Medix-AI/coqui-tts
Medix-AI
/
coqui-tts
mirror of https://github.com/coqui-ai/TTS.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

update SS tests for multi-speaker case

This commit is contained in:
erogol 2020-12-30 14:24:04 +01:00
parent 71c382be14
commit ad3863df3c
1 changed files with 55 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

74
tests/test_speedy_speech_layers.py
View File

@ -15,31 +15,31 @@ def test_encoder():
input_dummy = torch.rand(8, 14, 37).to(device) input_dummy = torch.rand(8, 14, 37).to(device)
input_lengths = torch.randint(31, 37, (8, )).long().to(device) input_lengths = torch.randint(31, 37, (8, )).long().to(device)
input_lengths[-1] = 37 input_lengths[-1] = 37
input_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)), input_mask = torch.unsqueeze(
1).to(device) sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device)
# residual bn conv encoder # residual bn conv encoder
layer = Encoder(out_channels=11, layer = Encoder(out_channels=11,
hidden_channels=14, in_hidden_channels=14,
encoder_type='residual_conv_bn').to(device) encoder_type='residual_conv_bn').to(device)
output = layer(input_dummy, input_mask) output = layer(input_dummy, input_mask)
assert list(output.shape)==[8, 11, 37] assert list(output.shape) == [8, 11, 37]
# transformer encoder # transformer encoder
layer = Encoder(out_channels=11, layer = Encoder(out_channels=11,
hidden_channels=14, in_hidden_channels=14,
encoder_type='transformer', encoder_type='transformer',
encoder_params={ encoder_params={
'hidden_channels_ffn': 768, 'hidden_channels_ffn': 768,
'num_heads': 2, 'num_heads': 2,
"kernel_size": 3, "kernel_size": 3,
"dropout_p": 0.1, "dropout_p": 0.1,
"num_layers": 6, "num_layers": 6,
"rel_attn_window_size": 4, "rel_attn_window_size": 4,
"input_length": None "input_length": None
}).to(device) }).to(device)
output = layer(input_dummy, input_mask) output = layer(input_dummy, input_mask)
assert list(output.shape)==[8, 11, 37] assert list(output.shape) == [8, 11, 37]
def test_decoder(): def test_decoder():
@ -47,9 +47,10 @@ def test_decoder():
input_lengths = torch.randint(31, 37, (8, )).long().to(device) input_lengths = torch.randint(31, 37, (8, )).long().to(device)
input_lengths[-1] = 37 input_lengths[-1] = 37
input_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device) input_mask = torch.unsqueeze(
sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device)
layer = Decoder(out_channels=11, hidden_channels=128).to(device) layer = Decoder(out_channels=11, in_hidden_channels=128).to(device)
output = layer(input_dummy, input_mask) output = layer(input_dummy, input_mask)
assert list(output.shape) == [8, 11, 37] assert list(output.shape) == [8, 11, 37]
@ -59,12 +60,13 @@ def test_duration_predictor():
input_lengths = torch.randint(20, 27, (8, )).long().to(device) input_lengths = torch.randint(20, 27, (8, )).long().to(device)
input_lengths[-1] = 27 input_lengths[-1] = 27
x_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device) x_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)),
1).to(device)
layer = DurationPredictor(hidden_channels=128).to(device) layer = DurationPredictor(hidden_channels=128).to(device)
output = layer(input_dummy, x_mask) output = layer(input_dummy, x_mask)
assert list(output.shape)==[8, 1, 27] assert list(output.shape) == [8, 1, 27]
def test_speedy_speech(): def test_speedy_speech():
@ -93,6 +95,40 @@ def test_speedy_speech():
# forward pass # forward pass
o_de, o_dr, attn = model(x_dummy, x_lengths, y_lengths, durations) o_de, o_dr, attn = model(x_dummy, x_lengths, y_lengths, durations)
assert list(o_de.shape) == [B, 80, T_de], f"{list(o_de.shape)}"
assert list(attn.shape) == [B, T_de, T_en]
assert list(o_dr.shape) == [B, T_en]
# with speaker embedding
model = SpeedySpeech(num_chars,
out_channels=80,
hidden_channels=128,
num_speakers=10,
c_in_channels=256).to(device)
model.forward(x_dummy,
x_lengths,
y_lengths,
durations,
g=torch.randint(0, 10, (B,)).to(device))
assert list(o_de.shape) == [B, 80, T_de], f"{list(o_de.shape)}"
assert list(attn.shape) == [B, T_de, T_en]
assert list(o_dr.shape) == [B, T_en]
# with speaker external embedding
model = SpeedySpeech(num_chars,
out_channels=80,
hidden_channels=128,
num_speakers=10,
external_c=True,
c_in_channels=256).to(device)
model.forward(x_dummy,
x_lengths,
y_lengths,
durations,
g=torch.rand((B,256)).to(device))
assert list(o_de.shape) == [B, 80, T_de], f"{list(o_de.shape)}" assert list(o_de.shape) == [B, 80, T_de], f"{list(o_de.shape)}"
assert list(attn.shape) == [B, T_de, T_en] assert list(attn.shape) == [B, T_de, T_en]
assert list(o_dr.shape) == [B, T_en] assert list(o_dr.shape) == [B, T_en]