Upload feature extractor

feature_extraction_maest.py

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+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# 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.
+"""
+Feature extractor class for Music Audio Efficient Spectrogram Transformer.
+"""
+
+
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+
+from transformers.audio_utils import mel_filter_bank, spectrogram, window_function
+from transformers.feature_extraction_sequence_utils import SequenceFeatureExtractor
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.utils import TensorType, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MAESTFeatureExtractor(SequenceFeatureExtractor):
+    r"""
+    Constructs a Music Audio Efficient Spectrogram Transformer (MAEST) feature extractor.
+
+    This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
+    most of the main methods. Users should refer to this superclass for more information regarding those methods.
+
+    This class extracts mel-filter bank features from raw audio, pads/truncates them to a fixed length and normalizes
+    them using a mean and standard deviation.
+
+    Args:
+        feature_size (`int`, *optional*, defaults to 1):
+            The feature dimension of the extracted features.
+        sampling_rate (`int`, *optional*, defaults to 16000):
+            The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
+        num_mel_bins (`int`, *optional*, defaults to 96):
+            Number of Mel-frequency bins.
+        max_length (`int`, *optional*, defaults to 1876):
+            Maximum length to which to pad/truncate the extracted features. Set to -1 to deactivate the functionallity.
+        padding_value (`int`, *optional*, defaults to 0.0):
+            The value used to pad the input waveform.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether or not to normalize the log-Mel features using `mean` and `std`.
+        mean (`float`, *optional*, defaults to 2.06755686098554):
+            The mean value used to normalize the log-Mel features. Uses the Discogs20 mean by default.
+        std (`float`, *optional*, defaults to 1.268292820667291):
+            The standard deviation value used to normalize the log-Mel features. Uses the Discogs20 standard deviation
+            by default.
+        return_attention_mask (`bool`, *optional*, defaults to `False`):
+            Whether or not [`~ASTFeatureExtractor.__call__`] should return `attention_mask`.
+        n_fft (`int`, *optional*, defaults to 512):
+            Length of the FFT window.
+        hop_length (`int`, *optional*, defaults to 256):
+            Number of samples between successive frames.
+        log_compression (`str`, *optional*, defaults to `"logC"`):
+            Type of log compression to apply to the mel-spectrogram. Can be one of [`None`, `log`, `logC`].
+    """
+
+    model_input_names = ["input_values", "attention_mask"]
+
+    def __init__(
+        self,
+        feature_size=1,
+        sampling_rate=16000,
+        num_mel_bins=96,
+        max_length=1876,
+        padding_value=0.0,
+        do_normalize=True,
+        mean=2.06755686098554,
+        std=1.268292820667291,
+        return_attention_mask=False,
+        n_fft=512,
+        hop_length=256,
+        log_compression="logC",
+        **kwargs,
+    ):
+        super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)
+        self.sampling_rate = sampling_rate
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.log_compression = log_compression
+        self.num_mel_bins = num_mel_bins
+        self.max_length = max_length
+        self.do_normalize = do_normalize
+        self.mean = mean
+        self.std = std
+        self.return_attention_mask = return_attention_mask
+
+        self.window = window_function(
+            window_length=self.n_fft,
+            name="hann",
+        ).tolist()
+
+        self.mel_fb = mel_filter_bank(
+            num_frequency_bins=self.n_fft // 2 + 1,
+            num_mel_filters=self.num_mel_bins,
+            min_frequency=0,
+            max_frequency=self.sampling_rate / 2,
+            sampling_rate=self.sampling_rate,
+            norm="slaney",
+            mel_scale="slaney",
+        ).tolist()
+
+    def _extract_fbank_features(
+        self,
+        waveform: np.ndarray,
+        max_length: int,
+    ) -> np.ndarray:
+        """
+        Get mel-spectrogram features using audio_utils.
+        """
+
+        melspec = spectrogram(
+            waveform,
+            window=np.array(self.window),
+            frame_length=self.n_fft,
+            hop_length=self.hop_length,
+            power=2,
+            mel_filters=np.array(self.mel_fb),
+            min_value=1e-30,
+            mel_floor=1e-30,
+            pad_mode="constant",
+        ).T
+
+        if not self.log_compression:
+            pass
+        elif self.log_compression == "log":
+            melspec = np.log(melspec + np.finfo(float).eps)
+        elif self.log_compression == "logC":
+            melspec = np.log10(1 + melspec * 10000)
+        else:
+            raise ValueError(
+                f"`log_compression` can only be one of [None, 'log', 'logC'], but got: {self.log_compression}"
+            )
+
+        melspec = torch.Tensor(melspec)
+        n_frames = melspec.shape[0]
+
+        if max_length > 0:
+            difference = max_length - n_frames
+
+            # pad or truncate, depending on difference
+            if difference > 0:
+                pad_module = torch.nn.ZeroPad2d((0, 0, 0, difference))
+                melspec = pad_module(melspec)
+            elif difference < 0:
+                melspec = melspec[0:max_length, :]
+
+        return melspec.numpy()
+
+    def normalize(self, input_values: np.ndarray) -> np.ndarray:
+        return (input_values - (self.mean)) / (self.std * 2)
+
+    def __call__(
+        self,
+        raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
+        sampling_rate: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        Main method to featurize and prepare for the model one or several sequence(s).
+
+        Args:
+            raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
+                The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
+                values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not
+                stereo, i.e. single float per timestep.
+            sampling_rate (`int`, *optional*):
+                The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass
+                `sampling_rate` at the forward call to prevent silent errors.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors instead of list of python integers. Acceptable values are:
+
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return Numpy `np.ndarray` objects.
+        """
+
+        if sampling_rate is not None:
+            if sampling_rate != self.sampling_rate:
+                raise ValueError(
+                    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
+                    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
+                    f" {self.sampling_rate} and not {sampling_rate}."
+                )
+        else:
+            logger.warning(
+                "It is strongly recommended to pass the `sampling_rate` argument to this function. "
+                "Failing to do so can result in silent errors that might be hard to debug."
+            )
+
+        is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
+        if is_batched_numpy and len(raw_speech.shape) > 2:
+            raise ValueError(f"Only mono-channel audio is supported for input to {self}")
+        is_batched = is_batched_numpy or (
+            isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
+        )
+
+        if is_batched:
+            raw_speech = [np.asarray(speech, dtype=np.float32) for speech in raw_speech]
+        elif not is_batched and not isinstance(raw_speech, np.ndarray):
+            raw_speech = np.asarray(raw_speech, dtype=np.float32)
+        elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.dtype(np.float64):
+            raw_speech = raw_speech.astype(np.float32)
+
+        # always return batch
+        if not is_batched:
+            raw_speech = [raw_speech]
+
+        # extract fbank features and pad/truncate to max_length
+        features = [self._extract_fbank_features(waveform, max_length=self.max_length) for waveform in raw_speech]
+
+        # convert into BatchFeature
+        padded_inputs = BatchFeature({"input_values": features})
+
+        # make sure list is in array format
+        input_values = padded_inputs.get("input_values")
+        if isinstance(input_values[0], list):
+            padded_inputs["input_values"] = [np.asarray(feature, dtype=np.float32) for feature in input_values]
+
+        # normalization
+        if self.do_normalize:
+            padded_inputs["input_values"] = [self.normalize(feature) for feature in input_values]
+
+        if return_tensors is not None:
+            padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
+
+        return padded_inputs