Instructions to use Cyanbox/Prompt-Singer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Fairseq
How to use Cyanbox/Prompt-Singer with Fairseq:
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub models, cfg, task = load_model_ensemble_and_task_from_hf_hub( "Cyanbox/Prompt-Singer" ) - Notebooks
- Google Colab
- Kaggle
| ########### model config ########### | |
| generator: | |
| name: SoundStream | |
| config: | |
| n_filters: 32 | |
| D: 256 | |
| #target_bandwidths: [6,] # [1, 1.5, 2, 4, 6] # [0.5, 1, 1.5, 2, 4, 6] | |
| target_bandwidths: [0.5, 1, 1.5, 2, 4] | |
| ratios: [8, 5, 4, 2] # downsampling by 320 | |
| sample_rate: 16000 | |
| bins: 1024 | |
| # Discriminator list | |
| #d_list: ['mpd', 'msd', 'mfd'] | |
| d_list: ['mfd'] | |
| mfd: | |
| name: MultiFrequencyDiscriminator | |
| config: | |
| hop_lengths: [32, 64, 128, 256, 512, 1024] | |
| hidden_channels: [64, 128, 256, 512, 512, 512] | |
| domain: double | |
| mel_scale: true | |
| sample_rate: 16000 | |
| mpd: | |
| name: MultiPeriodDiscriminator | |
| config: | |
| period_sizes: [2, 3, 5, 7, 11] | |
| period_kernel_size: 5 | |
| msd: | |
| name: MultiScaleDiscriminator | |
| config: | |
| num_scales: 3 | |
| pool_kernel_size: 4 | |
| pool_stride: 2 | |
| ########### optimizer config ########### | |
| optimizer: | |
| g: | |
| name: AdamW | |
| config: | |
| lr: 2e-4 | |
| betas: [0.8, 0.99] | |
| eps: 1.0e-6 | |
| d: | |
| name: AdamW | |
| config: | |
| lr: 2e-4 | |
| betas: [0.8, 0.99] | |
| eps: 1.0e-6 | |
| lr_scheduler: | |
| g: | |
| name: ExponentialLR | |
| config: | |
| gamma: 0.999 | |
| d: | |
| name: ExponentialLR | |
| config: | |
| gamma: 0.999 | |
| ########### criterion config ########### | |
| criterion: | |
| g_criterion: | |
| name: losses.generator_loss.GeneratorSTFTLoss | |
| config: | |
| use_mel_loss: false | |
| #adv_criterion: LeastDLoss | |
| adv_criterion: MSEGLoss | |
| mel_loss_weight: 45 | |
| use_feature_match: true | |
| feat_match_loss_weight: 20 | |
| use_full_stft_loss: true # Magnitude | |
| use_sub_stft_loss: true # PQMF loss | |
| full_stft_loss_weight: 1 | |
| sub_stft_loss_weight: 1 | |
| mel_scale_loss: | |
| sampling_rate: 16000 | |
| n_fft: 1024 | |
| num_mels: 80 | |
| hop_size: 160 | |
| win_size: 800 | |
| fmin: 0 | |
| full_multi_scale_stft_loss: # Full-band multi-scale STFT loss. | |
| fft_sizes: [512, 1024, 2048] | |
| win_sizes: [480, 960, 1200] | |
| hop_sizes: [120, 240, 300] | |
| sub_multi_scale_stft_loss: # Sub-band multi-scale STFT loss. | |
| num_bands: 6 | |
| fft_sizes: [128, 256, 256] | |
| win_sizes: [80, 120, 200] | |
| hop_sizes: [20, 40, 50] | |
| d_criterion: | |
| name: losses.discriminator_loss.MSEDiscriminatorLoss | |
| config: null | |
| commit_loss_weight: 1. #1000 | |
| ########### training and data config ########### | |
| seed: 2333 | |
| cudnn_deterministic: false | |
| tensorboard: true # whether to use tensorboard | |
| #checkpoint_interval: 5 | |
| #summary_interval: 10 | |
| #validation_interval: 10 | |
| checkpoint_interval: 5000 | |
| summary_interval: 100 | |
| validation_interval: 5000 | |
| num_epoches: 5000 | |
| print_freq: 10 | |
| discriminator_iter_start: 0 # start step after which we update discriminators | |
| num_ckpt_keep: 10 | |
| segment_size: 24000 | |
| audio_norm_scale: 1.0 | |
| batch_size: 6 | |
| num_workers: 8 | |
| num_plots: 8 | |