# Datasets, Page 127 of 132
Source: https://savrn.com/datasets/page/127
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

---

SAVRN Model Hub

# AI Training Datasets

Each dataset with its card, its structure (every split, row count and column type), its files, its license, and the models that disclose training on it.

11,122Models

3,164Datasets

285Papers

6,792Publishers

11,852Sourced relationships

Updated 2026-10-18 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Datasets

Task

- [Text generation127](https://savrn.com/datasets?task=Text+generation)
- [Robotics106](https://savrn.com/datasets?task=Robotics)
- [Other48](https://savrn.com/datasets?task=Other)
- [Text classification47](https://savrn.com/datasets?task=Text+classification)
- [Text retrieval44](https://savrn.com/datasets?task=Text+retrieval)
- [Question answering36](https://savrn.com/datasets?task=Question+answering)
- [Speech recognition33](https://savrn.com/datasets?task=Speech+recognition)
- [Tabular classification21](https://savrn.com/datasets?task=Tabular+classification)
- [Time series forecasting21](https://savrn.com/datasets?task=Time+series+forecasting)
- [Image to text17](https://savrn.com/datasets?task=Image+to+text)
- [Image classification16](https://savrn.com/datasets?task=Image+classification)
- [Object detection14](https://savrn.com/datasets?task=Object+detection)
- [Text to speech14](https://savrn.com/datasets?task=Text+to+speech)
- [Visual question answering14](https://savrn.com/datasets?task=Visual+question+answering)
- [Image to image11](https://savrn.com/datasets?task=Image+to+image)
- [Image segmentation9](https://savrn.com/datasets?task=Image+segmentation)
- [Image to 3d9](https://savrn.com/datasets?task=Image+to+3d)
- [Translation9](https://savrn.com/datasets?task=Translation)
- [Video classification9](https://savrn.com/datasets?task=Video+classification)
- [Audio classification7](https://savrn.com/datasets?task=Audio+classification)
- [Feature extraction7](https://savrn.com/datasets?task=Feature+extraction)
- [Reinforcement learning7](https://savrn.com/datasets?task=Reinforcement+learning)
- [Text to image7](https://savrn.com/datasets?task=Text+to+image)
- [Text to video7](https://savrn.com/datasets?task=Text+to+video)

License

- [other250](https://savrn.com/datasets?license=other)
- [apache-2.0236](https://savrn.com/datasets?license=apache-2.0)
- [mit232](https://savrn.com/datasets?license=mit)
- [cc-by-4.0224](https://savrn.com/datasets?license=cc-by-4.0)
- [cc-by-nc-4.077](https://savrn.com/datasets?license=cc-by-nc-4.0)
- [cc0-1.030](https://savrn.com/datasets?license=cc0-1.0)
- [cc-by-sa-4.029](https://savrn.com/datasets?license=cc-by-sa-4.0)
- [odc-by24](https://savrn.com/datasets?license=odc-by)
- [cc-by-nc-sa-4.020](https://savrn.com/datasets?license=cc-by-nc-sa-4.0)
- [unknown14](https://savrn.com/datasets?license=unknown)
- [cc-by-sa-3.06](https://savrn.com/datasets?license=cc-by-sa-3.0)
- [odbl6](https://savrn.com/datasets?license=odbl)
- [gpl-3.05](https://savrn.com/datasets?license=gpl-3.0)
- [cc-by-3.04](https://savrn.com/datasets?license=cc-by-3.0)
- [cc3](https://savrn.com/datasets?license=cc)
- [cc-by-nc-nd-4.03](https://savrn.com/datasets?license=cc-by-nc-nd-4.0)
- [gpl3](https://savrn.com/datasets?license=gpl)
- [cc-by-2.02](https://savrn.com/datasets?license=cc-by-2.0)
- [cc-by-nd-4.02](https://savrn.com/datasets?license=cc-by-nd-4.0)
- [cdla-sharing-1.02](https://savrn.com/datasets?license=cdla-sharing-1.0)
- [agpl-3.01](https://savrn.com/datasets?license=agpl-3.0)
- [bsl-1.01](https://savrn.com/datasets?license=bsl-1.0)
- [c-uda1](https://savrn.com/datasets?license=c-uda)
- [cc-by-nc-3.01](https://savrn.com/datasets?license=cc-by-nc-3.0)

