mirror of
https://github.com/wassname/TTS.git
synced 2026-09-09 11:16:00 +08:00
small fixes and test fixes
This commit is contained in:
erogol
@@ -86,7 +86,19 @@
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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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"seq_len_norm": false, // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
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"encoder_type": "transformer",
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"hidden_channels_encoder": 192,
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"hidden_channels_decoder": 192,
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"hidden_channels_duration_predictor": 256,
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"use_encoder_prenet": true,
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"encoder_type": "rel_pos_transformer",
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"encoder_params": {
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"kernel_size":3,
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"dropout_p": 0.1,
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"num_layers": 6,
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"num_heads": 2,
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"hidden_channels_ffn": 768,
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"input_length": null
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},
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// TENSORBOARD and LOGGING
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"print_step": 25, // Number of steps to log training on console.
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@@ -107,6 +119,7 @@
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"max_seq_len": 500, // DATASET-RELATED: maximum text length
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"compute_f0": false, // compute f0 values in data-loader
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"compute_input_seq_cache": true,
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"use_noise_augment": true,
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// PATHS
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"output_path": "tests/train_outputs/",
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@@ -64,6 +64,8 @@
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// MODEL PARAMETERS
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"positional_encoding": true,
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"hidden_channels": 128,
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"encoder_type": "residual_conv_bn",
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"encoder_type": "residual_conv_bn",
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"encoder_params":{
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"kernel_size": 4,
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@@ -71,13 +73,15 @@
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"num_conv_blocks": 2,
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"num_res_blocks": 13
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},
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"decoder_residual_conv_bn_params":{
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"decoder_type": "residual_conv_bn",
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"decoder_params":{
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"kernel_size": 4,
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"dilations": [1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1],
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"num_conv_blocks": 2,
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"num_res_blocks": 17
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},
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// TRAINING
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"batch_size":64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
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"eval_batch_size":32,
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+54
-50
@@ -42,58 +42,62 @@ class GlowTTSTrainTest(unittest.TestCase):
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criterion = criterion = GlowTTSLoss()
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# model to train
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model = GlowTts(num_chars=32,
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hidden_channels=128,
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hidden_channels_ffn=32,
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hidden_channels_dp=32,
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out_channels=80,
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num_heads=2,
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num_layers_enc=6,
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dropout_p=0.1,
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num_flow_blocks_dec=12,
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kernel_size_dec=5,
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dilation_rate=5,
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num_block_layers=4,
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dropout_p_dec=0.,
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num_speakers=0,
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c_in_channels=0,
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num_splits=4,
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num_sqz=1,
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sigmoid_scale=False,
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rel_attn_window_size=None,
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input_length=None,
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mean_only=False,
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hidden_channels_enc=None,
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hidden_channels_dec=None,
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use_encoder_prenet=False,
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encoder_type="transformer").to(device)
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model = GlowTts(
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num_chars=32,
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hidden_channels_enc=128,
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hidden_channels_dec=128,
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hidden_channels_dp=32,
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out_channels=80,
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encoder_type='rel_pos_transformer',
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encoder_params={
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'kernel_size': 3,
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'dropout_p': 0.1,
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'num_layers': 6,
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'num_heads': 2,
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'hidden_channels_ffn': 768, # 4 times the hidden_channels
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'input_length': None
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},
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use_encoder_prenet=True,
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num_flow_blocks_dec=12,
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kernel_size_dec=5,
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dilation_rate=5,
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num_block_layers=4,
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dropout_p_dec=0.,
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num_speakers=0,
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c_in_channels=0,
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num_splits=4,
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num_squeeze=1,
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sigmoid_scale=False,
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mean_only=False).to(device)
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# reference model to compare model weights
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model_ref = GlowTts(num_chars=32,
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hidden_channels=128,
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hidden_channels_ffn=32,
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hidden_channels_dp=32,
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out_channels=80,
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num_heads=2,
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num_layers_enc=6,
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dropout_p=0.1,
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num_flow_blocks_dec=12,
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kernel_size_dec=5,
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dilation_rate=5,
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num_block_layers=4,
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dropout_p_dec=0.,
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num_speakers=0,
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c_in_channels=0,
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num_splits=4,
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num_sqz=1,
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sigmoid_scale=False,
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rel_attn_window_size=None,
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input_length=None,
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mean_only=False,
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hidden_channels_enc=None,
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hidden_channels_dec=None,
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use_encoder_prenet=False,
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encoder_type="transformer").to(device)
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model_ref = GlowTts(
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num_chars=32,
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hidden_channels_enc=128,
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hidden_channels_dec=128,
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hidden_channels_dp=32,
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out_channels=80,
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encoder_type='rel_pos_transformer',
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encoder_params={
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'kernel_size': 3,
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'dropout_p': 0.1,
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'num_layers': 6,
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'num_heads': 2,
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'hidden_channels_ffn': 768, # 4 times the hidden_channels
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'input_length': None
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},
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use_encoder_prenet=True,
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num_flow_blocks_dec=12,
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kernel_size_dec=5,
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dilation_rate=5,
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num_block_layers=4,
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dropout_p_dec=0.,
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num_speakers=0,
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c_in_channels=0,
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num_splits=4,
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num_squeeze=1,
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sigmoid_scale=False,
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mean_only=False).to(device)
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model.train()
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print(" > Num parameters for GlowTTS model:%s" %
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