docs: correct dataset card and viewer config

README.md

@@ -9,28 +9,12 @@ tags:
 - noise
 pretty_name: RIRS NOISES
 size_categories:
-- 10K<n<100K
+- 1K<n<10K
 configs:
 - config_name: default
   data_files:
   - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: audio
-    dtype: audio
-  - name: filename
-    dtype: string
-  - name: category
-    dtype: string
-  - name: relative_path
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 84740338437
-    num_examples: 61260
-  download_size: 1777922276
-  dataset_size: 84740338437
+    path: "**/*.wav"
 ---
 
 # RIRS NOISES
@@ -41,17 +25,17 @@ dataset_info:
 from datasets import load_dataset
 
 # Stream to avoid downloading the entire dataset
-ds = load_dataset("schismaudio/rirs-noises", streaming=True)
+ds = load_dataset("schism-audio/rirs-noises", streaming=True)
 
 # Or download locally
-ds = load_dataset("schismaudio/rirs-noises")
+ds = load_dataset("schism-audio/rirs-noises")
 ```
 
 ## Dataset Description
 
-**RIRS NOISES** is a collection of simulated and real room impulse responses (RIRs) plus isotropic and point-source noises from [OpenSLR](https://openslr.org/28/). The dataset aggregates RIRs from the RWCP Sound Scene database, the REVERB challenge, and the Aachen Impulse Response (AIR) database, alongside noise recordings from the MUSAN corpus. All audio is 16 kHz, 16-bit.
+**RIRS NOISES** is the real/isotropic RIR and point-source noise subset from [OpenSLR SLR28](https://openslr.org/28/). This Hugging Face repo contains 1,260 WAV files: 417 files under `real_rirs_isotropic_noises/` and 843 files under `pointsource_noises/`. All audio is 16 kHz WAV.
 
-This dataset is designed for audio data augmentation — convolving clean audio with RIRs to simulate reverberant environments and adding noise at controlled SNRs. It is widely used for training robust speech and audio models.
+This subset is useful for audio data augmentation: convolving clean audio with real RIRs to simulate reverberant environments and adding noise at controlled SNRs. For the full 61,260-file OpenSLR SLR28 mirror, including simulated RIRs, see [`schism-audio/openslr-rirs`](https://huggingface.co/datasets/schism-audio/openslr-rirs).
 
 ## Dataset Structure
 
@@ -60,13 +44,23 @@ This dataset is designed for audio data augmentation — convolving clean audio
 | Field | Type | Description |
 |-------|------|-------------|
 | `audio` | `Audio` | WAV file at 16 kHz, 16-bit |
-| `filename` | `string` | Original filename |
-| `type` | `string` | Category: `rir_simulated`, `rir_real`, `noise_isotropic`, or `noise_pointsource` |
+| `label` | `ClassLabel` | Top-level source directory: `pointsource_noises` or `real_rirs_isotropic_noises` |
 
 ### Data Splits
 
 This dataset has no predefined splits. All files are in the default `train` split.
 
+| Split | Files |
+|-------|------:|
+| `train` | 1,260 |
+
+File counts by directory:
+
+| Directory | Files |
+|-----------|------:|
+| `pointsource_noises/` | 843 |
+| `real_rirs_isotropic_noises/` | 417 |
+
 ## Usage Examples
 
 ### Load RIRs for augmentation
@@ -74,10 +68,12 @@ This dataset has no predefined splits. All files are in the default `train` spli
 ```python
 from datasets import load_dataset
 
-ds = load_dataset("schismaudio/rirs-noises")
+ds = load_dataset("schism-audio/rirs-noises")
 
 # Filter for real RIRs only
-rirs = ds["train"].filter(lambda x: x["type"] == "rir_real")
+rirs = ds["train"].filter(
+    lambda x: ds["train"].features["label"].int2str(x["label"]) == "real_rirs_isotropic_noises"
+)
 print(f"Real RIRs: {len(rirs)}")
 ```
 
@@ -88,7 +84,7 @@ import numpy as np
 from scipy.signal import fftconvolve
 from datasets import load_dataset
 
-ds = load_dataset("schismaudio/rirs-noises")
+ds = load_dataset("schism-audio/rirs-noises")
 rir = ds["train"][0]["audio"]["array"]
 
 # Normalize the RIR
@@ -104,11 +100,8 @@ rir = rir / np.max(np.abs(rir))
 
 The dataset was compiled from multiple publicly available acoustic databases:
 
-- **RWCP Sound Scene Database:** Real-world RIRs recorded in various rooms and halls in Japan.
-- **REVERB Challenge:** RIRs and multi-channel recordings for distant speech recognition research.
-- **Aachen Impulse Response (AIR) Database:** Binaural and mono RIRs from diverse real environments.
-- **MUSAN:** Music, speech, and noise recordings used for audio augmentation.
-- **Simulated RIRs:** Generated using image-source methods for controlled room geometries.
+- **Real/isotropic RIR and noise files:** 417 WAV files under `real_rirs_isotropic_noises/`.
+- **Point-source noises:** 843 WAV files under `pointsource_noises/`.
 
 ### Annotations
 
@@ -118,13 +111,13 @@ No manual annotations are included. File organization and naming encode the sour
 
 - **Speech-centric design:** The RIRs and noise profiles were originally curated for speech processing. Drum and music applications may require additional filtering or selection.
 - **16 kHz only:** The fixed 16 kHz sample rate may be limiting for music applications that require higher fidelity.
-- **Simulated RIRs:** The simulated subset uses simplified room models that may not fully capture real-world acoustic complexity.
+- **Subset only:** This repo does not include the 60,000 simulated RIR files from OpenSLR SLR28. Use [`schism-audio/openslr-rirs`](https://huggingface.co/datasets/schism-audio/openslr-rirs) for the complete mirror.
 
 ## Related Datasets
 
-This dataset is part of the [Drum Audio Datasets](https://huggingface.co/collections/schismaudio/drum-audio-datasets) collection by [schismaudio](https://huggingface.co/schismaudio). Related datasets:
+This dataset is part of the [Drum Audio Datasets](https://huggingface.co/collections/schism-audio/drum-audio-datasets) collection by [schism-audio](https://huggingface.co/schism-audio). Related datasets:
 
-- [schismaudio/dechorate](https://huggingface.co/datasets/schismaudio/dechorate) — Calibrated multichannel RIRs with echo annotations and 3D positions
+- [schism-audio/dechorate](https://huggingface.co/datasets/schism-audio/dechorate) — Calibrated multichannel RIRs with echo annotations and 3D positions
 
 ## Citation