| --- |
| 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` |
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|
