Medix-AI/coqui-tts
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coqui-tts
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small fixes and test fixes

This commit is contained in:
erogol 2021-01-08 15:48:40 +01:00
parent a6259041d3
commit d382d759b3
6 changed files with 75 additions and 55 deletions
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2
TTS/bin/train_vocoder_wavernn.py
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@ -514,7 +514,7 @@ if __name__ == "__main__":
new_fields["restore_path"] = args.restore_path
new_fields["github_branch"] = get_git_branch()
copy_model_files(
args.config_path, os.path.join(OUT_PATH, "config.json"), new_fields
c, args.config_path, OUT_PATH, new_fields
)
os.chmod(AUDIO_PATH, 0o775)
os.chmod(OUT_PATH, 0o775)

2
TTS/tts/layers/speedy_speech/encoder.py
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@ -122,7 +122,7 @@ class Encoder(nn.Module):
num_layers=3,
dropout_p=0.5)
# text encoder
self.encoder = Transformer(self.hidden_channels, **encoder_params) # pylint: disable=unexpected-keyword-arg
self.encoder = RelativePositionTransformer(self.hidden_channels, **encoder_params) # pylint: disable=unexpected-keyword-arg
elif encoder_type.lower() == 'residual_conv_bn':
self.pre = nn.Sequential(
nn.Conv1d(self.in_channels, self.hidden_channels, 1),

1
TTS/tts/models/speedy_speech.py
View File

@ -169,7 +169,6 @@ class SpeedySpeech(nn.Module):
dr: [B, T_max]
g: [B, C]
"""
breakpoint()
o_en, o_en_dp, x_mask, g = self._forward_encoder(x, x_lengths, g)
o_dr_log = self.duration_predictor(o_en_dp.detach(), x_mask)
o_de, attn= self._forward_decoder(o_en, o_en_dp, dr, x_mask, y_lengths, g=g)

15
tests/inputs/test_glow_tts.json
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@ -86,7 +86,19 @@
"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
"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.
"encoder_type": "transformer",
"hidden_channels_encoder": 192,
"hidden_channels_decoder": 192,
"hidden_channels_duration_predictor": 256,
"use_encoder_prenet": true,
"encoder_type": "rel_pos_transformer",
"encoder_params": {
"kernel_size":3,
"dropout_p": 0.1,
"num_layers": 6,
"num_heads": 2,
"hidden_channels_ffn": 768,
"input_length": null
},
// TENSORBOARD and LOGGING
"print_step": 25, // Number of steps to log training on console.
@ -107,6 +119,7 @@
"max_seq_len": 500, // DATASET-RELATED: maximum text length
"compute_f0": false, // compute f0 values in data-loader
"compute_input_seq_cache": true,
"use_noise_augment": true,
// PATHS
"output_path": "tests/train_outputs/",

6
tests/inputs/test_speedy_speech.json
View File

@ -64,6 +64,8 @@
// MODEL PARAMETERS
"positional_encoding": true,
"hidden_channels": 128,
"encoder_type": "residual_conv_bn",
"encoder_type": "residual_conv_bn",
"encoder_params":{
"kernel_size": 4,
@ -71,13 +73,15 @@
"num_conv_blocks": 2,
"num_res_blocks": 13
},
"decoder_residual_conv_bn_params":{
"decoder_type": "residual_conv_bn",
"decoder_params":{
"kernel_size": 4,
"dilations": [1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1],
"num_conv_blocks": 2,
"num_res_blocks": 17
},
// TRAINING
"batch_size":64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
"eval_batch_size":32,

104
tests/test_glow_tts.py
View File

@ -42,58 +42,62 @@ class GlowTTSTrainTest(unittest.TestCase):
criterion = criterion = GlowTTSLoss()
# model to train
model = GlowTts(num_chars=32,
hidden_channels=128,
hidden_channels_ffn=32,
hidden_channels_dp=32,
out_channels=80,
num_heads=2,
num_layers_enc=6,
dropout_p=0.1,
num_flow_blocks_dec=12,
kernel_size_dec=5,
dilation_rate=5,
num_block_layers=4,
dropout_p_dec=0.,
num_speakers=0,
c_in_channels=0,
num_splits=4,
num_sqz=1,
sigmoid_scale=False,
rel_attn_window_size=None,
input_length=None,
mean_only=False,
hidden_channels_enc=None,
hidden_channels_dec=None,
use_encoder_prenet=False,
encoder_type="transformer").to(device)
model = GlowTts(
num_chars=32,
hidden_channels_enc=128,
hidden_channels_dec=128,
hidden_channels_dp=32,
out_channels=80,
encoder_type='rel_pos_transformer',
encoder_params={
'kernel_size': 3,
'dropout_p': 0.1,
'num_layers': 6,
'num_heads': 2,
'hidden_channels_ffn': 768, # 4 times the hidden_channels
'input_length': None
},
use_encoder_prenet=True,
num_flow_blocks_dec=12,
kernel_size_dec=5,
dilation_rate=5,
num_block_layers=4,
dropout_p_dec=0.,
num_speakers=0,
c_in_channels=0,
num_splits=4,
num_squeeze=1,
sigmoid_scale=False,
mean_only=False).to(device)
# reference model to compare model weights
model_ref = GlowTts(num_chars=32,
hidden_channels=128,
hidden_channels_ffn=32,
hidden_channels_dp=32,
out_channels=80,
num_heads=2,
num_layers_enc=6,
dropout_p=0.1,
num_flow_blocks_dec=12,
kernel_size_dec=5,
dilation_rate=5,
num_block_layers=4,
dropout_p_dec=0.,
num_speakers=0,
c_in_channels=0,
num_splits=4,
num_sqz=1,
sigmoid_scale=False,
rel_attn_window_size=None,
input_length=None,
mean_only=False,
hidden_channels_enc=None,
hidden_channels_dec=None,
use_encoder_prenet=False,
encoder_type="transformer").to(device)
model_ref = GlowTts(
num_chars=32,
hidden_channels_enc=128,
hidden_channels_dec=128,
hidden_channels_dp=32,
out_channels=80,
encoder_type='rel_pos_transformer',
encoder_params={
'kernel_size': 3,
'dropout_p': 0.1,
'num_layers': 6,
'num_heads': 2,
'hidden_channels_ffn': 768, # 4 times the hidden_channels
'input_length': None
},
use_encoder_prenet=True,
num_flow_blocks_dec=12,
kernel_size_dec=5,
dilation_rate=5,
num_block_layers=4,
dropout_p_dec=0.,
num_speakers=0,
c_in_channels=0,
num_splits=4,
num_squeeze=1,
sigmoid_scale=False,
mean_only=False).to(device)
model.train()
print(" > Num parameters for GlowTTS model:%s" %