Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
| 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() |