Update tts.py

tts.py

@@ -1,176 +1,3 @@
-# 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 os
-import re
-import tempfile
-import torch
-import sys
-import gradio as gr
-import numpy as np
-
-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.strip()] = name.strip()
-
-
-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=None, lang=None, speed=None):
-    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()
-        )
-
-    hyp = (hyp * 32768).astype(np.int16)
-    return (hps.data.sampling_rate, hyp), text
-
 
 TTS_EXAMPLES = [
     ["I am going to the store.", "eng (English)", 1.0],