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import gradio as gr
import os
os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..')
import json
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
import commons
import utils
from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate
from models import SynthesizerTrn
from text.symbols import symbols as symbols_default
from scipy.io.wavfile import write
from text import cleaners
model_configs = {
"Graphemes": {
"path": "french_model_vits/G_700000.pth",
"symbols": symbols_default
}
}
# Global variables
net_g = None
symbols = []
_symbol_to_id = {}
_id_to_symbol = {}
def text_to_sequence(text, cleaner_names):
sequence = []
clean_text = _clean_text(text, cleaner_names)
for symbol in clean_text:
symbol_id = _symbol_to_id[symbol]
sequence += [symbol_id]
return sequence
def _clean_text(text, cleaner_names):
for name in cleaner_names:
cleaner = getattr(cleaners, name)
if not cleaner:
raise Exception('Unknown cleaner: %s' % name)
text = cleaner(text)
return text
def get_text(text, hps):
text_norm = 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 load_model_and_symbols(tab_name):
global net_g, symbols, _symbol_to_id, _id_to_symbol
model_config = model_configs[tab_name]
symbols = model_config["symbols"]
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model)
_ = net_g.eval()
_ = utils.load_checkpoint(model_config["path"], net_g, None)
def tts(text, speaker_id, tab_name):
load_model_and_symbols(tab_name)
sid = torch.LongTensor([speaker_id]) # speaker identity
stn_tst = get_text(text, hps)
with torch.no_grad():
x_tst = stn_tst.unsqueeze(0)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][
0, 0].data.float().numpy()
return "Success", (hps.data.sampling_rate, audio)
def create_tab(tab_name):
with gr.TabItem(tab_name):
gr.Markdown(f"### {tab_name} TTS Model")
tts_input1 = gr.TextArea(label="Text in french", value="")
tts_input2 = gr.Dropdown(label="Speaker", choices=["Male", "Female"], type="index", value="Male")
tts_submit = gr.Button("Generate", variant="primary")
tts_output1 = gr.Textbox(label="Message")
tts_output2 = gr.Audio(label="Output")
tts_submit.click(lambda text, speaker_id: tts(text, speaker_id, tab_name), [tts_input1, tts_input2], [tts_output1, tts_output2])
hps = utils.get_hparams_from_file("configs/vctk_base.json")
app = gr.Blocks()
with app:
gr.Markdown(
"""
# VITS Implementation for French
Based on VITS (https://github.com/jaywalnut310/vits).
## How to use:
Write the text on the box below. For faster inference, it is recommended to use short sentences.
## Hint: Some sample texts are available at the bottom of the web site.
"""
)
with gr.Tabs():
create_tab("French TTS")
gr.Markdown(
"""
## Examples
| Input Text | Speaker |
|------------|---------|
| On ne voit bien qu'avec le cœur, l'essentiel est invisible pour les yeux. | Female |
| Voilà plusieurs fois, Monsieur, que je vous rencontre sur mon chemin. C’est autant de fois de trop, et j’en ai assez de perdre mon temps à déjouer les pièges que vous me tendez. | Male |
| Je pense, donc je suis. | Female |
| La vie est un sommeil, l'amour en est le rêve, et vous aurez vécu si vous avez aimé. | Male |
"""
)
app.launch() |