1) Add hifigan json files

2) Rename MPD disc
3) Re-format remove weight norm generator

TTS/vocoder/configs/modified_hifigan.json

@@ -0,0 +1,140 @@
+{
+    "run_name": "hifigan",
+    "run_description": "hifigan mean-var scaling",
+
+    // AUDIO PARAMETERS
+    "audio":{
+        "fft_size": 1024,         // number of stft frequency levels. Size of the linear spectogram frame.
+        "win_length": 1024,      // stft window length in ms.
+        "hop_length": 256,       // stft window hop-lengh in ms.
+        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
+
+        // Audio processing parameters
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "ref_level_db": 0,     // reference level db, theoretically 20db is the sound of air.
+
+        // Silence trimming
+        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
+
+        // MelSpectrogram parameters
+        "num_mels": 80,         // size of the mel spec frame.
+        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
+        "spec_gain": 1.0,         // scaler value appplied after log transform of spectrogram.
+
+        // Normalization parameters
+        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
+        "min_level_db": -100,   // lower bound for normalization
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "stats_path": "/home/erogol/Data/libritts/LibriTTS/scale_stats.npy"    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
+    },
+
+    // DISTRIBUTED TRAINING
+    "distributed":{
+        "backend": "nccl",
+        "url": "tcp:\/\/localhost:54324"
+    },
+
+    // MODEL PARAMETERS
+    "use_pqmf": false,
+
+    // LOSS PARAMETERS
+    "use_stft_loss": false,
+    "use_subband_stft_loss": false,
+    "use_mse_gan_loss": true,
+    "use_hinge_gan_loss": false,
+    "use_feat_match_loss": false,  // use only with melgan discriminators
+
+    // loss weights
+    "stft_loss_weight": 0.5,
+    "subband_stft_loss_weight": 0.5,
+    "mse_G_loss_weight": 2.5,
+    "hinge_G_loss_weight": 2.5,
+    "feat_match_loss_weight": 25,
+
+    // multiscale stft loss parameters
+    "stft_loss_params": {
+        "n_ffts": [1024, 2048, 512],
+        "hop_lengths": [120, 240, 50],
+        "win_lengths": [600, 1200, 240]
+    },
+
+    "target_loss": "avg_G_loss",  // loss value to pick the best model to save after each epoch
+
+    // DISCRIMINATOR
+    "discriminator_model": "hifigan_mpd_discriminator",
+    "discriminator_model_params":{
+        "peroids": [2, 3, 5, 7, 11],
+        "base_channels": 16,
+        "max_channels":512,
+        "downsample_factors":[4, 4, 4]
+    },
+    "steps_to_start_discriminator": 1,      // steps required to start GAN trainining.1
+
+    // GENERATOR
+    "generator_model": "hifigan_generator",
+    "generator_model_params": {
+        "upsample_factors":[8,8,2,2],
+        "upsample_kernel_sizes": [16,16,4,4],
+        "upsample_initial_channel": 512,
+        "resblock_kernel_sizes": [3,7,11],
+        "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]]
+    },
+
+    // DATASET
+    "data_path": "/home/erogol/Data/libritts/LibriTTS/train-clean-360/",
+    "feature_path": null,
+    "seq_len": 16384,
+    "pad_short": 2000,
+    "conv_pad": 0,
+    "use_noise_augment": false,
+    "use_cache": true,
+
+    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
+
+    // TRAINING
+    "batch_size": 48,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+
+    // VALIDATION
+    "run_eval": true,
+    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
+    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
+
+    // OPTIMIZER
+    "epochs": 10000,                // total number of epochs to train.
+    "wd": 0.0,                // Weight decay weight.
+    "gen_clip_grad": -1,      // Generator gradient clipping threshold. Apply gradient clipping if > 0
+    "disc_clip_grad": -1,     // Discriminator gradient clipping threshold.
+    "lr_scheduler_gen": "ExponentialLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    "lr_scheduler_gen_params": {
+       "gamma": 0.999
+    },
+    "lr_scheduler_disc": "ExponentialLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    "lr_scheduler_disc_params": {
+    	  "gamma": 0.999
+    },
+    "lr_gen": 0.0002,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_disc": 0.0002,
+
+    // TENSORBOARD and LOGGING
+    "print_step": 25,       // Number of steps to log traning on console.
+    "print_eval": false,     // If True, it prints loss values for each step in eval run.
+    "save_step": 25000,      // Number of training steps expected to plot training stats on TB and save model checkpoints.
+    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
+    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
+
+    // DATA LOADING
+    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
+    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
+    "eval_split_size": 10,
+
+    // PATHS
+    "output_path": "/home/erogol/Models/"
+}
+
+

