model/dataset.py

import json
import random
from tqdm import tqdm

import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, Sampler
import torchaudio
from datasets import load_dataset, load_from_disk
from datasets import Dataset as Dataset_

from model.modules import MelSpec


class HFDataset(Dataset):
    def __init__(
        self,
        hf_dataset: Dataset,
        target_sample_rate = 24_000,
        n_mel_channels = 100,
        hop_length = 256,
    ):
        self.data = hf_dataset
        self.target_sample_rate = target_sample_rate
        self.hop_length = hop_length
        self.mel_spectrogram = MelSpec(target_sample_rate=target_sample_rate, n_mel_channels=n_mel_channels, hop_length=hop_length)
        
    def get_frame_len(self, index):
        row = self.data[index]
        audio = row['audio']['array']
        sample_rate = row['audio']['sampling_rate']
        return audio.shape[-1] / sample_rate * self.target_sample_rate / self.hop_length

    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, index):
        row = self.data[index]
        audio = row['audio']['array']

        # logger.info(f"Audio shape: {audio.shape}")

        sample_rate = row['audio']['sampling_rate']
        duration = audio.shape[-1] / sample_rate

        if duration > 30 or duration < 0.3:
            return self.__getitem__((index + 1) % len(self.data))
        
        audio_tensor = torch.from_numpy(audio).float()
        
        if sample_rate != self.target_sample_rate:
            resampler = torchaudio.transforms.Resample(sample_rate, self.target_sample_rate)
            audio_tensor = resampler(audio_tensor)
        
        audio_tensor = audio_tensor.unsqueeze(0)  # 't -> 1 t')
        
        mel_spec = self.mel_spectrogram(audio_tensor)
        
        mel_spec = mel_spec.squeeze(0)  # '1 d t -> d t'
        
        text = row['text']
        
        return dict(
            mel_spec = mel_spec,
            text = text,
        )


class CustomDataset(Dataset):
    def __init__(
        self,
        custom_dataset: Dataset,
        durations = None,
        target_sample_rate = 24_000,
        hop_length = 256,
        n_mel_channels = 100,
        preprocessed_mel = False,
    ):
        self.data = custom_dataset
        self.durations = durations
        self.target_sample_rate = target_sample_rate
        self.hop_length = hop_length
        self.preprocessed_mel = preprocessed_mel
        if not preprocessed_mel:
            self.mel_spectrogram = MelSpec(target_sample_rate=target_sample_rate, hop_length=hop_length, n_mel_channels=n_mel_channels)

    def get_frame_len(self, index):
        if self.durations is not None:  # Please make sure the separately provided durations are correct, otherwise 99.99% OOM
            return self.durations[index] * self.target_sample_rate / self.hop_length
        return self.data[index]["duration"] * self.target_sample_rate / self.hop_length
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, index):
        row = self.data[index]
        audio_path = row["audio_path"]
        text = row["text"]
        duration = row["duration"]

        if self.preprocessed_mel:
            mel_spec = torch.tensor(row["mel_spec"])

        else:
            audio, source_sample_rate = torchaudio.load(audio_path)

            if duration > 30 or duration < 0.3:
                return self.__getitem__((index + 1) % len(self.data))
            
            if source_sample_rate != self.target_sample_rate:
                resampler = torchaudio.transforms.Resample(source_sample_rate, self.target_sample_rate)
                audio = resampler(audio)
            
            mel_spec = self.mel_spectrogram(audio)
            mel_spec = mel_spec.squeeze(0)  # '1 d t -> d t')
        
        return dict(
            mel_spec = mel_spec,
            text = text,
        )
    

# Dynamic Batch Sampler

class DynamicBatchSampler(Sampler[list[int]]):
    """ Extension of Sampler that will do the following:
        1.  Change the batch size (essentially number of sequences)
            in a batch to ensure that the total number of frames are less
            than a certain threshold.
        2.  Make sure the padding efficiency in the batch is high.
    """

    def __init__(self, sampler: Sampler[int], frames_threshold: int, max_samples=0, random_seed=None, drop_last: bool = False):
        self.sampler = sampler
        self.frames_threshold = frames_threshold
        self.max_samples = max_samples

