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NeMo / tests /collections /speaker_tasks /utils /test_multispeaker_utils.py
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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import pytest
import torch
from nemo.collections.asr.parts.utils.asr_multispeaker_utils import (
find_best_permutation,
find_first_nonzero,
get_ats_targets,
get_hidden_length_from_sample_length,
get_pil_targets,
reconstruct_labels,
)
def reconstruct_labels_forloop(labels: torch.Tensor, batch_perm_inds: torch.Tensor) -> torch.Tensor:
"""
This is a for-loop implementation of reconstruct_labels built for testing purposes.
"""
# Expanding batch_perm_inds to align with labels dimensions
batch_size, num_frames, num_speakers = labels.shape
batch_perm_inds_exp = batch_perm_inds.unsqueeze(1).expand(-1, num_frames, -1)
# Reconstructing the labels using advanced indexing
reconstructed_labels = torch.gather(labels, 2, batch_perm_inds_exp)
return reconstructed_labels
class TestSortingUtils:
@pytest.mark.unit
@pytest.mark.parametrize(
"mat, max_cap_val, thres, expected",
[
# Test 1: Basic case with clear first nonzero values
(torch.tensor([[0.1, 0.6, 0.0], [0.0, 0.0, 0.9]]), -1, 0.5, torch.tensor([1, 2])),
# Test 2: All elements are below threshold
(torch.tensor([[0.1, 0.2], [0.3, 0.4]]), -1, 0.5, torch.tensor([-1, -1])),
# Test 3: No nonzero elements, should return max_cap_val (-1)
(torch.tensor([[0.0, 0.0], [0.0, 0.0]]), -1, 0.5, torch.tensor([-1, -1])),
# Test 4: Large matrix with mixed values, some rows with all values below threshold
(torch.tensor([[0.1, 0.7, 0.3], [0.0, 0.0, 0.9], [0.5, 0.6, 0.7]]), -1, 0.5, torch.tensor([1, 2, 0])),
# Test 5: Single row matrix
(torch.tensor([[0.0, 0.0, 0.6]]), -1, 0.5, torch.tensor([2])),
# Test 6: Single column matrix
(torch.tensor([[0.1], [0.6], [0.0]]), -1, 0.5, torch.tensor([-1, 0, -1])),
# Test 7: One element matrix
(torch.tensor([[0.501]]), -1, 0.5, torch.tensor([0], dtype=torch.long)),
# Test 8: All values are zero, should return max_cap_val
(torch.tensor([[0.0, 0.0], [0.0, 0.0]]), -1, 0.5, torch.tensor([-1, -1])),
# Test 9: All values are above threshold
(torch.tensor([[0.6, 0.7], [0.8, 0.9]]), -1, 0.5, torch.tensor([0, 0])),
# Test 10: Custom max_cap_val different from default
(torch.tensor([[0.0, 0.0], [0.0, 0.0]]), 99, 0.5, torch.tensor([99, 99])),
# Test 11: Matrix with 101 columns, first nonzero value is towards the end
(torch.cat([torch.zeros(1, 100), torch.ones(1, 1)], dim=1), -1, 0.5, torch.tensor([100])),
# Test 12: Matrix with 1000 columns, all below threshold except one near the middle
(
torch.cat([torch.zeros(1, 499), torch.tensor([[0.6]]), torch.zeros(1, 500)], dim=1),
-1,
0.5,
torch.tensor([499]),
),
],
)
def test_find_first_nonzero(self, mat, max_cap_val, thres, expected):
result = find_first_nonzero(mat, max_cap_val, thres)
assert torch.equal(result, expected), f"Expected {expected} but got {result}"
@pytest.mark.unit
@pytest.mark.parametrize(