TTS/vocoder/layers/hifigan.py

@@ -24,15 +24,13 @@ class ResStack(nn.Module):
       return  x1 + x2
 
     def remove_weight_norm(self):
-        # nn.utils.remove_weight_norm(self.resstack[2])
-        # nn.utils.remove_weight_norm(self.resstack[4])
-        for idx, layer in enumerate(self.resstack):
-            if len(layer.state_dict()) != 0:
-                try:
-                    nn.utils.remove_weight_norm(layer)
-                except:
-                    layer.remove_weight_norm()
         nn.utils.remove_weight_norm(self.shortcut)
+        nn.utils.remove_weight_norm(self.resstack[2])
+        nn.utils.remove_weight_norm(self.resstack[5])
+        nn.utils.remove_weight_norm(self.resstack[8])
+        nn.utils.remove_weight_norm(self.resstack[11])
+        nn.utils.remove_weight_norm(self.resstack[14])
+        nn.utils.remove_weight_norm(self.resstack[17])
 
 class MRF(nn.Module):
     def __init__(self, kernels, channel, dilations = [[1,1], [3,1], [5,1]]):

TTS/vocoder/models/hifigan_generator.py

@@ -39,19 +39,9 @@ class Generator(nn.Module):
         return out
 
     def remove_weight_norm(self):
-        for idx, layer in enumerate(self.input):
+        nn.utils.remove_weight_norm(self.input[1])
-            if len(layer.state_dict()) != 0:
+        nn.utils.remove_weight_norm(self.output[2])
-                try:
-                    nn.utils.remove_weight_norm(layer)
-                except:
-                    layer.remove_weight_norm()
 
-        for idx, layer in enumerate(self.output):
-            if len(layer.state_dict()) != 0:
-                try:
-                    nn.utils.remove_weight_norm(layer)
-                except:
-                    layer.remove_weight_norm()
         for idx, layer in enumerate(self.generator):
             if len(layer.state_dict()) != 0:
                 try:

TTS/vocoder/models/multi_period_discriminator.py

@@ -46,9 +46,20 @@ class PeriodDiscriminator(nn.Module):
         return features[-1], features[:-1]
 
 
-class HiFiDiscriminator(nn.Module):
+class MultiPeriodDiscriminator(nn.Module):
-    def __init__(self, periods=[2, 3, 5, 7, 11]):
+    def __init__(self,
-        super(HiFiDiscriminator, self).__init__()
+                 periods=[2, 3, 5, 7, 11],
+                 in_channels=1,
+                 out_channels=1,
+                 num_scales=3,
+                 kernel_sizes=(5, 3),
+                 base_channels=16,
+                 max_channels=1024,
+                 downsample_factors=(4, 4, 4),
+                 pooling_kernel_size=4,
+                 pooling_stride=2,
+                 pooling_padding=1):
+        super(MultiPeriodDiscriminator, self).__init__()
         self.discriminators = nn.ModuleList([ PeriodDiscriminator(periods[0]),
                                               PeriodDiscriminator(periods[1]),
                                               PeriodDiscriminator(periods[2]),
@@ -56,7 +67,18 @@ class HiFiDiscriminator(nn.Module):
                                               PeriodDiscriminator(periods[4]),
                                             ])
 
-        self.msd = MelganMultiscaleDiscriminator()
+        self.msd = MelganMultiscaleDiscriminator(
+                                             in_channels=1,
+                                             out_channels=1,
+                                             num_scales=3,
+                                             kernel_sizes=(5, 3),
+                                             base_channels=16,
+                                             max_channels=1024,
+                                             downsample_factors=(4, 4, 4),
+                                             pooling_kernel_size=4,
+                                             pooling_stride=2,
+                                             pooling_padding=1
+        )
 
     def forward(self, x):
         scores, feats = self.msd(x)