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.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+g_dns_best filter=lfs diff=lfs merge=lfs -text
+g_phase_retrieval_voicebank_best filter=lfs diff=lfs merge=lfs -text
+g_universal_best filter=lfs diff=lfs merge=lfs -text
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config.json

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+    "dist_config": {
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+        "world_size": 1
+    }
+}

config_small.json

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+    "batch_size": 4,
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+        "dist_url": "tcp://localhost:12345",
+        "world_size": 1
+    }
+}

config_universal.json

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+    "sampling_rate": 16000,
+    "segment_size": 32000,
+    "n_fft": 400,
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+    "win_size": 400,
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+    "ang_chn":16,
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+    "num_workers": 16,
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+        "dist_backend": "nccl",
+        "dist_url": "tcp://localhost:12345",
+        "world_size": 1
+    }
+}

g_dns_best

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readme.md

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+# Global Rotation Equivariance Phase Modeling for Speech Enhancement with Deep Magnitude-Phase Interaction
+This repository hosts the official implementation for the paper:
+
+**Global Rotation Equivariant Phase Modeling for Speech Enhancement with Deep Magnitude-Phase Interaction** (submitted to IEEE TASLP).
+
+Authors: Chengzhong Wang, Andong Li, Dingding Yao and Junfeng Li*
+
+A manifold-aware magnitude–phase dual-stream framework is proposed, that enforces **Global Rotation Equivariance (GRE)** in the phase stream, enabling robust phase modeling with strong generalization across denoising, dereverberation, bandwidth extension, and mixed distortions.
+
+Training logs, audio samples and supplementary analysis: https://wangchengzhong.github.io/RENet-Supplementary-Materials/
+
+---
+
+## Implementation Summary
+- **GRE as inductive bias:** explicit global rotation equivariance for phase modeling.
+- **Deep Magnitude–Phase Interaction:** MPICM for cross-stream gating without breaking equivariance.
+- **Hybrid Attention Dual-FFN (HADF):** attention fusion in score domain + stream-specific FFNs.
+- **Strong results with compact model:** 1.55M parameters, competitive or better quality than advanced baselines across SE tasks.
+
+---
+
+## Method Overview
+
+### Architecture
+![Overview of RENet architecture](assets/fig_architecture.png)
+
+The model uses a dual-stream encoder–decoder with a GRE-constrained complex phase branch and a real-valued magnitude branch. Key modules:
+
+- **MPICM (Magnitude-Phase Interactive Convolutional Module):**
+    - bias-free complex convolution for phase stream
+    - RMSNorm + SiLU for magnitude stream
+    - cross-stream modulus-based gating preserving GRE
+
+- **HADF (Hybrid-Attention Dual-FFN):**
+    - hybrid attention with a shared score map
+    - independent magnitude/phase value projections
+    - GRU-based FFN for magnitude, complex-valued convolutional GLU for phase
+
+### Global Rotation Equivariance
+GRE ensures $
+\mathcal{F}(\mathbf{x}e^{j\theta}) = \mathcal{F}(\mathbf{x})e^{j\theta}
+$, preventing the phase stream from learning arbitrary absolute orientations while preserving relative phase structure (GD/IP).
+
+---
+
+## 🔵 Experiments
+
+We evaluate on three settings:
+1. **Phase Retrieval** (clean magnitude, zero phase)
+2. **Denoising** (VoiceBank+DEMAND, DNS-2020)
+3. **Universal SE** (DNS-2021 training, WSJ0+WHAMR! test; DN/DR/BWE/mixed)
+
+### Phase Retrieval (VoiceBank)
+
+| Model | Params (M) | MACs (G/s) | PESQ | SI-SDR | WOPD $\downarrow$ | PD $\downarrow$ |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| Griffin-Lim | - | - | 4.23 | -17.07 | 0.342 | 90.07 |
+| DiffPhase | 65.6 | 3330 | 4.41 | -11.75 | 0.230 | 85.66 |
+| MP-SENet Up.* | 1.99 | 38.80 | 4.60 | 14.64 | 0.058 | 11.38 |
+| SEMamba* | 1.88 | 38.01 | 4.59 | 13.63 | 0.059 | 12.46 |
+| **Proposed (Small)** | **0.90** | **22.89** | **4.61** | **16.03** | **0.044** | **8.47** |
+
+\* Single phase decoder for phase retrieval.
+
+### Denoising (VBD & DNS-2020)
+![Denoising results](assets/fig_denoising.png)
+
+\* SEMamba reported w/o PCS.
+
+Key result: strong zero-shot transfer from VBD to DNS-2020 with consistent gains across PESQ, STOI, UTMOS, and PD; SOTA results on larget-scale DNS-2020.
+
+### Universal SE (DNS-2021 → WSJ0+WHAMR!)
+![Universal SE results](assets/fig_universal_results.png)
+
+Our model achieves top-tier performance across DN/DR/BWE and mixed distortions.
+
+---
+
+## Repository Structure
+- Training:
+    - train_denoising_dns.py
+    - train_denoising_vbd.py
+    - train_phase_retrieval.py
+    - train_universal_dns.py
+- Inference:
+    - inference_denoising.py
+    - inference_phase.py
+    - inference_universal.py
+- Core modules:
+    - models/model.py
+    - models/transformer.py
+    - models/mpd_and_metricd.py
+- Data:
+    - dataset.py
