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import json
import os
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import torch
from torch.utils.data import Dataset
from models.config import PreprocessingConfigUnivNet as PreprocessingConfig
from models.config import get_lang_map, lang2id
from training.preprocess import PreprocessLibriTTS
from training.tools import pad_1D, pad_2D, pad_3D
from .libritts_r import LIBRITTS_R
class LibriTTSDatasetAcoustic(Dataset):
r"""Loading preprocessed acoustic model data."""
def __init__(
self,
lang: str = "en",
root: str = "datasets_cache/LIBRITTS",
url: str = "train-clean-360",
download: bool = False,
cache: bool = False,
mem_cache: bool = False,
cache_dir: str = "datasets_cache",
selected_speaker_ids: Optional[List[int]] = None,
):
r"""A PyTorch dataset for loading preprocessed acoustic data.
Args:
root (str): Path to the directory where the dataset is found or downloaded.
lang (str): The language of the dataset.
url (str): The dataset url, default "train-clean-360".
download (bool, optional): Whether to download the dataset if it is not found. Defaults to True.
cache (bool, optional): Whether to cache the preprocessed data to RAM. Defaults to False.
mem_cache (bool, optional): Whether to cache the preprocessed data. Defaults to False.
cache_dir (str, optional): Path to the directory where the cache is stored. Defaults to "datasets_cache".
selected_speaker_ids (Optional[List[int]], optional): A list of selected speakers. Defaults to None.
"""
lang_map = get_lang_map(lang)
processing_lang_type = lang_map.processing_lang_type
preprocess_config = PreprocessingConfig(processing_lang_type)
self.dataset = LIBRITTS_R(
root=root,
download=download,
url=url,
selected_speaker_ids=selected_speaker_ids,
min_audio_length=preprocess_config.min_seconds,
max_audio_length=preprocess_config.max_seconds,
)
self.cache = cache
# Calculate the directory for the cache file
self.cache_subdir = lambda idx: str(((idx // 1000) + 1) * 1000)
self.cache_dir = os.path.join(cache_dir, f"cache-{url}")
self.mem_cache = mem_cache
self.memory_cache = {}
# Load the id_mapping dictionary from the JSON file
with open("speaker_id_mapping_libri.json") as f:
self.id_mapping = json.load(f)
self.preprocess_libtts = PreprocessLibriTTS(
preprocess_config,
lang,
)
def __len__(self) -> int:
r"""Returns the number of samples in the dataset.
Returns
int: Number of samples in the dataset.
"""
return len(self.dataset)
def __getitem__(self, idx: int) -> Dict[str, Any]:
r"""Returns a sample from the dataset at the given index.
Args:
idx (int): Index of the sample to return.
Returns:
Dict[str, Any]: A dictionary containing the sample data.
"""
# Check if the data is in the memory cache
if self.mem_cache and idx in self.memory_cache:
return self.memory_cache[idx]
# Check if the data is in the cache
cache_subdir_path = os.path.join(self.cache_dir, self.cache_subdir(idx))
cache_file = os.path.join(cache_subdir_path, f"{idx}.pt")
# Check if the data is in the cache
if self.cache and os.path.exists(cache_file):
# If the data is in the cache, load it from the cache file and return it
data = torch.load(cache_file)
return data
# Retrive the dataset row
data = self.dataset[idx]
data = self.preprocess_libtts.acoustic(data)
# TODO: bad way to do filtering, fix this!
if data is None:
# print("Skipping due to preprocessing error")
rand_idx = np.random.randint(0, self.__len__())
return self.__getitem__(rand_idx)
data.wav = data.wav.unsqueeze(0)
result = {
"id": data.utterance_id,
"wav": data.wav,
"mel": data.mel,
"pitch": data.pitch,
"text": data.phones,
"attn_prior": data.attn_prior,
"energy": data.energy,
"raw_text": data.raw_text,
"normalized_text": data.normalized_text,
"speaker": self.id_mapping.get(str(data.speaker_id)),
"pitch_is_normalized": data.pitch_is_normalized,
# TODO: fix lang!
"lang": lang2id["en"],
}
# Add the data to the memory cache
if self.mem_cache:
self.memory_cache[idx] = result
if self.cache:
# Create the cache subdirectory if it doesn't exist
os.makedirs(cache_subdir_path, exist_ok=True)
# Save the preprocessed data to the cache
torch.save(result, cache_file)
return result
def __iter__(self):
r"""Method makes the class iterable. It iterates over the `_walker` attribute
and for each item, it gets the corresponding item from the dataset using the
`__getitem__` method.
Yields:
The item from the dataset corresponding to the current item in `_walker`.
"""
for item in range(self.__len__()):
yield self.__getitem__(item)
def collate_fn(self, data: List) -> List:
r"""Collates a batch of data samples.
Args:
data (List): A list of data samples.
Returns:
List: A list of reprocessed data batches.
"""
data_size = len(data)
idxs = list(range(data_size))
# Initialize empty lists to store extracted values
empty_lists: List[List] = [[] for _ in range(12)]
(
ids,
speakers,
texts,
raw_texts,
mels,
pitches,
attn_priors,
langs,
src_lens,
mel_lens,
wavs,
energy,
) = empty_lists
# Extract fields from data dictionary and populate the lists
for idx in idxs:
data_entry = data[idx]
ids.append(data_entry["id"])
speakers.append(data_entry["speaker"])
texts.append(data_entry["text"])
raw_texts.append(data_entry["raw_text"])
mels.append(data_entry["mel"])
pitches.append(data_entry["pitch"])
attn_priors.append(data_entry["attn_prior"])
langs.append(data_entry["lang"])
src_lens.append(data_entry["text"].shape[0])
mel_lens.append(data_entry["mel"].shape[1])
wavs.append(data_entry["wav"])
energy.append(data_entry["energy"])
# Convert langs, src_lens, and mel_lens to numpy arrays
langs = np.array(langs)
src_lens = np.array(src_lens)
mel_lens = np.array(mel_lens)
# NOTE: Instead of the pitches for the whole dataset, used stat for the batch
# Take only min and max values for pitch
pitches_stat = list(self.normalize_pitch(pitches)[:2])
texts = pad_1D(texts)
mels = pad_2D(mels)
pitches = pad_1D(pitches)
attn_priors = pad_3D(attn_priors, len(idxs), max(src_lens), max(mel_lens))
speakers = np.repeat(
np.expand_dims(np.array(speakers), axis=1),
texts.shape[1],
axis=1,
)
langs = np.repeat(
np.expand_dims(np.array(langs), axis=1),
texts.shape[1],
axis=1,
)
wavs = pad_2D(wavs)
energy = pad_2D(energy)
return [
ids,
raw_texts,
torch.from_numpy(speakers),
texts.int(),
torch.from_numpy(src_lens),
mels,
pitches,
pitches_stat,
torch.from_numpy(mel_lens),
torch.from_numpy(langs),
attn_priors,
wavs,
energy,
]
def normalize_pitch(
self,
pitches: List[torch.Tensor],
) -> Tuple[float, float, float, float]:
r"""Normalizes the pitch values.
Args:
pitches (List[torch.Tensor]): A list of pitch values.
Returns:
Tuple: A tuple containing the normalized pitch values.
"""
pitches_t = torch.concatenate(pitches)
min_value = torch.min(pitches_t).item()
max_value = torch.max(pitches_t).item()
mean = torch.mean(pitches_t).item()
std = torch.std(pitches_t).item()
return min_value, max_value, mean, std
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