---
tags:
  - automatic-speech-recognition
  - robustness
  - speech
  - benchmark
license: cc-by-4.0
---

# Whisper-RIR-Mega: Paired Clean↔Reverberant Speech Robustness Benchmark

## Dataset Summary

**Whisper-RIR-Mega** is a benchmark dataset of *paired* clean and reverberant speech for evaluating ASR robustness to room acoustics. Each sample consists of:

- **audio_clean**: Clean speech (LibriSpeech test-clean, 16 kHz)
- **audio_reverb**: Same utterance convolved with one RIR from [RIR-Mega](https://huggingface.co/datasets/mandipgoswami/rirmega) (v2)
- **text_ref**: Ground-truth transcript
- **RIR metadata**: `rir_id`, RT60, DRR, C50, etc. when available
- **Technical paper**:([Whisper-RIR-Mega](https://arxiv.org/abs/2603.02252))

Splits are stratified by RT60 (or DRR) when metadata exists, so the benchmark is balanced across acoustic conditions.

**Use this dataset to:**

- Benchmark Whisper (or any ASR) on clean vs. reverberant speech and report **reverb penalty** (Δ WER)
- Evaluate robustness across RT60/DRR bins
- Reproduce the official Whisper-RIR-Mega leaderboard

---

## 30-Second Quickstart

```python
from huggingface_hub import snapshot_download
from datasets import load_from_disk
import whisper
import jiwer

# Download and load (use load_dataset if your HF datasets supports it)
path = snapshot_download("mandipgoswami/whisper-rirmega-bench", repo_type="dataset")
ds = load_from_disk(path + "/hf_dataset")["test"]
model = whisper.load_model("base")

# One sample
row = ds[0]
clean_wer = jiwer.wer(row["text_ref"], model.transcribe(row["audio_clean"]["path"], language="en")["text"])
reverb_wer = jiwer.wer(row["text_ref"], model.transcribe(row["audio_reverb"]["path"], language="en")["text"])
print(f"Clean WER: {clean_wer:.4f}  Reverb WER: {reverb_wer:.4f}")
```

---

## Dataset Structure

| Column        | Type   | Description                          |
|---------------|--------|--------------------------------------|
| sample_id     | string | Unique ID (from LibriSpeech + RIR)   |
| audio_clean   | Audio  | Clean 16 kHz audio                  |
| audio_reverb  | Audio  | Reverberant 16 kHz audio            |
| text_ref      | string | Reference transcript                |
| rir_id        | string | RIR-Mega sample ID                  |
| split         | string | train / validation / test           |
| rir_*         | mixed  | RIR metadata (RT60_T30_s, DRR_dB, …)|

Splits: **validation** and **test** for benchmarking; **train** optional (default config uses test + validation only).

---

## How It’s Built

1. **Speech**: LibriSpeech test-clean (CC BY 4.0), streamed from Hugging Face.
2. **RIRs**: [mandipgoswami/rirmega](https://huggingface.co/datasets/mandipgoswami/rirmega) (v2.0.0), with metadata (RT60, DRR, C50, etc.).
3. **Pipeline**: For each utterance we sample one RIR (stratified by RT60), convolve at 16 kHz, normalize RIR energy and peak-normalize output. No added noise by default.
4. **Splits**: Deterministic assignment to validation/test (e.g. 20% / 80%) with optional stratification by acoustic bins.

Full reproducibility: see the [GitHub repo](https://github.com/mandipgoswami/Whisper_RIRMega) and run:

```bash
python -m bench.build_and_publish --config configs/default.yaml
```

---

## Leaderboard

The leaderboard is generated by the same pipeline and updated on each release. Example (your run may vary):

| model_id           | clean   | reverb  | Δ WER   |
|--------------------|---------|---------|---------|
| openai/whisper-tiny | …       | …       | …       |
| openai/whisper-base | …       | …       | …       |
| openai/whisper-small| …       | …       | …       |
| openai/whisper-medium| …      | …       | …       |
| openai/whisper-large-v3| …    | …       | …       |

See the [Space](https://huggingface.co/spaces/mandipgoswami/whisper-rirmega-benchmark) for interactive charts (WER vs RT60/DRR) and the latest leaderboard.

---

## Limitations

- English only (LibriSpeech).
- Single RIR per utterance in the default setup; multi-RIR variants can be built by changing `k_rirs_per_utt` in the config.
- RIR metadata (RT60, DRR) may be missing for some RIR-Mega samples; the pipeline stores whatever is available.

---

## License & Citation

- **Speech**: LibriSpeech (CC BY 4.0).
- **RIRs**: RIR-Mega license (see [mandipgoswami/rirmega](https://huggingface.co/datasets/mandipgoswami/rirmega)).
- **Benchmark curation**: MIT (this repo).

**Citation (BibTeX):**

```bibtex
@misc{whisper-rirmega-bench,
  title        = {Whisper-RIR-Mega: Paired Clean-Reverberant Speech Robustness Benchmark},
  author       = {Goswami, Mandip},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/mandipgoswami/whisper-rirmega-bench},
  note         = {Dataset built with LibriSpeech and RIR-Mega.}
}
```

**RIR-Mega citation:**

```bibtex
@misc{goswami2025rirmega,
  title        = {RIR-Mega: A Large-Scale Room Impulse Response Corpus with Benchmarks},
  author       = {Goswami, Mandip},
  year         = {2025},
  eprint       = {2510.18917},
  archivePrefix= {arXiv},
  primaryClass = {cs.SD},
  url          = {https://arxiv.org/abs/2510.18917}
}
```

---

## How to Reproduce

1. Clone the repo and install: `pip install -e .`
2. Set `HF_TOKEN` (and optionally reduce `n_utterances` in `configs/default.yaml` for a quick run).
3. Run: `python -m bench.build_and_publish --config configs/default.yaml`
4. This builds the dataset, runs Whisper baselines, generates reports, and can push the dataset and Space to the Hub (if `HF_TOKEN` is set).

For a <5 minute smoke test: `python scripts/sanity_check.py`