app.py

# -*- coding: utf-8 -*-
import gradio as gr
import numpy as np
import torch
import torch.nn as nn
import audiofile
from tts import StyleTTS2
from textual import only_greek_or_only_latin, transliterate_number, fix_vocals
import textwrap

from audionar import VitsModel, VitsTokenizer






language_names = ['Ancient greek',
                  'English',
                  'Deutsch',
                  'French',
                  'Hungarian',
                  'Romanian',
                  'Serbian (Approx.)']


def audionar_tts(text=None,
                 lang='Romanian'):

    # https://huggingface.co/dkounadis/artificial-styletts2/blob/main/msinference.py


    lang_map = {
            'ancient greek': 'grc',
            'english': 'eng',
            'deutsch': 'deu',
            'french': 'fra',
            'hungarian': 'hun',
            'romanian': 'ron',
            'serbian (approx.)': 'rmc-script_latin',
        }

    if text is None or text.strip() == '':
        text = 'No Txt Has been typed'



    fs = 16000

    if lang not in language_names:  # StyleTTS2
        
        fs = 24000
        
        text = only_greek_or_only_latin(text, lang='eng')

        x = _tts.inference(text,
                           ref_s='wav/' + lang + '.wav')[0, 0, :].numpy()  # 24 Khz
    
    else:  # VITS

        lang_code = lang_map.get(lang.lower(), lang.lower().split()[0].strip())

        global cached_lang_code, cached_net_g, cached_tokenizer

        if 'cached_lang_code' not in globals() or cached_lang_code != lang_code:
            cached_lang_code = lang_code
            cached_net_g = VitsModel.from_pretrained(f'facebook/mms-tts-{lang_code}').eval()
            cached_tokenizer = VitsTokenizer.from_pretrained(f'facebook/mms-tts-{lang_code}')

        net_g = cached_net_g
        tokenizer = cached_tokenizer
        text = only_greek_or_only_latin(text, lang=lang_code)
        text = transliterate_number(text, lang=lang_code)
        text = fix_vocals(text, lang=lang_code)


        sentences = textwrap.wrap(text, width=439)

        total_audio_parts = []
        for sentence in sentences:
            inputs = cached_tokenizer(sentence, return_tensors="pt")
            with torch.no_grad():
                audio_part = cached_net_g(
                    input_ids=inputs.input_ids,
                    attention_mask=inputs.attention_mask,
                    lang_code=lang_code,
                )[0, :]
            total_audio_parts.append(audio_part)

        x = torch.cat(total_audio_parts).cpu().numpy()






    wavfile = '_vits_.wav'
    audiofile.write(wavfile, x, fs)
    return wavfile  # 2x file for [audio out & state to pass to the Emotion reco tAB]







