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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
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