From 6f4eed94f5fd9f20271b1c77dc6c768c0727794c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 7 May 2021 15:33:18 +0200 Subject: [PATCH] remove *.json vocoder configs --- .../multiband-melgan_and_rwd_config.json | 158 ------------------ .../configs/multiband_melgan_config.json | 148 ---------------- .../configs/parallel_wavegan_config.json | 149 ----------------- .../configs/universal_fullband_melgan.json | 145 ---------------- TTS/vocoder/configs/wavegrad_libritts.json | 118 ------------- TTS/vocoder/configs/wavernn_config.json | 103 ------------ 6 files changed, 821 deletions(-) delete mode 100644 TTS/vocoder/configs/multiband-melgan_and_rwd_config.json delete mode 100644 TTS/vocoder/configs/multiband_melgan_config.json delete mode 100644 TTS/vocoder/configs/parallel_wavegan_config.json delete mode 100644 TTS/vocoder/configs/universal_fullband_melgan.json delete mode 100644 TTS/vocoder/configs/wavegrad_libritts.json delete mode 100644 TTS/vocoder/configs/wavernn_config.json diff --git a/TTS/vocoder/configs/multiband-melgan_and_rwd_config.json b/TTS/vocoder/configs/multiband-melgan_and_rwd_config.json deleted file mode 100644 index d52893f0..00000000 --- a/TTS/vocoder/configs/multiband-melgan_and_rwd_config.json +++ /dev/null @@ -1,158 +0,0 @@ -{ - "run_name": "multiband-melgan-rwd", - "run_description": "multiband melgan with random window discriminator from https://arxiv.org/pdf/1909.11646.pdf", - - // AUDIO PARAMETERS - "audio":{ - // stft parameters - "num_freq": 513, // 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": 20, // 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. - - // Griffin-Lim - "power": 1.5, // value to sharpen wav signals after GL algorithm. - "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation. - - // 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!! - - // 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": null // 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:54321" - // }, - - // MODEL PARAMETERS - "use_pqmf": true, - - // LOSS PARAMETERS - "use_stft_loss": true, - "use_subband_stft_loss": true, - "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] - }, - - // subband multiscale stft loss parameters - "subband_stft_loss_params":{ - "n_ffts": [384, 683, 171], - "hop_lengths": [30, 60, 10], - "win_lengths": [150, 300, 60] - }, - - "target_loss": "avg_G_loss", // loss value to pick the best model to save after each epoch - - // DISCRIMINATOR - "discriminator_model": "random_window_discriminator", - "discriminator_model_params":{ - "uncond_disc_donwsample_factors": [8, 4], - "cond_disc_downsample_factors": [[8, 4, 2, 2, 2], [8, 4, 2, 2], [8, 4, 2], [8, 4], [4, 2, 2]], - "cond_disc_out_channels": [[128, 128, 256, 256], [128, 256, 256], [128, 256], [256], [128, 256]], - "window_sizes": [512, 1024, 2048, 4096, 8192] - }, - "steps_to_start_discriminator": 200000, // steps required to start GAN trainining.1 - - // GENERATOR - "generator_model": "multiband_melgan_generator", - "generator_model_params": { - "upsample_factors":[8, 4, 2], - "num_res_blocks": 4 - }, - - // DATASET - "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/", - "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": 64, // 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 - "noam_schedule": false, // use noam warmup and lr schedule. - "warmup_steps_gen": 4000, // Noam decay steps to increase the learning rate from 0 to "lr" - "warmup_steps_disc": 4000, - "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_gen": 0.0002, // Initial learning rate. If Noam decay is active, maximum learning rate. - "lr_disc": 0.0002, - "optimizer": "AdamW", - "optimizer_params":{ - "betas": [0.8, 0.99], - "weight_decay": 0.0 - }, - "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, - "last_epoch": -1 - }, - "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, - "last_epoch": -1 - }, - - // 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" - "keep_all_best": false, // If true, keeps all best_models after keep_after steps - "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true - "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/LJSpeech/" -} - diff --git a/TTS/vocoder/configs/multiband_melgan_config.json b/TTS/vocoder/configs/multiband_melgan_config.json deleted file mode 100644 index 5aea4a61..00000000 --- a/TTS/vocoder/configs/multiband_melgan_config.json +++ /dev/null @@ -1,148 +0,0 @@ -{ - "run_name": "multiband-melgan", - "run_description": "multiband melgan 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": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!! - "mel_fmax": 7600.0, // 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/LJSpeech-1.1/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:54321" - // }, - - // LOSS PARAMETERS - "use_stft_loss": true, - "use_subband_stft_loss": true, // use only with multi-band models. - "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] - }, - - // subband multiscale stft loss parameters - "subband_stft_loss_params":{ - "n_ffts": [384, 683, 171], - "hop_lengths": [30, 60, 10], - "win_lengths": [150, 300, 60] - }, - - "target_loss": "avg_G_loss", // loss value to pick the best model to save after each epoch - - // DISCRIMINATOR - "discriminator_model": "melgan_multiscale_discriminator", - "discriminator_model_params":{ - "base_channels": 16, - "max_channels":512, - "downsample_factors":[4, 4, 4] - }, - "steps_to_start_discriminator": 200000, // steps required to start GAN trainining.1 - - // GENERATOR - "generator_model": "multiband_melgan_generator", - "generator_model_params": { - "upsample_factors":[8, 4, 2], - "num_res_blocks": 