MoeGoe.py

import sys
from torch import no_grad, LongTensor
import logging

logging.getLogger('numba').setLevel(logging.WARNING)

import commons
import utils
from models import SynthesizerTrn
from text import text_to_sequence
from mel_processing import spectrogram_torch

from scipy.io.wavfile import write

def get_text(text, hps):
    text_norm = text_to_sequence(text, hps_ms.symbols, hps.data.text_cleaners)
    if hps.data.add_blank:
        text_norm = commons.intersperse(text_norm, 0)
    text_norm = LongTensor(text_norm)
    return text_norm

def ask_if_continue():
    while True:
        answer = input('Continue? (y/n): ')
        if answer == 'y':
            break
        elif answer == 'n':
            sys.exit(0)

def print_speakers(speakers):
    print('ID\tSpeaker')
    for id, name in enumerate(speakers):
        print(str(id) + '\t' + name)

def get_speaker_id(message):
    speaker_id = input(message)
    try:
        speaker_id = int(speaker_id)
    except:
        print(str(speaker_id) + ' is not a valid ID!')
        sys.exit(1)
    return speaker_id

if __name__ == '__main__':
    model = input('Path of a VITS model: ')
    config = input('Path of a config file: ')
    try:
        hps_ms = utils.get_hparams_from_file(config)
        net_g_ms = SynthesizerTrn(
            len(hps_ms.symbols),
            hps_ms.data.filter_length // 2 + 1,
            hps_ms.train.segment_size // hps_ms.data.hop_length,
            n_speakers=hps_ms.data.n_speakers,
            **hps_ms.model)
        _ = net_g_ms.eval()
        _ = utils.load_checkpoint(model, net_g_ms, None)
    except:
        print('Failed to load!')
        sys.exit(1)

    while True:
        choice = input('TTS or VC? (t/v):')
        if choice == 't':
            text = input('Text to read: ')
            try:
                stn_tst = get_text(text, hps_ms)
            except:
                print('Invalid text!')
                sys.exit(1)
            
            print_speakers(hps_ms.speakers)
            speaker_id = get_speaker_id('Speaker ID: ')

            out_path = input('Path to save: ')

            try:
                with no_grad():
                    x_tst = stn_tst.unsqueeze(0)
                    x_tst_lengths = LongTensor([stn_tst.size(0)])
                    sid = LongTensor([speaker_id])
                    audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()
                write(out_path, hps_ms.data.sampling_rate, audio)
            except:
                print('Failed to generate!')
                sys.exit(1)
            
            print('Successfully saved!')
            ask_if_continue()
            
                
        elif choice == 'v':
            wav_path = input('Path of a WAV file (22050 Hz, 16 bits, 1 channel) to convert:\n')
            print_speakers(hps_ms.speakers)
            audio, sampling_rate = utils.load_wav_to_torch(wav_path)

            originnal_id = get_speaker_id('Original speaker ID: ')
            target_id = get_speaker_id('Target speaker ID: ')
            out_path = input('Path to save: ')

            y = audio / hps_ms.data.max_wav_value
            y = y.unsqueeze(0)

            spec = spectrogram_torch(y, hps_ms.data.filter_length,
                hps_ms.data.sampling_rate, hps_ms.data.hop_length, hps_ms.data.win_length,
                center=False)
            spec_lengths = LongTensor([spec.size(-1)])
            sid_src = LongTensor([originnal_id])

            try:
                with no_grad():
                    sid_tgt = LongTensor([target_id])
                    audio = net_g_ms.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][0,0].data.cpu().float().numpy()
                write(out_path, hps_ms.data.sampling_rate, audio)
            except:
                print('Failed to generate!')
                sys.exit(1)
                    
            print('Successfully saved!')
            ask_if_continue()