app.py

INTROTXT = """# 


**a Large Scale Model with a whole new pre-processing pipeline, added features and also some tweaks to the architecture is being trained right now.**


_______________________________________________________________________________________________________________________________________________________________

kudos to mrfakename for the base gradio code I'm borrowing here. 

**日本語用**

You will probably experience slight artifacts at the beginning or at the end of the output, which is not there on my server.

Unfortunately, due to the variation in how floating-point operations are performed across different devices,
and given the intrinsic characteristics of models that incorporate diffusion components,
it is unlikely that you will achieve identical results to those obtained on my server, where the model was originally trained. 
So, the output you're about to hear may not accurately reflect the true performance of the model.
it is also not limited to the artifacts, even the prosody and natural-ness of the speech is affected.


by [Soshyant](https://twitter.com/MystiqCaleid)

**NOTE: Punctuations are important!**


=========
音声の開始時または終了時に、もともと存在しなかったはずのアーティファクトが、ここで発生する可能性があります。
残念ながら、異なるデバイスで浮動小数点演算が異なる方法で行われるため、およびDiffusionコンポーネントを取り入れたモデルの固有の特性を考慮すると、
モデルが元々トレーニングされたデバイスで得られた結果と同じ結果を得ることは難しいでしょう。
その結果、以下で体験するパフォーマンスはモデルの真の性能を正確に反映していません。
そのため、アーティファクトの問題だけではなく、ナチュラルネスや音声クオリティーにも及びます。

**NOTE: 句読点はとても大事です!**

"""
import gradio as gr
import random
import styletts2importable
import ljspeechimportable
import torch
import os
from txtsplit import txtsplit
import numpy as np
import pickle
theme = gr.themes.Base(
    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
)

from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule

voicelist = ['1','2','3']
voices = {}
# import phonemizer
# global_phonemizer = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True,  with_stress=True)
# todo: cache computed style, load using pickle
# if os.path.exists('voices.pkl'):
    # with open('voices.pkl', 'rb') as f:
        # voices = pickle.load(f)
# else:
for v in voicelist:
    voices[v] = styletts2importable.compute_style(f'voices/{v}.wav')
# def synthesize(text, voice, multispeakersteps):
#     if text.strip() == "":
#         raise gr.Error("You must enter some text")
#     # if len(global_phonemizer.phonemize([text])) > 300:
#     if len(text) > 300:
#         raise gr.Error("Text must be under 300 characters")
#     v = voice.lower()
#     # return (24000, styletts2importable.inference(text, voices[v], alpha=0.3, beta=0.7, diffusion_steps=7, embedding_scale=1))
#     return (24000, styletts2importable.inference(text, voices[v], alpha=0.3, beta=0.7, diffusion_steps=multispeakersteps, embedding_scale=1))
if not torch.cuda.is_available(): INTROTXT += "\n\n### on CPU, it'll run rather slower, but not too much."
def synthesize(text, voice, lngsteps,embscale,alpha, beta, password, progress=gr.Progress()):
    if text.strip() == "":
        raise gr.Error("You must enter some text")
    if len(text) > 50000:
        raise gr.Error("Text must be <50k characters")
    print("*** saying ***")
    print(text)
    print("*** end ***")
    texts = txtsplit(text)
    v = voice.lower()
    audios = []
    for t in progress.tqdm(texts):
        print(t)
        audios.append(styletts2importable.inference(t, voices[v], alpha=alpha, beta=beta, diffusion_steps=lngsteps, embedding_scale=embscale))
    return (24000, np.concatenate(audios))
# def longsynthesize(text, voice, lngsteps, password, progress=gr.Progress()):
#     if password == os.environ['ACCESS_CODE']:
#         if text.strip() == "":
#             raise gr.Error("You must enter some text")
#         if lngsteps > 25:
#             raise gr.Error("Max 25 steps")
#         if lngsteps < 5:
#             raise gr.Error("Min 5 steps")
#         texts = split_and_recombine_text(text)
#         v = voice.lower()
#         audios = []
#         for t in progress.tqdm(texts):
#             audios.append(styletts2importable.inference(t, voices[v], alpha=0.3, beta=0.7, diffusion_steps=lngsteps, embedding_scale=1))
#         return (24000, np.concatenate(audios))
#     else:
#         raise gr.Error('Wrong access code')
    
def clsynthesize(text, voice, vcsteps, embscale, alpha, beta, progress=gr.Progress()):


    torch.manual_seed(0)
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True


    random.seed(0)