4 - }, - - // DATASET - "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/", - "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": 64, // 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_gen": 0.0002, // Initial learning rate. If Noam decay is active, maximum learning rate. - "lr_disc": 0.0002, - "optimizer": "AdamW", - "optimizer_params":{ - "betas": [0.8, 0.99], - "weight_decay": 0.0 - }, - "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, - "last_epoch": -1 - }, - "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, - "last_epoch": -1 - }, - - // 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" - "keep_all_best": false, // If true, keeps all best_models after keep_after steps - "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true - "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/LJSpeech/" -} - diff --git a/TTS/vocoder/configs/parallel_wavegan_config.json b/TTS/vocoder/configs/parallel_wavegan_config.json deleted file mode 100644 index 5ea7dbcd..00000000 --- a/TTS/vocoder/configs/parallel_wavegan_config.json +++ /dev/null @@ -1,149 +0,0 @@ -{ - "run_name": "pwgan", - "run_description": "parallel-wavegan training", - - // 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": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!! - "mel_fmax": 7600.0, // 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/LJSpeech-1.1/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:54321" - // }, - - // MODEL PARAMETERS - "use_pqmf": true, - - // LOSS PARAMETERS - "use_stft_loss": true, - "use_subband_stft_loss": false, // USE ONLY WITH MULTIBAND MODELS - "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] - }, - - // subband multiscale stft loss parameters - "subband_stft_loss_params":{ - "n_ffts": [384, 683, 171], - "hop_lengths": [30, 60, 10], - "win_lengths": [150, 300, 60] - }, - - "target_loss": "avg_G_loss", // loss value to pick the best model to save after each epoch - - // DISCRIMINATOR - "discriminator_model": "parallel_wavegan_discriminator", - "discriminator_model_params":{ - "num_layers": 10 - }, - "steps_to_start_discriminator": 200000, // steps required to start GAN trainining.1 - - // GENERATOR - "generator_model": "parallel_wavegan_generator", - "generator_model_params": { - "upsample_factors":[4, 4, 4, 4], - "stacks": 3, - "num_res_blocks": 30 - }, - - // DATASET - "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/", - "feature_path": null, - "seq_len": 25600, - "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": 6, // 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_gen": 0.0002, // Initial learning rate. If Noam decay is active, maximum learning rate. - "lr_disc": 0.0002, - "optimizer": "AdamW", - "optimizer_params":{ - "betas": [0.8, 0.99], - "weight_decay": 0.0 - }, - "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, - "last_epoch": -1 - }, - "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, - "last_epoch": -1 - }, - // 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" - "keep_all_best": false, // If true, keeps all best_models after keep_after steps - "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true - "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/LJSpeech/" -} - diff --git a/TTS/vocoder/configs/universal_fullband_melgan.json b/TTS/vocoder/configs/universal_fullband_melgan.json deleted file mode 100644 index 245de2d3..00000000 --- a/TTS/vocoder/configs/universal_fullband_melgan.json +++ /dev/null @@ -1,145 +0,0 @@ -{ - "run_name": "fullband-melgan", - "run_description": "fullband melgan 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": 24000, // 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": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!! - "mel_fmax": 7600.0, // 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": true, - "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": "melgan_multiscale_discriminator", - "discriminator_model_params":{ - "base_channels": 16, - "max_channels":512, - "downsample_factors":[4, 4, 4] - }, - "steps_to_start_discriminator": 200000, // steps required to start GAN trainining.1 - - // GENERATOR - "generator_model": "fullband_melgan_generator", - "generator_model_params": { - "upsample_factors":[8, 8, 4], - "num_res_blocks": 4 - }, - - // 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_gen": 0.0002, // Initial learning rate. If Noam decay is active, maximum learning rate. - "lr_disc": 0.0002, - "optimizer": "AdamW", - "optimizer_params":{ - "betas": [0.8, 0.99], - "weight_decay": 0.0 - }, - "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, - "last_epoch": -1 - }, - "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, - "last_epoch": -1 - }, - - // 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" - "keep_all_best": false, // If true, keeps all best_models after keep_after steps - "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true - "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/" -} - - diff --git a/TTS/vocoder/configs/wavegrad_libritts.json b/TTS/vocoder/configs/wavegrad_libritts.json deleted file mode 100644 index ade20a8f..00000000 --- a/TTS/vocoder/configs/wavegrad_libritts.json +++ /dev/null @@ -1,118 +0,0 @@ -{ - "run_name": "wavegrad-libritts", - "run_description": "wavegrad libritts", - - "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": 24000, // 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": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!! - "mel_fmax": 7600.0, // 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_wavegrad.