Access

- [Publicly accessible2,933](https://savrn.com/datasets?access=open_access)
- [Access requested at publisher231](https://savrn.com/datasets?access=gated)

Publisher

- [Benjamin J Barber35](https://savrn.com/datasets?publisher=endomorphosis)
- [Chang Yu24](https://savrn.com/datasets?publisher=g1n0st)
- [Ji Xie23](https://savrn.com/datasets?publisher=sanaka87)
- [Justicedao18](https://savrn.com/datasets?publisher=justicedao)
- [LeDuy13](https://savrn.com/datasets?publisher=duyle2408)
- [Nguyen13](https://savrn.com/datasets?publisher=icdn11)
- [Nguyen13](https://savrn.com/datasets?publisher=icdn12)
- [Nguyen13](https://savrn.com/datasets?publisher=icdn13)
- [National Engineering Research Center for Big Data Software13](https://savrn.com/datasets?publisher=thulab)
- [Nguyen12](https://savrn.com/datasets?publisher=icdn10)
- [Nguyen12](https://savrn.com/datasets?publisher=icdn14)
- [Byte I12](https://savrn.com/datasets?publisher=icdn15)
- [Rebrowser.net12](https://savrn.com/datasets?publisher=rebrowser)
- [Shufan Jiang12](https://savrn.com/datasets?publisher=tsumugii)
- [Ai210](https://savrn.com/datasets?publisher=allenai)
- [Ps Yo10](https://savrn.com/datasets?publisher=atokforps)
- [Dougall DeepMind10](https://savrn.com/datasets?publisher=dougalldeepmind)
- [Deesced9](https://savrn.com/datasets?publisher=ameluce)
- [Jiahao Zhang9](https://savrn.com/datasets?publisher=hi-todayis-jh)
- [Julia Dollis9](https://savrn.com/datasets?publisher=juliadollis)
- [Massive Text Embedding Benchmark9](https://savrn.com/datasets?publisher=mteb)
- [Pranab Pattanaik9](https://savrn.com/datasets?publisher=p2samapa)
- [Sota9](https://savrn.com/datasets?publisher=sotaagi2030)
- [Sunsetcanyon9](https://savrn.com/datasets?publisher=sunsetcanyon)

Size

- [10K<n<100K125](https://savrn.com/datasets?size=10K%3Cn%3C100K)
- [1K<n<10K106](https://savrn.com/datasets?size=1K%3Cn%3C10K)
- [n<1K100](https://savrn.com/datasets?size=n%3C1K)
- [100K<n<1M70](https://savrn.com/datasets?size=100K%3Cn%3C1M)
- [1M<n<10M51](https://savrn.com/datasets?size=1M%3Cn%3C10M)
- [10M<n<100M33](https://savrn.com/datasets?size=10M%3Cn%3C100M)
- [100M<n<1B21](https://savrn.com/datasets?size=100M%3Cn%3C1B)
- [n>1T16](https://savrn.com/datasets?size=n%3E1T)
- [1B<n<10B8](https://savrn.com/datasets?size=1B%3Cn%3C10B)
- [100B<n<1T5](https://savrn.com/datasets?size=100B%3Cn%3C1T)
- [10B<n<100B2](https://savrn.com/datasets?size=10B%3Cn%3C100B)
- [10GB<n<100GB1](https://savrn.com/datasets?size=10GB%3Cn%3C100GB)
- [120,8981](https://savrn.com/datasets?size=120%2C898)
- [1B-tokens1](https://savrn.com/datasets?size=1B-tokens)
- [1T<n<10T1](https://savrn.com/datasets?size=1T%3Cn%3C10T)
- [n1K<n10K1](https://savrn.com/datasets?size=n1K%3Cn10K)
- [n<100K1](https://savrn.com/datasets?size=n%3C100K)
- [n<10K1](https://savrn.com/datasets?size=n%3C10K)
- [n<1GB1](https://savrn.com/datasets?size=n%3C1GB)
- [n>1M1](https://savrn.com/datasets?size=n%3E1M)

