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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 einops import rearrange
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 = rearrange(audio_tensor, 't -> 1 t')
mel_spec = self.mel_spectrogram(audio_tensor)
mel_spec = rearrange(mel_spec, '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 = rearrange(mel_spec, '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,
)
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