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