Spaces:
Running
Running
File size: 5,142 Bytes
17e27a2 |
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 |
import os
import re
import tempfile
import torch
import sys
import gradio as gr
from huggingface_hub import hf_hub_download
# Setup TTS env
if "vits" not in sys.path:
sys.path.append("vits")
from vits import commons, utils
from vits.models import SynthesizerTrn
TTS_LANGUAGES = {}
with open(f"data/tts/all_langs.tsv") as f:
for line in f:
iso, name = line.split(" ", 1)
TTS_LANGUAGES[iso] = name
class TextMapper(object):
def __init__(self, vocab_file):
self.symbols = [
x.replace("\n", "") for x in open(vocab_file, encoding="utf-8").readlines()
]
self.SPACE_ID = self.symbols.index(" ")
self._symbol_to_id = {s: i for i, s in enumerate(self.symbols)}
self._id_to_symbol = {i: s for i, s in enumerate(self.symbols)}
def text_to_sequence(self, text, cleaner_names):
"""Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
Args:
text: string to convert to a sequence
cleaner_names: names of the cleaner functions to run the text through
Returns:
List of integers corresponding to the symbols in the text
"""
sequence = []
clean_text = text.strip()
for symbol in clean_text:
symbol_id = self._symbol_to_id[symbol]
sequence += [symbol_id]
return sequence
def uromanize(self, text, uroman_pl):
iso = "xxx"
with tempfile.NamedTemporaryFile() as tf, tempfile.NamedTemporaryFile() as tf2:
with open(tf.name, "w") as f:
f.write("\n".join([text]))
cmd = f"perl " + uroman_pl
cmd += f" -l {iso} "
cmd += f" < {tf.name} > {tf2.name}"
os.system(cmd)
outtexts = []
with open(tf2.name) as f:
for line in f:
line = re.sub(r"\s+", " ", line).strip()
outtexts.append(line)
outtext = outtexts[0]
return outtext
def get_text(self, text, hps):
text_norm = self.text_to_sequence(text, hps.data.text_cleaners)
if hps.data.add_blank:
text_norm = commons.intersperse(text_norm, 0)
text_norm = torch.LongTensor(text_norm)
return text_norm
def filter_oov(self, text, lang=None):
text = self.preprocess_char(text, lang=lang)
val_chars = self._symbol_to_id
txt_filt = "".join(list(filter(lambda x: x in val_chars, text)))
return txt_filt
def preprocess_char(self, text, lang=None):
"""
Special treatement of characters in certain languages
"""
if lang == "ron":
text = text.replace("ț", "ţ")
print(f"{lang} (ț -> ţ): {text}")
return text
def synthesize(text,speed,lang):
#lang = "spa"
#speed =1
if speed is None:
speed = 1.0
lang_code = lang.split()[0].strip()
vocab_file = hf_hub_download(
repo_id="facebook/mms-tts",
filename="vocab.txt",
subfolder=f"models/{lang_code}",
)
config_file = hf_hub_download(
repo_id="facebook/mms-tts",
filename="config.json",
subfolder=f"models/{lang_code}",
)
g_pth = hf_hub_download(
repo_id="facebook/mms-tts",
filename="G_100000.pth",
subfolder=f"models/{lang_code}",
)
if torch.cuda.is_available():
device = torch.device("cuda")
elif (
hasattr(torch.backends, "mps")
and torch.backends.mps.is_available()
and torch.backends.mps.is_built()
):
device = torch.device("mps")
else:
device = torch.device("cpu")
print(f"Run inference with {device}")
assert os.path.isfile(config_file), f"{config_file} doesn't exist"
hps = utils.get_hparams_from_file(config_file)
text_mapper = TextMapper(vocab_file)
net_g = SynthesizerTrn(
len(text_mapper.symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model,
)
net_g.to(device)
_ = net_g.eval()
_ = utils.load_checkpoint(g_pth, net_g, None)
is_uroman = hps.data.training_files.split(".")[-1] == "uroman"
if is_uroman:
uroman_dir = "uroman"
assert os.path.exists(uroman_dir)
uroman_pl = os.path.join(uroman_dir, "bin", "uroman.pl")
text = text_mapper.uromanize(text, uroman_pl)
text = text.lower()
text = text_mapper.filter_oov(text, lang=lang)
stn_tst = text_mapper.get_text(text, hps)
with torch.no_grad():
x_tst = stn_tst.unsqueeze(0).to(device)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
hyp = (
net_g.infer(
x_tst,
x_tst_lengths,
noise_scale=0.667,
noise_scale_w=0.8,
length_scale=1.0 / speed,
)[0][0, 0]
.cpu()
.float()
.numpy()
)
return hps.data.sampling_rate,hyp
#return gr.Audio.update(value=(hps.data.sampling_rate, hyp)), text
|