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Repository: RoyChao19477/SEMamba
Branch: main
Commit: 750ff5d8f0e1
Files: 115
Total size: 2.4 MB
Directory structure:
gitextract_9ebfdj2x/
├── .gitignore
├── LICENSE
├── README.md
├── ckpts/
│ ├── SEMamba_advanced.pth
│ ├── config.yaml
│ ├── pretrained_discriminator.pth
│ └── vd.pth
├── data/
│ ├── make_dataset_json.py
│ ├── test_clean.json
│ ├── test_noisy.json
│ ├── train_clean.json
│ ├── train_noisy.json
│ ├── valid_clean.json
│ └── valid_noisy.json
├── dataloaders/
│ └── dataloader_vctk.py
├── inference.py
├── make_dataset.sh
├── mamba-1_2_0_post1/
│ ├── .github/
│ │ └── workflows/
│ │ └── publish.yaml
│ ├── .gitignore
│ ├── .gitmodules
│ ├── AUTHORS
│ ├── LICENSE
│ ├── README.md
│ ├── benchmarks/
│ │ └── benchmark_generation_mamba_simple.py
│ ├── csrc/
│ │ └── selective_scan/
│ │ ├── reverse_scan.cuh
│ │ ├── selective_scan.cpp
│ │ ├── selective_scan.h
│ │ ├── selective_scan_bwd_bf16_complex.cu
│ │ ├── selective_scan_bwd_bf16_real.cu
│ │ ├── selective_scan_bwd_fp16_complex.cu
│ │ ├── selective_scan_bwd_fp16_real.cu
│ │ ├── selective_scan_bwd_fp32_complex.cu
│ │ ├── selective_scan_bwd_fp32_real.cu
│ │ ├── selective_scan_bwd_kernel.cuh
│ │ ├── selective_scan_common.h
│ │ ├── selective_scan_fwd_bf16.cu
│ │ ├── selective_scan_fwd_fp16.cu
│ │ ├── selective_scan_fwd_fp32.cu
│ │ ├── selective_scan_fwd_kernel.cuh
│ │ ├── static_switch.h
│ │ └── uninitialized_copy.cuh
│ ├── evals/
│ │ └── lm_harness_eval.py
│ ├── mamba_ssm/
│ │ ├── __init__.py
│ │ ├── models/
│ │ │ ├── __init__.py
│ │ │ ├── config_mamba.py
│ │ │ └── mixer_seq_simple.py
│ │ ├── modules/
│ │ │ ├── __init__.py
│ │ │ └── mamba_simple.py
│ │ ├── ops/
│ │ │ ├── __init__.py
│ │ │ ├── selective_scan_interface.py
│ │ │ └── triton/
│ │ │ ├── __init__.py
│ │ │ ├── layernorm.py
│ │ │ └── selective_state_update.py
│ │ └── utils/
│ │ ├── __init__.py
│ │ ├── generation.py
│ │ └── hf.py
│ ├── setup.py
│ └── tests/
│ └── ops/
│ ├── test_selective_scan.py
│ └── triton/
│ └── test_selective_state_update.py
├── mamba_install/
│ ├── AUTHORS
│ ├── LICENSE
│ ├── README.md
│ ├── benchmarks/
│ │ └── benchmark_generation_mamba_simple.py
│ ├── csrc/
│ │ └── selective_scan/
│ │ ├── reverse_scan.cuh
│ │ ├── selective_scan.cpp
│ │ ├── selective_scan.h
│ │ ├── selective_scan_bwd_bf16_complex.cu
│ │ ├── selective_scan_bwd_bf16_real.cu
│ │ ├── selective_scan_bwd_fp16_complex.cu
│ │ ├── selective_scan_bwd_fp16_real.cu
│ │ ├── selective_scan_bwd_fp32_complex.cu
│ │ ├── selective_scan_bwd_fp32_real.cu
│ │ ├── selective_scan_bwd_kernel.cuh
│ │ ├── selective_scan_common.h
│ │ ├── selective_scan_fwd_bf16.cu
│ │ ├── selective_scan_fwd_fp16.cu
│ │ ├── selective_scan_fwd_fp32.cu
│ │ ├── selective_scan_fwd_kernel.cuh
│ │ ├── static_switch.h
│ │ └── uninitialized_copy.cuh
│ ├── evals/
│ │ └── lm_harness_eval.py
│ ├── mamba_ssm/
│ │ ├── __init__.py
│ │ ├── models/
│ │ │ ├── __init__.py
│ │ │ ├── config_mamba.py
│ │ │ └── mixer_seq_simple.py
│ │ ├── modules/
│ │ │ ├── __init__.py
│ │ │ └── mamba_simple.py
│ │ ├── ops/
│ │ │ ├── __init__.py
│ │ │ ├── selective_scan_interface.py
│ │ │ └── triton/
│ │ │ ├── __init__.py
│ │ │ ├── layernorm.py
│ │ │ └── selective_state_update.py
│ │ └── utils/
│ │ ├── __init__.py
│ │ ├── generation.py
│ │ └── hf.py
│ ├── setup.py
│ └── tests/
│ └── ops/
│ ├── test_selective_scan.py
│ └── triton/
│ └── test_selective_state_update.py
├── models/
│ ├── codec_module.py
│ ├── discriminator.py
│ ├── generator.py
│ ├── loss.py
│ ├── lsigmoid.py
│ ├── mamba_block.py
│ ├── pcs400.py
│ └── stfts.py
├── pretrained.sh
├── recipes/
│ ├── SEMamba_advanced/
│ │ ├── SEMamba_advanced.yaml
│ │ └── SEMamba_advanced_pretrainedD.yaml
│ └── SEMamba_advanced_PCS/
│ └── SEMamba_advanced_PCS.yaml
├── requirements.txt
├── run.sh
├── runPCS.sh
├── train.py
└── utils/
└── util.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
.DS_Store
__pycache__
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2024 RONG CHAO
Portions of this software are Copyright (c) 2023 YXLU-0102 (Original MP-SENet)
Portions of this software are Copyright (c) 2022 Ruizhe Cao (Original CMGAN)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
1. The above copyright notice, this permission notice, and the following
acknowledgment shall be included in all copies or substantial portions of the Software:
“This software is based on MP-SENet (https://github.com/yxlu-0102/MP-SENet)
and CMGAN (https://github.com/ruizhecao96/CMGAN), which are licensed under the MIT license.”
2. Redistributions in source code or binary form must retain the above copyright
notice, this permission notice, and acknowledgment of MP-SENet and CMGAN’s contributions.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# SEMamba (Accepted to IEEE SLT 2024)
This is the official implementation of the SEMamba paper.
For more details, please refer to: [An Investigation of Incorporating Mamba for Speech Enhancement](https://arxiv.org/abs/2405.06573)
---
### NeurIPS 2024 competition : URGENT challenge 2024 ( oral presentation )
- A speech enhancement (SE) challenge aiming to build universal, robust, diverse, and generalizable SE models.
- The challenge involves diverse distortions, including
- **additive noise**,
- **reverberation**,
- **clipping**,
- **bandwidth limitations**,
with **all types of sampling frequencies** supported by a single model.
- Requires handling a large-scale dataset (~1.5 TB) and includes ranking based on **13 metrics** in classes of
- non-intrusive,
- intrusive,
- downstream-task-independent,
- downstream-task-dependent,
- subjective
SE metrics.
- Achieved **4th place** among 70 participating teams (>20 teams joined to the final stage).
- Deliver an oral presentation at the **NeurIPS** 2024 workshop, Vancouver, Canada.
- **Demo website** **[Live Demo Website](https://roychao19477.github.io/speech-enhancement-demo-2024/)**
---
## ✅ (Updated June 3) Online Demo on HuggingFace
1. You can now upload or record audio directly on our Hugging Face demo [https://huggingface.co/spaces/rc19477/Speech_Enhancement_Mamba](https://huggingface.co/spaces/rc19477/Speech_Enhancement_Mamba) for speech enhancement.
2. We now provide another model, `ckpts/vd.pth`, trained on both the VCTK-Demand and DNS-2020 corpora. The model achieves the following performance:
| Dataset | PESQ | CSIG | CBAK | COVL | STOI |
|-------------------|------|------|------|------|------|
| DNS-2020 | 3.66 | 2.88 | 4.33 | 2.67 | 0.98 |
| DNS-2020 w/PCS | 3.70 | 2.87 | 3.45 | 2.67 | 0.98 |
| VCTK-Demand | 3.56 | 4.73 | 4.00 | 4.25 | 0.96 |
| VCTK-Demand w/PCS | 3.75 | 4.76 | 3.67 | 4.37 | 0.96 |
---
## ✅ (Updated May 14) Now supports `mamba-ssm 2.2.x`
If you already installed `mamba-ssm 2.2.x`, you can still run this repo with a few modifications:
### Steps to run:
```bash
# Step 1: Copy local override
cp -R mamba_install/mamba_ssm/ .