Domain

- [LeRobot31](https://savrn.com/datasets?domain=LeRobot)
- [legal, law, graphrag12](https://savrn.com/datasets?domain=legal%2C+law%2C+graphrag)
- [tsfile, timeseries, time-series12](https://savrn.com/datasets?domain=tsfile%2C+timeseries%2C+time-series)
- [open-data, data-science, web-scraping9](https://savrn.com/datasets?domain=open-data%2C+data-science%2C+web-scraping)
- [seo-cluster, web-nodes9](https://savrn.com/datasets?domain=seo-cluster%2C+web-nodes)
- [dpo, rlhf, ultrafeedback8](https://savrn.com/datasets?domain=dpo%2C+rlhf%2C+ultrafeedback)
- [mteb, text, image7](https://savrn.com/datasets?domain=mteb%2C+text%2C+image)
- [nepali, instruction-tuning, agriculture7](https://savrn.com/datasets?domain=nepali%2C+instruction-tuning%2C+agriculture)
- [science, stackexchange, provenance5](https://savrn.com/datasets?domain=science%2C+stackexchange%2C+provenance)
- [LeRobot, robotics, lerobot4](https://savrn.com/datasets?domain=LeRobot%2C+robotics%2C+lerobot)
- [ml-intern4](https://savrn.com/datasets?domain=ml-intern)
- [AMASS, Retarget, Robotics3](https://savrn.com/datasets?domain=AMASS%2C+Retarget%2C+Robotics)
- [benchmark, agents, terminal3](https://savrn.com/datasets?domain=benchmark%2C+agents%2C+terminal)
- [climate3](https://savrn.com/datasets?domain=climate)
- [code3](https://savrn.com/datasets?domain=code)
- [crypto, cryptocurrency, finance3](https://savrn.com/datasets?domain=crypto%2C+cryptocurrency%2C+finance)
- [data-attribution, influence-functions, interpretability3](https://savrn.com/datasets?domain=data-attribution%2C+influence-functions%2C+interpretability)
- [fireviewer, fire, smoke3](https://savrn.com/datasets?domain=fireviewer%2C+fire%2C+smoke)
- [video3](https://savrn.com/datasets?domain=video)
- [LeRobot, lerobot, grabette2](https://savrn.com/datasets?domain=LeRobot%2C+lerobot%2C+grabette)
- [LeRobot, ur7e, robotiq2](https://savrn.com/datasets?domain=LeRobot%2C+ur7e%2C+robotiq)
- [agentenv, rl-environment, agents2](https://savrn.com/datasets?domain=agentenv%2C+rl-environment%2C+agents)
- [ai-governance, measurement2](https://savrn.com/datasets?domain=ai-governance%2C+measurement)
- [autonomous, agents, evolution2](https://savrn.com/datasets?domain=autonomous%2C+agents%2C+evolution)

[Most downloaded](https://savrn.com/datasets?sort=downloads)[Most liked](https://savrn.com/datasets?sort=likes)[Recently updated](https://savrn.com/datasets?sort=updated)[Name](https://savrn.com/datasets?sort=name)

3,164 datasets, sorted by most downloaded.

