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dataset.py
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dataset.py
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import json
import math
import os
import numpy as np
from torch.utils.data import Dataset
from text import text_to_sequence
from utils.tools import get_variance_level, pad_1D, pad_2D, pad_3D
from utils.pitch_tools import norm_interp_f0, get_lf0_cwt
class Dataset(Dataset):
def __init__(
self, filename, preprocess_config, model_config, train_config, sort=False, drop_last=False
):
self.dataset_name = preprocess_config["dataset"]
self.preprocess_config = preprocess_config
self.preprocessed_path = preprocess_config["path"]["preprocessed_path"]
self.cleaners = preprocess_config["preprocessing"]["text"]["text_cleaners"]
self.batch_size = train_config["optimizer"]["batch_size"]
self.learn_alignment = model_config["duration_modeling"]["learn_alignment"]
self.dataset_tag = "unsup" if self.learn_alignment else "sup"
self.load_spker_embed = model_config["multi_speaker"] \
and preprocess_config["preprocessing"]["speaker_embedder"] != 'none'
self.energy_level_tag, *_ = get_variance_level(preprocess_config, model_config)
self.basename, self.speaker, self.text, self.raw_text = self.process_meta(
filename
)
with open(os.path.join(self.preprocessed_path, "speakers.json")) as f:
self.speaker_map = json.load(f)
self.sort = sort
self.drop_last = drop_last
# pitch stats
self.pitch_type = preprocess_config["preprocessing"]["pitch"]["pitch_type"]
with open(
os.path.join(preprocess_config["path"]["preprocessed_path"], "stats.json")
) as f:
stats = json.load(f)
self.f0_unsup_mean = float(stats["f0_unsup"][0])
self.f0_unsup_std = float(stats["f0_unsup"][1])
self.f0_sup_mean = float(stats["f0_sup"][0])
self.f0_sup_std = float(stats["f0_sup"][1])
def __len__(self):
return len(self.text)
def __getitem__(self, idx):
dataset_tag = self.dataset_tag
basename = self.basename[idx]
speaker = self.speaker[idx]
speaker_id = self.speaker_map[speaker]
raw_text = self.raw_text[idx]
phone = np.array(text_to_sequence(self.text[idx], self.cleaners))
mel_path = os.path.join(
self.preprocessed_path,
"mel_{}".format(dataset_tag),
"{}-mel-{}.npy".format(speaker, basename),
)
mel = np.load(mel_path)
pitch_path = os.path.join(
self.preprocessed_path,
"pitch_{}".format(dataset_tag),
"{}-pitch-{}.npy".format(speaker, basename),
)
pitch = np.load(pitch_path)
f0_path = os.path.join(
self.preprocessed_path,
"f0_{}".format(dataset_tag),
"{}-f0-{}.npy".format(speaker, basename),
)
f0 = np.load(f0_path)
f0, uv = norm_interp_f0(f0, self.preprocess_config["preprocessing"]["pitch"])
pitch = np.load(pitch_path)
energy_path = os.path.join(
self.preprocessed_path,
"energy_{}_{}".format(dataset_tag, self.energy_level_tag),
"{}-energy-{}.npy".format(speaker, basename),
)
energy = np.load(energy_path)
if self.learn_alignment:
attn_prior_path = os.path.join(
self.preprocessed_path,
"attn_prior",
"{}-attn_prior-{}.npy".format(speaker, basename),
)
attn_prior = np.load(attn_prior_path)
duration = None
mel2ph = None
else:
duration_path = os.path.join(
self.preprocessed_path,
"duration",
"{}-duration-{}.npy".format(speaker, basename),
)
duration = np.load(duration_path)
mel2ph_path = os.path.join(
self.preprocessed_path,
"mel2ph",
"{}-mel2ph-{}.npy".format(speaker, basename),
)
mel2ph = np.load(mel2ph_path)
attn_prior = None
spker_embed = np.load(os.path.join(
self.preprocessed_path,
"spker_embed",
"{}-spker_embed.npy".format(speaker),
)) if self.load_spker_embed else None
cwt_spec = f0_mean = f0_std = f0_ph = None
if self.pitch_type == "cwt":
cwt_spec_path = os.path.join(
self.preprocessed_path,
"cwt_spec_{}".format(dataset_tag),
"{}-cwt_spec-{}.npy".format(speaker, basename),
)
cwt_spec = np.load(cwt_spec_path)
f0cwt_mean_std_path = os.path.join(
self.preprocessed_path,
"f0cwt_mean_std_{}".format(dataset_tag),
"{}-f0cwt_mean_std-{}.npy".format(speaker, basename),
)
f0cwt_mean_std = np.load(f0cwt_mean_std_path)
f0_mean, f0_std = float(f0cwt_mean_std[0]), float(f0cwt_mean_std[1])
sample = {
"id": basename,
"speaker": speaker_id,
"text": phone,
"raw_text": raw_text,
"mel": mel,
"pitch": pitch,
"f0": f0,
"uv": uv,
"cwt_spec": cwt_spec,
"f0_mean": f0_mean,
"f0_std": f0_std,
"energy": energy,
"duration": duration,
"mel2ph": mel2ph,
"attn_prior": attn_prior,
"spker_embed": spker_embed,
}
return sample
def process_meta(self, filename):
