|
import torch as T |
|
|
|
from TTS.tts.models.forward_tts import ForwardTTS, ForwardTTSArgs |
|
from TTS.tts.utils.helpers import sequence_mask |
|
|
|
|
|
|
|
|
|
def expand_encoder_outputs_test(): |
|
model = ForwardTTS(ForwardTTSArgs(num_chars=10)) |
|
|
|
inputs = T.rand(2, 5, 57) |
|
durations = T.randint(1, 4, (2, 57)) |
|
|
|
x_mask = T.ones(2, 1, 57) |
|
y_mask = T.ones(2, 1, durations.sum(1).max()) |
|
|
|
expanded, _ = model.expand_encoder_outputs(inputs, durations, x_mask, y_mask) |
|
|
|
for b in range(durations.shape[0]): |
|
index = 0 |
|
for idx, dur in enumerate(durations[b]): |
|
diff = ( |
|
expanded[b, :, index : index + dur.item()] |
|
- inputs[b, :, idx].repeat(dur.item()).view(expanded[b, :, index : index + dur.item()].shape) |
|
).sum() |
|
assert abs(diff) < 1e-6, diff |
|
index += dur |
|
|
|
|
|
def model_input_output_test(): |
|
"""Assert the output shapes of the model in different modes""" |
|
|
|
|
|
model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=False, use_aligner=False)) |
|
|
|
x = T.randint(0, 10, (2, 21)) |
|
x_lengths = T.randint(10, 22, (2,)) |
|
x_lengths[-1] = 21 |
|
x_mask = sequence_mask(x_lengths).unsqueeze(1).long() |
|
durations = T.randint(1, 4, (2, 21)) |
|
durations = durations * x_mask.squeeze(1) |
|
y_lengths = durations.sum(1) |
|
y_mask = sequence_mask(y_lengths).unsqueeze(1).long() |
|
|
|
outputs = model.forward(x, x_lengths, y_lengths, dr=durations) |
|
|
|
assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80) |
|
assert outputs["durations_log"].shape == (2, 21) |
|
assert outputs["durations"].shape == (2, 21) |
|
assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21) |
|
assert (outputs["x_mask"] - x_mask).sum() == 0.0 |
|
assert (outputs["y_mask"] - y_mask).sum() == 0.0 |
|
|
|
assert outputs["alignment_soft"] is None |
|
assert outputs["alignment_mas"] is None |
|
assert outputs["alignment_logprob"] is None |
|
assert outputs["o_alignment_dur"] is None |
|
assert outputs["pitch_avg"] is None |
|
assert outputs["pitch_avg_gt"] is None |
|
|
|
|
|
model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=False)) |
|
|
|
x = T.randint(0, 10, (2, 21)) |
|
x_lengths = T.randint(10, 22, (2,)) |
|
x_lengths[-1] = 21 |
|
x_mask = sequence_mask(x_lengths).unsqueeze(1).long() |
|
durations = T.randint(1, 4, (2, 21)) |
|
durations = durations * x_mask.squeeze(1) |
|
y_lengths = durations.sum(1) |
|
y_mask = sequence_mask(y_lengths).unsqueeze(1).long() |
|
pitch = T.rand(2, 1, y_lengths.max()) |
|
|
|
outputs = model.forward(x, x_lengths, y_lengths, dr=durations, pitch=pitch) |
|
|
|
assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80) |
|
assert outputs["durations_log"].shape == (2, 21) |
|
assert outputs["durations"].shape == (2, 21) |
|
assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21) |
|
assert (outputs["x_mask"] - x_mask).sum() == 0.0 |
|
assert (outputs["y_mask"] - y_mask).sum() == 0.0 |
|
assert outputs["pitch_avg"].shape == (2, 1, 21) |
|
assert outputs["pitch_avg_gt"].shape == (2, 1, 21) |
|
|
|
assert outputs["alignment_soft"] is None |
|
assert outputs["alignment_mas"] is None |
|
assert outputs["alignment_logprob"] is None |
|
assert outputs["o_alignment_dur"] is None |
|
|
|
|
|
model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=False, use_aligner=True)) |
|
|
|
x = T.randint(0, 10, (2, 21)) |
|
x_lengths = T.randint(10, 22, (2,)) |
|
x_lengths[-1] = 21 |
|
x_mask = sequence_mask(x_lengths).unsqueeze(1).long() |
|
durations = T.randint(1, 4, (2, 21)) |
|
durations = durations * x_mask.squeeze(1) |
|
y_lengths = durations.sum(1) |
|
y_mask = sequence_mask(y_lengths).unsqueeze(1).long() |
|
y = T.rand(2, y_lengths.max(), 80) |
|
|
|
outputs = model.forward(x, x_lengths, y_lengths, dr=durations, y=y) |
|
|
|
assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80) |
|
assert outputs["durations_log"].shape == (2, 21) |
|
assert outputs["durations"].shape == (2, 21) |
|
assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21) |
|
assert (outputs["x_mask"] - x_mask).sum() == 0.0 |
|
assert (outputs["y_mask"] - y_mask).sum() == 0.0 |
|
assert outputs["alignment_soft"].shape == (2, durations.sum(1).max(), 21) |
|
assert outputs["alignment_mas"].shape == (2, durations.sum(1).max(), 21) |
|
assert outputs["alignment_logprob"].shape == (2, 1, durations.sum(1).max(), 21) |
|
assert outputs["o_alignment_dur"].shape == (2, 21) |
|
|
|
assert outputs["pitch_avg"] is None |
|
assert outputs["pitch_avg_gt"] is None |
|
|
|
|
|
model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=True)) |
|
|
|
x = T.randint(0, 10, (2, 21)) |
|
x_lengths = T.randint(10, 22, (2,)) |
|
x_lengths[-1] = 21 |
|
x_mask = sequence_mask(x_lengths).unsqueeze(1).long() |
|
durations = T.randint(1, 4, (2, 21)) |
|
durations = durations * x_mask.squeeze(1) |
|
y_lengths = durations.sum(1) |
|
y_mask = sequence_mask(y_lengths).unsqueeze(1).long() |
|
y = T.rand(2, y_lengths.max(), 80) |
|
pitch = T.rand(2, 1, y_lengths.max()) |
|
|
|
outputs = model.forward(x, x_lengths, y_lengths, dr=durations, pitch=pitch, y=y) |
|
|
|
assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80) |
|
assert outputs["durations_log"].shape == (2, 21) |
|
assert outputs["durations"].shape == (2, 21) |
|
assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21) |
|
assert (outputs["x_mask"] - x_mask).sum() == 0.0 |
|
assert (outputs["y_mask"] - y_mask).sum() == 0.0 |
|
assert outputs["alignment_soft"].shape == (2, durations.sum(1).max(), 21) |
|
assert outputs["alignment_mas"].shape == (2, durations.sum(1).max(), 21) |
|
assert outputs["alignment_logprob"].shape == (2, 1, durations.sum(1).max(), 21) |
|
assert outputs["o_alignment_dur"].shape == (2, 21) |
|
assert outputs["pitch_avg"].shape == (2, 1, 21) |
|
assert outputs["pitch_avg_gt"].shape == (2, 1, 21) |
|
|
