mirror of
https://github.com/Mintplex-Labs/pyannote-audio-legacy.git
synced 2026-07-01 20:24:10 -04:00
Clément Pagés
132 lines
3.7 KiB
Python
132 lines
3.7 KiB
Python
# MIT License
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#
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# Copyright (c) 2023- CNRS
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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import torch
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from pyannote.audio.models.blocks.pooling import StatsPool
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def test_stats_pool_weightless():
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x = torch.Tensor([[[2.0, 4.0], [2.0, 4.0]], [[1.0, 1.0], [1.0, 1.0]]])
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# (batch = 2, features = 2, frames = 2)
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stats_pool = StatsPool()
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y = stats_pool(x)
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# (batch = 2, features = 4)
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assert torch.equal(
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torch.round(y, decimals=4),
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torch.Tensor([[3.0, 3.0, 1.4142, 1.4142], [1.0, 1.0, 0.0, 0.0]]),
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)
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def test_stats_pool_one_speaker():
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x = torch.Tensor([[[2.0, 4.0], [2.0, 4.0]], [[1.0, 1.0], [1.0, 1.0]]])
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# (batch = 2, features = 2, frames = 2)
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w = torch.Tensor(
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[
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[0.5, 0.01],
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[0.2, 0.1],
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]
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)
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# (batch = 2, frames = 2)
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stats_pool = StatsPool()
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y = stats_pool(x, weights=w)
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# (batch = 2, features = 4)
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assert torch.equal(
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torch.round(y, decimals=4),
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torch.Tensor([[2.0392, 2.0392, 1.4142, 1.4142], [1.0, 1.0, 0.0, 0.0]]),
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)
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def test_stats_pool_multi_speaker():
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x = torch.Tensor([[[2.0, 4.0], [2.0, 4.0]], [[1.0, 1.0], [1.0, 1.0]]])
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# (batch = 2, features = 2, frames = 2)
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w = torch.Tensor([[[0.1, 0.2], [0.2, 0.3]], [[0.001, 0.001], [0.2, 0.3]]])
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# (batch = 2, speakers = 2, frames = 2)
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stats_pool = StatsPool()
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y = stats_pool(x, weights=w)
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# (batch = 2, speakers = 2, features = 4)
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assert torch.equal(
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torch.round(y, decimals=4),
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torch.Tensor(
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[
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[[3.3333, 3.3333, 1.4142, 1.4142], [3.2, 3.2, 1.4142, 1.4142]],
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[[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]],
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]
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),
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)
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def test_stats_pool_frame_mismatch():
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x = torch.Tensor([[[2.0, 2.0], [2.0, 2.0]], [[1.0, 1.0], [1.0, 1.0]]])
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# (batch = 2, features = 2, frames = 2)
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stats_pool = StatsPool()
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w = torch.Tensor(
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[
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[0.5, 0.5, 0.0],
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[0.0, 0.5, 0.5],
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]
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)
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# (batch = 2, frames = 3)
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y = stats_pool(x, weights=w)
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# (batch = 2, features = 4)
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assert torch.equal(
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torch.round(y, decimals=4),
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torch.Tensor([[2.0, 2.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]]),
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)
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def test_stats_pool_all_zero_weights():
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x = torch.Tensor([[[2.0, 4.0], [2.0, 4.0]], [[1.0, 1.0], [1.0, 1.0]]])
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# (batch = 2, features = 2, frames = 2)
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w = torch.Tensor(
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[
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[0.5, 0.01],
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[0.0, 0.0], # all zero weights
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]
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)
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# (batch = 2, frames = 2)
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stats_pool = StatsPool()
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y = stats_pool(x, weights=w)
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# (batch = 2, features = 4)
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assert torch.equal(
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torch.round(y, decimals=4),
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torch.Tensor([[2.0392, 2.0392, 1.4142, 1.4142], [0.0, 0.0, 0.0, 0.0]]),
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)
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