79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
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import os
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from pathlib import Path
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import pickle
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import numpy as np
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import pandas
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from paddle.io import Dataset, DataLoader
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from parakeet.data.batch import batch_spec, batch_wav
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from parakeet.data import dataset
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from parakeet.audio import AudioProcessor
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class LJSpeech(Dataset):
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"""A simple dataset adaptor for the processed ljspeech dataset."""
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def __init__(self, root):
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self.root = Path(root).expanduser()
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meta_data = pandas.read_csv(
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str(self.root / "metadata.csv"),
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sep="\t",
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header=None,
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names=["fname", "frames", "samples"]
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)
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records = []
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for row in meta_data.itertuples() :
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mel_path = str(self.root / "mel" / (row.fname + ".npy"))
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wav_path = str(self.root / "wav" / (row.fname + ".npy"))
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records.append((mel_path, wav_path))
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self.records = records
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def __getitem__(self, i):
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mel_name, wav_name = self.records[i]
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mel = np.load(mel_name)
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wav = np.load(wav_name)
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return mel, wav
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def __len__(self):
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return len(self.records)
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class LJSpeechCollector(object):
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"""A simple callable to batch LJSpeech examples."""
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def __init__(self, padding_value=0.):
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self.padding_value = padding_value
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def __call__(self, examples):
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mels = [example[0] for example in examples]
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wavs = [example[1] for example in examples]
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mels = batch_spec(mels, pad_value=self.padding_value)
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wavs = batch_wav(wavs, pad_value=self.padding_value)
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return mels, wavs
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class LJSpeechClipCollector(object):
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def __init__(self, clip_frames=65, hop_length=256):
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self.clip_frames = clip_frames
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self.hop_length = hop_length
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def __call__(self, examples):
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mels = []
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wavs = []
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for example in examples:
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mel_clip, wav_clip = self.clip(example)
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mels.append(mel_clip)
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wavs.append(wav_clip)
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mels = np.stack(mels)
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wavs = np.stack(wavs)
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return mels, wavs
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def clip(self, example):
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mel, wav = example
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frames = mel.shape[-1]
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start = np.random.randint(0, frames - self.clip_frames)
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mel_clip = mel[:, start: start + self.clip_frames]
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wav_clip = wav[start * self.hop_length: (start + self.clip_frames) * self.hop_length]
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return mel_clip, wav_clip
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