# Step 2: Launch training
sh run.sh
```
---
### 🐳 Docker Support
We provide pre-built Docker environments to simplify setup:
1. **x86 systems**
Tested on A100, RTX 4090, and RTX 3090
👉 [Visit x86 Docker Repo](https://github.com/RoyChao19477/x86-semamba-docker)
2. **ARM systems**
Tested on NVIDIA GH200
👉 [Visit GH200 Docker Repo](https://github.com/RoyChao19477/gh200-semamba-docker)
---
⚠️ Notice: If you encounter CUDA-related issues while using the Mamba-1 framework, we suggest using the Mamba-2 framework (available in the mamba-2 branch).
The Mamba-2 framework is designed to support both Mamba-1 and Mamba-2 model structures.
```bash
git checkout mamba-2
```
## Requirement
* Python >= 3.9
* CUDA >= 12.0
* PyTorch == 2.2.2
## Model

## Speech Enhancement Results
VCTK-Demand

## ASR Word Error Rate
We have tested the ASR results using OpenAI Whisper on the test set of VoiceBank-DEMAND.
> The evaluation code will be released in the future.

## Additional Notes
1. Ensure that both the `nvidia-smi` and `nvcc -V` commands show CUDA version 12.0 or higher to verify proper installation and compatibility.
2. Currently, it supports only GPUs from the RTX series and newer models. Older GPU models, such as GTX 1080 Ti or Tesla V100, may not support the execution due to hardware limitations.
---
## Installation
### (Suggested:) Step 0 - Create a Python environment with Conda
It is highly recommended to create a separate Python environment to manage dependencies and avoid conflicts.
```bash
conda create --name mamba python=3.9
conda activate mamba
```
### Step 1 - Install PyTorch
Install PyTorch 2.2.2 from the official website. Visit [PyTorch Previous Versions](https://pytorch.org/get-started/previous-versions/) for specific installation commands based on your system configuration (OS, CUDA version, etc.).
### Step 2 - Install Required Packages
After setting up the environment and installing PyTorch, install the required Python packages listed in requirements.txt.
```bash
pip install -r requirements.txt
```
### Step 3 - Install the Mamba Package
Navigate to the mamba_install directory and install the package. This step ensures all necessary components are correctly installed.
```bash
cd mamba_install
pip install .
```
📌 Note: You might run `pip install numpy==1.26.4` after these to prevent unpredictable issues.
⚠️ Note: Installing from source (provided `mamba_install`) can help prevent package issues and ensure compatibility between different dependencies. It is recommended to follow these steps carefully to avoid potential conflicts.
⚠️ Notice: If you encounter CUDA-related issues while you already have `CUDA>=12.0` and installed `pytorch 2.2.2`, you could try mamba 1.2.0.post1 instead of mamba 1.2.0 as follow:
```bash
cd mamba-1_2_0_post1
pip install .
```
---
## Training the Model
### Step 1: Prepare Dataset JSON
Create the dataset JSON file using the script `sh make_dataset.sh`. You may need to modify `make_dataset.sh` and `data/make_dataset_json.py`.
Alternatively, you can directly modify the data paths in `data/train_clean.json`, `data/train_noisy.json`, etc.
### Step 2: Run the following script to train the model.
```bash
sh run.sh
```
📌 Note: You can use `tensorboard --logdir exp/path_to_your_exp/logs` to check your training log
📌 Note: If you would like to train SEMamba with the `pretrained_pesq_discriminator` weights, modify the `run.sh` to use the configuration: `--config recipes/SEMamba_advanced/SEMamba_advanced_pretrainedD.pth`.
---
## Using the Pretrained Model
Modify the `--input_folder` and `--output_folder` parameters in `pretrained.sh` to point to your desired input and output directories. Then, run the script.
```bash
sh pretrained.sh
```
---
## Implementing the PCS Method in SEMamba
> (Updated May 14, 2025) Fix issue: inference.py not parsing bool values correctly
There are two methods to implement the PCS (Perceptual Contrast Stretching) method in SEMamba:
1. Use PCS as Training Target:
- Run the `sh runPCS.sh` with the yaml configuration `use_PCS400=True`.
- Use the pretrained model `sh pretrained.sh` without post-processing `--post_processing_PCS False`.
2. Use PCS as Post-Processing:
- Run the `sh run.sh` with the yaml configuration `use_PCS400=False`.
- Use the pretrained model `sh pretrained.sh` with post-processing `--post_processing_PCS True`.
---
## Evaluation
The evaluation metrics is calculated via: [CMGAN](https://github.com/ruizhecao96/CMGAN/blob/main/src/tools/compute_metrics.py)
## Perceptual Contrast Stretching
The implementation of Perceptual Contrast Stretching (PCS) as discussed in our paper can be found at [PCS400](https://github.com/RoyChao19477/PCS/tree/main/PCS400).
## References and Acknowledgements
We would like to express our gratitude to the authors of [MP-SENet](https://github.com/yxlu-0102/MP-SENet/tree/main), [CMGAN](https://github.com/ruizhecao96/CMGAN), [HiFi-GAN](https://github.com/jik876/hifi-gan/blob/master/train.py), and [NSPP](https://github.com/YangAi520/NSPP).
## Citation:
If you find the paper useful in your research, please cite:
```
@article{chao2024investigation,
title={An Investigation of Incorporating Mamba for Speech Enhancement},
author={Chao, Rong and Cheng, Wen-Huang and La Quatra, Moreno and Siniscalchi, Sabato Marco and Yang, Chao-Han Huck and Fu, Szu-Wei and Tsao, Yu},
journal={arXiv preprint arXiv:2405.06573},
year={2024}
}
```
================================================
FILE: ckpts/config.yaml
================================================
# Environment Settings
# These settings specify the hardware and distributed setup for the model training.
# Adjust `num_gpus` and `dist_config` according to your distributed training environment.
env_setting:
num_gpus: 2 # Number of GPUs. Now we don't support CPU mode.
num_workers: 20 # Number of worker threads for data loading.
seed: 1234 # Seed for random number generators to ensure reproducibility.
stdout_interval: 10
checkpoint_interval: 1000 # save model to ckpt every N steps
validation_interval: 1000
summary_interval: 100
dist_cfg:
dist_backend: nccl # Distributed training backend, 'nccl' for NVIDIA GPUs.
dist_url: tcp://localhost:19477 # URL for initializing distributed training.
world_size: 1 # Total number of processes in the distributed training.
# Datapath Configuratoin
data_cfg:
train_clean_json: data/train_clean.json
train_noisy_json: data/train_noisy.json
valid_clean_json: data/valid_clean.json
valid_noisy_json: data/valid_noisy.json
test_clean_json: data/test_clean.json
test_noisy_json: data/test_noisy.json
# Training Configuration
# This section details parameters that directly influence the training process,
# including batch sizes, learning rates, and optimizer specifics.
training_cfg:
training_epochs: 200 # Training epoch.
batch_size: 4 # Training batch size.
learning_rate: 0.0005 # Initial learning rate.
adam_b1: 0.8 # Beta1 hyperparameter for the AdamW optimizer.
adam_b2: 0.99 # Beta2 hyperparameter for the AdamW optimizer.
lr_decay: 0.99 # Learning rate decay per epoch.
segment_size: 32000 # Audio segment size used during training, dependent on sampling rate.
loss:
metric: 0.05
magnitude: 0.9
phase: 0.3
complex: 0.1
time: 0.2
consistancy: 0.1
use_PCS400: False # Use PCS or not
# STFT Configuration
# Configuration for Short-Time Fourier Transform (STFT), crucial for audio processing models.
stft_cfg:
sampling_rate: 16000 # Audio sampling rate in Hz.
n_fft: 400 # FFT components for transforming audio signals.
hop_size: 100 # Samples between successive frames.
win_size: 400 # Window size used in FFT.
# Model Configuration
# Defines the architecture specifics of the model, including layer configurations and feature compression.
model_cfg:
hid_feature: 64 # Channels in dense layers.
compress_factor: 0.3 # Compression factor applied to extracted features.
num_tfmamba: 4 # Number of Time-Frequency Mamba (TFMamba) blocks in the model.
d_state: 16 # Dimensionality of the state vector in Mamba blocks.
d_conv: 4 # Convolutional layer dimensionality within Mamba blocks.
expand: 4 # Expansion factor for the layers within the Mamba blocks.
norm_epsilon: 0.00001 # Numerical stability in normalization layers within the Mamba blocks.
beta: 2.0 # Hyperparameter for the Learnable Sigmoid function.
