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5.5.0
DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism
Substantial update: We 1) abandon the explicit prediction of the F0 curve; 2) increase the receptive field of the denoiser; 3) make the linguistic encoder more robust. By doing so, 1) the synthesized recordings are more natural in terms of pitch; 2) the pipeline is simpler.
简而言之,把F0曲线的动态性交给生成式模型去捕捉,而不再是以前那样用MSE约束对数域F0。
DiffSinger (MIDI SVS | B version)
0. Data Acquirement
For Opencpop dataset: Please strictly follow the instructions of Opencpop. We have no right to give you the access to Opencpop.
The pipeline below is designed for Opencpop dataset:
1. Preparation
Data Preparation
a) Download and extract Opencpop, then create a link to the dataset folder: ln -s /xxx/opencpop data/raw/
b) Run the following scripts to pack the dataset for training/inference.
export PYTHONPATH=.
CUDA_VISIBLE_DEVICES=0 python data_gen/tts/bin/binarize.py --config usr/configs/midi/cascade/opencs/aux_rel.yaml
# `data/binary/opencpop-midi-dp` will be generated.
Vocoder Preparation
We provide the pre-trained model of HifiGAN-Singing which is specially designed for SVS with NSF mechanism.
Also, please unzip pre-trained vocoder and this pendant for vocoder into checkpoints
before training your acoustic model.
(Update: You can also move a ckpt with more training steps into this vocoder directory)
This singing vocoder is trained on ~70 hours singing data, which can be viewed as a universal vocoder.
Exp Name Preparation
export MY_DS_EXP_NAME=0228_opencpop_ds100_rel
.
|--data
|--raw
|--opencpop
|--segments
|--transcriptions.txt
|--wavs
|--checkpoints
|--MY_DS_EXP_NAME (optional)
|--0109_hifigan_bigpopcs_hop128 (vocoder)
|--model_ckpt_steps_1512000.ckpt
|--config.yaml
2. Training Example
CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/midi/e2e/opencpop/ds100_adj_rel.yaml --exp_name $MY_DS_EXP_NAME --reset
3. Inference from packed test set
CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/midi/e2e/opencpop/ds100_adj_rel.yaml --exp_name $MY_DS_EXP_NAME --reset --infer
We also provide:
- the pre-trained model of DiffSinger;
They can be found in here.
Remember to put the pre-trained models in checkpoints
directory.
4. Inference from raw inputs
python inference/svs/ds_e2e.py --config usr/configs/midi/e2e/opencpop/ds100_adj_rel.yaml --exp_name $MY_DS_EXP_NAME
Raw inputs:
inp = {
'text': '小酒窝长睫毛AP是你最美的记号',
'notes': 'C#4/Db4 | F#4/Gb4 | G#4/Ab4 | A#4/Bb4 F#4/Gb4 | F#4/Gb4 C#4/Db4 | C#4/Db4 | rest | C#4/Db4 | A#4/Bb4 | G#4/Ab4 | A#4/Bb4 | G#4/Ab4 | F4 | C#4/Db4',
'notes_duration': '0.407140 | 0.376190 | 0.242180 | 0.509550 0.183420 | 0.315400 0.235020 | 0.361660 | 0.223070 | 0.377270 | 0.340550 | 0.299620 | 0.344510 | 0.283770 | 0.323390 | 0.360340',
'input_type': 'word'
} # user input: Chinese characters
or,
inp = {
'text': '小酒窝长睫毛AP是你最美的记号',
'ph_seq': 'x iao j iu w o ch ang ang j ie ie m ao AP sh i n i z ui m ei d e j i h ao',
'note_seq': 'C#4/Db4 C#4/Db4 F#4/Gb4 F#4/Gb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 F#4/Gb4 F#4/Gb4 F#4/Gb4 C#4/Db4 C#4/Db4 C#4/Db4 rest C#4/Db4 C#4/Db4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 F4 F4 C#4/Db4 C#4/Db4',
'note_dur_seq': '0.407140 0.407140 0.376190 0.376190 0.242180 0.242180 0.509550 0.509550 0.183420 0.315400 0.315400 0.235020 0.361660 0.361660 0.223070 0.377270 0.377270 0.340550 0.340550 0.299620 0.299620 0.344510 0.344510 0.283770 0.283770 0.323390 0.323390 0.360340 0.360340',
'is_slur_seq': '0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0',
'input_type': 'phoneme'
} # input like Opencpop dataset.
5. Some issues.
a) the HifiGAN-Singing is trained on our vocoder dataset and the training set of PopCS. Opencpop is the out-of-domain dataset (unseen speaker). This may cause the deterioration of audio quality, and we are considering fine-tuning this vocoder on the training set of Opencpop.
b) in this version of codes, we used the melody frontend ([lyric + MIDI]->[ph_dur]) to predict phoneme duration. F0 curve is implicitly predicted together with mel-spectrogram.
c) example generated audio. More generated audio demos can be found in DiffSinger.