metrics.py

import math
from typing import Optional
from itertools import permutations

import torch


def sdr(
        estimate: torch.Tensor,
        reference: torch.Tensor,
        mask: Optional[torch.Tensor] = None,
        epsilon: float = 1e-8
) -> torch.Tensor:
    """Computes source-to-distortion ratio.

    1. scale the reference signal with power(s_est * s_ref) / powr(s_ref * s_ref)
    2. compute SNR between adjusted estimate and reference.

    Args:
        estimate (torch.Tensor): Estimtaed signal.
            Shape: [batch, speakers (can be 1), time frame]
        reference (torch.Tensor): Reference signal.
            Shape: [batch, speakers, time frame]
        mask (torch.Tensor or None, optional): Binary mask to indicate padded value (0) or valid value (1).
            Shape: [batch, 1, time frame]
        epsilon (float, optional): constant value used to stabilize division.

    Returns:
        torch.Tensor: scale-invariant source-to-distortion ratio.
        Shape: [batch, speaker]

    References:
        - Single-channel multi-speaker separation using deep clustering
          Y. Isik, J. Le Roux, Z. Chen, S. Watanabe, and J. R. Hershey,
        - Conv-TasNet: Surpassing Ideal Time--Frequency Magnitude Masking for Speech Separation
          Luo, Yi and Mesgarani, Nima
          https://arxiv.org/abs/1809.07454

    Notes:
        This function is tested to produce the exact same result as
        https://github.com/naplab/Conv-TasNet/blob/e66d82a8f956a69749ec8a4ae382217faa097c5c/utility/sdr.py#L34-L56
    """
    reference_pow = reference.pow(2).mean(axis=2, keepdim=True)
    mix_pow = (estimate * reference).mean(axis=2, keepdim=True)
    scale = mix_pow / (reference_pow + epsilon)

    reference = scale * reference
    error = estimate - reference

    reference_pow = reference.pow(2)
    error_pow = error.pow(2)

    if mask is None:
        reference_pow = reference_pow.mean(axis=2)
        error_pow = error_pow.mean(axis=2)
    else:
        denom = mask.sum(axis=2)
        reference_pow = (mask * reference_pow).sum(axis=2) / denom
        error_pow = (mask * error_pow).sum(axis=2) / denom

    return 10 * torch.log10(reference_pow) - 10 * torch.log10(error_pow)


class PIT(torch.nn.Module):
    """Applies utterance-level speaker permutation

    Computes the maxium possible value of the given utility function
    over the permutations of the speakers.

    Args:
        utility_func (function):
            Function that computes the utility (opposite of loss) with signature of
            (extimate: torch.Tensor, reference: torch.Tensor) -> torch.Tensor
            where input Tensors are shape of [batch, speakers, frame] and
            the output Tensor is shape of [batch, speakers].

    References:
        - Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of
          Deep Recurrent Neural Networks
          Morten Kolbæk, Dong Yu, Zheng-Hua Tan and Jesper Jensen
          https://arxiv.org/abs/1703.06284
    """

    def __init__(self, utility_func):
        super().__init__()
        self.utility_func = utility_func

    def forward(
            self,
            estimate: torch.Tensor,
            reference: torch.Tensor,
            mask: Optional[torch.Tensor] = None,
            epsilon: float = 1e-8
    ) -> torch.Tensor:
        """Compute utterance-level PIT Loss

        Args:
            estimate (torch.Tensor): Estimated source signals.
                Shape: [bacth, speakers, time frame]
            reference (torch.Tensor): Reference (original) source signals.
                Shape: [batch, speakers, time frame]
            mask (torch.Tensor or None, optional): Binary mask to indicate padded value (0) or valid value (1).
                Shape: [batch, 1, time frame]
            epsilon (float, optional): constant value used to stabilize division.

