PaddleSpeech/audio/paddleaudio/datasets/dataset.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from typing import List

import numpy as np
import paddle

from ..backends import load as load_audio
from ..compliance.kaldi import fbank as kaldi_fbank
from ..compliance.kaldi import mfcc as kaldi_mfcc
from ..compliance.librosa import melspectrogram
from ..compliance.librosa import mfcc

feat_funcs = {
    'raw': None,
    'melspectrogram': melspectrogram,
    'mfcc': mfcc,
    'kaldi_fbank': kaldi_fbank,
    'kaldi_mfcc': kaldi_mfcc,
}


class AudioClassificationDataset(paddle.io.Dataset):
    """
    Base class of audio classification dataset.
    """

    def __init__(self,
                 files: List[str],
                 labels: List[int],
                 feat_type: str='raw',
                 sample_rate: int=None,
                 **kwargs):
        """
        Ags:
            files (:obj:`List[str]`): A list of absolute path of audio files.
            labels (:obj:`List[int]`): Labels of audio files.
            feat_type (:obj:`str`, `optional`, defaults to `raw`):
                It identifies the feature type that user wants to extrace of an audio file.
        """
        super(AudioClassificationDataset, self).__init__()

        if feat_type not in feat_funcs.keys():
            raise RuntimeError(
                f"Unknown feat_type: {feat_type}, it must be one in {list(feat_funcs.keys())}"
            )

        self.files = files
        self.labels = labels

        self.feat_type = feat_type
        self.sample_rate = sample_rate
        self.feat_config = kwargs  # Pass keyword arguments to customize feature config

    def _get_data(self, input_file: str):
        raise NotImplementedError

    def _convert_to_record(self, idx):
        file, label = self.files[idx], self.labels[idx]

        if self.sample_rate is None:
            waveform, sample_rate = load_audio(file)
        else:
            waveform, sample_rate = load_audio(file, sr=self.sample_rate)

        feat_func = feat_funcs[self.feat_type]

        record = {}
        if self.feat_type in ['kaldi_fbank', 'kaldi_mfcc']:
            waveform = paddle.to_tensor(waveform).unsqueeze(0)  # (C, T)
            record['feat'] = feat_func(
                waveform=waveform, sr=self.sample_rate, **self.feat_config)
        else:
            record['feat'] = feat_func(
                waveform, sample_rate,
                **self.feat_config) if feat_func else waveform
        record['label'] = label
        return record

    def __getitem__(self, idx):
        record = self._convert_to_record(idx)
        if self.feat_type in ['kaldi_fbank', 'kaldi_mfcc']:
            return self.keys[idx], record['feat'], record['label']
        else:
            return np.array(record['feat']).transpose(), np.array(
                record['label'], dtype=np.int64)

    def __len__(self):
        return len(self.files)