+    - dns_dataset.py
+    - data_gen/
+- Metrics:
+    - cal_metrics_singledir.py
+    - cal_metrics_hierarchicaldir.py
+---
+
+## Configurations
+We provide multiple configs for different settings:
+- config.json (Standard)
+- config_small.json (Phase Retrieval)
+- config_universal.json (Universal SE)
+
+---
+
+## Setup
+This project depends on PyTorch and common audio/metric libraries. Make sure your environment includes:
+- torch
+- librosa
+- soundfile
+- numpy
+- pesq
+- pystoi
+- tablib[xlsx]
+- tqdm
+
+---
+
+## Data Preparation
+
+### 1) VoiceBank+DEMAND (Denoising/Phase Retrieval)
+Place 16 kHz wavs here:
+- filelist_VBD/wavs_clean
+- filelist_VBD/wavs_noisy
+
+
+The Filelists are with the same formulation as that of MP-SENet:
+- filelist_VBD/training.txt
+- filelist_VBD/test.txt
+
+### 2) DNS-2020 (Denoising)
+Place clean wavs and noisy wavs in two separate folders and create the filelist (3000h).
+
+Filelist format (the clean files path is set in the training script):
+```
+clean_fileid_118096.wav|/abs/path/to/noisy_fileid_118096.wav
+```
+
+You can generate this list using:
+- data/generate_filelist.py
+
+Default path:
+- filelist_DNS20/training.txt
+- filelist_DNS20/test.txt
+
+### 3) DNS-2020 + WSJ-WHAMR test(Universal SE)
+Prepare a DNS-2021-style list with the same format as DNS-2020 (300h):
+```
+clean_fileid_000123.wav|/abs/path/to/noisy_fileid_000123.wav
+```
+
+Default path:
+- filelist_DNS21/training.txt
+
+We provide the generated WSJ+WHAMR universal SE test set [here](https://drive.google.com/file/d/123-WvyaKZkKqbh81Q_gMTOdTGgxPB_3z/view?usp=sharing).
+
+---
+
+## 🚀 Training
+
+Pre-trained checkpoints for each task are released in the `checkpoint/` folder.
+
+### VBD Denoising
+```
+python train_denoising_vbd.py --config config.json
+```
+
+### DNS-2020 Denoising
+```
+python train_denoising_dns.py --config config.json
+```
+
+### Phase Retrieval (Small)
+```
+python train_phase_retrieval.py --config config_small.json
+```
+
+### Universal SE (DNS-2021)
+```
+python train_universal_dns.py \
+    --test_noisy_dir /path/to/wsj_whamr/noisy_test \
+    --test_clean_dir /path/to/wsj_whamr/clean_test \
+    --config config_universal.json \
+```
+---
+
+## Inference
+
+### Unified Inference
+```
+python inference_{denoising|phase|universal}.py \
+    --checkpoint_file /path/to/checkpoint \
+    --input_noisy_wavs_dir /path/to/input_wavs \
+    --output_dir /path/to/output
+```
+
+Notes:
+- Use `inference_denoising.py` with a denoising checkpoint and a noisy input folder.
+- Use `inference_phase.py` with PR-trained checkpoint and a clean  input folder (the script drops the phase itself).
+- Use `inference_universal.py` with USE-trained checkpoint and universal degraded test input.
+
+The inference scripts load the corresponding config file from the checkpoint folder automatically.
+
+---
+
+## 📈 Evaluation
+
+Note: To evaluate UTMOS and DNSMOS, the required metric checkpoint files are not included in this repository. Please place them under `cal_metrics/dns` and `cal_metrics/UTMOS_demo` before running the evaluator.
+
+We provide a single-directory evaluator that computes PESQ/STOI/SI-SNR/CSIG/CBAK/COVL/UTMOS/DNSMOS and phase metrics (PD/WOPD):
+```
+python cal_metrics_singledir.py \
+    --clean_dir /path/to/clean \
+    --enhanced_dir /path/to/enhanced \
+    --excel_name results.xlsx
+```
+
+To compute only a subset of metrics, use `--metrics` with a comma-separated list (or `all` for everything):
+```
+python cal_metrics_singledir.py \
+    --clean_dir /path/to/clean \
+    --enhanced_dir /path/to/enhanced \
+    --excel_name results.xlsx \
+    --metrics PESQ,STOI,SISNR,PD,WOPD
+```
+
+For hierarchical test sets (e.g., universal subfolders), the enhanced wavs should be in the same relative structure as the clean set.
+
+Hierarchical-directory example ( `cal_metrics_hierarchicaldir.py`):
+```
+python cal_metrics_hierarchicaldir.py \
+        --clean_dir /path/to/clean_root \
+        --enhanced_dir /path/to/enhanced_root \
+        --excel_name results_hierarchical.csv \
+        --metrics PESQ,STOI,SISNR,PD,WOPD
+```
+
+Target directory layout (clean and enhanced must mirror each other):
+```
+clean_root/
+    noise_limit/|noise_reverb/|noise_reverb_limit/|only_noise/
+        -5db/|0db/|5db/|10db/|15db/
+            0001.wav ...
+    only_reverb/
+        0001.wav ...
+    only_bandlimit/
+        2khz/|4khz/
+            0001.wav ...
+
+enhanced_root/
+  (same structure and filenames as clean_root)
+```
+
+
+
+---
+
+## Calculating MACs
+Since we use multiple custom operations, only counting standard conv/deconv/GRU/MHA underestimates MACs. We implement MAC counting for InteConvBlock(Transpose), CustomAttention, and ComplexFFN.
+
+Run:
+```
+python cal_mac.py
+```
+
+To modify the model size, edit the configuration near the bottom of cal_mac.py.
+
+
+## Acknowledgements
+We acknowledge the contributions of the following repositories, which served as important references for our code implementation:
+- [MP-SENet](https://github.com/yxlu-0102/MP-SENet)
+- [SEMamba](https://github.com/RoyChao19477/SEMamba)
+
+---
+
+## Citation
+If you find this work useful, please cite the paper.
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