        Returns:
            torch.Tensor: Maximum criterion over the speaker permutation.
                Shape: [batch, ]
        """
        assert estimate.shape == reference.shape

        batch_size, num_speakers = reference.shape[:2]
        num_permute = math.factorial(num_speakers)

        util_mat = torch.zeros(
            batch_size, num_permute, dtype=estimate.dtype, device=estimate.device
        )
        for i, idx in enumerate(permutations(range(num_speakers))):
            util = self.utility_func(estimate, reference[:, idx, :], mask=mask, epsilon=epsilon)
            util_mat[:, i] = util.mean(dim=1)  # take the average over speaker dimension
        return util_mat.max(dim=1).values


_sdr_pit = PIT(utility_func=sdr)


def sdr_pit(
        estimate: torch.Tensor,
        reference: torch.Tensor,
        mask: Optional[torch.Tensor] = None,
        epsilon: float = 1e-8):
    """Computes scale-invariant source-to-distortion ratio.

    1. adjust both estimate and reference to have 0-mean
    2. scale the reference signal with power(s_est * s_ref) / powr(s_ref * s_ref)
    3. compute SNR between adjusted estimate and reference.

    Args:
        estimate (torch.Tensor): Estimtaed signal.
            Shape: [batch, speakers (can be 1), time frame]
        reference (torch.Tensor): Reference signal.
            Shape: [batch, speakers, time frame]
        mask (torch.Tensor or None, optional): Binary mask to indicate padded value (0) or valid value (1).
            Shape: [batch, 1, time frame]
        epsilon (float, optional): constant value used to stabilize division.

    Returns:
        torch.Tensor: scale-invariant source-to-distortion ratio.
        Shape: [batch, speaker]

    References:
        - Single-channel multi-speaker separation using deep clustering
          Y. Isik, J. Le Roux, Z. Chen, S. Watanabe, and J. R. Hershey,
        - Conv-TasNet: Surpassing Ideal Time--Frequency Magnitude Masking for Speech Separation
          Luo, Yi and Mesgarani, Nima
          https://arxiv.org/abs/1809.07454

    Notes:
        This function is tested to produce the exact same result as the reference implementation,
        *when the inputs have 0-mean*
        https://github.com/naplab/Conv-TasNet/blob/e66d82a8f956a69749ec8a4ae382217faa097c5c/utility/sdr.py#L107-L153
    """
    return _sdr_pit(estimate, reference, mask, epsilon)


def sdri(
        estimate: torch.Tensor,
        reference: torch.Tensor,
        mix: torch.Tensor,
        mask: Optional[torch.Tensor] = None,
        epsilon: float = 1e-8,
) -> torch.Tensor:
    """Compute the improvement of SDR (SDRi).

    This function compute how much SDR is improved if the estimation is changed from
    the original mixture signal to the actual estimated source signals. That is,
    ``SDR(estimate, reference) - SDR(mix, reference)``.

    For computing ``SDR(estimate, reference)``, PIT (permutation invariant training) is applied,
    so that best combination of sources between the reference signals and the esimate signals
    are picked.

    Args:
        estimate (torch.Tensor): Estimated source signals.
            Shape: [batch, speakers, time frame]
        reference (torch.Tensor): Reference (original) source signals.
            Shape: [batch, speakers, time frame]
        mix (torch.Tensor): Mixed souce signals, from which the setimated signals were generated.
            Shape: [batch, speakers == 1, time frame]
        mask (torch.Tensor or None, optional): Binary mask to indicate padded value (0) or valid value (1).
            Shape: [batch, 1, time frame]
        epsilon (float, optional): constant value used to stabilize division.

    Returns:
        torch.Tensor: Improved SDR. Shape: [batch, ]

    References:
        - Conv-TasNet: Surpassing Ideal Time--Frequency Magnitude Masking for Speech Separation
          Luo, Yi and Mesgarani, Nima
          https://arxiv.org/abs/1809.07454
    """
    sdr_ = sdr_pit(estimate, reference, mask=mask, epsilon=epsilon)  # [batch, ]
    base_sdr = sdr(mix, reference, mask=mask, epsilon=epsilon)  # [batch, speaker]
    return (sdr_.unsqueeze(1) - base_sdr).mean(dim=1)