app.py

import os
import torch
import argparse
import numpy as np
from scipy.io.wavfile import write
import torchaudio
import utils
from Mels_preprocess import MelSpectrogramFixed

from hierspeechpp_speechsynthesizer import (
    SynthesizerTrn
)
from ttv_v1.text import text_to_sequence
from ttv_v1.t2w2v_transformer import SynthesizerTrn as Text2W2V
from speechsr24k.speechsr import SynthesizerTrn as SpeechSR24
from speechsr48k.speechsr import SynthesizerTrn as SpeechSR48
from denoiser.generator import MPNet
from denoiser.infer import denoise

import gradio as gr

css = """
body {
    font-family: 'Arial', sans-serif;
}
footer {
    visibility: hidden;
}
/* 여기에 추가적인 CSS 스타일을 정의할 수 있습니다. */
"""

def load_text(fp):
    with open(fp, 'r') as f:
        filelist = [line.strip() for line in f.readlines()]
    return filelist
def load_checkpoint(filepath, device):
    print(filepath)
    assert os.path.isfile(filepath)
    print("Loading '{}'".format(filepath))
    checkpoint_dict = torch.load(filepath, map_location=device)
    print("Complete.")
    return checkpoint_dict
def get_param_num(model):
    num_param = sum(param.numel() for param in model.parameters())
    return num_param
def intersperse(lst, item):
  result = [item] * (len(lst) * 2 + 1)
  result[1::2] = lst
  return result
def add_blank_token(text):

    text_norm = intersperse(text, 0)
    text_norm = torch.LongTensor(text_norm)
    return text_norm

def tts(text, 
        prompt, 
        ttv_temperature, 
        vc_temperature, 
        duratuion_temperature, 
        duratuion_length, 
        denoise_ratio, 
        random_seed):
    
    torch.manual_seed(random_seed)
    torch.cuda.manual_seed(random_seed)
    np.random.seed(random_seed)

    text_len = len(text)
    if text_len > 400:
        raise gr.Error("Text length limited to 400 characters for this demo. Current text length is " + str(text_len))
       
    else:
        text = text_to_sequence(str(text), ["english_cleaners2"])
        
        token = add_blank_token(text).unsqueeze(0).cuda()
        token_length = torch.LongTensor([token.size(-1)]).cuda() 

        # Prompt load
        # sample_rate, audio = prompt
        # audio = torch.FloatTensor([audio]).cuda()
        # if audio.shape[0] != 1:
        #     audio = audio[:1,:] 
        # audio = audio / 32768 
        audio, sample_rate = torchaudio.load(prompt)

        # support only single channel
        if audio.shape[0] != 1:
            audio = audio[:1,:] 
        # Resampling
        if sample_rate != 16000:
            audio = torchaudio.functional.resample(audio, sample_rate, 16000, resampling_method="kaiser_window") 

        # We utilize a hop size of 320 but denoiser uses a hop size of 400 so we utilize a hop size of 1600
        ori_prompt_len = audio.shape[-1]
        p = (ori_prompt_len // 1600 + 1) * 1600 - ori_prompt_len
        audio = torch.nn.functional.pad(audio, (0, p), mode='constant').data

        # If you have a memory issue during denosing the prompt, try to denoise the prompt with cpu before TTS 
        # We will have a plan to replace a memory-efficient denoiser 
        if denoise == 0:
            audio = torch.cat([audio.cuda(), audio.cuda()], dim=0)
        else:
            with torch.no_grad():
                
                if ori_prompt_len > 80000:
                    denoised_audio = []
                    for i in range((ori_prompt_len//80000)):
                        denoised_audio.append(denoise(audio.squeeze(0).cuda()[i*80000:(i+1)*80000], denoiser, hps_denoiser))
                    
                    denoised_audio.append(denoise(audio.squeeze(0).cuda()[(i+1)*80000:], denoiser, hps_denoiser))
                    denoised_audio = torch.cat(denoised_audio, dim=1)
                else:
                    denoised_audio = denoise(audio.squeeze(0).cuda(), denoiser, hps_denoiser)

            audio = torch.cat([audio.cuda(), denoised_audio[:,:audio.shape[-1]]], dim=0)

        audio = audio[:,:ori_prompt_len]  # 20231108 We found that large size of padding decreases a performance so we remove the paddings after denosing.

        if audio.shape[-1]<48000:
            audio = torch.cat([audio,audio,audio,audio,audio], dim=1)

        src_mel = mel_fn(audio.cuda())

        src_length = torch.LongTensor([src_mel.size(2)]).to(device)
        src_length2 = torch.cat([src_length,src_length], dim=0)

        ## TTV (Text --> W2V, F0)
        with torch.no_grad():
            w2v_x, pitch = text2w2v.infer_noise_control(token, token_length, src_mel, src_length2, 
                                                        noise_scale=ttv_temperature, noise_scale_w=duratuion_temperature, 
                                                        length_scale=duratuion_length, denoise_ratio=denoise_ratio)
            src_length = torch.LongTensor([w2v_x.size(2)]).cuda()  
        
            pitch[pitch<torch.log(torch.tensor([55]).cuda())]  = 0

            ## Hierarchical Speech Synthesizer (W2V, F0 --> 16k Audio)
            converted_audio = \
                net_g.voice_conversion_noise_control(w2v_x, src_length, src_mel, src_length2, pitch, noise_scale=vc_temperature, denoise_ratio=denoise_ratio)
    
            converted_audio = speechsr(converted_audio)

        converted_audio = converted_audio.squeeze()

        converted_audio = converted_audio / (torch.abs(converted_audio).max()) * 32767.0 * 0.999 
        converted_audio = converted_audio.cpu().numpy().astype('int16')

        write('output.wav', 48000, converted_audio)
        return 'output.wav'

def main():
    print('Initializing Inference Process..')

