muzic
PDAugment
PDAugment: Data Augmentation by Pitch and Duration Adjustments for Automatic Lyrics Transcription, by Chen Zhang, Jiaxing Yu, LuChin Chang, Xu Tan, Jiawei Chen, Tao Qin, Kejun Zhang, ISMIR 2022, is a data augmentation method that adjusts pitch and duration of speech to help the training of automatic lyrics transcription. It adjusts the pitch and duration of each syllable in natural speech to those of the corresponding note extracted from music scores, to narrow the gap between natural speech and singing voice. PDAugment outperforms previous state-of-the-art systems by 5.9% and 18.1% WERs on lyrics transription datasets DSing30 and Dali respectively.
The pipeline of automatic lyrics transcription system equipped with PDAugment
Pitch (left) and duration (right) adjustment
1. Data Preparation
1.1 Speech (LibriSpeech)
Download
We use LibriSpeech ASR corpus as the speech corpus for augmentation.
Download and save the LibriSpeech ASR Corpus in data/speech/raw.
The default format of the downloaded audios is flac. We convert flac to wav by running:
sudo apt install ffmpeg # Note that `ffmpeg` is needed
python flac2wav.py <flac_dir> <output_wav_dir> # e.g., python flac2wav.py data/speech/raw/dev-clean data/speech/wav
wav file will be saved in data/speech/wav.
Take data/speech/wav/dev-clean for example, the directory of dev-clean is as follows:
├── dev-clean
│ └── 84
│ ├── 121123
│ ├── 84-121123-0000.wav
│ ├── 84-121123-0001.wav
│ ├── 84-121123-0002.wav
│ ├── ...
│ └── 84-121123.trans.txt
│ ├── ...
│ ├── ...
Text to Phone
PDAugment needs phoneme level text. So we should convert the text in data/speech/wav from word to phoneme by an open-source tool named phonemizer. Here we provide the python code (text2phone.py).
python text2phone.py <dataset_dir> <output_dir>
# e.g, python text2phone.py data/speech/wav/dev-clean data/speech/phone
The output of the above code is an csv file containing metadata information about LibriSpeech ASR corpus and is saved in data/speech/phone.
|wav |new_wav |txt |phone |new_phone|
|--------|---------|--------|---------|---------|
Phoneme Level Alignment
After converting the text from word to phoneme, we obtain the phoneme level alignment of the speech audio by the Montreal forced aligner (MFA) tool. Here we follow the official example.
After MFA and data processing, we get the pickle file as follows:
(the list corresponding to an audio contains the split positions between each adjacent phoneme in this utterance)
{
"174-168635-0000.wav" : [0, 12, 18, 20...],
"174-168635-0001.wav" : [0, 12, 27, 35...],
"174-168635-0002.wav" : [0, 13, 26, 33...],
...
}
We can view the file as a dictionary and save the pickle file in data/pickle.
1.2 Music Scores (FreeMidi)
Download
In addition to the speech dataset, we also need a music dataset to provide guidance of melody. FreeMidi Dataset is a good choice. We only use the pop songs among them which contains about 4000 midi files.
Download and save the original pop songs midis in data/midis/raw
Preprocessing
In order to extract the note information of the melody, we need to clean the original multitrack data and recognize the lead track.
conda create -n midi python=3.6 -y
conda activate midi
pip install -r midi_preprocess/requirements.txt
PYTHONPATH=. python midi_preprocess/preprocess.py --config midi_preprocess/configs/default.yaml
Then the processed midi files can be found in data/midis/processed/midi_6tracks
2. PDAugment
After getting the phonemes, alignment and melody, we can now augment the dataset by PDAugment.
Here we provide pdaugment.py.
Requirements
We need to meet all the requirements of PDAugment.
conda create -n pdaugment python=3.6 -y
conda activate pdaugment
sudo apt-get install libsasl2-dev python-dev libldap2-dev libssl-dev -y
pip install -r requirements.txt
Augmentation
We need following command line parameters.
pickle_path # directory of pickle data, e.g., data/pickle/mel_splits.pickle
frequency_json_file # mapping between semitone and frequency, e.g., utils/frequency.json
dataset_dir # directory of dataset, e.g., data/speech/wav/dev-clean
midi_file_fir # directory of midi file, e.g., data/midis/processed/midi_6tracks
metadata_dir # metadata information of dataset, e.g., data/speech/phone/dev-clean_metadata.csv
output_duration_dir # output directory of duration-augmented wav, e.g., data/duration
output_pitch_dir # output directory of pitch-augmented wav, e.g., data/pitch
output_pdaugment_dir # output directory of PDAugmented wav, e.g., data/pdaugment
selected_dir # select directory for training, validating and testing, e.g., ${output_pdaugment_dir}
You can augment the speech dataset with multiple threads by setting:
number_of_threads # number of threads to process, e.g., 16
Finally run the pdaugment.py.
python pdaugment.py <pickle_path> <frequency_json_file> <dataset_dir> <midi_file_fir> <metadata_dir> <output_duration_dir> <output_pitch_dir> <output_pdaugment_dir> <number_of_threads>
# e.g., python pdaugment.py data/pickle/mel_splits.pickle utils/frequency.json data/speech/wav/dev-clean data/midis/processed/midi_6tracks data/speech/phone/dev-clean_metadata.csv data/duration data/pitch data/pdaugment 16
The corresponding augmented audios will be generated and saved in certain directories.