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 - "mixed_precision": true, // enable torch mixed precision training (true, false) - "distributed":{ - "backend": "nccl", - "url": "tcp:\/\/localhost:54322" - }, - - "target_loss": "avg_wavegrad_loss", // loss value to pick the best model to save after each epoch - - // MODEL PARAMETERS - "generator_model": "wavegrad", - "model_params":{ - "use_weight_norm": true, - "y_conv_channels":32, - "x_conv_channels":768, - "ublock_out_channels": [512, 512, 256, 128, 128], - "dblock_out_channels": [128, 128, 256, 512], - "upsample_factors": [4, 4, 4, 2, 2], - "upsample_dilations": [ - [1, 2, 1, 2], - [1, 2, 1, 2], - [1, 2, 4, 8], - [1, 2, 4, 8], - [1, 2, 4, 8]] - }, - - // DATASET - "data_path": "/home/erogol/Data/libritts/LibriTTS/train-clean-360/", // root data path. It finds all wav files recursively from there. - "feature_path": null, // if you use precomputed features - "seq_len": 6144, // 24 * hop_length - "pad_short": 0, // additional padding for short wavs - "conv_pad": 0, // additional padding against convolutions applied to spectrograms - "use_noise_augment": false, // add noise to the audio signal for augmentation - "use_cache": false, // use in memory cache to keep the computed features. This might cause OOM. - - "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": 96, // Batch size for training. - - // NOISE SCHEDULE PARAMS - Only effective at training time. - "train_noise_schedule":{ - "min_val": 1e-6, - "max_val": 1e-2, - "num_steps": 1000 - }, - "test_noise_schedule":{ - "min_val": 1e-6, - "max_val": 1e-2, - "num_steps": 50 - }, - - // VALIDATION - "run_eval": true, // enable/disable evaluation run - - // OPTIMIZER - "epochs": 10000, // total number of epochs to train. - "clip_grad": 1.0, // Generator gradient clipping threshold. Apply gradient clipping if > 0 - "lr_scheduler": "MultiStepLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate - "lr_scheduler_params": { - "gamma": 0.5, - "milestones": [100000, 200000, 300000, 400000, 500000, 600000] - }, - "lr": 1e-4, // Initial learning rate. If Noam decay is active, maximum learning rate. - - // TENSORBOARD and LOGGING - "print_step": 50, // 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": 5000, // 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" - "keep_all_best": false, // If true, keeps all best_models after keep_after steps - "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true - "tb_model_param_stats": true, // 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": 256, - - // PATHS - "output_path": "/home/erogol/Models/LJSpeech/" -} - diff --git a/TTS/vocoder/configs/wavernn_config.json b/TTS/vocoder/configs/wavernn_config.json deleted file mode 100644 index aa2d7b9f..00000000 --- a/TTS/vocoder/configs/wavernn_config.json +++ /dev/null @@ -1,103 +0,0 @@ -{ - "run_name": "wavernn_librittts", - "run_description": "wavernn libritts training from LJSpeech model", - -// 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": 24000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled. - "preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis. - "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air. - // Silence trimming - "do_trim_silence": false, // 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": 40.0, // 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": 20.0, // 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": null // 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 - }, - -// Generating / Synthesizing - "batched": true, - "target_samples": 11000, // target number of samples to be generated in each batch entry - "overlap_samples": 550, // number of samples for crossfading between batches - // DISTRIBUTED TRAINING - // "distributed":{ - // "backend": "nccl", - // "url": "tcp:\/\/localhost:54321" - // }, - -// MODEL MODE - "mode": "mold", // mold [string], gauss [string], bits [int] - "mulaw": true, // apply mulaw if mode is bits - -// MODEL PARAMETERS - "wavernn_model_params": { - "rnn_dims": 512, - "fc_dims": 512, - "compute_dims": 128, - "res_out_dims": 128, - "num_res_blocks": 10, - "use_aux_net": true, - "use_upsample_net": true, - "upsample_factors": [4, 8, 8] // this needs to correctly factorise hop_length - }, - -// GENERATOR - for backward compatibility - "generator_model": "WaveRNN", - -// DATASET - //"use_gta": true, // use computed gta features from the tts model - "data_path": "/home/erogol/Data/libritts/LibriTTS/train-clean-360/", // path containing training wav files - "feature_path": null, // path containing computed features from wav files if null compute them - "seq_len": 1280, // has to be devideable by hop_length - "padding": 2, // pad the input for resnet to see wider input length - -// TRAINING - "batch_size": 256, // Batch size for training. - "epochs": 10000, // total number of epochs to train. - "mixed_precision": true, // enable/ disable mixed precision training - -// VALIDATION - "run_eval": true, - "test_every_epochs": 10, // Test after set number of epochs (Test every 10 epochs for example) - -// OPTIMIZER - "grad_clip": 4, // apply gradient clipping if > 0 - "lr_scheduler": "MultiStepLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate - "lr_scheduler_params": { - "gamma": 0.5, - "milestones": [200000, 400000, 600000] - }, - "lr": 1e-4, // initial learning rate - -// 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" - "keep_all_best": false, // If true, keeps all best_models after keep_after steps - "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true - "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": 50, // number of samples for testing - -// PATHS - "output_path": "/home/erogol/Models/LJSpeech/" -}