    parser = argparse.ArgumentParser()
    parser.add_argument('--input_prompt', default='example/steve-jobs-2005.wav')
    parser.add_argument('--input_txt', default='example/abstract.txt')
    parser.add_argument('--output_dir', default='output')
    parser.add_argument('--ckpt', default='./logs/hierspeechpp_eng_kor/hierspeechpp_v1.1_ckpt.pth')
    parser.add_argument('--ckpt_text2w2v', '-ct', help='text2w2v checkpoint path', default='./logs/ttv_libritts_v1/ttv_lt960_ckpt.pth')
    parser.add_argument('--ckpt_sr', type=str, default='./speechsr24k/G_340000.pth')  
    parser.add_argument('--ckpt_sr48', type=str, default='./speechsr48k/G_100000.pth')  
    parser.add_argument('--denoiser_ckpt', type=str, default='denoiser/g_best')
    parser.add_argument('--scale_norm', type=str, default='max')
    parser.add_argument('--output_sr', type=float, default=48000)
    parser.add_argument('--noise_scale_ttv', type=float,
                        default=0.333)
    parser.add_argument('--noise_scale_vc', type=float,
                        default=0.333)
    parser.add_argument('--denoise_ratio', type=float,
                        default=0.8)
    parser.add_argument('--duration_ratio', type=float,
                        default=0.8)
    parser.add_argument('--seed', type=int,
                        default=1111)
    a = parser.parse_args()

    global device, hps, hps_t2w2v,h_sr,h_sr48, hps_denoiser
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    hps = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt)[0], 'config.json'))
    hps_t2w2v = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt_text2w2v)[0], 'config.json'))
    h_sr = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt_sr)[0], 'config.json') )
    h_sr48 = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt_sr48)[0], 'config.json') )
    hps_denoiser = utils.get_hparams_from_file(os.path.join(os.path.split(a.denoiser_ckpt)[0], 'config.json'))

    global mel_fn, net_g, text2w2v, speechsr, denoiser

    mel_fn = MelSpectrogramFixed(
        sample_rate=hps.data.sampling_rate,
        n_fft=hps.data.filter_length,
        win_length=hps.data.win_length,
        hop_length=hps.data.hop_length,
        f_min=hps.data.mel_fmin,
        f_max=hps.data.mel_fmax,
        n_mels=hps.data.n_mel_channels,
        window_fn=torch.hann_window
    ).cuda()  

    net_g = SynthesizerTrn(hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        **hps.model).cuda()
    net_g.load_state_dict(torch.load(a.ckpt))
    _ = net_g.eval()

    text2w2v = Text2W2V(hps.data.filter_length // 2 + 1,
    hps.train.segment_size // hps.data.hop_length,
    **hps_t2w2v.model).cuda()
    text2w2v.load_state_dict(torch.load(a.ckpt_text2w2v))
    text2w2v.eval()
  
    speechsr = SpeechSR48(h_sr48.data.n_mel_channels,
        h_sr48.train.segment_size // h_sr48.data.hop_length,
        **h_sr48.model).cuda()
    utils.load_checkpoint(a.ckpt_sr48, speechsr, None)
    speechsr.eval()
           
    denoiser = MPNet(hps_denoiser).cuda()
    state_dict = load_checkpoint(a.denoiser_ckpt, device)
    denoiser.load_state_dict(state_dict['generator'])
    denoiser.eval()


  
    demo_play = gr.Interface(
        fn=tts,
        inputs=[
            gr.Textbox(max_lines=6, label="Input Text", value="I am Taylor Swift. Be the change that you wish to see in the world", info="Up to 400 characters"), 
            gr.Audio(type='filepath', label="Input Audio", value="./example/TaylorSwift.wav"),
            gr.Slider(0, 1, 0.333, label="TTV Temperature"), 
            gr.Slider(0, 1, 0.333, label="VC Temperature"), 
            gr.Slider(0, 1, 1.0, label="Duration Temperature"), 
            gr.Slider(0.5, 2, 1.0, label="Duration Length"), 
            gr.Slider(0, 1, 0, label="Denoise Ratio"),
            gr.Slider(0, 9999, 1111, label="Random Seed")],  
        outputs='audio',
        title='ZeroShot Voice',
        description='''<div>
            <p style="text-align: left"> ZeroShot Voice is a 'Zero shot' speech synthesis model.</p>
        </div>''',
                 examples=[["I am Dasvader of Starwars. I am your Father. Be the change that you wish to see in the world", "./example/dasvader.wav", 0.333,0.333, 1.0, 1.0, 0, 1111],
                           ["I am Taylor Swift. Be the change that you wish to see in the world", "./example/TaylorSwift.wav", 0.333,0.667, 1.0, 1.0, 0, 1790],
                           ["I am Marlon Brando of God Father. Be the change that you wish to see in the world", "./example/MarlonBrando.wav", 0.333,0.333, 1.0, 1.0, 0, 1111],
                           ["I am Obama. Be the change that you wish to see in the world", "./example/obama.wav", 0.333,0.333, 1.0, 1.0, 0, 1111],
                           ["I am Trump. Be the change that you wish to see in the world", "./example/trump.wav", 0.333,0.333, 1.0, 1.0, 0, 1111]],
                 css=css
                )

    demo_play.launch()

if __name__ == '__main__':
    main()