    # if text.strip() == "":
    #     raise gr.Error("You must enter some text")
    # # if global_phonemizer.phonemize([text]) > 300:
    # if len(text) > 400:
    #     raise gr.Error("Text must be under 400 characters")
    # # return (24000, styletts2importable.inference(text, styletts2importable.compute_style(voice), alpha=0.3, beta=0.7, diffusion_steps=20, embedding_scale=1))
    # return (24000, styletts2importable.inference(text, styletts2importable.compute_style(voice), alpha=0.3, beta=0.7, diffusion_steps=vcsteps, embedding_scale=1))
    if text.strip() == "":
        raise gr.Error("You must enter some text")
    if len(text) > 50000:
        raise gr.Error("Text must be <50k characters")
    if embscale > 1.3 and len(text) < 20:
        gr.Warning("WARNING: You entered short text, you may get static!")
    print("*** saying ***")
    print(text)
    print("*** end ***")
    texts = txtsplit(text)
    
    audios = []
    # vs = styletts2importable.compute_style(voice)
    
    # print(vs)
    for t in progress.tqdm(texts):
        audios.append(styletts2importable.inference(t, voices[v], alpha=alpha, beta=beta, diffusion_steps=vcsteps, embedding_scale=embscale))
        # audios.append(styletts2importable.inference(t, vs, diffusion_steps=10, alpha=0.3, beta=0.7, embedding_scale=5))
    return (24000, np.concatenate(audios))


    
def ljsynthesize(text, steps,embscale, progress=gr.Progress()):



    torch.manual_seed(0)
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True


    random.seed(0)


    
    # if text.strip() == "":
    #     raise gr.Error("You must enter some text")
    # # if global_phonemizer.phonemize([text]) > 300:
    # if len(text) > 400:
    #     raise gr.Error("Text must be under 400 characters")
    noise = torch.tanh(torch.randn(1,1,256).to('cuda' if torch.cuda.is_available() else 'cpu'))
    # return (24000, Text-guided Inferenceimportable.inference(text, noise, diffusion_steps=7, embedding_scale=1))
    if text.strip() == "":
        raise gr.Error("You must enter some text")
    if len(text) > 150000:
        raise gr.Error("Text must be <150k characters")
    print("*** saying ***")
    print(text)
    print("*** end ***")
    texts = txtsplit(text)
    audios = []
    for t in progress.tqdm(texts):
        audios.append(ljspeechimportable.inference(t, noise, diffusion_steps=steps, embedding_scale=embscale))
    return (24000, np.concatenate(audios))


