mirror of https://github.com/coqui-ai/TTS.git
1) Add hifigan json files
2) Rename MPD disc 3) Re-format remove weight norm generatorpull/422/head
rishikksh20
Eren Gölge
parent
7b7c5d635f
commit
39b5845810
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@ -0,0 +1,140 @@
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{
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"run_name": "hifigan",
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"run_description": "hifigan mean-var scaling",
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// AUDIO PARAMETERS
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"audio":{
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"fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
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"win_length": 1024, // stft window length in ms.
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"hop_length": 256, // stft window hop-lengh in ms.
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"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
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"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
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// Audio processing parameters
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"sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
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"preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
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"ref_level_db": 0, // reference level db, theoretically 20db is the sound of air.
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// Silence trimming
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"do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
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"trim_db": 60, // threshold for timming silence. Set this according to your dataset.
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// MelSpectrogram parameters
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"num_mels": 80, // size of the mel spec frame.
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"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
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"spec_gain": 1.0, // scaler value appplied after log transform of spectrogram.
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// Normalization parameters
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"signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
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"min_level_db": -100, // lower bound for normalization
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"symmetric_norm": true, // move normalization to range [-1, 1]
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"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
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"clip_norm": true, // clip normalized values into the range.
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"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
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},
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// DISTRIBUTED TRAINING
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"distributed":{
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"backend": "nccl",
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"url": "tcp:\/\/localhost:54324"
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},
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// MODEL PARAMETERS
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"use_pqmf": false,
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// LOSS PARAMETERS
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"use_stft_loss": false,
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"use_subband_stft_loss": false,
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"use_mse_gan_loss": true,
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"use_hinge_gan_loss": false,
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"use_feat_match_loss": false, // use only with melgan discriminators
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// loss weights
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"stft_loss_weight": 0.5,
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"subband_stft_loss_weight": 0.5,
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"mse_G_loss_weight": 2.5,
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"hinge_G_loss_weight": 2.5,
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"feat_match_loss_weight": 25,
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// multiscale stft loss parameters
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"stft_loss_params": {
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"n_ffts": [1024, 2048, 512],
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"hop_lengths": [120, 240, 50],
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"win_lengths": [600, 1200, 240]
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},
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"target_loss": "avg_G_loss", // loss value to pick the best model to save after each epoch
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// DISCRIMINATOR
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"discriminator_model": "hifigan_mpd_discriminator",
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"discriminator_model_params":{
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"peroids": [2, 3, 5, 7, 11],
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"base_channels": 16,
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"max_channels":512,
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"downsample_factors":[4, 4, 4]
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},
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"steps_to_start_discriminator": 1, // steps required to start GAN trainining.1
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// GENERATOR
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"generator_model": "hifigan_generator",
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"generator_model_params": {
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"upsample_factors":[8,8,2,2],
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"upsample_kernel_sizes": [16,16,4,4],
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"upsample_initial_channel": 512,
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"resblock_kernel_sizes": [3,7,11],
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"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]]
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},
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// DATASET
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"data_path": "/home/erogol/Data/libritts/LibriTTS/train-clean-360/",
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"feature_path": null,
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"seq_len": 16384,
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"pad_short": 2000,
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"conv_pad": 0,
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"use_noise_augment": false,
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"use_cache": true,
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"reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
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// TRAINING
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"batch_size": 48, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
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// VALIDATION
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"run_eval": true,
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"test_delay_epochs": 10, //Until attention is aligned, testing only wastes computation time.
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"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.
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// OPTIMIZER
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"epochs": 10000, // total number of epochs to train.
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"wd": 0.0, // Weight decay weight.
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"gen_clip_grad": -1, // Generator gradient clipping threshold. Apply gradient clipping if > 0
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"disc_clip_grad": -1, // Discriminator gradient clipping threshold.
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"lr_scheduler_gen": "ExponentialLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
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"lr_scheduler_gen_params": {
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"gamma": 0.999
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},
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"lr_scheduler_disc": "ExponentialLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
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"lr_scheduler_disc_params": {
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"gamma": 0.999
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},
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"lr_gen": 0.0002, // Initial learning rate. If Noam decay is active, maximum learning rate.
