Medix-AI/coqui-tts
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coqui-tts
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Merge branch 'upstream_clean' of https://github.com/geneing/TTS into geneing-upstream_clean

This commit is contained in:
root 2020-01-06 09:52:08 +01:00
parent 6e09224697 748cbbc403
commit 71af8da293
3 changed files with 14 additions and 11 deletions

13
layers/common_layers.py
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@ -127,8 +127,8 @@ class GravesAttention(nn.Module):
self.init_layers() self.init_layers()
def init_layers(self): def init_layers(self):
torch.nn.init.constant_(self.N_a[2].bias[10:15], 0.5) torch.nn.init.constant_(self.N_a[2].bias[(2*self.K):(3*self.K)], 1.)
torch.nn.init.constant_(self.N_a[2].bias[5:10], 10) torch.nn.init.constant_(self.N_a[2].bias[self.K:(2*self.K)], 10)
def init_states(self, inputs): def init_states(self, inputs):
if self.J is None or inputs.shape[1] > self.J.shape[-1]: if self.J is None or inputs.shape[1] > self.J.shape[-1]:
@ -159,20 +159,21 @@ class GravesAttention(nn.Module):
k_t = gbk_t[:, 2, :] k_t = gbk_t[:, 2, :]
# attention GMM parameters # attention GMM parameters
inv_sig_t = torch.exp(-torch.clamp(b_t, min=-6, max=9)) # variance sig_t = torch.nn.functional.softplus(b_t)+self.eps
mu_t = self.mu_prev + torch.nn.functional.softplus(k_t) mu_t = self.mu_prev + torch.nn.functional.softplus(k_t)
g_t = torch.softmax(g_t, dim=-1) * inv_sig_t + self.eps g_t = torch.softmax(g_t, dim=-1) / sig_t + self.eps
# each B x K x T_in # each B x K x T_in
g_t = g_t.unsqueeze(2).expand(g_t.size(0), g_t = g_t.unsqueeze(2).expand(g_t.size(0),
g_t.size(1), g_t.size(1),
inputs.size(1)) inputs.size(1))
inv_sig_t = inv_sig_t.unsqueeze(2).expand_as(g_t) sig_t = sig_t.unsqueeze(2).expand_as(g_t)
mu_t_ = mu_t.unsqueeze(2).expand_as(g_t) mu_t_ = mu_t.unsqueeze(2).expand_as(g_t)
j = self.J[:g_t.size(0), :, :inputs.size(1)] j = self.J[:g_t.size(0), :, :inputs.size(1)]
# attention weights # attention weights
phi_t = g_t * torch.exp(-0.5 * inv_sig_t * (mu_t_ - j)**2) phi_t = g_t * torch.exp(-0.5 * (mu_t_ - j)**2 / (sig_t**2))
alpha_t = self.COEF * torch.sum(phi_t, 1) alpha_t = self.COEF * torch.sum(phi_t, 1)
# apply masking # apply masking

5
server/server.py
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@ -55,8 +55,11 @@ def tts():
if __name__ == '__main__': if __name__ == '__main__':
if not config or not synthesizer:
args = create_argparser().parse_args() args = create_argparser().parse_args()
# Setup synthesizer from CLI args if they're specified or no embedded model
# is present.
if not config or not synthesizer or args.tts_checkpoint or args.tts_config:
synthesizer = Synthesizer(args) synthesizer = Synthesizer(args)
app.run(debug=config.debug, host='0.0.0.0', port=config.port) app.run(debug=config.debug, host='0.0.0.0', port=config.port)

5
server/synthesizer.py
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@ -53,15 +53,14 @@ class Synthesizer(object):
num_speakers = 0 num_speakers = 0
self.tts_model = setup_model(self.input_size, num_speakers=num_speakers, c=self.tts_config) self.tts_model = setup_model(self.input_size, num_speakers=num_speakers, c=self.tts_config)
# load model state # load model state
map_location = None if use_cuda else torch.device('cpu') cp = torch.load(tts_checkpoint, map_location=torch.device('cpu'))
cp = torch.load(tts_checkpoint, map_location=map_location)
# load the model # load the model
self.tts_model.load_state_dict(cp['model']) self.tts_model.load_state_dict(cp['model'])
if use_cuda: if use_cuda:
self.tts_model.cuda() self.tts_model.cuda()
self.tts_model.eval() self.tts_model.eval()
self.tts_model.decoder.max_decoder_steps = 3000 self.tts_model.decoder.max_decoder_steps = 3000
if 'r' in cp and self.tts_config.model in ["Tacotron", "TacotronGST"]: if 'r' in cp:
self.tts_model.decoder.set_r(cp['r']) self.tts_model.decoder.set_r(cp['r'])
def load_wavernn(self, lib_path, model_path, model_file, model_config, use_cuda): def load_wavernn(self, lib_path, model_path, model_file, model_config, use_cuda):