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T4
| 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 | |
| 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 > 200: | |
| raise gr.Error("Text length limited to 200 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 | |
| # 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_v2_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="HierSpeech is a zero shot speech synthesis model, which can generate high-quality audio", info="Up to 200 characters"), | |
| gr.Audio(type='filepath', value="./example/3_rick_gt.wav"), | |
| gr.Slider(0,1,0.333), | |
| gr.Slider(0,1,0.333), | |
| gr.Slider(0,1,1.0), | |
| gr.Slider(0.5,2,1.0), | |
| gr.Slider(0,1,0), | |
| gr.Slider(0,9999,1111)], | |
| outputs = 'audio', | |
| title = 'HierSpeech++', | |
| description = '''<div> | |
| <p style="text-align: left"> HierSpeech++ is a zero-shot speech synthesis model.</p> | |
| <p style="text-align: left"> Our model is trained with LibriTTS dataset so this model only supports english. We will release a multi-lingual HierSpeech++ soon.</p> | |
| <p style="text-align: left"> <a href="https://sh-lee-prml.github.io/HierSpeechpp-demo/">[Demo Page]</a> <a href="https://github.com/sh-lee-prml/HierSpeechpp">[Source Code]</a></p> | |
| </div>''', | |
| examples=[["HierSpeech is a zero-shot speech synthesis model, which can generate high-quality audio", "./example/3_rick_gt.wav", 0.333,0.333, 1.0, 1.0, 0, 1111], | |
| ["HierSpeech is a zero-shot speech synthesis model, which can generate high-quality audio", "./example/ex01_whisper_00359.wav", 0.333,0.333, 1.0, 1.0, 0, 1111], | |
| ["Hi there, I'm your new voice clone. Try your best to upload quality audio", "./example/female.wav", 0.333,0.333, 1.0, 1.0, 0, 1111], | |
| ["Hello I'm HierSpeech++", "./example/reference_1.wav", 0.333,0.333, 1.0, 1.0, 0, 1111], | |
| ] | |
| ) | |
| demo_play.launch() | |
| if __name__ == '__main__': | |
| main() |