Sesotho (Southern Sotho) trained by DSFSI on the African Next Voices corpus. The weights are unchanged apart from the format conversion, for use with Apple silicon. All credit for the model goes to the original authors. Please cite and refer to the source model and the African Next Voices dataset. Whisper has no Sesotho language token. The fine-tune decodes under the English token (en), which Whisper's language detection narrowly picks for it, so pass it explicitly to avoid detection drifting to another language. Turning off conditiononprevioustext avoids repetition loops on long audio. Converted from source revision…

Model · Speech recognition

whisper-large-v3-turbo-anv-zul-ct2

Gyori Lab for Computational Biomedicine

A CTranslate2 conversion of a Whisper large-v3-turbo fine-tune for Zulu trained by DSFSI on the African Next Voices corpus. The weights are unchanged apart from the format conversion and int8 quantization, for use with All credit for the model goes to the original authors. Please cite and refer to the source model and the African Next Voices dataset. Whisper has no isiZulu language token. The fine-tune decodes under the English token (en), which Whisper's language detection narrowly picks for it, so pass it explicitly to avoid detection drifting to another language. Turning off conditiononprevioustext avoids repetition loops on long audio. Converted from source revision…

Model · Speech recognition

whisper-large-v3-turbo-anv-zul-mlx

Gyori Lab for Computational Biomedicine

An MLX conversion of a Whisper large-v3-turbo fine-tune for Zulu trained by DSFSI on the African Next Voices corpus. The weights are unchanged apart from the format conversion, for use with Apple silicon. All credit for the model goes to the original authors. Please cite and refer to the source model and the African Next Voices dataset. Whisper has no isiZulu language token. The fine-tune decodes under the English token (en), which Whisper's language detection narrowly picks for it, so pass it explicitly to avoid detection drifting to another language. Turning off conditiononprevioustext avoids repetition loops on long audio. Converted from source revision…

This model is a fine-tuned version of openai/whisper-medium on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: constantwithwarmup - lrschedulerwarmupsteps: 500 - numepochs: 100 - Transformers 5.0.0.dev0 - Pytorch 2.9.0+cu126 - Datasets 3.6.0 - Tokenizers 0.22.2