"match_score, speaker_permutations, expected",
[
# Test 1: Simple case with batch size 1, clear best match
(
torch.tensor([[0.1, 0.9, 0.2]]), # match_score (batch_size=1, num_permutations=3)
torch.tensor([[0, 1], [1, 0], [0, 1]]), # speaker_permutations (num_permutations=3, num_speakers=2)
torch.tensor([[1, 0]]), # expected best permutation for the batch
),
# Test 2: Batch size 2, different best matches for each batch
(
torch.tensor([[0.5, 0.3, 0.7], [0.2, 0.6, 0.4]]), # match_score (batch_size=2, num_permutations=3)
torch.tensor([[0, 1], [1, 0], [0, 1]]), # speaker_permutations
torch.tensor([[0, 1], [1, 0]]), # expected best permutations
),
# Test 3: Larger number of speakers and permutations
(
torch.tensor(
[[0.1, 0.4, 0.9, 0.5], [0.6, 0.3, 0.7, 0.2]]
), # match_score (batch_size=2, num_permutations=4)
torch.tensor(
[[0, 1, 2], [1, 0, 2], [2, 1, 0], [1, 2, 0]]
), # speaker_permutations (num_permutations=4, num_speakers=3)
torch.tensor([[2, 1, 0], [2, 1, 0]]), # expected best permutations
),
# Test 4: All match scores are the same, should pick the first permutation (argmax behavior)
(
torch.tensor([[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]), # equal match_score across permutations
torch.tensor([[0, 1], [1, 0], [0, 1]]), # speaker_permutations
torch.tensor([[0, 1], [0, 1]]), # first permutation is chosen as tie-breaker
),
# Test 5: Single speaker case (num_speakers = 1)
(
torch.tensor([[0.8, 0.2]]), # match_score (batch_size=1, num_permutations=2)
torch.tensor([[0], [0]]), # speaker_permutations (num_permutations=2, num_speakers=1)
torch.tensor([[0]]), # expected best permutation
),
# Test 6: Batch size 3, varying permutations
(
torch.tensor([[0.3, 0.6], [0.4, 0.1], [0.2, 0.7]]), # match_score (batch_size=3, num_permutations=2)
torch.tensor([[0, 1], [1, 0]]), # speaker_permutations
torch.tensor([[1, 0], [0, 1], [1, 0]]), # expected best permutations for each batch
),
],
)
def test_find_best_permutation(self, match_score, speaker_permutations, expected):
result = find_best_permutation(match_score, speaker_permutations)
assert torch.equal(result, expected), f"Expected {expected} but got {result}"
@pytest.mark.parametrize(
"batch_size, num_frames, num_speakers",
[
(2, 4, 3), # Original test case
(3, 5, 2), # More frames and speakers
(1, 6, 4), # Single batch with more frames and speakers
(5, 3, 5), # More batch size with equal frames and speakers
],
)
def test_reconstruct_labels_with_forloop_ver(self, batch_size, num_frames, num_speakers):
# Generate random labels and batch_perm_inds tensor for testing
labels = torch.rand(batch_size, num_frames, num_speakers)
batch_perm_inds = torch.stack([torch.randperm(num_speakers) for _ in range(batch_size)])
# Call both functions
result_matrix = reconstruct_labels(labels, batch_perm_inds)
result_forloop = reconstruct_labels_forloop(labels, batch_perm_inds)
# Assert that both methods return the same result
assert torch.allclose(result_matrix, result_forloop), "The results are not equal!"
