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GAN training now supports DistributedDataParallel (DDP) (#558)
* The new vocoder Fre-GAN is now supported * Improved some fregan details * Fixed the problem that the existing model could not be loaded to continue training when training GAN * Updated reference papers * GAN training now supports DistributedDataParallel (DDP) * Added requirements.txt * GAN training uses single card training by default * Added note about GAN vocoder training with multiple GPUs
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flysmart
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@ -90,6 +90,7 @@
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* 训练fregan声码器:
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`python vocoder_train.py <trainid> <datasets_root> --config config.json fregan`
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> `<trainid>`替换为你想要的标识，同一标识再次训练时会延续原模型
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* 将GAN声码器的训练切换为多GPU模式：修改GAN文件夹下.json文件中的"num_gpus"参数
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### 3. 启动程序或工具箱
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您可以尝试使用以下命令：
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@ -25,3 +25,4 @@ streamlit==1.8.0
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PyYAML==5.4.1
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torch_complex
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espnet
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PyWavelets
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@ -1,25 +0,0 @@
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# Fre-GAN Vocoder
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[Fre-GAN: Adversarial Frequency-consistent Audio Synthesis](https://arxiv.org/abs/2106.02297)
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## Training:
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```
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python train.py --config config.json
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```
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## Citation:
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```
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@misc{kim2021fregan,
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title={Fre-GAN: Adversarial Frequency-consistent Audio Synthesis},
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author={Ji-Hoon Kim and Sang-Hoon Lee and Ji-Hyun Lee and Seong-Whan Lee},
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year={2021},
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eprint={2106.02297},
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archivePrefix={arXiv},
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primaryClass={eess.AS}
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}
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```
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## Note
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* For more complete and end to end Voice cloning or Text to Speech (TTS) toolbox please visit [Deepsync Technologies](https://deepsync.co/).
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## References:
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* [Hi-Fi-GAN repo](https://github.com/jik876/hifi-gan)
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* [WaveSNet repo](https://github.com/LiQiufu/WaveSNet)
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@ -7,6 +7,7 @@
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"adam_b2": 0.99,
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"lr_decay": 0.999,
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"seed": 1234,
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"disc_start_step":0,
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"upsample_rates": [5,5,2,2,2],
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@ -1 +0,0 @@
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PyWavelets
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@ -135,8 +135,7 @@ def train(rank, a, h):
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h.win_size,
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h.fmin, h.fmax_for_loss)
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if steps > h.disc_start_step:
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optim_d.zero_grad()
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# MPD
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@ -162,7 +161,7 @@ def train(rank, a, h):
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# sc_loss, mag_loss = stft_loss(y_g_hat[:, :, :y.size(2)].squeeze(1), y.squeeze(1))
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# loss_mel = h.lambda_aux * (sc_loss + mag_loss) # STFT Loss
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if steps > h.disc_start_step:
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y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat)
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y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat)
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loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)
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@ -170,7 +169,8 @@ def train(rank, a, h):
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loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)
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loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
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loss_gen_all = loss_gen_s + loss_gen_f + (2 * (loss_fm_s + loss_fm_f)) + loss_mel
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else:
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loss_gen_all = loss_mel
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loss_gen_all.backward()
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optim_g.step()
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@ -7,6 +7,7 @@
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"adam_b2": 0.99,
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"lr_decay": 0.999,
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"seed": 1234,
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"disc_start_step":0,
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"upsample_rates": [5,5,4,2],
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"upsample_kernel_sizes": [10,10,8,4],
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@ -27,5 +28,11 @@
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"fmax": 7600,
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"fmax_for_loss": null,
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"num_workers": 4
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"num_workers": 4,
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"dist_config": {
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"dist_backend": "nccl",
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"dist_url": "tcp://localhost:54321",
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"world_size": 1
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}
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}
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@ -137,7 +137,7 @@ def train(rank, a, h):
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y_g_hat = generator(x)
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y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size,
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h.fmin, h.fmax_for_loss)
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if steps > h.disc_start_step:
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optim_d.zero_grad()
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# MPD
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@ -159,6 +159,7 @@ def train(rank, a, h):
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# L1 Mel-Spectrogram Loss
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loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45
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if steps > h.disc_start_step:
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y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat)
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y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat)
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loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)
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@ -166,6 +167,8 @@ def train(rank, a, h):
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loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)
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loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
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loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel
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else:
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loss_gen_all = loss_mel
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loss_gen_all.backward()
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optim_g.step()
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@ -6,7 +6,8 @@ from utils.util import AttrDict
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from pathlib import Path
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import argparse
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import json
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import torch
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import torch.multiprocessing as mp
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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@ -69,11 +70,23 @@ if __name__ == "__main__":
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with open(args.config) as f:
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json_config = json.load(f)
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h = AttrDict(json_config)
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if h.num_gpus > 1:
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h.num_gpus = torch.cuda.device_count()
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h.batch_size = int(h.batch_size / h.num_gpus)
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print('Batch size per GPU :', h.batch_size)
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mp.spawn(train_hifigan, nprocs=h.num_gpus, args=(args, h,))
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else:
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train_hifigan(0, args, h)
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elif args.vocoder_type == "fregan":
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with open('vocoder/fregan/config.json') as f:
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json_config = json.load(f)
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h = AttrDict(json_config)
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if h.num_gpus > 1:
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h.num_gpus = torch.cuda.device_count()
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h.batch_size = int(h.batch_size / h.num_gpus)
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print('Batch size per GPU :', h.batch_size)
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mp.spawn(train_fregan, nprocs=h.num_gpus, args=(args, h,))
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else:
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train_fregan(0, args, h)
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