input_channel: 2 # Magnitude and Phase
output_channel: 1 # Single Channel Speech Enhancement
================================================
FILE: data/make_dataset_json.py
================================================
import os
import json
import argparse
def list_files_in_directory(directory_path):
# List all files in the directory
files = []
for root, dirs, filenames in os.walk(directory_path):
for filename in filenames:
if filename.endswith('.wav'): # only add .wav files
files.append(os.path.join(root, filename))
return files
def save_files_to_json(files, output_file):
with open(output_file, 'w') as json_file:
json.dump(files, json_file, indent=4)
def make_json(directory_path, output_file):
# Get the list of files and save to JSON
files = list_files_in_directory(directory_path)
save_files_to_json(files, output_file)
# create training set json
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--prefix_path', default='None')
args = parser.parse_args()
prefix = args.prefix_path if (args.prefix_path is not None) else "/mnt/e/Corpora/noisy_vctk/"
## You can manualy modify the clean and noisy path
# ----------------------------------------------------------#
## train_clean
make_json(
os.path.join(prefix, 'clean_trainset_28spk_wav_16k/'),
'data/train_clean.json'
)
## train_noisy
make_json(
os.path.join(prefix, 'noisy_trainset_28spk_wav_16k/'),
'data/train_noisy.json'
)
# ----------------------------------------------------------#
# ----------------------------------------------------------#
# create valid set json
## valid_clean
make_json(
os.path.join(prefix, 'clean_testset_wav_16k/'),
'data/valid_clean.json'
)
## valid_noisy
make_json(
os.path.join(prefix, 'noisy_testset_wav_16k/'),
'data/valid_noisy.json'
)
# ----------------------------------------------------------#
# ----------------------------------------------------------#
# create testing set json
## test_clean
make_json(
os.path.join(prefix, 'clean_testset_wav_16k/'),
'data/test_clean.json'
)
## test_noisy
make_json(
os.path.join(prefix, 'noisy_testset_wav_16k/'),
'data/test_noisy.json'
)
# ----------------------------------------------------------#
if __name__ == '__main__':
main()
================================================
FILE: data/test_clean.json
================================================
[
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_001.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_196.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_002.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_197.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_003.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_198.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_005.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_199.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_006.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_200.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_007.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_201.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_009.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_202.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_010.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_203.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_011.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_204.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_012.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_205.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_013.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_206.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_014.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_207.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_015.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_208.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_016.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_209.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_017.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_210.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_019.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_211.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_020.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_212.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_021.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_213.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_022.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_214.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_023.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_215.wav",
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"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_084.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_085.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_086.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_087.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_088.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_089.wav",
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"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_117.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_118.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_119.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_120.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_121.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_122.wav",
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"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_124.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_125.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_126.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_127.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_128.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_129.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_130.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_131.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_132.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_133.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_135.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_136.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_137.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_138.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_139.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_140.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_141.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_142.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_143.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_144.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_145.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_146.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_147.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_148.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_149.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_150.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_151.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_152.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_153.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_154.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_155.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_156.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_157.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_158.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_159.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_160.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_161.wav",
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"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_163.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_164.wav",
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"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_166.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_167.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_168.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_169.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_170.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_171.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_172.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_173.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_174.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_175.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_176.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_177.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_178.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_179.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_180.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_181.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_182.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_183.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_184.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_185.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_186.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_187.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_188.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_189.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_190.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_191.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_192.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_193.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_194.wav",
"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p257_195.wav"
]
================================================
FILE: data/test_noisy.json
================================================
[
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p232_001.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_196.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p232_002.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_197.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p232_006.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_218.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p232_028.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_219.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p232_029.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_229.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p232_040.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_232.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p232_044.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_235.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_236.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_117.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_118.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_119.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_120.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_121.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_130.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_131.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_133.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_137.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_138.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_140.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_141.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_142.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_144.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_145.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_148.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_149.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_150.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_151.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_152.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_154.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_155.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_158.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_161.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_170.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_171.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_172.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_173.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_174.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_175.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_177.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_178.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_181.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_184.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_185.wav",
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"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_187.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_188.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_189.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_190.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_191.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_192.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_193.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_194.wav",
"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p257_195.wav"
]
================================================
FILE: data/train_clean.json
================================================
[
"/mnt/e/Corpora/noisy_vctk/clean_trainset_28spk_wav_16k/p231_272.wav",
"/mnt/e/Corpora/noisy_vctk/clean_trainset_28spk_wav_16k/p226_001.wav",
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gitextract_9ebfdj2x/
├── .gitignore
├── LICENSE
├── README.md
├── ckpts/
│ ├── SEMamba_advanced.pth
│ ├── config.yaml
│ ├── pretrained_discriminator.pth
│ └── vd.pth
├── data/
│ ├── make_dataset_json.py
│ ├── test_clean.json
│ ├── test_noisy.json
│ ├── train_clean.json
│ ├── train_noisy.json
│ ├── valid_clean.json
│ └── valid_noisy.json
├── dataloaders/
│ └── dataloader_vctk.py
├── inference.py
├── make_dataset.sh
├── mamba-1_2_0_post1/
│ ├── .github/
│ │ └── workflows/
│ │ └── publish.yaml
│ ├── .gitignore
│ ├── .gitmodules
│ ├── AUTHORS
│ ├── LICENSE
│ ├── README.md
│ ├── benchmarks/
│ │ └── benchmark_generation_mamba_simple.py
│ ├── csrc/
│ │ └── selective_scan/
│ │ ├── reverse_scan.cuh
│ │ ├── selective_scan.cpp
│ │ ├── selective_scan.h
│ │ ├── selective_scan_bwd_bf16_complex.cu
│ │ ├── selective_scan_bwd_bf16_real.cu
│ │ ├── selective_scan_bwd_fp16_complex.cu
│ │ ├── selective_scan_bwd_fp16_real.cu
│ │ ├── selective_scan_bwd_fp32_complex.cu
│ │ ├── selective_scan_bwd_fp32_real.cu
│ │ ├── selective_scan_bwd_kernel.cuh
│ │ ├── selective_scan_common.h
│ │ ├── selective_scan_fwd_bf16.cu
│ │ ├── selective_scan_fwd_fp16.cu
│ │ ├── selective_scan_fwd_fp32.cu
│ │ ├── selective_scan_fwd_kernel.cuh
│ │ ├── static_switch.h
│ │ └── uninitialized_copy.cuh
│ ├── evals/
│ │ └── lm_harness_eval.py
│ ├── mamba_ssm/
│ │ ├── __init__.py
│ │ ├── models/
│ │ │ ├── __init__.py
│ │ │ ├── config_mamba.py
│ │ │ └── mixer_seq_simple.py
│ │ ├── modules/
│ │ │ ├── __init__.py
│ │ │ └── mamba_simple.py
│ │ ├── ops/
│ │ │ ├── __init__.py
│ │ │ ├── selective_scan_interface.py
│ │ │ └── triton/
│ │ │ ├── __init__.py
│ │ │ ├── layernorm.py
│ │ │ └── selective_state_update.py
│ │ └── utils/
│ │ ├── __init__.py
│ │ ├── generation.py
│ │ └── hf.py
│ ├── setup.py
│ └── tests/
│ └── ops/
│ ├── test_selective_scan.py
│ └── triton/
│ └── test_selective_state_update.py
├── mamba_install/
│ ├── AUTHORS
│ ├── LICENSE
│ ├── README.md
│ ├── benchmarks/
│ │ └── benchmark_generation_mamba_simple.py
│ ├── csrc/
│ │ └── selective_scan/
│ │ ├── reverse_scan.cuh
│ │ ├── selective_scan.cpp
│ │ ├── selective_scan.h
│ │ ├── selective_scan_bwd_bf16_complex.cu
│ │ ├── selective_scan_bwd_bf16_real.cu
│ │ ├── selective_scan_bwd_fp16_complex.cu
│ │ ├── selective_scan_bwd_fp16_real.cu
│ │ ├── selective_scan_bwd_fp32_complex.cu
│ │ ├── selective_scan_bwd_fp32_real.cu
│ │ ├── selective_scan_bwd_kernel.cuh
│ │ ├── selective_scan_common.h
│ │ ├── selective_scan_fwd_bf16.cu
│ │ ├── selective_scan_fwd_fp16.cu
│ │ ├── selective_scan_fwd_fp32.cu
│ │ ├── selective_scan_fwd_kernel.cuh
│ │ ├── static_switch.h
│ │ └── uninitialized_copy.cuh
│ ├── evals/
│ │ └── lm_harness_eval.py
│ ├── mamba_ssm/
│ │ ├── __init__.py
│ │ ├── models/
│ │ │ ├── __init__.py
│ │ │ ├── config_mamba.py
│ │ │ └── mixer_seq_simple.py
│ │ ├── modules/
│ │ │ ├── __init__.py
│ │ │ └── mamba_simple.py
│ │ ├── ops/
│ │ │ ├── __init__.py
│ │ │ ├── selective_scan_interface.py
│ │ │ └── triton/
│ │ │ ├── __init__.py
│ │ │ ├── layernorm.py
│ │ │ └── selective_state_update.py
│ │ └── utils/
│ │ ├── __init__.py
│ │ ├── generation.py
│ │ └── hf.py
│ ├── setup.py
│ └── tests/
│ └── ops/
│ ├── test_selective_scan.py
│ └── triton/
│ └── test_selective_state_update.py
├── models/
│ ├── codec_module.py
│ ├── discriminator.py
│ ├── generator.py
│ ├── loss.py
│ ├── lsigmoid.py
│ ├── mamba_block.py
│ ├── pcs400.py
│ └── stfts.py
├── pretrained.sh
├── recipes/
│ ├── SEMamba_advanced/
│ │ ├── SEMamba_advanced.yaml
│ │ └── SEMamba_advanced_pretrainedD.yaml
│ └── SEMamba_advanced_PCS/
│ └── SEMamba_advanced_PCS.yaml
├── requirements.txt
├── run.sh
├── runPCS.sh
├── train.py
└── utils/
└── util.py
SYMBOL INDEX (300 symbols across 43 files)
FILE: data/make_dataset_json.py
function list_files_in_directory (line 5) | def list_files_in_directory(directory_path):
function save_files_to_json (line 14) | def save_files_to_json(files, output_file):
function make_json (line 18) | def make_json(directory_path, output_file):
function main (line 26) | def main():
FILE: dataloaders/dataloader_vctk.py
function list_files_in_directory (line 10) | def list_files_in_directory(directory_path):
function load_json_file (line 18) | def load_json_file(file_path):
function extract_identifier (line 23) | def extract_identifier(file_path):
function get_clean_path_for_noisy (line 26) | def get_clean_path_for_noisy(noisy_file_path, clean_path_dict):
class VCTKDemandDataset (line 30) | class VCTKDemandDataset(torch.utils.data.Dataset):
method __init__ (line 49) | def __init__(
method __getitem__ (line 89) | def __getitem__(self, index):
method __len__ (line 136) | def __len__(self):
FILE: inference.py
function str2bool (line 21) | def str2bool(v):
function inference (line 31) | def inference(args, device):
function main (line 77) | def main():
FILE: mamba-1_2_0_post1/csrc/selective_scan/selective_scan.cpp
function set_ssm_params_fwd (line 59) | void set_ssm_params_fwd(SSMParamsBase ¶ms,
function set_ssm_params_bwd (line 143) | void set_ssm_params_bwd(SSMParamsBwd ¶ms,
function selective_scan_fwd (line 226) | std::vector<at::Tensor>
function selective_scan_bwd (line 338) | std::vector<at::Tensor>
function PYBIND11_MODULE (line 494) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
FILE: mamba-1_2_0_post1/csrc/selective_scan/selective_scan.h
type SSMScanParamsBase (line 9) | struct SSMScanParamsBase {
type SSMParamsBase (line 26) | struct SSMParamsBase {
function SSMParamsBase (line 71) | struct SSMParamsBwd: public SSMParamsBase {
FILE: mamba-1_2_0_post1/csrc/selective_scan/selective_scan_common.h
type BytesToType (line 31) | struct BytesToType
type BytesToType (line 36) | struct BytesToType
type BytesToType (line 41) | struct BytesToType
type BytesToType (line 46) | struct BytesToType
type BytesToType (line 51) | struct BytesToType
function __device__ (line 60) | static inline __device__ void to_float(const scalar_t (&src)[N], float (...
