39 lines
980 B
Python
39 lines
980 B
Python
import pytest
|
|
import torch
|
|
|
|
from mayavoz.inference import Inference
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"audio",
|
|
["tests/data/vctk/clean_testset_wav/p257_166.wav", torch.rand(1, 2, 48000)],
|
|
)
|
|
def test_read_input(audio):
|
|
|
|
read_audio = Inference.read_input(audio, 48000, 16000)
|
|
assert isinstance(read_audio, torch.Tensor)
|
|
assert read_audio.shape[0] == 1
|
|
|
|
|
|
def test_batchify():
|
|
rand = torch.rand(1, 1000)
|
|
batched_rand = Inference.batchify(rand, window_size=100, step_size=100)
|
|
assert batched_rand.shape[0] == 12
|
|
|
|
|
|
def test_aggregate():
|
|
rand = torch.rand(12, 1, 100)
|
|
agg_rand = Inference.aggreagate(
|
|
data=rand, window_size=100, total_frames=1000, step_size=100
|
|
)
|
|
assert agg_rand.shape[-1] == 1000
|
|
|
|
|
|
def test_pretrained():
|
|
from mayavoz.models import Mayamodel
|
|
|
|
model = Mayamodel.from_pretrained(
|
|
"shahules786/mayavoz-waveunet-valentini-28spk"
|
|
)
|
|
_ = model.enhance("tests/data/vctk/clean_testset_wav/p257_166.wav")
|