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 as symbols_default # import symbols graphemes
from fr_wa_graphemes.symbols import symbols as symbols_ft # import symbols finetuned 
from wa_ft_graphemes_v2.symbols import symbols as symbols_ft_v2
from wa_graphemes_v2.symbols import symbols as symbols_v2
from scipy.io.wavfile import write
from text import cleaners

model_configs = {
    "Graphemes_finetuned": {
        "path": "fr_wa_graphemes/G_80000.pth",
        "symbols": symbols_ft
    },
    "Graphemes": {
        "path": "wa_graphemes/G_258000.pth",
        "symbols": symbols_default
    },
    "Graphemes_v2": {
        "path": "wa_graphemes_v2/G_228000.pth",
        "symbols": symbols_v2
    },
    "Graphemes_finetuned_v2": {
        "path": "wa_ft_graphemes_v2/G_207000.pth",
        "symbols": symbols_ft_v2
    },
    "Graphemes_v3": {
        "path": "wa_graphemes_v3/G_394000.pth",
        "symbols": symbols_v2
    },
    "Graphemes_finetuned_v3": {
        "path": "wa_ft_v3/G_213000.pth",
        "symbols": symbols_ft_v2
    }
}

# 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 Walloon", 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])

def tts_comparison(text, speaker_id):
    result1 = tts(text, speaker_id, "Graphemes")
    result2 = tts(text, speaker_id, "Graphemes_finetuned")
    #result3 = tts(text, speaker_id, "Graphemes_v2")
    #result4 = tts(text, speaker_id, "Graphemes_finetuned_v2")
    result5 = tts(text, speaker_id, "Graphemes_v3")
    result6 = tts(text, speaker_id, "Graphemes_finetuned_v3")
    return result1[1], result2[1], result5[1], result6[1] #result3[1], result4[1], 

def create_comparison_tab():
    with gr.TabItem("Compare Models"):
        gr.Markdown("### Compare TTS Models")
        tts_input = gr.TextArea(label="Text in Walloon", value="")
        tts_speaker = gr.Dropdown(label="Speaker", choices=["Male", "Female"], type="index", value="Male")
        tts_submit = gr.Button("Generate", variant="primary")
        tts_output1 = gr.Audio(label="Graphemes")
        tts_output2 = gr.Audio(label="Graphemes French Data")
        #tts_output3 = gr.Audio(label="Graphemes v2")
        #tts_output4 = gr.Audio(label="Graphemes v2 French Data")
        tts_output5 = gr.Audio(label="Graphemes v3")
        tts_output6 = gr.Audio(label="Graphemes v3 French Data")
        tts_submit.click(lambda text, speaker_id: tts_comparison(text, speaker_id), [tts_input, tts_speaker], [tts_output1, tts_output2,tts_output5, tts_output6]) #tts_output3, tts_output4

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

app = gr.Blocks()
with app:
    gr.Markdown(
        """
        # First Text to Speech (TTS) for Walloon
        Based on VITS (https://github.com/jaywalnut310/vits).
        ## How to use:
        Write the text in graphemes. 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 phonemes follow the link below:
        https://huggingface.co/spaces/Pipe1213/VITS_Walloon_Phonemes
        ## Hint: Some sample texts are available at the bottom of the web site.
        """
    )
    with gr.Tabs():
        create_tab("Graphemes")
        create_tab("Graphemes_finetuned")
        #create_tab("Graphemes_v2")
        #create_tab("Graphemes_finetuned_v2")
        create_tab("Graphemes_v3")
        create_tab("Graphemes_finetuned_v3")        
        create_comparison_tab()

    gr.Markdown(
        """
        ## Examples
        | Input Text | Speaker |
        |------------|---------|
        | Portant, c' est l' seu ki n' doereut nén fé rire di lu, a mi idêye.  | Female | 
        | Li bijhe et l’ solea estént ki s’ margayént po sawè kî çki, des deus, esteut l’ pus foirt. Mins ç’ côp la, la k’ i veyèt on tchminåd k' arivéve pyim piam, dins on bea noû tchôd paltot.  | Male |
        | Ci fourit co l' bedot les cåzes ca, a on moumint, li Ptit Prince mi dmanda yåk, come onk k' est so dotance, tot d' on côp  | Female | 
        | Li Ptit Prince, da Antoenne di Sint-Spuri, ratourné e walon pa Lorint Enchel | Female | 
        """
    )

app.launch()