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Merge pull request #269 from edwardhdlu/wavenet-pull-request
WaveNet Implementation
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,103 @@ | ||
| # pylint: skip-file | ||
| import tensorflow as tf | ||
| from open_seq2seq.models import Text2SpeechWavenet | ||
| from open_seq2seq.encoders import WavenetEncoder | ||
| from open_seq2seq.decoders import FakeDecoder | ||
| from open_seq2seq.losses import WavenetLoss | ||
| from open_seq2seq.data import WavenetDataLayer | ||
| from open_seq2seq.optimizers.lr_policies import exp_decay | ||
| from open_seq2seq.parts.convs2s.utils import gated_linear_units | ||
|
|
||
| base_model = Text2SpeechWavenet | ||
|
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||
| base_params = { | ||
| "random_seed": 0, | ||
| "use_horovod": False, | ||
| "max_steps": 1000000, | ||
|
|
||
| "num_gpus": 1, | ||
| "batch_size_per_gpu": 2, | ||
|
|
||
| "save_summaries_steps": 50, | ||
| "print_loss_steps": 50, | ||
| "print_samples_steps": 500, | ||
| "eval_steps": 500, | ||
| "save_checkpoint_steps": 2500, | ||
| "logdir": "result/wavenet-LJ-float", | ||
|
|
||
| "optimizer": "Adam", | ||
| "optimizer_params": {}, | ||
| "lr_policy": exp_decay, | ||
| "lr_policy_params": { | ||
| "learning_rate": 1e-3, | ||
| "decay_steps": 20000, | ||
| "decay_rate": 0.1, | ||
| "use_staircase_decay": False, | ||
| "begin_decay_at": 45000, | ||
| "min_lr": 1e-5, | ||
| }, | ||
| "dtype": tf.float32, | ||
| "regularizer": tf.contrib.layers.l2_regularizer, | ||
| "regularizer_params": { | ||
| "scale": 1e-6 | ||
| }, | ||
| "initializer": tf.contrib.layers.xavier_initializer, | ||
|
|
||
| "summaries": [], | ||
|
|
||
| "encoder": WavenetEncoder, | ||
| "encoder_params": { | ||
| "layer_type": "conv1d", | ||
| "kernel_size": 3, | ||
| "strides": 1, | ||
| "padding": "VALID", | ||
| "blocks": 3, | ||
| "layers_per_block": 10, | ||
| "filters": 64, | ||
| "quantization_channels": 256 | ||
| }, | ||
|
|
||
| "decoder": FakeDecoder, | ||
|
|
||
| "loss": WavenetLoss, | ||
|
|
||
| "data_layer": WavenetDataLayer, | ||
| "data_layer_params": { | ||
| "num_audio_features": 80, | ||
| "dataset_location": "data/speech/LJSpeech/wavs/" | ||
| } | ||
| } | ||
|
|
||
| train_params = { | ||
| "data_layer_params": { | ||
| "dataset_files": [ | ||
| "data/speech/LJSpeech/train.csv", | ||
| ], | ||
| "shuffle": True, | ||
| }, | ||
| } | ||
|
|
||
| eval_params = { | ||
| "data_layer_params": { | ||
| "dataset_files": [ | ||
| "data/speech/LJSpeech/val.csv", | ||
| ], | ||
| "shuffle": False, | ||
| }, | ||
| } | ||
|
|
||
| infer_params = { | ||
| "data_layer_params": { | ||
| "dataset_files": [ | ||
| "data/speech/LJSpeech/test.csv", | ||
| ], | ||
| "shuffle": False, | ||
| }, | ||
| } | ||
|
|
||
| interactive_infer_params = { | ||
| "data_layer_params": { | ||
| "dataset_files": [], | ||
| "shuffle": False, | ||
| }, | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,103 @@ | ||
| # pylint: skip-file | ||
| import tensorflow as tf | ||
| from open_seq2seq.models import Text2SpeechWavenet | ||
| from open_seq2seq.encoders import WavenetEncoder | ||
| from open_seq2seq.decoders import FakeDecoder | ||
| from open_seq2seq.losses import WavenetLoss | ||
| from open_seq2seq.data import WavenetDataLayer | ||
| from open_seq2seq.optimizers.lr_policies import exp_decay | ||
| from open_seq2seq.parts.convs2s.utils import gated_linear_units | ||
|
|
||
| base_model = Text2SpeechWavenet | ||
|
|
||
| base_params = { | ||
| "random_seed": 0, | ||
| "use_horovod": True, | ||
| "max_steps": 1000000, | ||
|
|
||
| "num_gpus": 8, | ||
| "batch_size_per_gpu": 1, | ||
|
|
||
| "save_summaries_steps": 50, | ||
| "print_loss_steps": 50, | ||
| "print_samples_steps": 500, | ||
| "eval_steps": 500, | ||
| "save_checkpoint_steps": 2500, | ||
| "logdir": "result/wavenet-LJ-float", | ||
|
|
||
| "optimizer": "Adam", | ||
| "optimizer_params": {}, | ||
| "lr_policy": exp_decay, | ||
| "lr_policy_params": { | ||
| "learning_rate": 1e-3, | ||
| "decay_steps": 20000, | ||
| "decay_rate": 0.1, | ||
| "use_staircase_decay": False, | ||
| "begin_decay_at": 45000, | ||
| "min_lr": 1e-5, | ||
| }, | ||
| "dtype": tf.float32, | ||
| "regularizer": tf.contrib.layers.l2_regularizer, | ||
