Duplicate from mandipgoswami/whisper-rirmega-bench

Co-authored-by: Mandip Goswami <mandipgoswami@users.noreply.huggingface.co>

.gitattributes

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+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
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+*.jpg filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+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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