Full Code of ASLP-lab/SongEval for AI

Repository: ASLP-lab/SongEval
Branch: main
Commit: 848fb2ff3a2a
Files: 7
Total size: 96.1 MB

Directory structure:
gitextract_fjw2lbx4/

├── LICENSE
├── README.md
├── ckpt/
│   └── model.safetensors
├── config.yaml
├── eval.py
├── model.py
└── requirements.txt

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FILE CONTENTS
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FILE: LICENSE
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FILE: README.md
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# 🎵 SongEval: A Benchmark Dataset for Song Aesthetics Evaluation

[![Hugging Face Dataset](https://img.shields.io/badge/HuggingFace-Dataset-blue)](https://huggingface.co/datasets/ASLP-lab/SongEval)
[![Arxiv Paper](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/pdf/2505.10793)
[![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)  


This repository provides a **trained aesthetic evaluation toolkit** based on [SongEval](https://huggingface.co/datasets/ASLP-lab/SongEval), the first large-scale, open-source dataset for human-perceived song aesthetics. The toolkit enables **automatic scoring of generated song** across five perceptual aesthetic dimensions aligned with professional musician judgments.

---

## 🌟 Key Features

- 🧠 **Pretrained neural models** for perceptual aesthetic evaluation
- 🎼 Predicts **five aesthetic dimensions**:
  - Overall Coherence
  - Memorability
  - Naturalness of Vocal Breathing and Phrasing
  - Clarity of Song Structure
  - Overall Musicality
<!-- - 🧪 Supports **batch evaluation** for model benchmarking -->
- 🎧 Accepts **full-length songs** (vocals + accompaniment) as input
- ⚙️ Simple inference interface

---

## 📦 Installation

Clone the repository and install dependencies:

```bash
git clone https://github.com/ASLP-lab/SongEval.git
cd SongEval
pip install -r requirements.txt
```

## 🚀 Quick Start

- Evaluate a single audio file:

```bash
python eval.py -i /path/to/audio.mp3 -o /path/to/output
```

- Evaluate a list of audio files:

```bash
python eval.py -i /path/to/audio_list.txt -o /path/to/output
```

- Evaluate all audio files in a directory:

```bash
python eval.py -i /path/to/audio_directory -o /path/to/output
```

- Force evaluation on CPU  (⚠️ CPU evaluation may be significantly slower) :


```bash
python eval.py -i /path/to/audio.wav -o /path/to/output --use_cpu True
```


## 🙏 Acknowledgement
This project is mainly organized by the audio, speech and language processing lab [(ASLP@NPU)](http://www.npu-aslp.org/).

We sincerely thank the **Shanghai Conservatory of Music** for their expert guidance on music theory, aesthetics, and annotation design.
Meanwhile, we thank AISHELL to help with the orgnization of the song annotations.

<p align="center"> <img src="assets/logo.png" alt="Shanghai Conservatory of Music Logo"/> </p>

## 📑 License
This project is released under the CC BY-NC-SA 4.0 license. 

You are free to use, modify, and build upon it for non-commercial purposes, with attribution.

## 📚 Citation
If you use this toolkit or the SongEval dataset, please cite the following:
```
@article{yao2025songeval,
  title   = {SongEval: A Benchmark Dataset for Song Aesthetics Evaluation},
  author  = {Yao, Jixun and Ma, Guobin and Xue, Huixin and Chen, Huakang and Hao, Chunbo and Jiang, Yuepeng and Liu, Haohe and Yuan, Ruibin and Xu, Jin and Xue, Wei and others},
  journal = {arXiv preprint arXiv:2505.10793},
  year={2025}
}

```


================================================
FILE: ckpt/model.safetensors
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[File too large to display: 96.1 MB]

================================================
FILE: config.yaml
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generator:
  _target_: model.Generator
  in_features: 1024
  ffd_hidden_size: 4096
  num_classes: 5
  attn_layer_num: 4

================================================
FILE: eval.py
================================================
import glob
import os
import json
import librosa
import numpy as np
import torch
import argparse
from muq import MuQ
from hydra.utils import instantiate
from omegaconf import OmegaConf
from safetensors.torch import load_file
from tqdm import tqdm



class Synthesizer(object):

    def __init__(self,
                 checkpoint_path,
                 input_path,
                 output_dir,
                 use_cpu: bool = False):
    
        self.checkpoint_path = checkpoint_path
        self.input_path = input_path
        self.output_dir = output_dir
        os.makedirs(self.output_dir, exist_ok=True)
        self.device = torch.device('cuda') if (torch.cuda.is_available() and (not use_cpu)) else torch.device('cpu')
    
    @torch.no_grad()
    def setup(self):
        
        train_config = OmegaConf.load(os.path.join(os.path.dirname(self.checkpoint_path), '../config.yaml'))
        model = instantiate(train_config.generator).to(self.device).eval()
        state_dict = load_file(self.checkpoint_path, device="cpu")
        model.load_state_dict(state_dict, strict=False)

        self.model = model
        self.muq = MuQ.from_pretrained("OpenMuQ/MuQ-large-msd-iter")
        self.muq = self.muq.to(self.device).eval()
        self.result_dcit = {}
        
