Spaces:
Sleeping
Sleeping
File size: 6,307 Bytes
85ba398 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import random
from pathlib import Path
from typing import Dict, Optional, Tuple
import torch
import torch.nn as nn
logger = logging.getLogger(__name__)
class TTSHubInterface(nn.Module):
def __init__(self, cfg, task, model):
super().__init__()
self.cfg = cfg
self.task = task
self.model = model
self.model.eval()
self.update_cfg_with_data_cfg(self.cfg, self.task.data_cfg)
self.generator = self.task.build_generator([self.model], self.cfg)
@classmethod
def phonemize(
cls,
text: str,
lang: Optional[str],
phonemizer: Optional[str] = None,
preserve_punct: bool = False,
to_simplified_zh: bool = False,
):
if to_simplified_zh:
import hanziconv
text = hanziconv.HanziConv.toSimplified(text)
if phonemizer == "g2p":
import g2p_en
g2p = g2p_en.G2p()
if preserve_punct:
return " ".join("|" if p == " " else p for p in g2p(text))
else:
res = [{",": "sp", ";": "sp"}.get(p, p) for p in g2p(text)]
return " ".join(p for p in res if p.isalnum())
if phonemizer == "g2pc":
import g2pc
g2p = g2pc.G2pC()
return " ".join([w[3] for w in g2p(text)])
elif phonemizer == "ipa":
assert lang is not None
import phonemizer
from phonemizer.separator import Separator
lang_map = {"en": "en-us", "fr": "fr-fr"}
return phonemizer.phonemize(
text,
backend="espeak",
language=lang_map.get(lang, lang),
separator=Separator(word="| ", phone=" "),
)
else:
return text
@classmethod
def tokenize(cls, text: str, tkn_cfg: Dict[str, str]):
sentencepiece_model = tkn_cfg.get("sentencepiece_model", None)
if sentencepiece_model is not None:
assert Path(sentencepiece_model).exists()
import sentencepiece as sp
spm = sp.SentencePieceProcessor()
spm.Load(sentencepiece_model)
return " ".join(spm.Encode(text, out_type=str))
else:
return text
@classmethod
def update_cfg_with_data_cfg(cls, cfg, data_cfg):
cfg["task"].vocoder = data_cfg.vocoder.get("type", "griffin_lim")
@classmethod
def get_model_input(
cls, task, text: str, speaker: Optional[int] = None, verbose: bool = False
):
phonemized = cls.phonemize(
text,
task.data_cfg.hub.get("lang", None),
task.data_cfg.hub.get("phonemizer", None),
task.data_cfg.hub.get("preserve_punct", False),
task.data_cfg.hub.get("to_simplified_zh", False),
)
tkn_cfg = task.data_cfg.bpe_tokenizer
tokenized = cls.tokenize(phonemized, tkn_cfg)
if verbose:
logger.info(f"text: {text}")
logger.info(f"phonemized: {phonemized}")
logger.info(f"tokenized: {tokenized}")
spk = task.data_cfg.hub.get("speaker", speaker)
n_speakers = len(task.speaker_to_id or {})
if spk is None and n_speakers > 0:
spk = random.randint(0, n_speakers - 1)
if spk is not None:
spk = max(0, min(spk, n_speakers - 1))
if verbose:
logger.info(f"speaker: {spk}")
spk = None if spk is None else torch.Tensor([[spk]]).long()
src_tokens = task.src_dict.encode_line(tokenized, add_if_not_exist=False).view(
1, -1
)
src_lengths = torch.Tensor([len(tokenized.split())]).long()
return {
"net_input": {
"src_tokens": src_tokens,
"src_lengths": src_lengths,
"prev_output_tokens": None,
},
"target_lengths": None,
"speaker": spk,
}
@classmethod
def get_prediction(cls, task, model, generator, sample) -> Tuple[torch.Tensor, int]:
prediction = generator.generate(model, sample)
return prediction[0]["waveform"], task.sr
def predict(
self, text: str, speaker: Optional[int] = None, verbose: bool = False
) -> Tuple[torch.Tensor, int]:
sample = self.get_model_input(self.task, text, speaker, verbose=verbose)
return self.get_prediction(self.task, self.model, self.generator, sample)
class VocoderHubInterface(nn.Module):
"""Vocoder interface to run vocoder models through hub. Currently we only support unit vocoder"""
def __init__(self, cfg, model):
super().__init__()
self.vocoder = model
self.vocoder.eval()
self.sr = 16000
self.multispkr = self.vocoder.model.multispkr
if self.multispkr:
logger.info("multi-speaker vocoder")
self.num_speakers = cfg.get(
"num_speakers",
200,
) # following the default in codehifigan to set to 200
def get_model_input(
self,
text: str,
speaker: Optional[int] = -1,
):
units = list(map(int, text.strip().split()))
x = {
"code": torch.LongTensor(units).view(1, -1),
}
if not speaker:
speaker = -1
if self.multispkr:
assert (
speaker < self.num_speakers
), f"invalid --speaker-id ({speaker}) with total #speakers = {self.num_speakers}"
spk = random.randint(0, self.num_speakers - 1) if speaker == -1 else speaker
x["spkr"] = torch.LongTensor([spk]).view(1, 1)
return x
def get_prediction(self, sample, dur_prediction: Optional[bool] = True):
wav = self.vocoder(sample, dur_prediction)
return wav, self.sr
def predict(
self,
text: str,
speaker: Optional[int] = None,
dur_prediction: Optional[bool] = True,
):
sample = self.get_model_input(text, speaker)
return self.get_prediction(sample, dur_prediction)
|