mandipgoswami/whisper-rirmega-bench

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 (v2)
• text_ref: Ground-truth transcript
• RIR metadata: rir_id, RT60, DRR, C50, etc. when available

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

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30-Second Quickstart

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}")

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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).

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How It’s Built

1. Speech: LibriSpeech test-clean (CC BY 4.0), streamed from Hugging Face.
2. RIRs: 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 and run:

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

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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 for interactive charts (WER vs RT60/DRR) and the latest leaderboard.

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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.

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License & Citation

• Speech: LibriSpeech (CC BY 4.0).
• RIRs: RIR-Mega license (see mandipgoswami/rirmega).
• Benchmark curation: MIT (this repo).

Citation (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:

@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}
}

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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