    @torch.no_grad()
    def synthesis(self):
        if os.path.isfile(self.input_path):
            if self.input_path.endswith(('.wav', '.mp3')):
                lines = []
                lines.append(self.input_path)
            else:
                with open(self.input_path, "r") as f:
                    lines = [line for line in f]
            input_files = [{
                "input_path": line.strip(), 
            } for line in lines]
            print(f"input filelst: {self.input_path}")
        elif os.path.isdir(self.input_path):
            input_files = [{
                "input_path": file,
            }for file in glob.glob(os.path.join(self.input_path, '*')) if file.lower().endswith(('.wav', '.mp3'))]
        else:
            raise ValueError(f"input_path {self.input_path} is not a file or directory")
        
        
        for input in tqdm(input_files):
            self.handle(**input)
        with open(os.path.join(self.output_dir, "result.json") , "w")as f:
            json.dump(self.result_dcit, f, indent=4, ensure_ascii=False)
        
    @torch.no_grad()
    def handle(self, input_path):
        
        fid = os.path.basename(input_path).split('.')[0]
        if input_path.endswith('.npy'):
            input = np.load(input_path)
            
            # check ssl
            if len(input.shape) == 3 and input.shape[0] != 1:
                print('ssl_shape error', input_path)
                return
            if np.isnan(input).any():
                print('ssl nan', input_path)
                return
            
            input = torch.from_numpy(input).to(self.device)
            if len(input.shape) == 2:
                input = input.unsqueeze(0)

        if input_path.endswith(('.wav', '.mp3')):
            wav, sr = librosa.load(input_path, sr=24000)
            audio = torch.tensor(wav).unsqueeze(0).to(self.device)
            output = self.muq(audio, output_hidden_states=True)
            input = output["hidden_states"][6]
            
        values = {} 
        scores_g = self.model(input).squeeze(0)
        values['Coherence'] = round(scores_g[0].item(), 4)
        values['Musicality'] = round(scores_g[1].item(), 4)
        values['Memorability'] = round(scores_g[2].item(), 4)
        values['Clarity'] = round(scores_g[3].item(), 4)
        values['Naturalness'] = round(scores_g[4].item(), 4)
        
        
        self.result_dcit[fid] = values
        
        
if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-i", "--input_path",
        type=str,
        required=True,
        help="Input audio: path to a single file, a text file listing audio paths, or a directory of audio files."
    )
    parser.add_argument(
        "-o", "--output_dir",
        type=str,
        required=True,
        help="Output directory for generated results (will be created if it doesn't exist)."
    )
    parser.add_argument(
        "--use_cpu",
        type=str,
        help="Force CPU mode even if a GPU is available.",
        default=False
    )
    
    args = parser.parse_args()
    
    ckpt_path = "ckpt/model.safetensors"
    
    synthesizer = Synthesizer(checkpoint_path=ckpt_path,
                              input_path=args.input_path,
                              output_dir=args.output_dir,
                              use_cpu=args.use_cpu)
    
    synthesizer.setup()
    
    synthesizer.synthesis()

================================================
FILE: model.py
================================================
from einops import rearrange
import numpy as np
import torch
import torch.nn as nn


class Generator(nn.Module):

    def __init__(self,
                 in_features,
                 ffd_hidden_size,
                 num_classes,
                 attn_layer_num,
                 
                 ):
        super(Generator, self).__init__()
        
        self.attn = nn.ModuleList(
            [
                nn.MultiheadAttention(
                    embed_dim=in_features,
                    num_heads=8,
                    dropout=0.2,
                    batch_first=True,
                )
                for _ in range(attn_layer_num)
            ]
        )
        
        self.ffd = nn.Sequential(
            nn.Linear(in_features, ffd_hidden_size),
            nn.ReLU(),
            nn.Linear(ffd_hidden_size, in_features)
        )
        
        self.dropout = nn.Dropout(0.2)
        
        self.fc =  nn.Linear(in_features * 2, num_classes)
        
        self.proj = nn.Tanh()
        

    def forward(self, ssl_feature, judge_id=None):
        '''
        ssl_feature: [B, T, D]   
        output: [B, num_classes]
        '''
        
        B, T, D = ssl_feature.shape
        
        ssl_feature = self.ffd(ssl_feature)
        
        tmp_ssl_feature = ssl_feature
        
        for attn in self.attn:
            tmp_ssl_feature, _ = attn(tmp_ssl_feature, tmp_ssl_feature, tmp_ssl_feature)
    
        ssl_feature = self.dropout(torch.concat([torch.mean(tmp_ssl_feature, dim=1), torch.max(ssl_feature, dim=1)[0]], dim=1))  # B, 2D
        
        x = self.fc(ssl_feature)  # B, num_classes
        
        x = self.proj(x) * 2.0 + 3
        
        return x
    
    


================================================
FILE: requirements.txt
================================================
librosa==0.11.0
torch==2.7.0
muq==0.1.0
hydra-core==1.3.2