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"lr_disc": 0.0002,
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// TENSORBOARD and LOGGING
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"print_step": 25, // Number of steps to log traning on console.
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"print_eval": false, // If True, it prints loss values for each step in eval run.
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"save_step": 25000, // Number of training steps expected to plot training stats on TB and save model checkpoints.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
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// DATA LOADING
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"num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values.
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"num_val_loader_workers": 4, // number of evaluation data loader processes.
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"eval_split_size": 10,
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// PATHS
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"output_path": "/home/erogol/Models/"
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}
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@ -24,15 +24,13 @@ class ResStack(nn.Module):
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return x1 + x2
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def remove_weight_norm(self):
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# nn.utils.remove_weight_norm(self.resstack[2])
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# nn.utils.remove_weight_norm(self.resstack[4])
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for idx, layer in enumerate(self.resstack):
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if len(layer.state_dict()) != 0:
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try:
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nn.utils.remove_weight_norm(layer)
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except:
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layer.remove_weight_norm()
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nn.utils.remove_weight_norm(self.shortcut)
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nn.utils.remove_weight_norm(self.resstack[2])
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nn.utils.remove_weight_norm(self.resstack[5])
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nn.utils.remove_weight_norm(self.resstack[8])
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nn.utils.remove_weight_norm(self.resstack[11])
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nn.utils.remove_weight_norm(self.resstack[14])
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nn.utils.remove_weight_norm(self.resstack[17])
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class MRF(nn.Module):
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def __init__(self, kernels, channel, dilations = [[1,1], [3,1], [5,1]]):
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@ -39,19 +39,9 @@ class Generator(nn.Module):
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return out
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def remove_weight_norm(self):
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for idx, layer in enumerate(self.input):
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if len(layer.state_dict()) != 0:
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try:
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nn.utils.remove_weight_norm(layer)
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except:
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layer.remove_weight_norm()
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nn.utils.remove_weight_norm(self.input[1])
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nn.utils.remove_weight_norm(self.output[2])
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for idx, layer in enumerate(self.output):
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if len(layer.state_dict()) != 0:
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try:
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nn.utils.remove_weight_norm(layer)
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except:
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layer.remove_weight_norm()
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for idx, layer in enumerate(self.generator):
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if len(layer.state_dict()) != 0:
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try:
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@ -46,9 +46,20 @@ class PeriodDiscriminator(nn.Module):
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return features[-1], features[:-1]
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class HiFiDiscriminator(nn.Module):
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def __init__(self, periods=[2, 3, 5, 7, 11]):
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super(HiFiDiscriminator, self).__init__()
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class MultiPeriodDiscriminator(nn.Module):
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def __init__(self,
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periods=[2, 3, 5, 7, 11],
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in_channels=1,
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out_channels=1,
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num_scales=3,
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kernel_sizes=(5, 3),
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base_channels=16,
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max_channels=1024,
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downsample_factors=(4, 4, 4),
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pooling_kernel_size=4,
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pooling_stride=2,
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pooling_padding=1):
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super(MultiPeriodDiscriminator, self).__init__()
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self.discriminators = nn.ModuleList([ PeriodDiscriminator(periods[0]),
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PeriodDiscriminator(periods[1]),
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PeriodDiscriminator(periods[2]),
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@ -56,7 +67,18 @@ class HiFiDiscriminator(nn.Module):
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PeriodDiscriminator(periods[4]),
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])
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self.msd = MelganMultiscaleDiscriminator()
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self.msd = MelganMultiscaleDiscriminator(
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in_channels=1,
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out_channels=1,
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num_scales=3,
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kernel_sizes=(5, 3),
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base_channels=16,
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max_channels=1024,
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downsample_factors=(4, 4, 4),
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pooling_kernel_size=4,
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pooling_stride=2,
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pooling_padding=1
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)
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def forward(self, x):
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scores, feats = self.msd(x)
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