function __device__ (line 68) | static inline __device__ void to_float(const at::Half (&src)[N], float (...
function __device__ (line 80) | static inline __device__ void to_float(const at::BFloat16 (&src)[N], flo...
function complex_t (line 94) | complex_t cexp2f(complex_t z) {
function complex_t (line 101) | complex_t cexpf(complex_t z) {
function float (line 111) | struct SSMScanOp<float> {
function complex_t (line 118) | struct SSMScanOp<complex_t> {
function __device__ (line 136) | __device__ SSMScanPrefixCallbackOp(scan_t running_prefix_) : running_pre...
function __device__ (line 139) | __device__ scan_t operator()(scan_t block_aggregate) {
function load_input (line 149) | void load_input(typename Ktraits::input_t *u,
function load_weight (line 166) | void load_weight(typename Ktraits::input_t *Bvar,
function store_output (line 204) | void store_output(typename Ktraits::input_t *out,
FILE: mamba-1_2_0_post1/evals/lm_harness_eval.py
class MambaEvalWrapper (line 15) | class MambaEvalWrapper(HFLM):
method __init__ (line 19) | def __init__(self, pretrained="state-spaces/mamba-2.8b", max_length=20...
method batch_size (line 31) | def batch_size(self):
method _model_generate (line 34) | def _model_generate(self, context, max_length, stop, **generation_kwar...
FILE: mamba-1_2_0_post1/mamba_ssm/models/config_mamba.py
class MambaConfig (line 5) | class MambaConfig:
FILE: mamba-1_2_0_post1/mamba_ssm/models/mixer_seq_simple.py
function create_block (line 24) | def create_block(
function _init_weights (line 54) | def _init_weights(
class MixerModel (line 86) | class MixerModel(nn.Module):
method __init__ (line 87) | def __init__(
method allocate_inference_cache (line 145) | def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None,...
method forward (line 151) | def forward(self, input_ids, inference_params=None):
class MambaLMHeadModel (line 176) | class MambaLMHeadModel(nn.Module, GenerationMixin):
method __init__ (line 178) | def __init__(
method tie_weights (line 222) | def tie_weights(self):
method allocate_inference_cache (line 226) | def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None,...
method forward (line 229) | def forward(self, input_ids, position_ids=None, inference_params=None,...
method from_pretrained (line 242) | def from_pretrained(cls, pretrained_model_name, device=None, dtype=Non...
method save_pretrained (line 249) | def save_pretrained(self, save_directory):
FILE: mamba-1_2_0_post1/mamba_ssm/modules/mamba_simple.py
class Mamba (line 31) | class Mamba(nn.Module):
method __init__ (line 32) | def __init__(
method forward (line 119) | def forward(self, hidden_states, inference_params=None):
method step (line 208) | def step(self, hidden_states, conv_state, ssm_state):
method allocate_inference_cache (line 255) | def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None,...
method _get_states_from_cache (line 268) | def _get_states_from_cache(self, inference_params, batch_size, initial...
class Block (line 297) | class Block(nn.Module):
method __init__ (line 298) | def __init__(
method forward (line 324) | def forward(
method allocate_inference_cache (line 352) | def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None,...
FILE: mamba-1_2_0_post1/mamba_ssm/ops/selective_scan_interface.py
class SelectiveScanFn (line 19) | class SelectiveScanFn(torch.autograd.Function):
method forward (line 22) | def forward(ctx, u, delta, A, B, C, D=None, z=None, delta_bias=None, d...
method backward (line 55) | def backward(ctx, dout, *args):
function selective_scan_fn (line 82) | def selective_scan_fn(u, delta, A, B, C, D=None, z=None, delta_bias=None...
function selective_scan_ref (line 91) | def selective_scan_ref(u, delta, A, B, C, D=None, z=None, delta_bias=Non...
class MambaInnerFn (line 160) | class MambaInnerFn(torch.autograd.Function):
method forward (line 164) | def forward(ctx, xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_...
method backward (line 240) | def backward(ctx, dout):
function mamba_inner_fn (line 311) | def mamba_inner_fn(
function mamba_inner_ref (line 322) | def mamba_inner_ref(
FILE: mamba-1_2_0_post1/mamba_ssm/ops/triton/layernorm.py
function layer_norm_ref (line 19) | def layer_norm_ref(x, weight, bias, residual=None, eps=1e-6, prenorm=Fal...
function rms_norm_ref (line 35) | def rms_norm_ref(x, weight, bias, residual=None, eps=1e-6, prenorm=False...
function _layer_norm_fwd_1pass_kernel (line 65) | def _layer_norm_fwd_1pass_kernel(
function _layer_norm_fwd (line 123) | def _layer_norm_fwd(
function _layer_norm_bwd_kernel (line 196) | def _layer_norm_bwd_kernel(
function _layer_norm_bwd (line 293) | def _layer_norm_bwd(
class LayerNormFn (line 380) | class LayerNormFn(torch.autograd.Function):
method forward (line 382) | def forward(
method backward (line 425) | def backward(ctx, dy, *args):
function layer_norm_fn (line 464) | def layer_norm_fn(
function rms_norm_fn (line 477) | def rms_norm_fn(x, weight, bias, residual=None, prenorm=False, residual_...
class RMSNorm (line 481) | class RMSNorm(torch.nn.Module):
method __init__ (line 482) | def __init__(self, hidden_size, eps=1e-5, device=None, dtype=None):
method reset_parameters (line 490) | def reset_parameters(self):
method forward (line 493) | def forward(self, x, residual=None, prenorm=False, residual_in_fp32=Fa...
class LayerNormLinearFn (line 505) | class LayerNormLinearFn(torch.autograd.Function):
method forward (line 508) | def forward(
method backward (line 567) | def backward(ctx, dout, *args):
function layer_norm_linear_fn (line 612) | def layer_norm_linear_fn(
FILE: mamba-1_2_0_post1/mamba_ssm/ops/triton/selective_state_update.py
function _selective_scan_update_kernel (line 21) | def _selective_scan_update_kernel(
function selective_state_update (line 119) | def selective_state_update(state, x, dt, A, B, C, D=None, z=None, dt_bia...
function selective_state_update_ref (line 202) | def selective_state_update_ref(state, x, dt, A, B, C, D=None, z=None, dt...
