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import logging
import os
import torch
from torchaudio._internal import download_url_to_file, module_utils as _mod_utils
def _get_chars():
return (
"_",
"-",
"!",
"'",
"(",
")",
",",
".",
":",
";",
"?",
" ",
"a",
"b",
"c",
"d",
"e",
"f",
"g",
"h",
"i",
"j",
"k",
"l",
"m",
"n",
"o",
"p",
"q",
"r",
"s",
"t",
"u",
"v",
"w",
"x",
"y",
"z",
)
def _get_phones():
return (
"_",
"-",
"!",
"'",
"(",
")",
",",
".",
":",
";",
"?",
" ",
"AA",
"AA0",
"AA1",
"AA2",
"AE",
"AE0",
"AE1",
"AE2",
"AH",
"AH0",
"AH1",
"AH2",
"AO",
"AO0",
"AO1",
"AO2",
"AW",
"AW0",
"AW1",
"AW2",
"AY",
"AY0",
"AY1",
"AY2",
"B",
"CH",
"D",
"DH",
"EH",
"EH0",
"EH1",
"EH2",
"ER",
"ER0",
"ER1",
"ER2",
"EY",
"EY0",
"EY1",
"EY2",
"F",
"G",
"HH",
"IH",
"IH0",
"IH1",
"IH2",
"IY",
"IY0",
"IY1",
"IY2",
"JH",
"K",
"L",
"M",
"N",
"NG",
"OW",
"OW0",
"OW1",
"OW2",
"OY",
"OY0",
"OY1",
"OY2",
"P",
"R",
"S",
"SH",
"T",
"TH",
"UH",
"UH0",
"UH1",
"UH2",
"UW",
"UW0",
"UW1",
"UW2",
"V",
"W",
"Y",
"Z",
"ZH",
)
def _to_tensor(indices):
lengths = torch.tensor([len(i) for i in indices], dtype=torch.int32)
values = [torch.tensor(i) for i in indices]
values = torch.nn.utils.rnn.pad_sequence(values, batch_first=True)
return values, lengths
def _load_phonemizer(file, dl_kwargs):
if not _mod_utils.is_module_available("dp"):
raise RuntimeError("DeepPhonemizer is not installed. Please install it.")
from dp.phonemizer import Phonemizer
# By default, dp issues DEBUG level log.
logger = logging.getLogger("dp")
orig_level = logger.level
logger.setLevel(logging.INFO)
try:
url = f"https://public-asai-dl-models.s3.eu-central-1.amazonaws.com/DeepPhonemizer/{file}"
directory = os.path.join(torch.hub.get_dir(), "checkpoints")
os.makedirs(directory, exist_ok=True)
path = os.path.join(directory, file)
if not os.path.exists(path):
dl_kwargs = {} if dl_kwargs is None else dl_kwargs
download_url_to_file(url, path, **dl_kwargs)
return Phonemizer.from_checkpoint(path)
finally:
logger.setLevel(orig_level)
def _unnormalize_waveform(waveform: torch.Tensor, bits: int) -> torch.Tensor:
r"""Transform waveform [-1, 1] to label [0, 2 ** bits - 1]"""
waveform = torch.clamp(waveform, -1, 1)
waveform = (waveform + 1.0) * (2**bits - 1) / 2
return torch.clamp(waveform, 0, 2**bits - 1).int()
def _get_taco_params(n_symbols):
return {
"mask_padding": False,
"n_mels": 80,
"n_frames_per_step": 1,
"symbol_embedding_dim": 512,
"encoder_embedding_dim": 512,
"encoder_n_convolution": 3,
"encoder_kernel_size": 5,
"decoder_rnn_dim": 1024,
"decoder_max_step": 2000,
"decoder_dropout": 0.1,
"decoder_early_stopping": True,
"attention_rnn_dim": 1024,
"attention_hidden_dim": 128,
"attention_location_n_filter": 32,
"attention_location_kernel_size": 31,
"attention_dropout": 0.1,
"prenet_dim": 256,
"postnet_n_convolution": 5,
"postnet_kernel_size": 5,
"postnet_embedding_dim": 512,
"gate_threshold": 0.5,
"n_symbol": n_symbols,
}
def _get_wrnn_params():
return {
"upsample_scales": [5, 5, 11],
"n_classes": 2**8, # n_bits = 8
"hop_length": 275,
"n_res_block": 10,
"n_rnn": 512,
"n_fc": 512,
"kernel_size": 5,
"n_freq": 80,
"n_hidden": 128,
"n_output": 128,
}
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