        indices, batches = [], []
        data_source = self.sampler.data_source
        
        for idx in tqdm(self.sampler, desc=f"Sorting with sampler... if slow, check whether dataset is provided with duration"):
            indices.append((idx, data_source.get_frame_len(idx)))
        indices.sort(key=lambda elem : elem[1])

        batch = []
        batch_frames = 0
        for idx, frame_len in tqdm(indices, desc=f"Creating dynamic batches with {frames_threshold} audio frames per gpu"):
            if batch_frames + frame_len <= self.frames_threshold and (max_samples == 0 or len(batch) < max_samples):
                batch.append(idx)
                batch_frames += frame_len
            else:
                if len(batch) > 0:
                    batches.append(batch)
                if frame_len <= self.frames_threshold:
                    batch = [idx]
                    batch_frames = frame_len
                else:
                    batch = []
                    batch_frames = 0

        if not drop_last and len(batch) > 0:
            batches.append(batch)

        del indices

        # if want to have different batches between epochs, may just set a seed and log it in ckpt
        # cuz during multi-gpu training, although the batch on per gpu not change between epochs, the formed general minibatch is different
        # e.g. for epoch n, use (random_seed + n)
        random.seed(random_seed)
        random.shuffle(batches)

        self.batches = batches

    def __iter__(self):
        return iter(self.batches)

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


# Load dataset

def load_dataset(
        dataset_name: str,
        tokenizer: str = "pinyin",
        dataset_type: str = "CustomDataset", 
        audio_type: str = "raw", 
        mel_spec_kwargs: dict = dict()
        ) -> CustomDataset | HFDataset:
    '''
    dataset_type    - "CustomDataset" if you want to use tokenizer name and default data path to load for train_dataset
                    - "CustomDatasetPath" if you just want to pass the full path to a preprocessed dataset without relying on tokenizer
    '''
    
    print("Loading dataset ...")

    if dataset_type == "CustomDataset":
        if audio_type == "raw":
            try:
                train_dataset = load_from_disk(f"data/{dataset_name}_{tokenizer}/raw")
            except:
                train_dataset = Dataset_.from_file(f"data/{dataset_name}_{tokenizer}/raw.arrow")
            preprocessed_mel = False
        elif audio_type == "mel":
            train_dataset = Dataset_.from_file(f"data/{dataset_name}_{tokenizer}/mel.arrow")
            preprocessed_mel = True
        with open(f"data/{dataset_name}_{tokenizer}/duration.json", 'r', encoding='utf-8') as f:
            data_dict = json.load(f)
        durations = data_dict["duration"]
        train_dataset = CustomDataset(train_dataset, durations=durations, preprocessed_mel=preprocessed_mel, **mel_spec_kwargs)
        
    elif dataset_type == "CustomDatasetPath":
        try:
            train_dataset = load_from_disk(f"{dataset_name}/raw")
        except:
            train_dataset = Dataset_.from_file(f"{dataset_name}/raw.arrow")
            
        with open(f"{dataset_name}/duration.json", 'r', encoding='utf-8') as f:
            data_dict = json.load(f)
        durations = data_dict["duration"]
        train_dataset = CustomDataset(train_dataset, durations=durations, preprocessed_mel=preprocessed_mel, **mel_spec_kwargs)
            
    elif dataset_type == "HFDataset":
        print("Should manually modify the path of huggingface dataset to your need.\n" +
              "May also the corresponding script cuz different dataset may have different format.")
        pre, post = dataset_name.split("_")
        train_dataset = HFDataset(load_dataset(f"{pre}/{pre}", split=f"train.{post}", cache_dir="./data"),)

    return train_dataset


# collation

def collate_fn(batch):
    mel_specs = [item['mel_spec'].squeeze(0) for item in batch]
    mel_lengths = torch.LongTensor([spec.shape[-1] for spec in mel_specs])
    max_mel_length = mel_lengths.amax()

    padded_mel_specs = []
    for spec in mel_specs:  # TODO. maybe records mask for attention here
        padding = (0, max_mel_length - spec.size(-1))
        padded_spec = F.pad(spec, padding, value = 0)
        padded_mel_specs.append(padded_spec)
    
    mel_specs = torch.stack(padded_mel_specs)

    text = [item['text'] for item in batch]
    text_lengths = torch.LongTensor([len(item) for item in text])

    return dict(
        mel = mel_specs,
        mel_lengths = mel_lengths,
        text = text,
        text_lengths = text_lengths,
    )