102 lines
3.6 KiB
Python
102 lines
3.6 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import numpy as np
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import pandas as pd
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import librosa
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from .. import g2p
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from ..data.sampler import SequentialSampler, RandomSampler, BatchSampler
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from ..data.dataset import DatasetMixin
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from ..data.datacargo import DataCargo
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from ..data.batch import TextIDBatcher, SpecBatcher
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class LJSpeech(DatasetMixin):
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def __init__(self, root):
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super(LJSpeech, self).__init__()
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self.root = root
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self.metadata = self._prepare_metadata()
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def _prepare_metadata(self):
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csv_path = os.path.join(self.root, "metadata.csv")
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metadata = pd.read_csv(
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csv_path,
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sep="|",
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header=None,
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quoting=3,
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names=["fname", "raw_text", "normalized_text"])
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return metadata
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def _get_example(self, metadatum):
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"""All the code for generating an Example from a metadatum. If you want a
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different preprocessing pipeline, you can override this method.
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This method may require several processor, each of which has a lot of options.
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In this case, you'd better pass a composed transform and pass it to the init
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method.
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"""
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fname, raw_text, normalized_text = metadatum
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wav_path = os.path.join(self.root, "wavs", fname + ".wav")
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# load -> trim -> preemphasis -> stft -> magnitude -> mel_scale -> logscale -> normalize
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wav, sample_rate = librosa.load(
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wav_path,
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sr=None) # we would rather use functor to hold its parameters
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trimed, _ = librosa.effects.trim(wav)
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preemphasized = librosa.effects.preemphasis(trimed)
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D = librosa.stft(preemphasized)
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mag, phase = librosa.magphase(D)
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mel = librosa.feature.melspectrogram(S=mag)
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mag = librosa.amplitude_to_db(S=mag)
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mel = librosa.amplitude_to_db(S=mel)
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ref_db = 20
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max_db = 100
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mel = np.clip((mel - ref_db + max_db) / max_db, 1e-8, 1)
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mel = np.clip((mag - ref_db + max_db) / max_db, 1e-8, 1)
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phonemes = np.array(
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g2p.en.text_to_sequence(normalized_text), dtype=np.int64)
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return (mag, mel, phonemes
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) # maybe we need to implement it as a map in the future
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def _batch_examples(self, minibatch):
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mag_batch = []
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mel_batch = []
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phoneme_batch = []
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for example in minibatch:
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mag, mel, phoneme = example
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mag_batch.append(mag)
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mel_batch.append(mel)
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phoneme_batch.append(phoneme)
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mag_batch = SpecBatcher(pad_value=0.)(mag_batch)
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mel_batch = SpecBatcher(pad_value=0.)(mel_batch)
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phoneme_batch = TextIDBatcher(pad_id=0)(phoneme_batch)
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return (mag_batch, mel_batch, phoneme_batch)
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def __getitem__(self, index):
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metadatum = self.metadata.iloc[index]
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example = self._get_example(metadatum)
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return example
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def __iter__(self):
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for i in range(len(self)):
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yield self[i]
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def __len__(self):
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return len(self.metadata)
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