app.py

import os
import gradio as gr
import random

os.system("pip install --upgrade Cython==0.29.35")
os.system("pip install pysptk --no-build-isolation")
os.system("pip install kantts -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html")
os.system("pip install librosa==0.9.2")
os.system("pip install numpy==1.22.0")

from modelscope.models.audio.tts import SambertHifigan
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

from voicefixer import VoiceFixer
voicefixer = VoiceFixer()

# model_0

model_dir = os.path.abspath("./pretrain_work_dir")

custom_infer_abs = {
    'voice_name':
    'F7',
    'am_ckpt':
    os.path.join(model_dir, 'tmp_am', 'ckpt'),
    'am_config':
    os.path.join(model_dir, 'tmp_am', 'config.yaml'),
    'voc_ckpt':
    os.path.join(model_dir, 'orig_model', 'basemodel_16k', 'hifigan', 'ckpt'),
    'voc_config':
    os.path.join(model_dir, 'orig_model', 'basemodel_16k', 'hifigan',
             'config.yaml'),
    'audio_config':
    os.path.join(model_dir, 'data', 'audio_config.yaml'),
    'se_file':
    os.path.join(model_dir, 'data', 'se', 'se.npy')
}
kwargs = {'custom_ckpt': custom_infer_abs}

model_id = SambertHifigan(os.path.join(model_dir, "orig_model"), **kwargs)

inference = pipeline(task=Tasks.text_to_speech, model=model_id)

# model_1

model_dir1 = os.path.abspath("./jay/pretrain_work_dir")

custom_infer_abs1 = {
    'voice_name':
    'F7',
    'am_ckpt':
    os.path.join(model_dir1, 'tmp_am', 'ckpt'),
    'am_config':
    os.path.join(model_dir1, 'tmp_am', 'config.yaml'),
    'voc_ckpt':
    os.path.join(model_dir1, 'orig_model', 'basemodel_16k', 'hifigan', 'ckpt'),
    'voc_config':
    os.path.join(model_dir1, 'orig_model', 'basemodel_16k', 'hifigan',
             'config.yaml'),
    'audio_config':
    os.path.join(model_dir1, 'data', 'audio_config.yaml'),
    'se_file':
    os.path.join(model_dir1, 'data', 'se', 'se.npy')
}
kwargs1 = {'custom_ckpt': custom_infer_abs1}

model_id1 = SambertHifigan(os.path.join(model_dir1, "orig_model"), **kwargs1)

inference1 = pipeline(task=Tasks.text_to_speech, model=model_id1)


# functions

def infer(text):
    output = inference(input=text)
    filename = str(random.randint(1, 1000000000000))
    
    with open(filename + "myfile.wav", mode='bx') as f:
        f.write(output["output_wav"])
    return filename + "myfile.wav"

def infer1(text):
    output = inference1(input=text)
    filename = str(random.randint(1, 1000000000000))
    
    with open(filename + "file.wav", mode='bx') as f:
        f.write(output["output_wav"])
    return filename + "file.wav"

# upsample

import numpy as np
import torch
from hifi_gan_bwe import BandwidthExtender
from scipy.io.wavfile import write

MAX_LENGTH = 600.0

model = BandwidthExtender.from_pretrained("hifi-gan-bwe-10-42890e3-vctk-48kHz")

def extend(audio):
    fs, x = audio
    x = x[:int(MAX_LENGTH * fs)]
    x = x.astype(np.float32) / 32767.0
    if len(x.shape) == 1:
        x = x[:, np.newaxis]

    with torch.no_grad():
        y = np.stack([model(torch.from_numpy(x), fs) for x in x.T]).T
        y = (y * 32767.0).astype(np.int16)
        fs = int(model.sample_rate)
        write("upsample.wav", fs, y)

    return "upsample.wav"

# denoise

def inference_denoise(audio):
    voicefixer.restore(input=audio, # input wav file path
                    output="output.wav", # output wav file path
                    cuda=False, # whether to use gpu acceleration
                    mode = int(0)) # You can try out mode 0, 1 to find out the best result
    return 'output.wav'


app = gr.Blocks()

with app:
    gr.Markdown("# <center>🥳🎶🎡 - KanTTS中文声音克隆</center>")
    gr.Markdown("## <center>🌊 - 更多精彩应用，敬请关注[xm火种堂](http://xmaigc.top)；社恐患者杨老师💕</center>")

    with gr.Row():
        with gr.Column():
            inp = gr.Textbox(lines=5, label="请填写您想要转换的中文文本")
            with gr.Row():
                btn = gr.Button("使用定制AI钟老师的声音", variant="primary")
                btn1 = gr.Button("使用AI周杰伦的声音", variant="primary")
        with gr.Column():
            with gr.Row():
                out = gr.Audio(label="为您生成的专属音频")
                out1 = gr.Audio(label="更高采样率的专属音频", type="filepath")
                out2 = gr.Audio(label="降噪后的高采样率音频", type="filepath")
            with gr.Row():
                btn2 = gr.Button("一键提高采样率")
                btn3 = gr.Button("一键降噪")
    
        btn.click(fn=infer, inputs=[inp], outputs=[out])
        btn1.click(fn=infer1, inputs=[inp], outputs=[out])
        btn2.click(fn=extend, inputs=[out], outputs=[out1])
        btn3.click(fn=inference_denoise, inputs=[out1], outputs=[out2])

    gr.Markdown("### <center>注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。</center>")
    gr.HTML('''
        <div class="footer">
                    <p>🌊🏞️🎶 - 雪夜围炉煎暖茶，泊船瓜洲宿酒家。 
                               - 此生不关风与月，执手仗剑闯天涯。 社恐患者杨老师
                    </p>
        </div>
    ''')
app.launch(show_error=True)