fairseq-0.10.2/fairseq/data/audio/feature_transforms/specaugment.py

import math
import numbers
from typing import Optional

import numpy as np
from fairseq.data.audio.feature_transforms import (
    AudioFeatureTransform,
    register_audio_feature_transform,
)


@register_audio_feature_transform("specaugment")
class SpecAugmentTransform(AudioFeatureTransform):
    """SpecAugment (https://arxiv.org/abs/1904.08779)"""

    @classmethod
    def from_config_dict(cls, config=None):
        _config = {} if config is None else config
        return SpecAugmentTransform(
            _config.get("time_warp_W", 0),
            _config.get("freq_mask_N", 0),
            _config.get("freq_mask_F", 0),
            _config.get("time_mask_N", 0),
            _config.get("time_mask_T", 0),
            _config.get("time_mask_p", 0.0),
            _config.get("mask_value", None),
        )

    def __init__(
        self,
        time_warp_w: int = 0,
        freq_mask_n: int = 0,
        freq_mask_f: int = 0,
        time_mask_n: int = 0,
        time_mask_t: int = 0,
        time_mask_p: float = 0.0,
        mask_value: Optional[float] = 0.0,
    ):
        # Sanity checks
        assert mask_value is None or isinstance(
            mask_value, numbers.Number
        ), f"mask_value (type: {type(mask_value)}) must be None or a number"
        if freq_mask_n > 0:
            assert freq_mask_f > 0, (
                f"freq_mask_F ({freq_mask_f}) "
                f"must be larger than 0 when doing freq masking."
            )
        if time_mask_n > 0:
            assert time_mask_t > 0, (
                f"time_mask_T ({time_mask_t}) must be larger than 0 when "
                f"doing time masking."
            )

        self.time_warp_w = time_warp_w
        self.freq_mask_n = freq_mask_n
        self.freq_mask_f = freq_mask_f
        self.time_mask_n = time_mask_n
        self.time_mask_t = time_mask_t
        self.time_mask_p = time_mask_p
        self.mask_value = mask_value

    def __repr__(self):
        return (
            self.__class__.__name__
            + "("
            + ", ".join(
                [
                    f"time_warp_w={self.time_warp_w}",
                    f"freq_mask_n={self.freq_mask_n}",
                    f"freq_mask_f={self.freq_mask_f}",
                    f"time_mask_n={self.time_mask_n}",
                    f"time_mask_t={self.time_mask_t}",
                    f"time_mask_p={self.time_mask_p}",
                ]
            )
            + ")"
        )

    def __call__(self, spectrogram):
        assert len(spectrogram.shape) == 2, "spectrogram must be a 2-D tensor."

        distorted = spectrogram.copy()  # make a copy of input spectrogram.
        num_frames = spectrogram.shape[0]  # or 'tau' in the paper.
        num_freqs = spectrogram.shape[1]  # or 'miu' in the paper.
        mask_value = self.mask_value

        if mask_value is None:  # if no value was specified, use local mean.
            mask_value = spectrogram.mean()

        if num_frames == 0:
            return spectrogram

        if num_freqs < self.freq_mask_f:
            return spectrogram

        if self.time_warp_w > 0:
            if 2 * self.time_warp_w < num_frames:
                import cv2

                w0 = np.random.randint(self.time_warp_w, num_frames - self.time_warp_w)
                w = np.random.randint(0, self.time_warp_w)
                upper, lower = distorted[:w0, :], distorted[w0:, :]
                upper = cv2.resize(
                    upper, dsize=(num_freqs, w0 + w), interpolation=cv2.INTER_LINEAR
                )
                lower = cv2.resize(
                    lower,
                    dsize=(num_freqs, num_frames - w0 - w),
                    interpolation=cv2.INTER_LINEAR,
                )
                distorted = np.concatenate((upper, lower), axis=0)

        for _i in range(self.freq_mask_n):
            f = np.random.randint(0, self.freq_mask_f)
            f0 = np.random.randint(0, num_freqs - f)
            if f != 0:
                distorted[:, f0 : f0 + f] = mask_value

        max_time_mask_t = min(
            self.time_mask_t, math.floor(num_frames * self.time_mask_p)
        )
        if max_time_mask_t < 1:
            return distorted

        for _i in range(self.time_mask_n):
            t = np.random.randint(0, max_time_mask_t)
            t0 = np.random.randint(0, num_frames - t)
            if t != 0:
                distorted[t0 : t0 + t, :] = mask_value

        return distorted