Dataset · Text to speech

### [sl-cobalt](https://savrn.com/datasets/sl-cobalt)

[Tohir Saidzoda](https://savrn.com/model-publishers/tohirju)

Single-speaker synthetic Tajik speech, generated 2026-09-22..24. Sources: khazina (book readings), law (legal texts), books (book texts). CC-BY-NC-4.0. Research use only. Generated with an OmniVoice LoRA (omnivoice-lora-sl-panther2); OmniVoice is CC-BY-NC, so this corpus inherits the non-commercial restriction. Every manifest row carries licensenote: "CC-BY-NC-derived, research-only, never Sado product". This data must not be used in a commercial product. Manifest fields: clipid, text, wav, duration, refvoice, generator, synthetic, rmsdbfs, normalizationtargetdbfs, peakceilingdbfs, normalizationmethod, licensenote, sourcerow. All audio is loudness-normalised to -16 dBFS RMS with a -1 dBFS…

Access requested at publisher cc-by-nc-4.0

[View dataset](https://savrn.com/datasets/sl-cobalt)

Dataset · Text generation

### [smolified-aiexpense](https://savrn.com/datasets/smolified-aiexpense)

[Smolify AI](https://savrn.com/model-publishers/smolify)

This is a synthetic training corpus generated by the Smolify Foundry. It was used to train the corresponding model smolify/smolified-aiexpense. This dataset is a sovereign asset owned by smolify. Generated via Smolify.ai.

Publicly accessible apache-2.0 1K<n<10K

[View dataset](https://savrn.com/datasets/smolified-aiexpense)

Dataset

### [smoothloopv3_dataset](https://savrn.com/datasets/smoothloopv3-dataset)

[Adwolf Saint Czar](https://savrn.com/model-publishers/adwolfczar)

Access requested at publisher

[View dataset](https://savrn.com/datasets/smoothloopv3-dataset)

Dataset

### [snb_SFT](https://savrn.com/datasets/snb-sft)

[Zhenqian Ji](https://savrn.com/model-publishers/crowdj996)

Verified recovery SFT trajectories for visual web agents. - 2000 multi-turn conversations in policysft.jsonl - Benchmarks (as even as the accepted pool allowed): MiniWoB 639, TimeWarp 639, WebArena-Verified 362, VisualWebArena 360 Image files referenced by path live on the collection machine under /root/SFT200020260916/batches/. This JSONL is the training text; pair it with those PNGs if you train a vision model.

Publicly accessible

[View dataset](https://savrn.com/datasets/snb-sft)

Dataset · Tabular classification

### [snowfall-news-2019-2026](https://savrn.com/datasets/snowfall-news-2019-2026)

[Fliar BI](https://savrn.com/model-publishers/fliarbi)

A dataset of news about snowfall, snowstorms and blizzards from a global news index, January 2019 – July 2026, built with the Prism News API (historical and real-time news, https://radar-api.fliarbi.com/news). Every row is one news article about snowfall, with its date, location, tone, editorial priority, themes and the organisations it names, plus a link that returns the full article through the API. - 59.76% of each year's snowfall coverage falls in December–February (2019–2025 average); June–August holds 5.57%. - Snowfall's share of news ranged from 4.08 to 7.97 articles per 10,000 between 2019 and 2025; the 2025 level was 39.4% below 2019. - January 2026 (the January 23–27 North…

Publicly accessible cc-by-nc-4.0 10K<n<100K

[View dataset](https://savrn.com/datasets/snowfall-news-2019-2026)

Dataset

### [so101-ai20k-multitask](https://savrn.com/datasets/so101-ai20k-multitask)

[Hung Ho](https://savrn.com/model-publishers/hungho77)

Publicly accessible

[View dataset](https://savrn.com/datasets/so101-ai20k-multitask)

Dataset · Robotics

### [so101-cube-pinkbowl_20260930_232713](https://savrn.com/datasets/so101-cube-pinkbowl-20260930-232713)

[Jing](https://savrn.com/model-publishers/fatdove)

This dataset was created using LeRobot.