FILE: mamba-1_2_0_post1/mamba_ssm/utils/generation.py
class InferenceParams (line 18) | class InferenceParams:
method reset (line 29) | def reset(self, max_seqlen, max_batch_size):
function modify_logits_for_min_p_filtering (line 37) | def modify_logits_for_min_p_filtering(logits, min_p):
function modify_logits_for_top_k_filtering (line 45) | def modify_logits_for_top_k_filtering(logits, top_k):
function modify_logits_for_top_p_filtering (line 53) | def modify_logits_for_top_p_filtering(logits, top_p):
function modify_logit_for_repetition_penalty (line 69) | def modify_logit_for_repetition_penalty(logits, prev_output_tokens, repe...
function sample (line 83) | def sample(logits, top_k=1, top_p=0.0, min_p=0.0, temperature=1.0):
function decode (line 121) | def decode(
class GenerationMixin (line 244) | class GenerationMixin:
method allocate_inference_cache (line 245) | def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None,...
method generate (line 248) | def generate(
class DecodingCGCache (line 269) | class DecodingCGCache:
function update_graph_cache (line 281) | def update_graph_cache(
function capture_graph (line 340) | def capture_graph(
FILE: mamba-1_2_0_post1/mamba_ssm/utils/hf.py
function load_config_hf (line 9) | def load_config_hf(model_name):
function load_state_dict_hf (line 14) | def load_state_dict_hf(model_name, device=None, dtype=None):
FILE: mamba-1_2_0_post1/setup.py
function get_platform (line 47) | def get_platform():
function get_cuda_bare_metal_version (line 62) | def get_cuda_bare_metal_version(cuda_dir):
function check_if_cuda_home_none (line 73) | def check_if_cuda_home_none(global_option: str) -> None:
function append_nvcc_threads (line 85) | def append_nvcc_threads(nvcc_extra_args):
function get_package_version (line 171) | def get_package_version():
function get_wheel_url (line 182) | def get_wheel_url():
class CachedWheelsCommand (line 207) | class CachedWheelsCommand(_bdist_wheel):
method run (line 215) | def run(self):
FILE: mamba-1_2_0_post1/tests/ops/test_selective_scan.py
function test_selective_scan (line 38) | def test_selective_scan(is_variable_B, is_variable_C, varBC_groups, has_...
function test_mamba_inner_fn (line 160) | def test_mamba_inner_fn(is_variable_B, is_variable_C, seqlen, itype, wty...
FILE: mamba-1_2_0_post1/tests/ops/triton/test_selective_state_update.py
function test_selective_state_update (line 22) | def test_selective_state_update(dim, dstate, has_z, itype):
FILE: mamba_install/csrc/selective_scan/selective_scan.cpp
function set_ssm_params_fwd (line 59) | void set_ssm_params_fwd(SSMParamsBase ¶ms,
function set_ssm_params_bwd (line 143) | void set_ssm_params_bwd(SSMParamsBwd ¶ms,
function selective_scan_fwd (line 226) | std::vector<at::Tensor>
function selective_scan_bwd (line 338) | std::vector<at::Tensor>
function PYBIND11_MODULE (line 494) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
FILE: mamba_install/csrc/selective_scan/selective_scan.h
type SSMScanParamsBase (line 9) | struct SSMScanParamsBase {
type SSMParamsBase (line 26) | struct SSMParamsBase {
function SSMParamsBase (line 71) | struct SSMParamsBwd: public SSMParamsBase {
FILE: mamba_install/csrc/selective_scan/selective_scan_common.h
type BytesToType (line 31) | struct BytesToType
type BytesToType (line 36) | struct BytesToType
type BytesToType (line 41) | struct BytesToType
type BytesToType (line 46) | struct BytesToType
type BytesToType (line 51) | struct BytesToType
function __device__ (line 60) | static inline __device__ void to_float(const scalar_t (&src)[N], float (...
function __device__ (line 68) | static inline __device__ void to_float(const at::Half (&src)[N], float (...
function __device__ (line 80) | static inline __device__ void to_float(const at::BFloat16 (&src)[N], flo...
function complex_t (line 94) | complex_t cexp2f(complex_t z) {
function complex_t (line 101) | complex_t cexpf(complex_t z) {
function float (line 111) | struct SSMScanOp<float> {
function complex_t (line 118) | struct SSMScanOp<complex_t> {
function __device__ (line 136) | __device__ SSMScanPrefixCallbackOp(scan_t running_prefix_) : running_pre...
function __device__ (line 139) | __device__ scan_t operator()(scan_t block_aggregate) {
function load_input (line 149) | void load_input(typename Ktraits::input_t *u,
function load_weight (line 166) | void load_weight(typename Ktraits::input_t *Bvar,
function store_output (line 204) | void store_output(typename Ktraits::input_t *out,
FILE: mamba_install/evals/lm_harness_eval.py
class MambaEvalWrapper (line 15) | class MambaEvalWrapper(HFLM):
method __init__ (line 19) | def __init__(self, pretrained="state-spaces/mamba-2.8b", max_length=20...
method batch_size (line 31) | def batch_size(self):
method _model_generate (line 34) | def _model_generate(self, context, max_length, stop, **generation_kwar...
FILE: mamba_install/mamba_ssm/models/config_mamba.py
class MambaConfig (line 5) | class MambaConfig:
FILE: mamba_install/mamba_ssm/models/mixer_seq_simple.py
function create_block (line 24) | def create_block(
function _init_weights (line 54) | def _init_weights(
class MixerModel (line 86) | class MixerModel(nn.Module):
method __init__ (line 87) | def __init__(
method allocate_inference_cache (line 145) | def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None,...
method forward (line 151) | def forward(self, input_ids, inference_params=None):
class MambaLMHeadModel (line 176) | class MambaLMHeadModel(nn.Module, GenerationMixin):
method __init__ (line 178) | def __init__(
method tie_weights (line 222) | def tie_weights(self):
method allocate_inference_cache (line 226) | def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None,...
method forward (line 229) | def forward(self, input_ids, position_ids=None, inference_params=None,...
method from_pretrained (line 242) | def from_pretrained(cls, pretrained_model_name, device=None, dtype=Non...
method save_pretrained (line 249) | def save_pretrained(self, save_directory):
FILE: mamba_install/mamba_ssm/modules/mamba_simple.py
class Mamba (line 31) | class Mamba(nn.Module):
method __init__ (line 32) | def __init__(
method forward (line 119) | def forward(self, hidden_states, inference_params=None):
method step (line 208) | def step(self, hidden_states, conv_state, ssm_state):
method allocate_inference_cache (line 255) | def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None,...
method _get_states_from_cache (line 268) | def _get_states_from_cache(self, inference_params, batch_size, initial...
class Block (line 297) | class Block(nn.Module):
method __init__ (line 298) | def __init__(
method forward (line 324) | def forward(
method allocate_inference_cache (line 352) | def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None,...
FILE: mamba_install/mamba_ssm/ops/selective_scan_interface.py
class SelectiveScanFn (line 19) | class SelectiveScanFn(torch.autograd.Function):
method forward (line 22) | def forward(ctx, u, delta, A, B, C, D=None, z=None, delta_bias=None, d...
method backward (line 55) | def backward(ctx, dout, *args):
function selective_scan_fn (line 82) | def selective_scan_fn(u, delta, A, B, C, D=None, z=None, delta_bias=None...
function selective_scan_ref (line 91) | def selective_scan_ref(u, delta, A, B, C, D=None, z=None, delta_bias=Non...
class MambaInnerFn (line 160) | class MambaInnerFn(torch.autograd.Function):
method forward (line 164) | def forward(ctx, xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_...
method backward (line 240) | def backward(ctx, dout):
function mamba_inner_fn (line 311) | def mamba_inner_fn(
function mamba_inner_ref (line 322) | def mamba_inner_ref(
FILE: mamba_install/mamba_ssm/ops/triton/layernorm.py
function layer_norm_ref (line 19) | def layer_norm_ref(x, weight, bias, residual=None, eps=1e-6, prenorm=Fal...
function rms_norm_ref (line 35) | def rms_norm_ref(x, weight, bias, residual=None, eps=1e-6, prenorm=False...
function _layer_norm_fwd_1pass_kernel (line 65) | def _layer_norm_fwd_1pass_kernel(
function _layer_norm_fwd (line 123) | def _layer_norm_fwd(
function _layer_norm_bwd_kernel (line 196) | def _layer_norm_bwd_kernel(
function _layer_norm_bwd (line 293) | def _layer_norm_bwd(
class LayerNormFn (line 380) | class LayerNormFn(torch.autograd.Function):
method forward (line 382) | def forward(
method backward (line 425) | def backward(ctx, dy, *args):
function layer_norm_fn (line 464) | def layer_norm_fn(
function rms_norm_fn (line 477) | def rms_norm_fn(x, weight, bias, residual=None, prenorm=False, residual_...
class RMSNorm (line 481) | class RMSNorm(torch.nn.Module):
method __init__ (line 482) | def __init__(self, hidden_size, eps=1e-5, device=None, dtype=None):
method reset_parameters (line 490) | def reset_parameters(self):
method forward (line 493) | def forward(self, x, residual=None, prenorm=False, residual_in_fp32=Fa...
class LayerNormLinearFn (line 505) | class LayerNormLinearFn(torch.autograd.Function):
method forward (line 508) | def forward(
method backward (line 567) | def backward(ctx, dout, *args):
function layer_norm_linear_fn (line 612) | def layer_norm_linear_fn(
FILE: mamba_install/mamba_ssm/ops/triton/selective_state_update.py
function _selective_scan_update_kernel (line 21) | def _selective_scan_update_kernel(
function selective_state_update (line 119) | def selective_state_update(state, x, dt, A, B, C, D=None, z=None, dt_bia...
function selective_state_update_ref (line 202) | def selective_state_update_ref(state, x, dt, A, B, C, D=None, z=None, dt...
