SOFA: Singing-Oriented Forced Aligner

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Introduction

SOFA (Singing-Oriented Forced Aligner) is a forced alignment tool designed specifically for singing voice.

On singing data, SOFA has the following advantages over MFA (Montreal Forced Aligner):

• Easier installation
• Better performance
• Faster inference speed

How to Use

Environment Setup

1. Use git clone to download the code from this repository
2. Install conda
3. Create a conda environment, requiring Python version 3.8

   conda create -n SOFA python=3.8 -y
   conda activate SOFA

4. Go to the pytorch official website to install torch
5. (Optional, to improve wav file reading speed) Go to the pytorch official website to install torchaudio
6. Install other Python libraries

   pip install -r requirements.txt

Inference

1. Download the model file. You can find the trained models in the pretrained model sharing category of the discussion section, with the file extension .ckpt.

2. Place the dictionary file in the /dictionary folder. The default dictionary is opencpop-extension.txt

3. Prepare the data for forced alignment and place it in a folder (by default in the /segments folder), with the following format

   - segments
       - singer1
           - segment1.lab
           - segment1.wav
           - segment2.lab
           - segment2.wav
           - ...
       - singer2
           - segment1.lab
           - segment1.wav
           - ...
   

   Ensure that the .wav files and their corresponding .lab files are in the same folder.

   The .lab file is the transcription for the .wav file with the same name. The file extension for the transcription can be changed using the --in_format parameter.

   After the transcription is converted into a phoneme sequence by the g2p module, it is fed into the model for alignment.

   For example, when using the DictionaryG2P module and the opencpop-extension dictionary by default, if the content of the transcription is: gan shou ting zai wo fa duan de zhi jian, the g2p module will convert it based on the dictionary into the phoneme sequence g an sh ou t ing z ai w o f a duan d e zh ir j ian. For how to use other g2p modules, see g2p module usage instructions.

4. Command-line inference

   Use python infer.py to perform inference.

   Parameters that need to be specified:

   • --ckpt: (must be specified) The path to the model weights;
   • --folder: The folder where the data to be aligned is stored (default is segments);
   • --in_format: The file extension of the transcription (default is lab);
   • --out_formats: The annotation format of the inferred files, multiple formats can be specified, separated by commas (default is TextGrid,htk,trans).
   • --save_confidence: Output confidence scores.
   • --dictionary: The dictionary file (default is dictionary/opencpop-extension.txt);

   python infer.py --ckpt checkpoint_path --folder segments_path --dictionary dictionary_path --out_formats output_format1,output_format2...

5. Retrieve the Final Annotation

   The final annotation is saved in a folder, the name of which is the annotation format you have chosen. This folder is located in the same directory as the wav files used for inference.

Advanced Features

• Using a custom g2p instead of a dictionary
  • See g2p module instructions
• In the matching mode, you can activate it by specifying -m during inference. It finds the most probable contiguous sequence segment within the given phoneme sequence, rather than having to use all the phonemes.

Training

1. Follow the steps above for setting up the environment. It is recommended to install torchaudio for faster binarization speed;

2. Place the training data in the data folder in the following format:

   - data
       - full_label
           - singer1
               - wavs
                   - audio1.wav
                   - audio2.wav
                   - ...
               - transcriptions.csv
           - singer2
               - wavs
                   - ...
               - transcriptions.csv
       - weak_label
           - singer3
               - wavs
                   - ...
               - transcriptions.csv
           - singer4
               - wavs
                   - ...
               - transcriptions.csv
       - no_label
           - audio1.wav
           - audio2.wav
           - ...
   

   Regarding the format of transcriptions.csv, see: qiuqiao#5

   Where:

   transcriptions.csv only needs to have the correct relative path to the wavs folder;

   The transcriptions.csv in weak_label does not need to have a ph_dur column;

3. Modify binarize_config.yaml as needed, then execute python binarize.py;

4. Download the pre-trained model you need from releases, modify train_config.yaml as needed, then execute python train.py -p path_to_your_pretrained_model;

5. For training visualization: tensorboard --logdir=ckpt/.

Evaluation (for model developers)

To measure the performance of a model, it is useful to calculate some objective evaluation metrics between the predictions (force-aligned labels) and the targets (manual labels), especially in a k-fold cross-validation.

Some useful metrics are:

• Boundary Edit Distance: the total moving distance from the predicted boundaries to the target boundaries.
• Boundary Edit Ratio: the boundary edit distance divided by the total duration of target intervals.
• Boundary Error Rate: the proportion of misplaced boundaries to all target boundaries under a given tolerance of distance.

To evaluate your model on a specific dataset, please first run the inference to get all predictions. You should put your predictions and targets in different folders, with same filenames and relative paths, containing the same phone sequences except for spaces. The script only supports TextGrid format currently.

Run the following command:

python evaluate.py <PRED_DIR> <TARGET_DIR> -r -s

where PRED_DIR is a directory containing all predictions and TARGET_DIR is a directory containing all targets.

Options:

• -r, --recursive: compare the files in subdirectories recursively
• -s, --strict: use strict mode (raise errors instead of skipping if the phones are not identical)
• --ignore: ignore some phone marks (default: AP,SP,<AP>,<SP>,,pau,cl)

The script will calculate:

• The boundary edit ratio
• The boundary error rate, under 10ms, 20ms and 50ms tolerance