# TTS

VOICES = ['jv_ID_google-gmu_04982.wav', #y
        'en_US_vctk_p303.wav', #
        'en_US_vctk_p306.wav', #, 
        'en_US_vctk_p318.wav', # y
        'en_US_vctk_p269.wav', #y
        'en_US_vctk_p316.wav', #y
        'en_US_vctk_p362.wav', #y cls
        'fr_FR_tom.wav', #y 
        'bn_multi_5958.wav', #y         
        'en_US_vctk_p287.wav', #y
        'en_US_vctk_p260.wav', #y cl
        'en_US_cmu_arctic_fem.wav', #t 
        'en_US_cmu_arctic_rms.wav', #t
        'fr_FR_m-ailabs_nadine_eckert_boulet.wav', #
        'en_US_vctk_p237.wav', #y
        'en_US_vctk_p317.wav',# 
        'tn_ZA_google-nwu_0378.wav',#y 
        'nl_pmk.wav',#fixst
        'tn_ZA_google-nwu_3342.wav',# 
        'ne_NP_ne-google_3997.wav', #
        'tn_ZA_google-nwu_8914.wav', #t
        'en_US_vctk_p238.wav', # y
        'en_US_vctk_p275.wav', # y
        'af_ZA_google-nwu_0184.wav',#
        'af_ZA_google-nwu_8148.wav',#y 
        'en_US_vctk_p326.wav', #t
        'en_US_vctk_p264.wav', #y
        'en_US_vctk_p295.wav', #
        'en_US_vctk_p294.wav', # 
        'en_US_vctk_p330.wav', #y
        'gu_IN_cmu-indic_cmu_indic_guj_ad.wav',#y 
        'jv_ID_google-gmu_05219.wav',#y 
        'en_US_vctk_p284.wav',#y
        'en_US_m-ailabs_mary_ann.wav', 
        'bn_multi_01701.wav',#y 
        'en_US_vctk_p262.wav',#y
        'en_US_vctk_p243.wav', #y
        'en_US_vctk_p278.wav', #y
        'en_US_vctk_p250.wav', #y cl
        'nl_femal.wav', #y
        'en_US_vctk_p228.wav', #y
        'ne_NP_ne-google_0649.wav',# 
        'en_US_cmu_arctic_gka.wav',#y
        'en_US_vctk_p361.wav', #y
        'jv_ID_google-gmu_02326.wav', #y 
        'tn_ZA_google-nwu_1932.wav', #y
        'de_DE_thorsten-emotion_amused.wav', #y
        'jv_ID_google-gmu_08002.wav', #y
        'tn_ZA_google-nwu_3629.wav',#y 
        'en_US_vctk_p230.wav', #y
        'af_ZA_google-nwu_7214.wav', #y
        'nl_nathalie.wav', #
        'en_US_cmu_arctic_lnh.wav',#y 
        'tn_ZA_google-nwu_6459.wav',  #y
        'tn_ZA_google-nwu_6206.wav', 
        'en_US_vctk_p323.wav', #y clips
        'en_US_m-ailabs_judy_bieber.wav',#y
        'en_US_vctk_p261.wav', #y
        'fa_haaniye.wav', #y
        # 'en_US_vctk_p339.wav', 
        'tn_ZA_google-nwu_7896.wav',#y 
        'en_US_vctk_p258.wav', #y clps
        'tn_ZA_google-nwu_7674.wav', #y        
        'en_US_hifi-tts_6097.wav', #y
        'en_US_vctk_p304.wav', #y clps
        'en_US_vctk_p307.wav', #y
        'fr_FR_m-ailabs_bernard.wav', #y
        'en_US_cmu_arctic_jmk.wav', #y
        'ne_NP_ne-google_0283.wav', #
        'en_US_vctk_p246.wav', #y
        'en_US_vctk_p276.wav', # y
        'style_o22050.wav', #y
        'en_US_vctk_s5.wav', #y
        'en_US_vctk_p268.wav', #y reduce clip
        'af_ZA_google-nwu_8924.wav', #y
        'en_US_vctk_p363.wav', #y
        # 'it_IT_mls_644.wav', 
        'ne_NP_ne-google_3614.wav', #
        'ne_NP_ne-google_3154.wav', #
        'en_US_cmu_arctic_eey.wav', # y fix styl
        'tn_ZA_google-nwu_2839.wav', # y
        'af_ZA_google-nwu_7130.wav', #
        'ne_NP_ne-google_2139.wav', #y
        'jv_ID_google-gmu_04715.wav', # 
        'en_US_vctk_p273.wav' #
        ]
VOICES = [t[:-4] for t in VOICES]  # crop .wav for visuals in gr.DropDown

_tts = StyleTTS2().to('cpu')
with gr.Blocks() as demo:
    with gr.Tabs() as tabs:
        with gr.Tab("TTS"):
            with gr.Column():
                text_input = gr.Textbox(
                    label="Type text for TTS:",
                    placeholder="Accepts Latin / Cyrillic / Greek",
                    lines=4,
                    value='Η γρηγορη καφετι αλεπου πειδαει πανω απο τον τεμπελη σκυλο.')
                choice_dropdown = gr.Dropdown(
                    choices=language_names + VOICES,
                    label="Vox",
                    value=language_names[0])
                generate_button = gr.Button("Produce Audio", variant="primary")
                output_audio = gr.Audio(label=".wav File")
            generate_button.click(
                fn=audionar_tts,
                inputs=[text_input, choice_dropdown],
                outputs=[output_audio])

        with gr.Tab(label="Videos"):
           gr.Markdown('''<a href="https://huggingface.co/dkounadis/artificial-styletts2">Full Code and Videos</a>''')

demo.launch(debug=True)