FILE: mamba_install/mamba_ssm/utils/generation.py
class InferenceParams (line 18) | class InferenceParams:
method reset (line 29) | def reset(self, max_seqlen, max_batch_size):
function modify_logits_for_min_p_filtering (line 37) | def modify_logits_for_min_p_filtering(logits, min_p):
function modify_logits_for_top_k_filtering (line 45) | def modify_logits_for_top_k_filtering(logits, top_k):
function modify_logits_for_top_p_filtering (line 53) | def modify_logits_for_top_p_filtering(logits, top_p):
function modify_logit_for_repetition_penalty (line 69) | def modify_logit_for_repetition_penalty(logits, prev_output_tokens, repe...
function sample (line 83) | def sample(logits, top_k=1, top_p=0.0, min_p=0.0, temperature=1.0):
function decode (line 121) | def decode(
class GenerationMixin (line 244) | class GenerationMixin:
method allocate_inference_cache (line 245) | def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None,...
method generate (line 248) | def generate(
class DecodingCGCache (line 269) | class DecodingCGCache:
function update_graph_cache (line 281) | def update_graph_cache(
function capture_graph (line 340) | def capture_graph(
FILE: mamba_install/mamba_ssm/utils/hf.py
function load_config_hf (line 9) | def load_config_hf(model_name):
function load_state_dict_hf (line 14) | def load_state_dict_hf(model_name, device=None, dtype=None):
FILE: mamba_install/setup.py
function get_platform (line 47) | def get_platform():
function get_cuda_bare_metal_version (line 62) | def get_cuda_bare_metal_version(cuda_dir):
function check_if_cuda_home_none (line 73) | def check_if_cuda_home_none(global_option: str) -> None:
function append_nvcc_threads (line 85) | def append_nvcc_threads(nvcc_extra_args):
function get_package_version (line 171) | def get_package_version():
function get_wheel_url (line 182) | def get_wheel_url():
class CachedWheelsCommand (line 207) | class CachedWheelsCommand(_bdist_wheel):
method run (line 215) | def run(self):
FILE: mamba_install/tests/ops/test_selective_scan.py
function test_selective_scan (line 38) | def test_selective_scan(is_variable_B, is_variable_C, varBC_groups, has_...
function test_mamba_inner_fn (line 160) | def test_mamba_inner_fn(is_variable_B, is_variable_C, seqlen, itype, wty...
FILE: mamba_install/tests/ops/triton/test_selective_state_update.py
function test_selective_state_update (line 22) | def test_selective_state_update(dim, dstate, has_z, itype):
FILE: models/codec_module.py
function get_padding (line 8) | def get_padding(kernel_size, dilation=1):
function get_padding_2d (line 21) | def get_padding_2d(kernel_size, dilation=(1, 1)):
class DenseBlock (line 35) | class DenseBlock(nn.Module):
method __init__ (line 39) | def __init__(self, cfg, kernel_size=(3, 3), depth=4):
method forward (line 56) | def forward(self, x):
class DenseEncoder (line 72) | class DenseEncoder(nn.Module):
method __init__ (line 76) | def __init__(self, cfg):
method forward (line 96) | def forward(self, x):
class MagDecoder (line 111) | class MagDecoder(nn.Module):
method __init__ (line 115) | def __init__(self, cfg):
method forward (line 132) | def forward(self, x):
class PhaseDecoder (line 149) | class PhaseDecoder(nn.Module):
method __init__ (line 153) | def __init__(self, cfg):
method forward (line 168) | def forward(self, x):
FILE: models/discriminator.py
function pesq_loss (line 10) | def pesq_loss(clean, noisy, sr=16000):
function batch_pesq (line 19) | def batch_pesq(clean, noisy, cfg):
class MetricDiscriminator (line 29) | class MetricDiscriminator(nn.Module):
method __init__ (line 30) | def __init__(self, dim=16, in_channel=2):
method forward (line 54) | def forward(self, x, y):
FILE: models/generator.py
class SEMamba (line 7) | class SEMamba(nn.Module):
method __init__ (line 14) | def __init__(self, cfg):
method forward (line 35) | def forward(self, noisy_mag, noisy_pha):
FILE: models/loss.py
function phase_losses (line 9) | def phase_losses(phase_r, phase_g, cfg):
function anti_wrapping_function (line 50) | def anti_wrapping_function(x):
function compute_stft (line 62) | def compute_stft(y: torch.Tensor, n_fft: int, hop_size: int, win_size: i...
function pesq_score (line 103) | def pesq_score(utts_r, utts_g, cfg):
FILE: models/lsigmoid.py
class LearnableSigmoid1D (line 6) | class LearnableSigmoid1D(nn.Module):
method __init__ (line 12) | def __init__(self, in_features, beta=1):
method forward (line 25) | def forward(self, x):
class LearnableSigmoid2D (line 37) | class LearnableSigmoid2D(nn.Module):
method __init__ (line 43) | def __init__(self, in_features, beta=1):
method forward (line 56) | def forward(self, x):
FILE: models/mamba_block.py
function create_block (line 16) | def create_block(
class MambaBlock (line 38) | class MambaBlock(nn.Module):
method __init__ (line 39) | def __init__(self, in_channels, cfg):
method forward (line 52) | def forward(self, x):
class TFMambaBlock (line 68) | class TFMambaBlock(nn.Module):
method __init__ (line 79) | def __init__(self, cfg):
method forward (line 92) | def forward(self, x):
FILE: models/pcs400.py
function Sp_and_phase (line 23) | def Sp_and_phase(signal):
function SP_to_wav (line 38) | def SP_to_wav(mag, phase, signal_length):
function cal_pcs (line 48) | def cal_pcs(signal_wav):
FILE: models/stfts.py
function mag_phase_stft (line 4) | def mag_phase_stft(y, n_fft, hop_size, win_size, compress_factor=1.0, ce...
function mag_phase_istft (line 47) | def mag_phase_istft(mag, pha, n_fft, hop_size, win_size, compress_factor...
FILE: train.py
function setup_optimizers (line 29) | def setup_optimizers(models, cfg):
function setup_schedulers (line 40) | def setup_schedulers(optimizers, cfg, last_epoch):
function create_dataset (line 50) | def create_dataset(cfg, train=True, split=True, device='cuda:0'):
function create_dataloader (line 73) | def create_dataloader(dataset, cfg, train=True):
function train (line 95) | def train(rank, args, cfg):
function main (line 330) | def main():
FILE: utils/util.py
function load_config (line 8) | def load_config(config_path):
function initialize_seed (line 13) | def initialize_seed(seed):
function print_gpu_info (line 19) | def print_gpu_info(num_gpus, cfg):
function initialize_process_group (line 26) | def initialize_process_group(cfg, rank):
function log_model_info (line 35) | def log_model_info(rank, model, exp_path):
function load_ckpts (line 44) | def load_ckpts(args, device):
function load_checkpoint (line 56) | def load_checkpoint(filepath, device):
function save_checkpoint (line 64) | def save_checkpoint(filepath, obj):
function scan_checkpoint (line 70) | def scan_checkpoint(cp_dir, prefix):
function build_env (line 77) | def build_env(config, config_name, exp_path):
function load_optimizer_states (line 83) | def load_optimizer_states(optimizers, state_dict_do):
Condensed preview — 115 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,659K chars).
