DL-Art-School/codes/scripts/audio/gen/use_diffuse_tts.py

import argparse
import os

import torch
import torchaudio

from data.audio.unsupervised_audio_dataset import load_audio
from scripts.audio.gen.speech_synthesis_utils import do_spectrogram_diffusion, \
    load_discrete_vocoder_diffuser, wav_to_mel, convert_mel_to_codes
from utils.audio import plot_spectrogram
from utils.util import load_model_from_config


def ceil_multiple(base, multiple):
    res = base % multiple
    if res == 0:
        return base
    return base + (multiple - res)


if __name__ == '__main__':
    conditioning_clips = {
        # Male
        'simmons': 'Y:\\clips\\books1\\754_Dan Simmons - The Rise Of Endymion 356 of 450\\00026.wav',
        'carlin': 'Y:\\clips\\books1\\12_dchha13 Bubonic Nukes\\00097.wav',
        'entangled': 'Y:\\clips\\books1\\3857_25_The_Entangled_Bank__000000000\\00123.wav',
        'snowden': 'Y:\\clips\\books1\\7658_Edward_Snowden_-_Permanent_Record__000000004\\00027.wav',
        # Female
        'the_doctor': 'Y:\\clips\\books2\\37062___The_Doctor__000000003\\00206.wav',
        'puppy': 'Y:\\clips\\books2\\17830___3_Puppy_Kisses__000000002\\00046.wav',
        'adrift': 'Y:\\clips\\books2\\5608_Gear__W_Michael_-_Donovan_1-5_(2018-2021)_(book_4_Gear__W_Michael_-_Donovan_5_-_Adrift_(2021)_Gear__W_Michael_-_Adrift_(Donovan_5)_—_82__000000000\\00019.wav',
    }

    parser = argparse.ArgumentParser()
    parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium.yml')
    parser.add_argument('-diffusion_model_name', type=str, help='Name of the diffusion model in opt.', default='generator')
    parser.add_argument('-diffusion_model_path', type=str, help='Path to saved model weights', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium\\models\\14500_generator_ema.pth')
    parser.add_argument('-aligned_codes', type=str, help='Comma-delimited list of integer codes that defines text & prosody. Get this by apply W2V to an existing audio clip or from a bespoke generator.',
                        default='0,0,0,0,10,10,0,4,0,7,0,17,4,4,0,25,5,0,13,13,0,22,4,4,0,21,15,15,7,0,0,14,4,4,6,8,4,4,0,0,12,5,0,0,5,0,4,4,22,22,8,16,16,0,4,4,4,0,0,0,0,0,0,0')  # Default: 'i am very glad to see you', libritts/train-clean-100/103/1241/103_1241_000017_000001.wav.
    # -cond "Y:\libritts/train-clean-100/103/1241/103_1241_000017_000001.wav"
    parser.add_argument('-cond', type=str, help='Type of conditioning voice', default='adrift')
    parser.add_argument('-diffusion_steps', type=int, help='Number of diffusion steps to perform to create the generate. Lower steps reduces quality, but >40 is generally pretty good.', default=100)
    parser.add_argument('-diffusion_schedule', type=str, help='Type of diffusion schedule that was used', default='cosine')
    parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='../results/use_diffuse_tts')
    parser.add_argument('-sample_rate', type=int, help='Model sample rate', default=5500)
    parser.add_argument('-cond_sample_rate', type=int, help='Conditioning sample rate', default=5500)
    parser.add_argument('-device', type=str, help='Device to run on', default='cuda')
    args = parser.parse_args()
    os.makedirs(args.output_path, exist_ok=True)

    print("Loading Diffusion Model..")
    diffusion = load_model_from_config(args.opt, args.diffusion_model_name, also_load_savepoint=False,
                                       load_path=args.diffusion_model_path, device=args.device)
    aligned_codes_compression_factor = args.sample_rate * 221 // 11025

    print("Loading data..")
    aligned_codes = torch.tensor([int(s) for s in args.aligned_codes.split(',')]).to(args.device)
    diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=args.diffusion_steps, schedule=args.diffusion_schedule)
    cond = load_audio(conditioning_clips[args.cond], args.cond_sample_rate).to(args.device)
    if cond.shape[-1] > 88000:
        cond = cond[:,:88000]

    with torch.no_grad():
        print("Performing inference..")
        diffusion.eval()
        output_shape = (1, 1, ceil_multiple(aligned_codes.shape[-1]*aligned_codes_compression_factor, 2048))

        output = diffuser.p_sample_loop(diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device),
                                        model_kwargs={'tokens': aligned_codes.unsqueeze(0),
                                        'conditioning_input': cond.unsqueeze(0)})
        torchaudio.save(os.path.join(args.output_path, f'output_mean.wav'), output.cpu().squeeze(0), args.sample_rate)

        for k in range(5):
            output = diffuser.p_sample_loop(diffusion, output_shape, model_kwargs={'tokens': aligned_codes.unsqueeze(0),
                                                                                   'conditioning_input': cond.unsqueeze(0)})

            torchaudio.save(os.path.join(args.output_path, f'output_{k}.wav'), output.cpu().squeeze(0), args.sample_rate)