app.py

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
from text.symbols_pho import symbols_pho
#from text import text_to_sequence
from scipy.io.wavfile import write
from text import cleaners

symbols = symbols_pho # change this later 
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
_id_to_symbol = {i: s for i, s in enumerate(symbols)}

def text_to_sequence(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 = _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(model_path, hps):
    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_path, net_g, None)
    return net_g

#hps = utils.get_hparams_from_file("configs/vctk_base.json")
hps = utils.get_hparams_from_file("configs/vctk_base.json")

# Define a dictionary to store the model paths for each tab
model_paths = {
    "Phonemes_finetuned": "wallon_pho/G_277000.pth"
}

# Load the initial model
net_g = load_model(model_paths["Phonemes_finetuned"], hps)

def tts(text, speaker_id, tab_name):
    global net_g
    net_g = load_model(model_paths[tab_name], hps)
    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 Walloon on IPA phonemes", 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])

app = gr.Blocks()
with app:
    gr.Markdown(
        """
        # First Text to Speech (TTS) for Walloon
        Based on VITS (https://github.com/jaywalnut310/vits).

        Write the text in phonemes or graphemes depending on the model.
        For faster inference, it is recommended to use short sentences.
        
        The quality of the results varies between male and female voice due to the limited data for female voice on this language. 
        For better results with male voice, use the models fully trained on Walloon.
        For better results with female voice, use the models trained on french and fine-tuned on Walloon.
        
        To try the version trained in graphemes follow the link below:

        https://huggingface.co/spaces/Pipe1213/VITS_Walloon_Graphemes

        
        ## Hint: Some sample texts are available at the bottom of the web site.
        ## Hint: For faster inference speed it is recommended to use short sentences.
        """
    )
    with gr.Tabs():
        create_tab("Phonemes_finetuned")
        create_tab("Phonemes(not working yet!!!)")

    gr.Markdown(
        """
        ### Examples
        | Input Text | Speaker |
        |------------|---------|
        | li biːç ɛ l sɔlja ɛstẽ ki s maʁɡajẽ pɔ sawɛ kiː ski , dɛ døː , ɛstøː l py fwaʁ . m ɛ̃ s koː la , la k i vɛjɛ õ tsminɔː k aʁivef pjim pjam , d ɛ̃ õ bja nuː tsoː paltɔ .  | Female |
        | ɛl m õ ʁɛspõdu ,  duvẽ ɔːʁẽ n pøː d õ tsapja . | Male |
        | dɔ koː , dz a dvu tswɛzi ɛn oːt mɛstiː , dz ast apʁ ɛ̃ a mõne dɛz avjõ .| Female |
        """
    )

app.launch()