[
{
"path": ".gitignore",
"chars": 22,
"preview": ".DS_Store\n__pycache__\n"
},
{
"path": "LICENSE",
"chars": 1597,
"preview": "MIT License\n\nCopyright (c) 2024 RONG CHAO\nPortions of this software are Copyright (c) 2023 YXLU-0102 (Original MP-SENet)"
},
{
"path": "README.md",
"chars": 7856,
"preview": "# SEMamba (Accepted to IEEE SLT 2024)\nThis is the official implementation of the SEMamba paper. \nFor more details, plea"
},
{
"path": "ckpts/config.yaml",
"chars": 2986,
"preview": "# Environment Settings\n# These settings specify the hardware and distributed setup for the model training.\n# Adjust `num"
},
{
"path": "data/make_dataset_json.py",
"chars": 2296,
"preview": "import os\nimport json\nimport argparse\n\ndef list_files_in_directory(directory_path):\n # List all files in the director"
},
{
"path": "data/test_clean.json",
"chars": 56034,
"preview": "[\n \"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_001.wav\",\n \"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_1"
},
{
"path": "data/test_noisy.json",
"chars": 56034,
"preview": "[\n \"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p232_001.wav\",\n \"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_1"
},
{
"path": "data/train_clean.json",
"chars": 867902,
"preview": "[\n \"/mnt/e/Corpora/noisy_vctk/clean_trainset_28spk_wav_16k/p231_272.wav\",\n \"/mnt/e/Corpora/noisy_vctk/clean_trains"
},
{
"path": "data/train_noisy.json",
"chars": 867902,
"preview": "[\n \"/mnt/e/Corpora/noisy_vctk/noisy_trainset_28spk_wav_16k/p231_272.wav\",\n \"/mnt/e/Corpora/noisy_vctk/noisy_trains"
},
{
"path": "data/valid_clean.json",
"chars": 56034,
"preview": "[\n \"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_16k/p232_001.wav\",\n \"/mnt/e/Corpora/noisy_vctk/clean_testset_wav_1"
},
{
"path": "data/valid_noisy.json",
"chars": 56034,
"preview": "[\n \"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/p232_001.wav\",\n \"/mnt/e/Corpora/noisy_vctk/noisy_testset_wav_1"
},
{
"path": "dataloaders/dataloader_vctk.py",
"chars": 5618,
"preview": "import os\nimport json\nimport random\nimport torch\nimport torch.utils.data\nimport librosa\nfrom models.stfts import mag_pha"
},
{
"path": "inference.py",
"chars": 3780,
"preview": "import glob\nimport os\nimport argparse\nimport json\nimport torch\nimport librosa\nfrom models.stfts import mag_phase_stft, m"
},
{
"path": "make_dataset.sh",
"chars": 80,
"preview": "python data/make_dataset_json.py \\\n --prefix_path /mnt/e/Corpora/noisy_vctk/\n"
},
{
"path": "mamba-1_2_0_post1/.github/workflows/publish.yaml",
"chars": 9986,
"preview": "# This workflow will:\n# - Create a new Github release\n# - Build wheels for supported architectures\n# - Deploy the wheels"
},
{
"path": "mamba-1_2_0_post1/.gitignore",
"chars": 39,
"preview": "*__pycache__/\n*.egg-info/\nbuild/\n**.so\n"
},
{
"path": "mamba-1_2_0_post1/.gitmodules",
"chars": 144,
"preview": "[submodule \"3rdparty/lm-evaluation-harness\"]\n\tpath = 3rdparty/lm-evaluation-harness\n\turl = https://github.com/EleutherAI"
},
{
"path": "mamba-1_2_0_post1/AUTHORS",
"chars": 53,
"preview": "Tri Dao, tri@tridao.me\nAlbert Gu, agu@andrew.cmu.edu\n"
},
{
"path": "mamba-1_2_0_post1/LICENSE",
"chars": 11348,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "mamba-1_2_0_post1/README.md",
"chars": 8673,
"preview": "# This repository serves as a backup, cloned from the official Mamba Repository:\nhttps://github.com/state-spaces/mamba\n\n"
},
{
"path": "mamba-1_2_0_post1/benchmarks/benchmark_generation_mamba_simple.py",
"chars": 3243,
"preview": "# Copyright (c) 2023, Tri Dao, Albert Gu.\n\nimport argparse\nimport time\nimport json\n\nimport torch\nimport torch.nn.functio"
},
{
"path": "mamba-1_2_0_post1/csrc/selective_scan/reverse_scan.cuh",
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"chars": 2771,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
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"path": "mamba-1_2_0_post1/csrc/selective_scan/selective_scan_bwd_bf16_complex.cu",
"chars": 395,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
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"chars": 391,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
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"path": "mamba-1_2_0_post1/csrc/selective_scan/selective_scan_bwd_fp16_complex.cu",
"chars": 391,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
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"path": "mamba-1_2_0_post1/csrc/selective_scan/selective_scan_bwd_fp16_real.cu",
"chars": 387,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
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"path": "mamba-1_2_0_post1/csrc/selective_scan/selective_scan_bwd_fp32_complex.cu",
"chars": 388,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
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"chars": 384,
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"chars": 33634,
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"chars": 8573,
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"path": "mamba-1_2_0_post1/csrc/selective_scan/selective_scan_fwd_bf16.cu",
"chars": 500,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
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"path": "mamba-1_2_0_post1/csrc/selective_scan/selective_scan_fwd_fp16.cu",
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},
{
"path": "mamba-1_2_0_post1/csrc/selective_scan/static_switch.h",
"chars": 1278,
"preview": "// Inspired by https://github.com/NVIDIA/DALI/blob/main/include/dali/core/static_switch.h\n// and https://github.com/pyto"
},
{
"path": "mamba-1_2_0_post1/csrc/selective_scan/uninitialized_copy.cuh",
"chars": 2646,
"preview": "/******************************************************************************\n * Copyright (c) 2011-2022, NVIDIA CORPO"
},
{
"path": "mamba-1_2_0_post1/evals/lm_harness_eval.py",
"chars": 1287,
"preview": "import torch\n\nimport transformers\nfrom transformers import AutoTokenizer\n\nfrom mamba_ssm.models.mixer_seq_simple import "
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/__init__.py",
"chars": 226,
"preview": "__version__ = \"1.2.0.post1\"\n\nfrom mamba_ssm.ops.selective_scan_interface import selective_scan_fn, mamba_inner_fn\nfrom m"
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/models/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/models/config_mamba.py",
"chars": 357,
"preview": "from dataclasses import dataclass, field\n\n\n@dataclass\nclass MambaConfig:\n\n d_model: int = 2560\n n_layer: int = 64\n"
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/models/mixer_seq_simple.py",
"chars": 9970,
"preview": "# Copyright (c) 2023, Albert Gu, Tri Dao.\n\nimport math\nfrom functools import partial\nimport json\nimport os\n\nfrom collect"
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/modules/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/modules/mamba_simple.py",
"chars": 14291,
"preview": "# Copyright (c) 2023, Tri Dao, Albert Gu.\n\nimport math\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\ni"
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/ops/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/ops/selective_scan_interface.py",
"chars": 16835,
"preview": "# Copyright (c) 2023, Tri Dao, Albert Gu.\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.cuda.amp import custo"
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/ops/triton/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/ops/triton/layernorm.py",
"chars": 21303,
"preview": "# Copyright (c) 2023, Tri Dao.\n# Implement residual + layer_norm / rms_norm.\n\n# Based on the Triton LayerNorm tutorial: "
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/ops/triton/selective_state_update.py",
"chars": 10720,
"preview": "# Copyright (c) 2024, Tri Dao, Albert Gu.\n\n\"\"\"We want triton==2.1.0 or triton==2.2.0 or triton==2.3.0 for this\n\"\"\"\n\nimpo"
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/utils/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/utils/generation.py",
"chars": 15363,
"preview": "# Copyright (c) 2023, Albert Gu, Tri Dao.\nimport gc\nimport time\nfrom collections import namedtuple\nfrom dataclasses impo"
},
{
"path": "mamba-1_2_0_post1/mamba_ssm/utils/hf.py",
"chars": 966,
"preview": "import json\n\nimport torch\n\nfrom transformers.utils import WEIGHTS_NAME, CONFIG_NAME\nfrom transformers.utils.hub import c"
},
{
"path": "mamba-1_2_0_post1/setup.py",
"chars": 10784,
"preview": "# Copyright (c) 2023, Albert Gu, Tri Dao.\nimport sys\nimport warnings\nimport os\nimport re\nimport ast\nfrom pathlib import "
},
{
"path": "mamba-1_2_0_post1/tests/ops/test_selective_scan.py",
"chars": 13064,
"preview": "# Copyright (C) 2023, Tri Dao.\n\nimport math\n\nimport torch\nimport torch.nn.functional as F\nimport pytest\n\nfrom einops imp"
},
{
"path": "mamba-1_2_0_post1/tests/ops/triton/test_selective_state_update.py",
"chars": 2003,
"preview": "# Copyright (C) 2023, Tri Dao.\n\nimport math\n\nimport torch\nimport torch.nn.functional as F\nimport pytest\n\nfrom einops imp"
},
{
"path": "mamba_install/AUTHORS",
"chars": 53,
"preview": "Tri Dao, tri@tridao.me\nAlbert Gu, agu@andrew.cmu.edu\n"
