chore: import upstream snapshot with attribution

decoding/GAD/fairseq/data/audio/feature_transforms/__init__.py

@@ -0,0 +1,82 @@
+import importlib
+import os
+from abc import ABC, abstractmethod
+from typing import Dict, Optional
+
+
+class AudioFeatureTransform(ABC):
+    @classmethod
+    @abstractmethod
+    def from_config_dict(cls, config: Optional[Dict] = None):
+        pass
+
+
+AUDIO_FEATURE_TRANSFORM_REGISTRY = {}
+AUDIO_FEATURE_TRANSFORM_CLASS_NAMES = set()
+
+
+def register_audio_feature_transform(name):
+    def register_audio_feature_transform_cls(cls):
+        if name in AUDIO_FEATURE_TRANSFORM_REGISTRY:
+            raise ValueError(f"Cannot register duplicate transform ({name})")
+        if not issubclass(cls, AudioFeatureTransform):
+            raise ValueError(
+                f"Transform ({name}: {cls.__name__}) must extend "
+                "AudioFeatureTransform"
+            )
+        if cls.__name__ in AUDIO_FEATURE_TRANSFORM_CLASS_NAMES:
+            raise ValueError(
+                f"Cannot register audio feature transform with duplicate "
+                f"class name ({cls.__name__})"
+            )
+        AUDIO_FEATURE_TRANSFORM_REGISTRY[name] = cls
+        AUDIO_FEATURE_TRANSFORM_CLASS_NAMES.add(cls.__name__)
+        return cls
+
+    return register_audio_feature_transform_cls
+
+
+def get_audio_feature_transform(name):
+    return AUDIO_FEATURE_TRANSFORM_REGISTRY[name]
+
+
+transforms_dir = os.path.dirname(__file__)
+for file in os.listdir(transforms_dir):
+    path = os.path.join(transforms_dir, file)
+    if (
+        not file.startswith("_")
+        and not file.startswith(".")
+        and (file.endswith(".py") or os.path.isdir(path))
+    ):
+        name = file[: file.find(".py")] if file.endswith(".py") else file
+        importlib.import_module("fairseq.data.audio.feature_transforms." + name)
+
+
+class CompositeAudioFeatureTransform(AudioFeatureTransform):
+    @classmethod
+    def from_config_dict(cls, config=None):
+        _config = {} if config is None else config
+        _transforms = _config.get("transforms")
+        if _transforms is None:
+            return None
+        transforms = [
+            get_audio_feature_transform(_t).from_config_dict(_config.get(_t))
+            for _t in _transforms
+        ]
+        return CompositeAudioFeatureTransform(transforms)
+
+    def __init__(self, transforms):
+        self.transforms = [t for t in transforms if t is not None]
+
+    def __call__(self, x):
+        for t in self.transforms:
+            x = t(x)
+        return x
+
+    def __repr__(self):
+        format_string = (
+            [self.__class__.__name__ + "("]
+            + [f"    {t.__repr__()}" for t in self.transforms]
+            + [")"]
+        )
+        return "\n".join(format_string)

decoding/GAD/fairseq/data/audio/feature_transforms/global_cmvn.py

@@ -0,0 +1,29 @@
+import numpy as np
+from fairseq.data.audio.feature_transforms import (
+    AudioFeatureTransform,
+    register_audio_feature_transform,
+)
+
+
+@register_audio_feature_transform("global_cmvn")
+class GlobalCMVN(AudioFeatureTransform):
+    """Global CMVN (cepstral mean and variance normalization). The global mean
+    and variance need to be pre-computed and stored in NumPy format (.npz)."""
+
+    @classmethod
+    def from_config_dict(cls, config=None):
+        _config = {} if config is None else config
+        return GlobalCMVN(_config.get("stats_npz_path"))
+
+    def __init__(self, stats_npz_path):
+        self.stats_npz_path = stats_npz_path
+        stats = np.load(stats_npz_path)
+        self.mean, self.std = stats["mean"], stats["std"]
+
+    def __repr__(self):
+        return self.__class__.__name__ + f'(stats_npz_path="{self.stats_npz_path}")'
+
+    def __call__(self, x):
+        x = np.subtract(x, self.mean)
+        x = np.divide(x, self.std)
+        return x

decoding/GAD/fairseq/data/audio/feature_transforms/specaugment.py

@@ -0,0 +1,131 @@
+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(-self.time_warp_w + 1, 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

decoding/GAD/fairseq/data/audio/feature_transforms/utterance_cmvn.py

@@ -0,0 +1,40 @@
+import numpy as np
+from fairseq.data.audio.feature_transforms import (
+    AudioFeatureTransform,
+    register_audio_feature_transform,
+)
+
+
+@register_audio_feature_transform("utterance_cmvn")
+class UtteranceCMVN(AudioFeatureTransform):
+    """Utterance-level CMVN (cepstral mean and variance normalization)"""
+
+    @classmethod
+    def from_config_dict(cls, config=None):
+        _config = {} if config is None else config
+        return UtteranceCMVN(
+            _config.get("norm_means", True),
+            _config.get("norm_vars", True),
+        )
+
+    def __init__(self, norm_means=True, norm_vars=True):
+        self.norm_means, self.norm_vars = norm_means, norm_vars
+
+    def __repr__(self):
+        return (
+            self.__class__.__name__
+            + f"(norm_means={self.norm_means}, norm_vars={self.norm_vars})"
+        )
+
+    def __call__(self, x):
+        mean = x.mean(axis=0)
+        square_sums = (x ** 2).sum(axis=0)
+
+        if self.norm_means:
+            x = np.subtract(x, mean)
+        if self.norm_vars:
+            var = square_sums / x.shape[0] - mean ** 2
+            std = np.sqrt(np.maximum(var, 1e-10))
+            x = np.divide(x, std)
+
+        return x