mandipgoswami
/

whisper-medium-rirmega

@@ -1,199 +1,93 @@
----
-library_name: transformers
-tags: []
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+---
+language: en
+license: apache-2.0
+tags:
+  - whisper
+  - automatic-speech-recognition
+  - speech
+  - en
+  - reverberant-speech
+  - room-acoustics
+  - fine-tuned
+  - pytorch
+datasets:
+  - mandipgoswami/whisper-rirmega-bench
+base_model: openai/whisper-medium
+pipeline_tag: automatic-speech-recognition
+metrics:
+  - wer
+model-index:
+  - name: whisper-medium-rirmega
+    results:
+      - task:
+          type: automatic-speech-recognition
+        dataset:
+          name: Whisper-RIR-Mega (test)
+          type: mandipgoswami/whisper-rirmega-bench
+          split: test
+        metrics:
+          - name: WER
+            type: wer
+            value: 0.0430
+---
+# Whisper-Medium fine-tuned for reverberant speech (Whisper-RIR-Mega)
+**Use this model when:** transcribing speech that was recorded in reverberant or “roomy” conditions (meetings, lectures, far-field mics). It keeps the same WER as the base Whisper-medium on clean/reverberant benchmarks while being trained specifically on reverberant data.
+This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the [Whisper-RIR-Mega](https://huggingface.co/datasets/mandipgoswami/whisper-rirmega-bench) dataset for ASR robustness to room reverberation. One-line load; no PEFT needed.
+## Quick usage
+```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import librosa
+processor = WhisperProcessor.from_pretrained("mandipgoswami/whisper-medium-rirmega")
+model = WhisperForConditionalGeneration.from_pretrained("mandipgoswami/whisper-medium-rirmega")
+audio, sr = librosa.load("path/to/reverberant_audio.wav", sr=16000)
+input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features
+predicted_ids = model.generate(input_features)
+transcript = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+print(transcript)
+```
+## When to use
+- Reverberant or room-recorded speech (meetings, lectures, far-field).
+- You want English ASR with the same ease as base Whisper (single `from_pretrained`).
+- You care about robustness to room acoustics without losing clean-speech quality.
+## Training
+- **Base model:** openai/whisper-medium
+- **Dataset:** Whisper-RIR-Mega (reverberant speech with clean transcripts)
+- **Epochs:** 4
+- **Learning rate:** 8e-06
+- **Effective batch size:** 16 (2 × 8 gradient accumulation)
+- **Precision:** BF16/FP16 mixed precision
+- **Gradient checkpointing:** Enabled
+- **Hardware:** Single NVIDIA RTX 5080 (16 GB)
+## Evaluation
+| Dataset | Split | WER |
+|---------|-------|-----|
+| Whisper-RIR-Mega | test | 0.0430 |
+## Limitations
+English only. Trained on 400 reverberant samples; best used in conditions similar to the [Whisper-RIR-Mega](https://huggingface.co/datasets/mandipgoswami/whisper-rirmega-bench) benchmark.
+## Citation
+If you use this model, please cite:
+```bibtex
+@article{goswami2026whisperrirmega,
+  title={Whisper-RIR-Mega: A Paired Clean-Reverberant Speech Benchmark for ASR Robustness to Room Acoustics},
+  author={Goswami, Mandip},
+  journal={arXiv preprint arXiv:2603.02252},
+  year={2026}
+}
+```