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/so101-cube-pinkbowl-20260930-232713)

Dataset

### [so101-drawer-eval](https://savrn.com/datasets/so101-drawer-eval)

[Cheng Xu](https://savrn.com/model-publishers/acerola0v0)

这是一组公开的SO-101实机诊断录制，用于复核SmolVLA在“夹住抽屉边缘→松开→自主返回初始区域”任务中的正常表现与失败。当前只有2026-10-16的5条SmolVLA录制，1条用户反馈正常、4条用户确认失败，不是正式留出测试集，也不是ACT/DP/SmolVLA成功率排行榜。 五条共4282帧，top/wrist视频均640×480，名义30FPS。原始data/meta/videos保持完整；单条30秒录制中的重复动作周期不是多次独立试验。 - 每条目录的labels.json是附加标签，原始LeRobot文件没有被改写。 - 未明确确认的人工介入、重试次数、实际控制频率使用unknown/null，不根据视频背景或夹爪变化次数补猜。 - 这些录制已经参与问题分析，不能再作为未经接触的最终留出测试集。meta/info.json中的默认train split只保留录制格式，不代表训练用途。 模型为SmolVLA 15K，HF根目录本身是20K，不能混淆。实际使用权重SHA256为7a02885f974b22d50959c8d7e9cda94f5ab2c861af959a25391e814821449f85。 配置/提供的CLI为chunksize50、nactionsteps50、nobssteps1、Flow numsteps10，CUDA同步推理；日志没有完整配置转储。实际模型输入top→camera1、wrist→camera2，六维状态/动作。自动复位禁用。 本仓库根目录是评测合集，不是一个合并后的LeRobot根目录。以下下载轻微改写条件：…

Publicly accessible n<1K

[View dataset](https://savrn.com/datasets/so101-drawer-eval)

Dataset · Robotics

### [so101-red-cube-3d_pickup_20260918_174503](https://savrn.com/datasets/so101-red-cube-3d-pickup-20260918-174503)

[Solanki Dharak Deepak](https://savrn.com/model-publishers/dharak22)

This dataset was created using LeRobot.

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/so101-red-cube-3d-pickup-20260918-174503)

Dataset

### [so101-spinning-table-success-head](https://savrn.com/datasets/so101-spinning-table-success-head)

[Ethan Winchell](https://savrn.com/model-publishers/ewinchell07)

Publicly accessible

[View dataset](https://savrn.com/datasets/so101-spinning-table-success-head)

Dataset · Robotics

### [so101-spinning-table-success-head-wrist](https://savrn.com/datasets/so101-spinning-table-success-head-wrist)

[Ethan Winchell](https://savrn.com/model-publishers/ewinchell07)

This dataset was created using LeRobot. SO-101 turntable pick-and-place (robot lilguy), September 2026. A single SO-101 follower arm, teleoperated from an SO-101 leader arm, picks a cube off a slowly spinning turntable and places it in a basket. Two cube colours (blue, green) and two operator speeds (slow, fast). Recorded with MakerMods Lab on 2026-09-27 and 2026-09-29. - observation.state: 6 follower joint positions - action: 6 leader joint positions - observation.images.head: Intel RealSense D435if RGB, 640×480, overhead view - observation.images.headdepth: RealSense depth aligned to the head RGB, 12-bit lossless HEVC (decode with lerobot's depth support to get millimetres; values under…

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/so101-spinning-table-success-head-wrist)

Dataset · Robotics

### [so101-toy-plate_20260929_231320](https://savrn.com/datasets/so101-toy-plate-20260929-231320)

[Jing](https://savrn.com/model-publishers/fatdove)

This dataset was created using LeRobot.

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/so101-toy-plate-20260929-231320)

Dataset

### [so101_action_annotations_gripper_hints4](https://savrn.com/datasets/so101-action-annotations-gripper-hints4)

[Krish Daterao](https://savrn.com/model-publishers/kdaterao)

Action100M-inspired annotation artifacts using V-JEPA 2.1, configurable captioners (default Qwen2.5-VL video and Qwen3-VL image), and optional Claude aggregation. This is an adaptation, not a reproduction. Exact models are recorded in run.json. Each train row represents one captioned or requested aggregation node. Context-only nodes remain in the episode sidecars and are omitted from aggregation rows. selectedsubtask marks the controller cut; source frame intervals are half-open [startframe, endframe). The split name does not imply a held-out evaluation split or verified labels. Source dataset IDs/revisions and episode IDs identify the original demonstrations. captioning/ contains caption…

Publicly accessible

[View dataset](https://savrn.com/datasets/so101-action-annotations-gripper-hints4)

Dataset · Robotics

### [so101_amazing_hand_smoke_20260923_034214](https://savrn.com/datasets/so101-amazing-hand-smoke-20260923-034214)

[Francis Zhou](https://savrn.com/model-publishers/franciszhou18)

This dataset was created using LeRobot.