@pytest.mark.parametrize(
"labels, batch_perm_inds, expected_output",
[
# Example 1: Small batch size with a few frames and speakers
(
torch.tensor(
[
[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]], # First batch
[[0.9, 0.8, 0.7], [0.6, 0.5, 0.4], [0.3, 0.2, 0.1]], # Second batch
]
),
torch.tensor([[2, 0, 1], [1, 2, 0]]),
torch.tensor(
[
[[0.3, 0.1, 0.2], [0.6, 0.4, 0.5], [0.9, 0.7, 0.8]], # First batch reconstructed
[[0.8, 0.7, 0.9], [0.5, 0.4, 0.6], [0.2, 0.1, 0.3]], # Second batch reconstructed
]
),
),
# Example 2: batch_size = 1 with more frames and speakers
(
torch.tensor(
[[[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8], [0.9, 1.0, 1.1, 1.2], [1.3, 1.4, 1.5, 1.6]]]
),
torch.tensor([[3, 0, 1, 2]]),
torch.tensor(
[[[0.4, 0.1, 0.2, 0.3], [0.8, 0.5, 0.6, 0.7], [1.2, 0.9, 1.0, 1.1], [1.6, 1.3, 1.4, 1.5]]]
),
),
# Example 3: Larger batch size with fewer frames and speakers
(
torch.tensor(
[
[[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]], # First batch
[[0.7, 0.8], [0.9, 1.0], [1.1, 1.2]], # Second batch
[[1.3, 1.4], [1.5, 1.6], [1.7, 1.8]], # Third batch
[[1.9, 2.0], [2.1, 2.2], [2.3, 2.4]], # Fourth batch
]
),
torch.tensor([[1, 0], [0, 1], [1, 0], [0, 1]]),
torch.tensor(
[
[[0.2, 0.1], [0.4, 0.3], [0.6, 0.5]], # First batch reconstructed
[[0.7, 0.8], [0.9, 1.0], [1.1, 1.2]], # Second batch unchanged
[[1.4, 1.3], [1.6, 1.5], [1.8, 1.7]], # Third batch reconstructed
[[1.9, 2.0], [2.1, 2.2], [2.3, 2.4]], # Fourth batch unchanged
]
),
),
],
)
def test_reconstruct_labels(self, labels, batch_perm_inds, expected_output):
# Call the reconstruct_labels function
result = reconstruct_labels(labels, batch_perm_inds)
# Assert that the result matches the expected output
assert torch.allclose(result, expected_output), f"Expected {expected_output}, but got {result}"
class TestTargetGenerators:
@pytest.mark.parametrize(
"labels, preds, num_speakers, expected_output",
[
# Test 1: Basic case with simple permutations
(
torch.tensor(
[
[[0.9, 0.1, 0.0], [0.1, 0.8, 0.0], [0.0, 0.1, 0.9]], # Batch 1
[[0.0, 0.0, 0.9], [0.0, 0.9, 0.1], [0.9, 0.1, 0.0]], # Batch 2
]
),
torch.tensor(
[
[[0.8, 0.2, 0.0], [0.2, 0.7, 0.0], [0.0, 0.1, 0.9]], # Batch 1
[[0.0, 0.0, 0.8], [0.0, 0.8, 0.2], [0.9, 0.1, 0.0]], # Batch 2
]
),
3, # Number of speakers
torch.tensor(
[
[[0.9, 0.1, 0.0], [0.1, 0.8, 0.0], [0.0, 0.1, 0.9]], # Expected labels for Batch 1
[[0.9, 0.0, 0.0], [0.1, 0.9, 0.0], [0.0, 0.1, 0.9]], # Expected labels for Batch 2
]
),
),
# Test 2: Ambiguous case
(
torch.tensor([[[0.9, 0.8, 0.7], [0.2, 0.8, 0.7], [0.2, 0.3, 0.9]]]), # Labels
torch.tensor([[[0.6, 0.7, 0.2], [0.9, 0.4, 0.0], [0.1, 0.7, 0.1]]]), # Preds
3, # Number of speakers
torch.tensor([[[0.8, 0.7, 0.9], [0.8, 0.7, 0.2], [0.3, 0.9, 0.2]]]), # Expected output
),
# Test 3: Ambiguous case
(
torch.tensor([[[0, 0, 1, 1], [0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]]), # Labels
torch.tensor(
[[[0.6, 0.6, 0.1, 0.9], [0.7, 0.7, 0.2, 0.8], [0.4, 0.6, 0.2, 0.7], [0.1, 0.1, 0.1, 0.7]]]
), # Preds
4, # Number of speakers
torch.tensor([[[1, 1, 0, 0], [1, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0]]]), # Expected output
),
],
)
def test_get_ats_targets(self, labels, preds, num_speakers, expected_output):
# Generate all permutations for the given number of speakers
speaker_inds = list(range(num_speakers))
speaker_permutations = torch.tensor(list(itertools.permutations(speaker_inds)))