# with gr.Blocks() as vctk:
#     with gr.Row():
#         with gr.Column(scale=1):
#             clinp = gr.Textbox(label="Text", info="Enter the text | テキストを入れてください、短すぎるとひどくなります",value="あなたがいないと、世界は色褪せて見えます。あなたの笑顔が私の日々を明るく照らしています。あなたがいない日は、まるで冬のように寒く、暗いです.", interactive=True)
#             voice = gr.Dropdown(voicelist, label="Voice", info="Select a default voice.", interactive=True)
#             vcsteps = gr.Slider(minimum=3, maximum=20, value=5, step=1, label="Diffusion Steps", info="You'll get more variation in the results if you increase it, doesn't necessarily improve anything.| これを上げたらもっとエモーショナルな音声になります（下げたらその逆）、増やしすぎるとだめになるので、ご注意ください", interactive=True)
#             embscale = gr.Slider(minimum=1, maximum=10, value=1.8, step=0.1, label="Embedding Scale (READ WARNING BELOW)", info="これを上げたらもっとエモーショナルな音声になります（下げたらその逆）、増やしすぎるとだめになるので、ご注意ください", interactive=True)
#             alpha = gr.Slider(minimum=0, maximum=1, value=0.3, step=0.1, label="Alpha", interactive=True)
#             beta = gr.Slider(minimum=0, maximum=1, value=0.4, step=0.1, label="Beta", interactive=True)
#         with gr.Column(scale=1):
#             clbtn = gr.Button("Synthesize", variant="primary")
#             claudio = gr.Audio(interactive=False, label="Synthesized Audio", waveform_options={'waveform_progress_color': '#3C82F6'})
#             clbtn.click(clsynthesize, inputs=[clinp, voice, vcsteps, embscale, alpha, beta], outputs=[claudio], concurrency_limit=4)
    
with gr.Blocks() as vctk:
    with gr.Row():
        with gr.Column(scale=1):
            inp = gr.Textbox(label="Text", info="Enter the text | テキストを入れてください、短すぎるとひどくなります.", value="長い間、皆さんとの繋がりは、私にとってかけがえのないものでした。言葉を交わし、考えを共有し、互いの世界を少しずつ理解してきたこの時間は、私の人生において非常に重要なものです.",  interactive=True)
            voice = gr.Dropdown(voicelist, label="Voice", info="Select a default voice.", value='3', interactive=True)
            embscale = gr.Slider(minimum=1, maximum=10, value=1, step=0.1, label="Embedding Scale (READ WARNING BELOW)", info="これを上げたらもっとエモーショナルな音声になります（下げたらその逆）、増やしすぎるとだめになるので、ご注意ください", interactive=True)
            alpha = gr.Slider(minimum=0, maximum=1, value=0.3, step=0.1, label="Alpha",info ="Closer to 0 means similar to the Audio Reference | ゼロに近いほど、音声サンプルのスタイルに近い音声になります", interactive=True)
            beta = gr.Slider(minimum=0, maximum=1, value=0.5, step=0.1, label="Beta",info ="alphaと同じく", interactive=True)
            multispeakersteps = gr.Slider(minimum=3, maximum=15, value=3, step=1, label="Diffusion Steps", interactive=True)
            # use_gruut = gr.Checkbox(label="Use alternate phonemizer (Gruut) - Experimental")
        with gr.Column(scale=1):
            btn = gr.Button("Synthesize", variant="primary")
            audio = gr.Audio(interactive=False, label="Synthesized Audio", waveform_options={'waveform_progress_color': '#3C82F6'})
            btn.click(synthesize, inputs=[inp, voice, multispeakersteps,embscale,alpha,beta], outputs=[audio], concurrency_limit=4)




