Introduction

Welcome to the US English Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

Introduction

Welcome to the US Spanish Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

Introduction

Welcome to the Canadian French Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

"Data for removing derelict vehicle operations from city streets. Gives disposition (complaints) of derelict vehicles reported to DSNY from 311. For information on how to report an abandoned vehicle, go to: http://www1.nyc.gov/nyc-resources/service/989/abandoned-vehicle.

Related datasets: - https://data.cityofnewyork.us/City-Government/Derelict-Vehicle-Dispositions-Tow/vr8p-8shw - https://data.cityofnewyork.us/City-Government/Derelict-Vehicles-Dispositions-Complaints/pq5i-thsu

"Provides data on abandoned vehicles on city streets that did not meet DSNY guidelines to be classified as derelict and that were tagged and reported to the NYPD Rotation Tow (ro-tow) Program for towing. For information on how to report an abandoned vehicle, go to: http://www1.nyc.gov/nyc-resources/service/989/abandoned-vehicle Related datasets: - https://data.cityofnewyork.us/City-Government/Derelict-Vehicles-Dispositions-Complaints/pq5i-thsu - https://data.cityofnewyork.us/City-Government/Derelict-Vehicle-Dispositions-Vehicles/bjuu-44hx

Directory structure:

engine-sounds.zip
|----- car [car engine sounds]
|----- cv [commercial vehicles engine sounds]

locX.zip
|----- meta.json [contains meta information of traffic condition and sensor setup corresponding to the location]
|----- train [train flac files inside]
|----- train.csv
|----- val [val flac files inside]
|----- val.csv

simulation.zip
|----- locX
|----- car
| |_ left [flac files and label csv inside]
| |_ right [flac files and label csv inside]
|----- cv
|_ left [flac files and label csv inside]
|_ right [flac files and label csv inside]

NOTE: We split zip files for large size folders (i.e., loc1, loc3, loc6). Please make sure to download all splits before unzipping the data! For example, run

zip -s 0 loc1.zip --out unsplit-loc1.zip

unzip unsplit-loc1.zip

in your terminal once you have downloaded loc1.z01, loc1.z02, loc1.z03 and loc1.zip.

Contact:

If there is any question, please contact:

• Luca Bondi: luca (dot) bondi@us (dot) bosch (dot) com
• Shabnam Ghaffarzadegan: shabnam (dot) ghaffarzadegan@us (dot) bosch (dot) com
• Wei-Cheng (Winston) Lin: wei-cheng (dot) lin@us (dot) bosch (dot) com

Acknowledgement:

This work has partially received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 956962 and from the European Research Council under the European Union's Horizon 2020 research and innovation program / ERC Consolidator Grant: SONORA (no. 773268). This work reflects only the authors' views and the Union is not liable for any use that may be made of the contained information.

🚀 Update (August 8, 2025 – NuRec 25.07 Release)!!

We've added new neural reconstructions reconstructions with improved quality and a wider range of scenarios, including:

All-way stop and 2 way stop intersections Vulnerable road users (VRUs) at crosswalks Protected turns, curved lanes, and more.

The reconstructions were generated using 6 camera views (front-wide 120 deg, front-tele 30 deg, cross right/left 120 deg and rear right/left 70 deg). Find the new samples in the… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles-NuRec.

A digital record of all Tesla fires - including cars and other products, e.g. Tesla MegaPacks - that are corroborated by news articles or confirmed primary sources. Latest version hosted at https://www.tesla-fire.com.