# Call the function under test
result = get_ats_targets(labels, preds, speaker_permutations)
# Assert that the result matches the expected output
assert torch.allclose(result, expected_output), f"Expected {expected_output}, but got {result}"
@pytest.mark.unit
@pytest.mark.parametrize(
"labels, preds, num_speakers, expected_output",
[
# Test 1: Basic case with simple permutations
(
torch.tensor(
[[[1, 0], [0, 1]], [[1, 0], [0, 1]]]
), # Labels (batch_size=2, num_speakers=2, num_classes=2)
torch.tensor(
[[[1, 0], [0, 1]], [[0, 1], [1, 0]]]
), # Preds (batch_size=2, num_speakers=2, num_classes=2)
2, # Number of speakers
torch.tensor([[[1, 0], [0, 1]], [[0, 1], [1, 0]]]), # expected max_score_permed_labels
),
# Test 2: Batch size 1 with more complex permutations
(
torch.tensor([[[0.8, 0.2], [0.3, 0.7]]]), # Labels
torch.tensor([[[0.9, 0.1], [0.2, 0.8]]]), # Preds
2, # Number of speakers
torch.tensor(
[[[0.8, 0.2], [0.3, 0.7]]]
), # expected output (labels remain the same as preds are close)
),
# Test 3: Ambiguous case
(
torch.tensor([[[0, 0, 1, 1], [0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]]), # Labels
torch.tensor(
[[[0.61, 0.6, 0.1, 0.9], [0.7, 0.7, 0.2, 0.8], [0.4, 0.6, 0.2, 0.7], [0.1, 0.1, 0.1, 0.7]]]
), # Preds
4, # Number of speakers
torch.tensor([[[1, 0, 0, 1], [1, 0, 0, 1], [0, 0, 0, 1], [0, 0, 0, 0]]]), # Expected output
),
],
)
def test_get_pil_targets(self, labels, preds, num_speakers, expected_output):
# Generate all permutations for the given number of speakers
speaker_inds = list(range(num_speakers))
speaker_permutations = torch.tensor(list(itertools.permutations(speaker_inds)))
result = get_pil_targets(labels, preds, speaker_permutations)
assert torch.equal(result, expected_output), f"Expected {expected_output} but got {result}"
class TestGetHiddenLengthFromSampleLength:
@pytest.mark.parametrize(
"num_samples, num_sample_per_mel_frame, num_mel_frame_per_asr_frame, expected_hidden_length",
[
(160, 160, 8, 1),
(1280, 160, 8, 1),
(0, 160, 8, 0),
(159, 160, 8, 1),
(129, 100, 5, 1),
(300, 150, 3, 1),
],
)
def test_various_cases(
self, num_samples, num_sample_per_mel_frame, num_mel_frame_per_asr_frame, expected_hidden_length
):
result = get_hidden_length_from_sample_length(
num_samples, num_sample_per_mel_frame, num_mel_frame_per_asr_frame
)
assert result == expected_hidden_length
def test_default_parameters(self):
assert get_hidden_length_from_sample_length(160) == 1
assert get_hidden_length_from_sample_length(1280) == 1
assert get_hidden_length_from_sample_length(0) == 0
assert get_hidden_length_from_sample_length(159) == 1
def test_edge_cases(self):
assert get_hidden_length_from_sample_length(159, 160, 8) == 1
assert get_hidden_length_from_sample_length(160, 160, 8) == 1
assert get_hidden_length_from_sample_length(161, 160, 8) == 1
assert get_hidden_length_from_sample_length(1279, 160, 8) == 1
def test_real_life_examples(self):
# The samples tried when this function was designed.
assert get_hidden_length_from_sample_length(160000) == 125
assert get_hidden_length_from_sample_length(159999) == 125
assert get_hidden_length_from_sample_length(158720) == 124
assert get_hidden_length_from_sample_length(158719) == 124
assert get_hidden_length_from_sample_length(158880) == 125
assert get_hidden_length_from_sample_length(158879) == 125
assert get_hidden_length_from_sample_length(1600) == 2
assert get_hidden_length_from_sample_length(1599) == 2