rohansheth/tiro-qwen3-asr-1.7b-ffasr-v1
Automatic Speech Recognition • 2B • Updated • 31
audio dict | Room stringclasses 260
values | Room Description stringclasses 10
values | Receiver Label stringclasses 92
values | Direct Path Length [m] float32 0.5 26.3 | Avg C50 float32 -21.03 27.2 ⌀ | Avg T30 float32 0.02 5.49 |
|---|---|---|---|---|---|---|
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97,
116,
... | Apartments_idx_0 | Apartments | R10 | 4.527704 | -3.001645 | 0.849125 |
{
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... | Apartments_idx_0 | Apartments | R11 | 2.240925 | 4.259616 | 0.705375 |
{
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... | Apartments_idx_0 | Apartments | R18 | 3.961072 | -3.337958 | 0.848875 |
{
"bytes": [
82,
73,
70,
70,
36,
125,
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87,
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109,
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... | Apartments_idx_0 | Apartments | R1 | 2.258617 | 4.257056 | 0.695375 |
{"bytes":"UklGRiR9AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQB9AAACAAMAAQD9//n/8v/r/+P/2//T/83/xv(...TRUNCATED) | Apartments_idx_0 | Apartments | R20 | 1.831476 | 4.177169 | 0.707 |
{"bytes":"UklGRiR9AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQB9AAAAAP///v/9//z/+//4//b/9P/y//D/7f(...TRUNCATED) | Apartments_idx_0 | Apartments | R21 | 3.313822 | 2.026134 | 0.764375 |
{"bytes":"UklGRiR9AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQB9AAAAAP///v/7//j/9P/v/+r/5P/f/9n/1P(...TRUNCATED) | Apartments_idx_0 | Apartments | R22 | 2.585526 | 3.934752 | 0.69075 |
{"bytes":"UklGRiR9AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQB9AAAAAP//AAD//////v/+//3//f/8//v/+/(...TRUNCATED) | Apartments_idx_0 | Apartments | R23 | 4.434034 | 1.880208 | 0.792125 |
{"bytes":"UklGRiR9AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQB9AAD2//7/CQARABYAFAAMAAAA7//b/8j/tf(...TRUNCATED) | Apartments_idx_0 | Apartments | R24 | 1.004377 | 6.160553 | 0.68325 |
{"bytes":"UklGRiR9AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQB9AAD//wAA//8AAP//AAD//wAAAQAAAAAAAQ(...TRUNCATED) | Apartments_idx_0 | Apartments | R26 | 5.693389 | -0.678275 | 0.842625 |
End of preview. Expand in Data Studio
Dataset Description
This dataset is a Parquet-formatted conversion of the AcousticRooms dataset released by Liu et al. alongside the CVPR 2025 paper Hearing Anywhere in Any Environment.
The conversion changes the dataset's storage and access format to Parquet for easier loading and streaming. The underlying room impulse responses and associated metadata originate from the official AcousticRooms release.
This is an independent conversion and is not an official release by the original authors or Meta.
Original Dataset
AcousticRooms contains simulated room impulse responses (RIRs) generated across 260 rooms from 10 room categories. The original release includes more than 300,000 single-channel RIRs together with source and receiver positions and other simulation metadata.
Original repository: https://github.com/facebookresearch/AcousticRooms
Modifications
The original dataset was converted to Parquet format.
No changes were made to the underlying audio or metadata beyond those required for serialization and packaging.
License
The original AcousticRooms dataset is distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This Parquet conversion is redistributed under the same CC BY 4.0 license. Users must provide appropriate attribution to the original authors, include a link to the license, and indicate whether changes were made.
Citation
If you use this dataset, please cite the original AcousticRooms paper:
Xiulong Liu, Anurag Kumar, Paul Calamia, Sebastià V. Amengual Garí, Calvin Murdock, Ishwarya Ananthabhotla, Philip Robinson, Eli Shlizerman, Vamsi Krishna Ithapu, and Ruohan Gao. “Hearing Anywhere in Any Environment.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
@inproceedings{liu2025haae,
title = {Hearing Anywhere in Any Environment},
author = {Liu, Xiulong and Kumar, Anurag and Calamia, Paul and
Garí, Sebastià V. Amengual and Murdock, Calvin and
Ananthabhotla, Ishwarya and Robinson, Philip and
Shlizerman, Eli and Ithapu, Vamsi Krishna and Gao, Ruohan},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition (CVPR)},
year = {2025}
}
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