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/so101-amazing-hand-smoke-20260923-034214)

Dataset · Robotics

### [so101_pencil_holder_1](https://savrn.com/datasets/so101-pencil-holder-1)

[National Engineering Research Center for Big Data Software](https://savrn.com/model-publishers/thulab)

Apache TsFile version of nikodembartnik/so101pencilholder1. A SO follower dataset for the task "put the pen in the wooden box" (source pretty name: "Task: put pen in wooden box"). Each frame records the 6-D joint state and the 6-D commanded action. Task (taskindex 0): "put the pen in the wooden box". - Time (INT64, milliseconds) - per-episode sample time, round(timestamp 1000) in milliseconds (the source timestamp column equals Time / 1000 seconds and is therefore not stored separately). - episodeindex, taskindex (TAG) - device dimensions identifying one demonstration episode and one task; query a single episode with WHERE episodeindex=0. - action0..5, observationstate0..5 (FIELD) - FLOAT…

Publicly accessible apache-2.0 10K<n<100K

[View dataset](https://savrn.com/datasets/so101-pencil-holder-1)

Dataset

### [so101_state_machine_variations_300](https://savrn.com/datasets/so101-state-machine-variations-300)

[Maximilian Ofir](https://savrn.com/model-publishers/maximilianofir)

Publicly accessible

[View dataset](https://savrn.com/datasets/so101-state-machine-variations-300)

Dataset

### [so101_tape_to_box_v2](https://savrn.com/datasets/so101-tape-to-box-v2)

[Jaemin Kim](https://savrn.com/model-publishers/jammin0319)

Publicly accessible

[View dataset](https://savrn.com/datasets/so101-tape-to-box-v2)

Dataset · Robotics

### [soarm_amazing_hand_pick](https://savrn.com/datasets/soarm-amazing-hand-pick)

[Juxi Technology](https://savrn.com/model-publishers/juxi-technology)

Teleoperated demonstration dataset for training a grasping policy on an SO-ARM101 arm with an AmazingHand dexterous hand (imitation learning). Pick up the cube with the dexterous hand — grasp a cube on the table using the dexterous hand. Collection flow: an operator moves the leader arm → the follower tracks it while its gripper proportionally drives the hand's open/close → joint angles and both camera streams are recorded synchronously. Per-episode flow: start from a fixed initial pose → perform one complete grasp (approach → grasp → lift → move → place) → end. Position coverage: the cube was placed at 4 different table positions, 5 episodes each, to teach positional generalization.…

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/soarm-amazing-hand-pick)

Dataset

### [soc-rawq-multi-0921-control](https://savrn.com/datasets/soc-rawq-multi-0921-control)

[Franklalalala](https://savrn.com/model-publishers/franklalalala)

Publicly accessible

[View dataset](https://savrn.com/datasets/soc-rawq-multi-0921-control)

Dataset

### [social-audio-text-benchmark-2023](https://savrn.com/datasets/social-audio-text-benchmark-2023)

[Tyler Jackson](https://savrn.com/model-publishers/tylerjtj4205)

Preparation notes and schema examples for Social tasks using Audio Text data. Full source material is intentionally not bundled, so provenance and licensing remain explicit. - builddataset.py — loading, cleaning, and split preparation code. - datasetinfos.json — schema and split metadata. - metadatasample.jsonl — small, human-readable records for checking the schema. - README.md — data card and usage notes. Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the same rows. The repository is suitable for testing the…

Publicly accessible cc-by-4.0

[View dataset](https://savrn.com/datasets/social-audio-text-benchmark-2023)

