jlspeech.py

from pathlib import Path
import numpy as np
import pandas as pd
import librosa

from paddle import fluid
from parakeet import g2p
from parakeet import audio
from parakeet.data.sampler import *
from parakeet.data.datacargo import DataCargo
from parakeet.data.dataset import Dataset
from parakeet.data.batch import TextIDBatcher, SpecBatcher

class LJSpeechLoader:
    def __init__(self, config, nranks, rank, is_vocoder=False):
        place = fluid.CUDAPlace(rank) if config.use_gpu else fluid.CPUPlace()

        LJSPEECH_ROOT = Path(config.data_path)
        dataset = LJSpeech(LJSPEECH_ROOT, config)
        sampler = DistributedSampler(len(dataset), nranks, rank)

        assert config.batch_size % nranks == 0
        each_bs = config.batch_size // nranks
        if is_vocoder:
            dataloader = DataCargo(dataset, sampler=sampler, batch_size=each_bs, shuffle=True, collate_fn=batch_examples_vocoder, drop_last=True)
        else:
            dataloader = DataCargo(dataset, sampler=sampler, batch_size=each_bs, shuffle=True, collate_fn=batch_examples, drop_last=True)
        
        self.reader = fluid.io.DataLoader.from_generator(
            capacity=32,
            iterable=True,
            use_double_buffer=True,
            return_list=True)
        self.reader.set_batch_generator(dataloader, place)


class LJSpeech(Dataset):
    def __init__(self, root, config):
        super(LJSpeech, self).__init__()
        assert isinstance(root, (str, Path)), "root should be a string or Path object"
        self.root = root if isinstance(root, Path) else Path(root)
        self.metadata = self._prepare_metadata()
        self.config = config
        
    def _prepare_metadata(self):
        csv_path = self.root.joinpath("metadata.csv")
        metadata = pd.read_csv(csv_path, sep="|", header=None, quoting=3,
                               names=["fname", "raw_text", "normalized_text"])
        return metadata
            
    def _get_example(self, metadatum):
        """All the code for generating an Example from a metadatum. If you want a 
        different preprocessing pipeline, you can override this method. 
        This method may require several processor, each of which has a lot of options.
        In this case, you'd better pass a composed transform and pass it to the init
        method.
        """
        
        fname, raw_text, normalized_text = metadatum
        wav_path = self.root.joinpath("wavs", fname + ".wav")
        
        _ljspeech_processor = audio.AudioProcessor(
            sample_rate=22050, 
            num_mels=80, 
            min_level_db=-100, 
            ref_level_db=20, 
            n_fft=2048, 
            win_length= int(22050 * 0.05), 
            hop_length= int(22050 * 0.0125),
            power=1.2,
            preemphasis=0.97,
            signal_norm=True,
            symmetric_norm=False,
            max_norm=1.,
            mel_fmin=0,
            mel_fmax=None,
            clip_norm=True,
            griffin_lim_iters=60,
            do_trim_silence=False,
            sound_norm=False)
        # load -> trim -> preemphasis -> stft -> magnitude -> mel_scale -> logscale -> normalize
        wav = _ljspeech_processor.load_wav(str(wav_path))
        mag = _ljspeech_processor.spectrogram(wav).astype(np.float32)
        mel = _ljspeech_processor.melspectrogram(wav).astype(np.float32)
        phonemes = np.array(g2p.en.text_to_sequence(normalized_text), dtype=np.int64)
        return (mag, mel, phonemes) # maybe we need to implement it as a map in the future

    def __getitem__(self, index):
        metadatum = self.metadata.iloc[index]
        example = self._get_example(metadatum)
        return example
    
    def __iter__(self):
        for i in range(len(self)):
            yield self[i]
    
    def __len__(self):
        return len(self.metadata)


def batch_examples(batch):
    texts = []
    mels = []
    mel_inputs = []
    text_lens = []
    pos_texts = []
    pos_mels = []
    for data in batch:
        _, mel, text = data
        mel_inputs.append(np.concatenate([np.zeros([mel.shape[0], 1], np.float32), mel[:,:-1]], axis=-1))
        text_lens.append(len(text))
        pos_texts.append(np.arange(1, len(text) + 1))
        pos_mels.append(np.arange(1, mel.shape[1] + 1))
        mels.append(mel)
        texts.append(text)
    
    # Sort by text_len in descending order
    texts = [i for i,_ in sorted(zip(texts, text_lens), key=lambda x: x[1], reverse=True)]
    mels = [i for i,_ in sorted(zip(mels, text_lens), key=lambda x: x[1], reverse=True)]
    mel_inputs = [i for i,_ in sorted(zip(mel_inputs, text_lens), key=lambda x: x[1], reverse=True)]
    pos_texts = [i for i,_ in sorted(zip(pos_texts, text_lens), key=lambda x: x[1], reverse=True)]
    pos_mels = [i for i,_ in sorted(zip(pos_mels, text_lens), key=lambda x: x[1], reverse=True)]
    text_lens = sorted(text_lens, reverse=True)

    # Pad sequence with largest len of the batch
    texts = TextIDBatcher(pad_id=0)(texts)
    pos_texts = TextIDBatcher(pad_id=0)(pos_texts)
    pos_mels = TextIDBatcher(pad_id=0)(pos_mels)
    mels = np.transpose(SpecBatcher(pad_value=0.)(mels), axes=(0,2,1))
    mel_inputs = np.transpose(SpecBatcher(pad_value=0.)(mel_inputs), axes=(0,2,1))
    return (texts, mels, mel_inputs, pos_texts, pos_mels, np.array(text_lens))

def batch_examples_vocoder(batch):
    mels=[]
    mags=[]
    for data in batch:
        mag, mel, _ = data
        mels.append(mel)
        mags.append(mag)

    mels = np.transpose(SpecBatcher(pad_value=0.)(mels), axes=(0,2,1))
    mags = np.transpose(SpecBatcher(pad_value=0.)(mags), axes=(0,2,1))

    return (mels, mags)