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 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="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 = '''

HierSpeech++ is a zero-shot speech synthesis model.

Our model is trained with LibriTTS dataset so this model only supports english. We will release a multi-lingual HierSpeech++ soon.

[Demo Page] [Source Code]

''', 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()