Dataset

### [sorrel-T-qwen3-1.7b-base-seed0-documents](https://savrn.com/datasets/sorrel-t-qwen3-1-7b-base-seed0-documents)

[Joshua Fonseca](https://savrn.com/model-publishers/joshycodes)

Publicly accessible

[View dataset](https://savrn.com/datasets/sorrel-t-qwen3-1-7b-base-seed0-documents)

Dataset

### [sorrel-T-qwen3-4b-base-seed0-documents](https://savrn.com/datasets/sorrel-t-qwen3-4b-base-seed0-documents)

[Joshua Fonseca](https://savrn.com/model-publishers/joshycodes)

Publicly accessible

[View dataset](https://savrn.com/datasets/sorrel-t-qwen3-4b-base-seed0-documents)

Dataset · Audio classification

### [spanish-accents-speech](https://savrn.com/datasets/spanish-accents-speech)

[Silencio Voice AI](https://savrn.com/model-publishers/silencionetwork)

Spanish from 12 speakers born in 9 countries. 19 clips, 0.3 hours, each labelled with the speaker's country of birth, first language, self-reported accent or regional variety, declared Spanish proficiency, age band, gender and recording device. Audio and speaker metadata only. Human-validated transcription is available on request. Silencio's Spanish catalogue is 65% Latin American by recorded hours, led by Venezuela, with Spain at 9%. It also holds a substantial body of Spanish spoken as a second language: 18% of hours come from speakers born in Africa, notably Nigeria, Kenya and Egypt. This sample covers 9 countries of birth across Latin America, Spain, Africa, the Middle East and the…

Publicly accessible cc-by-nc-4.0 n<1K

[View dataset](https://savrn.com/datasets/spanish-accents-speech)

Dataset · Audio to audio

### [spansynth-edit-gallery](https://savrn.com/datasets/spansynth-edit-gallery)

[Sungkyun Chang](https://savrn.com/model-publishers/mimbres)

Finished MIDI-guided music edits made with SpanSynth-Edit. Open a work's shared link to compare the original and edited audio, explore its score, and make an editable copy. Each work has its own folder: - input.wav and output.wav: the original clip and saved generated audio, at 48 kHz mono. - original.mid and edited.mid: the source and edited score, aligned to the clip. - context.wav: the normalized audio used by the model, including the history before the clip. - project.json: the title, description, notes, editing region, generation settings, and piano-roll display settings. Shared links have the form https://mimbres-spansynth-edit.hf.space/?work=FOLDERNAME. An unlisted work is still…

Publicly accessible

[View dataset](https://savrn.com/datasets/spansynth-edit-gallery)

## Model Collections

Hand-picked starting points, each with the reason it exists.

Collection · 4 entries

### [Embedding models for retrieval](https://savrn.com/collections/embedding-models-for-retrieval)

Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.

[View collection](https://savrn.com/collections/embedding-models-for-retrieval)

Collection · 4 entries

### [Models that fit on one accelerator](https://savrn.com/collections/models-that-run-on-one-accelerator)

Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.

[View collection](https://savrn.com/collections/models-that-run-on-one-accelerator)

Collection · 6 entries

### [Open-weight text models worth knowing](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.

[View collection](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Collection · 3 entries

### [Speech and audio models](https://savrn.com/collections/speech-and-audio)

Recognition and synthesis models, grouped so the two directions are easy to compare.

[View collection](https://savrn.com/collections/speech-and-audio)

## Related SAVRN Research

The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.

SAVRN Index

### [What open models cost to run](https://savrn.com/ai-index/pricing/open-models)

The same open-weight model priced by every host that serves it, per million tokens.

Research Hub

### [Data center trackers and maps](https://savrn.com/data-center-research)

Moratoriums, permits, power, water and capital behind the facilities that run these models.

Method

### [How the Model Hub is built](https://savrn.com/model-hub/methodology)

Sources, evidence labels, refresh behaviour, and the limits of every comparison here.