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coqui-tts
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don't use sigmoid output for tacotron, fix bug for memory queue handling, remove maxout

This commit is contained in:
Eren Golge 2019-07-22 15:09:05 +02:00
parent ee706b50f6
commit 2bbb3f7a40
3 changed files with 9 additions and 15 deletions
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8
config.json
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@ -1,6 +1,6 @@
{ {
"run_name": "ljspeech", "run_name": "ljspeech",
"run_description": "gradual training with prenet frame size 1. Comparing to memory queue in gradual training. ", "run_description": "gradual training with prenet frame size 1 + no maxout for cbhg + symmetric norm.",
"audio":{ "audio":{
// Audio processing parameters // Audio processing parameters
@ -16,8 +16,8 @@
"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation. "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
// Normalization parameters // Normalization parameters
"signal_norm": true, // normalize the spec values in range [0, 1] "signal_norm": true, // normalize the spec values in range [0, 1]
"symmetric_norm": false, // move normalization to range [-1, 1] "symmetric_norm": true, // move normalization to range [-1, 1]
"max_norm": 1, // scale normalization to range [-max_norm, max_norm] or [0, max_norm] "max_norm": 4, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
"clip_norm": true, // clip normalized values into the range. "clip_norm": true, // clip normalized values into the range.
"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!! "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!! "mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
@ -71,7 +71,7 @@
"dataset": "ljspeech", // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py "dataset": "ljspeech", // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
"min_seq_len": 6, // DATASET-RELATED: minimum text length to use in training "min_seq_len": 6, // DATASET-RELATED: minimum text length to use in training
"max_seq_len": 150, // DATASET-RELATED: maximum text length "max_seq_len": 150, // DATASET-RELATED: maximum text length
"output_path": "../keep/", // DATASET-RELATED: output path for all training outputs. "output_path": "/media/erogol/data_ssd/Models/libri_tts/", // DATASET-RELATED: output path for all training outputs.
"num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values. "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. "num_val_loader_workers": 4, // number of evaluation data loader processes.
"phoneme_cache_path": "mozilla_us_phonemes", // phoneme computation is slow, therefore, it caches results in the given folder. "phoneme_cache_path": "mozilla_us_phonemes", // phoneme computation is slow, therefore, it caches results in the given folder.

12
layers/tacotron.py
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@ -135,9 +135,6 @@ class CBHG(nn.Module):
]) ])
# max pooling of conv bank, with padding # max pooling of conv bank, with padding
# TODO: try average pooling OR larger kernel size # TODO: try average pooling OR larger kernel size
self.max_pool1d = nn.Sequential(
nn.ConstantPad1d([0, 1], value=0),
nn.MaxPool1d(kernel_size=2, stride=1, padding=0))
out_features = [K * conv_bank_features] + conv_projections[:-1] out_features = [K * conv_bank_features] + conv_projections[:-1]
activations = [self.relu] * (len(conv_projections) - 1) activations = [self.relu] * (len(conv_projections) - 1)
activations += [None] activations += [None]
@ -186,7 +183,6 @@ class CBHG(nn.Module):
outs.append(out) outs.append(out)
x = torch.cat(outs, dim=1) x = torch.cat(outs, dim=1)
assert x.size(1) == self.conv_bank_features * len(self.conv1d_banks) assert x.size(1) == self.conv_bank_features * len(self.conv1d_banks)
x = self.max_pool1d(x)
for conv1d in self.conv1d_projections: for conv1d in self.conv1d_projections:
x = conv1d(x) x = conv1d(x)
# (B, T_in, hid_feature) # (B, T_in, hid_feature)
@ -387,7 +383,7 @@ class Decoder(nn.Module):
del decoder_input del decoder_input
# predict mel vectors from decoder vectors # predict mel vectors from decoder vectors
output = self.proj_to_mel(decoder_output) output = self.proj_to_mel(decoder_output)
output = torch.sigmoid(output) # output = torch.sigmoid(output)
# predict stop token # predict stop token
stopnet_input = torch.cat([decoder_output, output], -1) stopnet_input = torch.cat([decoder_output, output], -1)
del decoder_output del decoder_output
@ -403,15 +399,15 @@ class Decoder(nn.Module):
if self.memory_size > self.r: if self.memory_size > self.r:
# memory queue size is larger than number of frames per decoder iter # memory queue size is larger than number of frames per decoder iter
self.memory_input = torch.cat([ self.memory_input = torch.cat([
self.memory_input[:, self.r * self.memory_dim:].clone(), new_memory new_memory, self.memory_input[:, :(self.memory_size - self.r) * self.memory_dim].clone()
], ],
dim=-1) dim=-1)
else: else:
# memory queue size smaller than number of frames per decoder iter # memory queue size smaller than number of frames per decoder iter
self.memory_input = new_memory[:, (self.r - self.memory_size)*self.memory_dim:] self.memory_input = new_memory[:, :self.memory_size * self.memory_dim]
else: else:
# use only the last frame prediction # use only the last frame prediction
self.memory_input = new_memory[:, (self.r-1) * self.memory_dim:] self.memory_input = new_memory[:, :self.memory_dim]
def forward(self, inputs, memory, mask): def forward(self, inputs, memory, mask):
""" """

4
models/tacotron.py
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@ -36,9 +36,7 @@ class Tacotron(nn.Module):
forward_attn, trans_agent, forward_attn_mask, forward_attn, trans_agent, forward_attn_mask,
location_attn, separate_stopnet) location_attn, separate_stopnet)
self.postnet = PostCBHG(mel_dim) self.postnet = PostCBHG(mel_dim)
self.last_linear = nn.Sequential( self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim)
nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim),
nn.Sigmoid())
def forward(self, characters, text_lengths, mel_specs, speaker_ids=None): def forward(self, characters, text_lengths, mel_specs, speaker_ids=None):
B = characters.size(0) B = characters.size(0)