},
{
"path": "mamba_install/LICENSE",
"chars": 11348,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "mamba_install/README.md",
"chars": 8723,
"preview": "# This repository serves as a backup, cloned from the official Mamba Repository:\nhttps://github.com/state-spaces/mamba/t"
},
{
"path": "mamba_install/benchmarks/benchmark_generation_mamba_simple.py",
"chars": 3243,
"preview": "# Copyright (c) 2023, Tri Dao, Albert Gu.\n\nimport argparse\nimport time\nimport json\n\nimport torch\nimport torch.nn.functio"
},
{
"path": "mamba_install/csrc/selective_scan/reverse_scan.cuh",
"chars": 17978,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan.cpp",
"chars": 21714,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan.h",
"chars": 2771,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan_bwd_bf16_complex.cu",
"chars": 395,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan_bwd_bf16_real.cu",
"chars": 391,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan_bwd_fp16_complex.cu",
"chars": 391,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan_bwd_fp16_real.cu",
"chars": 387,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan_bwd_fp32_complex.cu",
"chars": 388,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan_bwd_fp32_real.cu",
"chars": 384,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan_bwd_kernel.cuh",
"chars": 33634,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan_common.h",
"chars": 8573,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan_fwd_bf16.cu",
"chars": 500,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan_fwd_fp16.cu",
"chars": 492,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan_fwd_fp32.cu",
"chars": 486,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/selective_scan_fwd_kernel.cuh",
"chars": 19247,
"preview": "/******************************************************************************\n * Copyright (c) 2023, Tri Dao.\n *******"
},
{
"path": "mamba_install/csrc/selective_scan/static_switch.h",
"chars": 1278,
"preview": "// Inspired by https://github.com/NVIDIA/DALI/blob/main/include/dali/core/static_switch.h\n// and https://github.com/pyto"
},
{
"path": "mamba_install/csrc/selective_scan/uninitialized_copy.cuh",
"chars": 2646,
"preview": "/******************************************************************************\n * Copyright (c) 2011-2022, NVIDIA CORPO"
},
{
"path": "mamba_install/evals/lm_harness_eval.py",
"chars": 1287,
"preview": "import torch\n\nimport transformers\nfrom transformers import AutoTokenizer\n\nfrom mamba_ssm.models.mixer_seq_simple import "
},
{
"path": "mamba_install/mamba_ssm/__init__.py",
"chars": 220,
"preview": "__version__ = \"1.2.2\"\n\nfrom mamba_ssm.ops.selective_scan_interface import selective_scan_fn, mamba_inner_fn\nfrom mamba_s"
},
{
"path": "mamba_install/mamba_ssm/models/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "mamba_install/mamba_ssm/models/config_mamba.py",
"chars": 357,
"preview": "from dataclasses import dataclass, field\n\n\n@dataclass\nclass MambaConfig:\n\n d_model: int = 2560\n n_layer: int = 64\n"
},
{
"path": "mamba_install/mamba_ssm/models/mixer_seq_simple.py",
"chars": 9970,
"preview": "# Copyright (c) 2023, Albert Gu, Tri Dao.\n\nimport math\nfrom functools import partial\nimport json\nimport os\n\nfrom collect"
},
{
"path": "mamba_install/mamba_ssm/modules/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "mamba_install/mamba_ssm/modules/mamba_simple.py",
"chars": 14291,
"preview": "# Copyright (c) 2023, Tri Dao, Albert Gu.\n\nimport math\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\ni"
},
{
"path": "mamba_install/mamba_ssm/ops/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "mamba_install/mamba_ssm/ops/selective_scan_interface.py",
"chars": 16835,
"preview": "# Copyright (c) 2023, Tri Dao, Albert Gu.\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.cuda.amp import custo"
},
{
"path": "mamba_install/mamba_ssm/ops/triton/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "mamba_install/mamba_ssm/ops/triton/layernorm.py",
"chars": 21303,
"preview": "# Copyright (c) 2023, Tri Dao.\n# Implement residual + layer_norm / rms_norm.\n\n# Based on the Triton LayerNorm tutorial: "
},
{
"path": "mamba_install/mamba_ssm/ops/triton/selective_state_update.py",
"chars": 10720,
"preview": "# Copyright (c) 2024, Tri Dao, Albert Gu.\n\n\"\"\"We want triton==2.1.0 or triton==2.2.0 or triton==2.3.0 for this\n\"\"\"\n\nimpo"
},
{
"path": "mamba_install/mamba_ssm/utils/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "mamba_install/mamba_ssm/utils/generation.py",
"chars": 15363,
"preview": "# Copyright (c) 2023, Albert Gu, Tri Dao.\nimport gc\nimport time\nfrom collections import namedtuple\nfrom dataclasses impo"
},
{
"path": "mamba_install/mamba_ssm/utils/hf.py",
"chars": 966,
"preview": "import json\n\nimport torch\n\nfrom transformers.utils import WEIGHTS_NAME, CONFIG_NAME\nfrom transformers.utils.hub import c"
},
{
"path": "mamba_install/setup.py",
"chars": 10784,
"preview": "# Copyright (c) 2023, Albert Gu, Tri Dao.\nimport sys\nimport warnings\nimport os\nimport re\nimport ast\nfrom pathlib import "
},
{
"path": "mamba_install/tests/ops/test_selective_scan.py",
"chars": 13064,
"preview": "# Copyright (C) 2023, Tri Dao.\n\nimport math\n\nimport torch\nimport torch.nn.functional as F\nimport pytest\n\nfrom einops imp"
},
{
"path": "mamba_install/tests/ops/triton/test_selective_state_update.py",
"chars": 2003,
"preview": "# Copyright (C) 2023, Tri Dao.\n\nimport math\n\nimport torch\nimport torch.nn.functional as F\nimport pytest\n\nfrom einops imp"
},
{
"path": "models/codec_module.py",
"chars": 6296,
"preview": "# Reference: https://github.com/yxlu-0102/MP-SENet/blob/main/models/generator.py\n\nimport torch\nimport torch.nn as nn\nfro"
},
{
"path": "models/discriminator.py",
"chars": 2057,
"preview": "# References: https://github.com/yxlu-0102/MP-SENet/blob/main/models/discriminator.py\n\nimport torch\nimport torch.nn as n"
},
{
"path": "models/generator.py",
"chars": 2666,
"preview": "import torch\nimport torch.nn as nn\nfrom einops import rearrange\nfrom .mamba_block import TFMambaBlock\nfrom .codec_module"
},
{
"path": "models/loss.py",
"chars": 5496,
"preview": "# Reference: https://github.com/yxlu-0102/MP-SENet/blob/main/models/generator.py\n\nimport torch\nimport torch.nn as nn\nimp"
},
{
"path": "models/lsigmoid.py",
"chars": 2139,
"preview": "# Reference: https://github.com/yxlu-0102/MP-SENet/blob/main/utils.py\n\nimport torch\nimport torch.nn as nn\n\nclass Learnab"
},
{
"path": "models/mamba_block.py",
"chars": 4306,
"preview": "# Reference: https://github.com/state-spaces/mamba/blob/9127d1f47f367f5c9cc49c73ad73557089d02cb8/mamba_ssm/models/mixer_"
},
{
"path": "models/pcs400.py",
"chars": 1490,
"preview": "import os\nimport torch\nimport torchaudio\nimport numpy as np\nimport argparse\nimport librosa\nimport scipy\n\n# PCS400 parame"
},
{
"path": "models/stfts.py",
"chars": 2731,
"preview": "import torch\nimport torch.nn as nn\n\ndef mag_phase_stft(y, n_fft, hop_size, win_size, compress_factor=1.0, center=True, a"
},
{
"path": "pretrained.sh",
"chars": 294,
"preview": "CUDA_VISIBLE_DEVICES='0' python inference.py \\\n --input_folder /mnt/e/Corpora/noisy_vctk/noisy_testset_wav_16k/ \\\n -"
},
{
"path": "recipes/SEMamba_advanced/SEMamba_advanced.yaml",
"chars": 2986,
"preview": "# Environment Settings\n# These settings specify the hardware and distributed setup for the model training.\n# Adjust `num"
},
{
"path": "recipes/SEMamba_advanced/SEMamba_advanced_pretrainedD.yaml",
"chars": 3010,
"preview": "# Environment Settings\n# These settings specify the hardware and distributed setup for the model training.\n# Adjust `num"
},
{
"path": "recipes/SEMamba_advanced_PCS/SEMamba_advanced_PCS.yaml",
"chars": 2985,
"preview": "# Environment Settings\n# These settings specify the hardware and distributed setup for the model training.\n# Adjust `num"
},
{
"path": "requirements.txt",
"chars": 99,
"preview": "packaging\nlibrosa\nsoundfile\npyyaml\nargparse\ntensorboard\npesq\neinops\ntorch==2.2.2\ntorchaudio==2.2.2\n"
},
{
"path": "run.sh",
"chars": 150,
"preview": "CUDA_VISIBLE_DEVICES='0' python train.py \\\n --config recipes/SEMamba_advanced/SEMamba_advanced.yaml \\\n --exp_folder ex"
},
{
"path": "runPCS.sh",
"chars": 162,
"preview": "CUDA_VISIBLE_DEVICES='0' python train.py \\\n --config recipes/SEMamba_advanced_PCS/SEMamba_advanced_PCS.yaml \\\n --exp_f"
},
{
"path": "train.py",
"chars": 17334,
"preview": "import warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\nimport os\nimport time\nimport argparse\nimp"
},
{
"path": "utils/util.py",
"chars": 3144,
"preview": "import yaml\nimport torch\nimport os\nimport shutil\nimport glob\nfrom torch.distributed import init_process_group\n\ndef load_"
}
]
// ... and 3 more files (download for full content)
About this extraction
This page contains the full source code of the RoyChao19477/SEMamba GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 115 files (2.4 MB), approximately 644.9k tokens, and a symbol index with 300 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.