    
# with gr.Blocks() as clone:
#     with gr.Row():
#         with gr.Column(scale=1):
#             clinp = gr.Textbox(label="Text", info="Enter the text | テキストを入れてください、短すぎるとひどくなります", interactive=True)
#             clvoice = gr.Audio(label="Voice", interactive=True, type='filepath', max_length=300, waveform_options={'waveform_progress_color': '#3C82F6'})
#             vcsteps = gr.Slider(minimum=3, maximum=10, value=2, step=1, label="Diffusion Steps", info="これを上げたらもっとエモーショナルな音声になります（下げたらその逆）、増やしすぎるとだめになるので、ご注意ください", interactive=True)
#             embscale = gr.Slider(minimum=1, maximum=10, value=1, step=0.1, label="Embedding Scale (READ WARNING BELOW)", info="Defaults to 1. WARNING: If you set this too high and generate text that's too short you will get static!", interactive=True)
#             alpha = gr.Slider(minimum=0, maximum=1, value=0.3, step=0.1, label="Alpha", info="Defaults to 0.3", interactive=True)
#             beta = gr.Slider(minimum=0, maximum=1, value=0.7, step=0.1, label="Beta", info="Defaults to 0.7", interactive=True)
#         with gr.Column(scale=1):
#             clbtn = gr.Button("Synthesize", variant="primary")
#             claudio = gr.Audio(interactive=False, label="Synthesized Audio", waveform_options={'waveform_progress_color': '#3C82F6'})
#             clbtn.click(clsynthesize, inputs=[clinp, clvoice, vcsteps, embscale, alpha, beta], outputs=[claudio], concurrency_limit=4)
# with gr.Blocks() as longText:
#     with gr.Row():
#         with gr.Column(scale=1):
#             lnginp = gr.Textbox(label="Text", info="What would you like StyleTTS 2 to read? It works better on full sentences.", interactive=True)
#             lngvoice = gr.Dropdown(voicelist, label="Voice", info="Select a default voice.", value='m-us-1', interactive=True)
#             lngsteps = gr.Slider(minimum=5, maximum=25, value=10, step=1, label="Diffusion Steps", info="Higher = better quality, but slower", interactive=True)
#             lngpwd = gr.Textbox(label="Access code", info="This feature is in beta. You need an access code to use it as it uses more resources and we would like to prevent abuse")
#         with gr.Column(scale=1):
#             lngbtn = gr.Button("Synthesize", variant="primary")
#             lngaudio = gr.Audio(interactive=False, label="Synthesized Audio")
#             lngbtn.click(longsynthesize, inputs=[lnginp, lngvoice, lngsteps, lngpwd], outputs=[lngaudio], concurrency_limit=4)
with gr.Blocks() as lj:
    with gr.Row():
        with gr.Column(scale=1):
            ljinp = gr.Textbox(label="Text", info="Enter the text | テキストを入れてください、短すぎるとひどくなります.", interactive=True, value="あなたがいないと、世界は色褪せて見えます。あなたの笑顔が私の日々を明るく照らしています。あなたがいない日は、まるで冬のように寒く、暗いです.")
            embscale = gr.Slider(minimum=1, maximum=3, value=1.1, step=0.1, label="Embedding Scale (READ WARNING BELOW)", info="これを上げたらもっとエモーショナルな音声になります（下げたらその逆）、増やしすぎるとだめになるので、ご注意ください(1から - 2までの範囲で最適。)", interactive=True)
            ljsteps = gr.Slider(minimum=3, maximum=20, value=5, step=1, label="Diffusion Steps", info="You'll get more variation in the results if you increase it, doesn't necessarily improve anything.| これを上げたらもっとエモーショナルな音声になります（下げたらその逆）、増やしすぎるとだめになるので、ご注意ください", interactive=True)
        with gr.Column(scale=1):
            ljbtn = gr.Button("Synthesize", variant="primary")
            ljaudio = gr.Audio(interactive=False, label="Synthesized Audio", waveform_options={'waveform_progress_color': '#3C82F6'})
            ljbtn.click(ljsynthesize, inputs=[ljinp, ljsteps, embscale], outputs=[ljaudio], concurrency_limit=4)
with gr.Blocks(title="StyleTTS 2", css="footer{display:none !important}", theme="NoCrypt/miku") as demo:
    gr.Markdown(INTROTXT)
    gr.DuplicateButton("Duplicate Space")
    # gr.TabbedInterface([vctk, clone, lj, longText], ['Multi-Voice', 'Voice Cloning', 'Text-guided Inference', 'Long Text [Beta]'])
    gr.TabbedInterface([lj, vctk], ['|Text-guided Inference|','With Reference Audio(Not Optimized)','Text-guided Inference', 'Long Text [Beta]'])
    gr.Markdown("""
the base code was borrowed from -> [mrfakename](https://twitter.com/realmrfakename). Neither of us are affiliated with the StyleTTS 2's authors.
""") # Please do not remove this line.
if __name__ == "__main__":
    # demo.queue(api_open=False, max_size=15).launch(show_api=False)
    demo.queue(api_open=False, max_size=15).launch(show_api=False)