with open(
os.path.join(self.preprocessed_path, filename), "r", encoding="utf-8"
) as f:
name = []
speaker = []
text = []
raw_text = []
for line in f.readlines():
n, s, t, r = line.strip("\n").split("|")
name.append(n)
speaker.append(s)
text.append(t)
raw_text.append(r)
return name, speaker, text, raw_text
def reprocess(self, data, idxs):
ids = [data[idx]["id"] for idx in idxs]
speakers = [data[idx]["speaker"] for idx in idxs]
texts = [data[idx]["text"] for idx in idxs]
raw_texts = [data[idx]["raw_text"] for idx in idxs]
mels = [data[idx]["mel"] for idx in idxs]
pitches = [data[idx]["pitch"] for idx in idxs]
f0s = [data[idx]["f0"] for idx in idxs]
uvs = [data[idx]["uv"] for idx in idxs]
cwt_specs = f0_means = f0_stds = f0_phs = None
if self.pitch_type == "cwt":
cwt_specs = [data[idx]["cwt_spec"] for idx in idxs]
f0_means = [data[idx]["f0_mean"] for idx in idxs]
f0_stds = [data[idx]["f0_std"] for idx in idxs]
cwt_specs = pad_2D(cwt_specs)
f0_means = np.array(f0_means)
f0_stds = np.array(f0_stds)
energies = [data[idx]["energy"] for idx in idxs]
durations = [data[idx]["duration"] for idx in idxs] if not self.learn_alignment else None
mel2phs = [data[idx]["mel2ph"] for idx in idxs] if not self.learn_alignment else None
attn_priors = [data[idx]["attn_prior"] for idx in idxs] if self.learn_alignment else None
spker_embeds = np.concatenate(np.array([data[idx]["spker_embed"] for idx in idxs]), axis=0) \
if self.load_spker_embed else None
text_lens = np.array([text.shape[0] for text in texts])
mel_lens = np.array([mel.shape[0] for mel in mels])
speakers = np.array(speakers)
texts = pad_1D(texts)
mels = pad_2D(mels)
pitches = pad_1D(pitches)
f0s = pad_1D(f0s)
uvs = pad_1D(uvs)
energies = pad_1D(energies)
if self.learn_alignment:
attn_priors = pad_3D(attn_priors, len(idxs), max(text_lens), max(mel_lens))
else:
durations = pad_1D(durations)
mel2phs = pad_1D(mel2phs)
return (
ids,
raw_texts,
speakers,
texts,
text_lens,
max(text_lens),
mels,
mel_lens,
max(mel_lens),
pitches,
f0s,
uvs,
cwt_specs,
f0_means,
f0_stds,
energies,
durations,
mel2phs,
attn_priors,
spker_embeds,
)
def collate_fn(self, data):
data_size = len(data)
if self.sort:
len_arr = np.array([d["text"].shape[0] for d in data])
idx_arr = np.argsort(-len_arr)
else:
idx_arr = np.arange(data_size)
tail = idx_arr[len(idx_arr) - (len(idx_arr) % self.batch_size) :]
idx_arr = idx_arr[: len(idx_arr) - (len(idx_arr) % self.batch_size)]
idx_arr = idx_arr.reshape((-1, self.batch_size)).tolist()
if not self.drop_last and len(tail) > 0:
idx_arr += [tail.tolist()]
output = list()
for idx in idx_arr:
output.append(self.reprocess(data, idx))
return output
class TextDataset(Dataset):
def __init__(self, filepath, preprocess_config, model_config):
self.cleaners = preprocess_config["preprocessing"]["text"]["text_cleaners"]
self.preprocessed_path = preprocess_config["path"]["preprocessed_path"]
self.load_spker_embed = model_config["multi_speaker"] \
and preprocess_config["preprocessing"]["speaker_embedder"] != 'none'
self.basename, self.speaker, self.text, self.raw_text = self.process_meta(
filepath
)
with open(
os.path.join(
preprocess_config["path"]["preprocessed_path"], "speakers.json"
)
) as f:
self.speaker_map = json.load(f)
def __len__(self):
return len(self.text)
def __getitem__(self, idx):
basename = self.basename[idx]
speaker = self.speaker[idx]
speaker_id = self.speaker_map[speaker]
raw_text = self.raw_text[idx]
phone = np.array(text_to_sequence(self.text[idx], self.cleaners))
spker_embed = np.load(os.path.join(
self.preprocessed_path,
"spker_embed",
"{}-spker_embed.npy".format(speaker),
)) if self.load_spker_embed else None
return (basename, speaker_id, phone, raw_text, spker_embed)
def process_meta(self, filename):
with open(filename, "r", encoding="utf-8") as f:
name = []
speaker = []
text = []
raw_text = []
for line in f.readlines():
n, s, t, r = line.strip("\n").split("|")
name.append(n)
speaker.append(s)
text.append(t)
raw_text.append(r)
return name, speaker, text, raw_text
def collate_fn(self, data):
ids = [d[0] for d in data]
speakers = np.array([d[1] for d in data])
texts = [d[2] for d in data]
raw_texts = [d[3] for d in data]
text_lens = np.array([text.shape[0] for text in texts])
spker_embeds = np.concatenate(np.array([d[4] for d in data]), axis=0) \
if self.load_spker_embed else None
texts = pad_1D(texts)
return ids, raw_texts, speakers, texts, text_lens, max(text_lens), spker_embeds