| "regularizer_params": { | ||
| "scale": 1e-6 | ||
| }, | ||
| "initializer": tf.contrib.layers.xavier_initializer, | ||
|
|
||
| "summaries": [], | ||
|
|
||
| "encoder": WavenetEncoder, | ||
| "encoder_params": { | ||
| "layer_type": "conv1d", | ||
| "kernel_size": 3, | ||
| "strides": 1, | ||
| "padding": "VALID", | ||
| "blocks": 3, | ||
| "layers_per_block": 10, | ||
| "filters": 64, | ||
| "quantization_channels": 256 | ||
| }, | ||
|
|
||
| "decoder": FakeDecoder, | ||
|
|
||
| "loss": WavenetLoss, | ||
|
|
||
| "data_layer": WavenetDataLayer, | ||
| "data_layer_params": { | ||
| "num_audio_features": 80, | ||
| "dataset_location": "/data/LJSpeech-1.1-partitioned/wavs/" | ||
| } | ||
| } | ||
|
|
||
| train_params = { | ||
| "data_layer_params": { | ||
| "dataset_files": [ | ||
| "/data/LJSpeech-1.1-partitioned/train.csv", | ||
| ], | ||
| "shuffle": True, | ||
| }, | ||
| } | ||
|
|
||
| eval_params = { | ||
| "data_layer_params": { | ||
| "dataset_files": [ | ||
| "/data/LJSpeech-1.1-partitioned/val.csv", | ||
| ], | ||
| "shuffle": False, | ||
| }, | ||
| } | ||
|
|
||
| infer_params = { | ||
| "data_layer_params": { | ||
| "dataset_files": [ | ||
| "/data/LJSpeech-1.1-partitioned/test.csv", | ||
| ], | ||
| "shuffle": False, | ||
| }, | ||
| } | ||
|
|
||
| interactive_infer_params = { | ||
| "data_layer_params": { | ||
| "dataset_files": [], | ||
| "shuffle": False, | ||
| }, | ||
| } |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,104 @@ | ||
| # pylint: skip-file | ||
| import tensorflow as tf | ||
| from open_seq2seq.models import Text2SpeechWavenet | ||
| from open_seq2seq.encoders import WavenetEncoder | ||
| from open_seq2seq.decoders import FakeDecoder | ||
| from open_seq2seq.losses import WavenetLoss | ||
| from open_seq2seq.data import WavenetDataLayer | ||
| from open_seq2seq.optimizers.lr_policies import exp_decay | ||
| from open_seq2seq.parts.convs2s.utils import gated_linear_units | ||
|
|
||
| base_model = Text2SpeechWavenet | ||
|
|
||
| base_params = { | ||
| "random_seed": 0, | ||
| "use_horovod": False, | ||
| "max_steps": 1000000, | ||
|
|
||
| "num_gpus": 1, | ||
| "batch_size_per_gpu": 4, | ||
|
|
||
| "save_summaries_steps": 50, | ||
| "print_loss_steps": 50, | ||
| "print_samples_steps": 500, | ||
| "eval_steps": 500, | ||
| "save_checkpoint_steps": 2500, | ||
| "logdir": "result/wavenet-LJ-mixed", | ||
|
|
||
| "optimizer": "Adam", | ||
| "optimizer_params": {}, | ||
| "lr_policy": exp_decay, | ||
| "lr_policy_params": { | ||
| "learning_rate": 1e-3, | ||
| "decay_steps": 20000, | ||
| "decay_rate": 0.1, | ||
| "use_staircase_decay": False, | ||
| "begin_decay_at": 45000, | ||
| "min_lr": 1e-5, | ||
| }, | ||
| "dtype": "mixed", | ||
| "loss_scaling": "Backoff", | ||
| "regularizer": tf.contrib.layers.l2_regularizer, | ||
| "regularizer_params": { | ||
| "scale": 1e-6 | ||
| }, | ||
| "initializer": tf.contrib.layers.xavier_initializer, | ||
|
|
||
| "summaries": [], | ||
|
|
||
| "encoder": WavenetEncoder, | ||
| "encoder_params": { | ||
| "layer_type": "conv1d", | ||
| "kernel_size": 3, | ||
| "strides": 1, | ||
| "padding": "VALID", | ||
| "blocks": 3, | ||
| "layers_per_block": 10, | ||
| "filters": 64, | ||
| "quantization_channels": 256 | ||
| }, | ||
|
|
||
| "decoder": FakeDecoder, | ||
|
|
||
| "loss": WavenetLoss, | ||
|
|
||
| "data_layer": WavenetDataLayer, | ||
| "data_layer_params": { | ||
| "num_audio_features": 80, | ||
| "dataset_location": "data/speech/LJSpeech/wavs/" | ||
| } | ||
| } | ||
|
|
||
| train_params = { | ||
| "data_layer_params": { | ||
| "dataset_files": [ | ||
| "data/speech/LJSpeech/train.csv", | ||
| ], | ||
| "shuffle": True, | ||
| }, | ||
| } | ||
|
|
||
| eval_params = { | ||
| "data_layer_params": { | ||
| "dataset_files": [ | ||
| "data/speech/LJSpeech/val.csv", | ||
| ], | ||
| "shuffle": False, | ||
| }, | ||
| } | ||
|
|
||
| infer_params = { | ||
| "data_layer_params": { | ||
| "dataset_files": [ | ||
| "data/speech/LJSpeech/test.csv", | ||
| ], | ||
| "shuffle": False, | ||
| }, | ||
| } | ||
|
|
||
| interactive_infer_params = { | ||
| "data_layer_params": { | ||
| "dataset_files": [], | ||
| "shuffle": False, | ||
| }, | ||
| } |
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