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import logging |
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logging.getLogger('numba').setLevel(logging.WARNING) |
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logging.getLogger('matplotlib').setLevel(logging.WARNING) |
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logging.getLogger('urllib3').setLevel(logging.WARNING) |
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import json |
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import re |
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import numpy as np |
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import IPython.display as ipd |
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import torch |
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import commons |
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import utils |
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from models import SynthesizerTrn |
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from text.symbols import symbols |
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from text import text_to_sequence |
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import gradio as gr |
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import time |
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import datetime |
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import os |
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import pickle |
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import openai |
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from scipy.io.wavfile import write |
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import librosa |
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import romajitable |
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from mel_processing import spectrogram_torch |
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def is_japanese(string): |
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for ch in string: |
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if ord(ch) > 0x3040 and ord(ch) < 0x30FF: |
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return True |
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return False |
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|
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def is_english(string): |
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import re |
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pattern = re.compile('^[A-Za-z0-9.,:;!?()_*"\' ]+$') |
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if pattern.fullmatch(string): |
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return True |
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else: |
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return False |
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|
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def extrac(text): |
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text = re.sub("<[^>]*>","",text) |
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result_list = re.split(r'\n', text) |
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final_list = [] |
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for i in result_list: |
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if is_english(i): |
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i = romajitable.to_kana(i).katakana |
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i = i.replace('\n','').replace(' ','') |
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|
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if len(i)>1: |
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if len(i) > 50: |
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try: |
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cur_list = re.split(r'。|！', i) |
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for i in cur_list: |
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if len(i)>1: |
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final_list.append(i+'。') |
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except: |
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pass |
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else: |
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final_list.append(i) |
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''' |
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final_list.append(i) |
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''' |
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final_list = [x for x in final_list if x != ''] |
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print(final_list) |
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return final_list |
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|
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def to_numpy(tensor: torch.Tensor): |
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return tensor.detach().cpu().numpy() if tensor.requires_grad \ |
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else tensor.detach().numpy() |
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|
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def chatgpt(text): |
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messages = [] |
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try: |
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if text != 'exist': |
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with open('log.pickle', 'rb') as f: |
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messages = pickle.load(f) |
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messages.append({"role": "user", "content": text},) |
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chat = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages) |
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reply = chat.choices[0].message.content |
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messages.append({"role": "assistant", "content": reply}) |
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print(messages[-1]) |
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if len(messages) == 12: |
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messages[6:10] = messages[8:] |
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del messages[-2:] |
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with open('log.pickle', 'wb') as f: |
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pickle.dump(messages, f) |
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return reply |
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except: |
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messages.append({"role": "user", "content": text},) |
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chat = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages) |
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reply = chat.choices[0].message.content |
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messages.append({"role": "assistant", "content": reply}) |
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print(messages[-1]) |
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if len(messages) == 12: |
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messages[6:10] = messages[8:] |
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del messages[-2:] |
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with open('log.pickle', 'wb') as f: |
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pickle.dump(messages, f) |
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return reply |
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|
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def get_symbols_from_json(path): |
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assert os.path.isfile(path) |
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with open(path, 'r') as f: |
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data = json.load(f) |
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return data['symbols'] |
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|
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def sle(language,text): |
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text = text.replace('\n', '').replace('\r', '').replace(" ", "") |
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if language == "中文": |
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tts_input1 = "[ZH]" + text + "[ZH]" |
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return tts_input1 |
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elif language == "自动": |
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tts_input1 = f"[JA]{text}[JA]" if is_japanese(text) else f"[ZH]{text}[ZH]" |
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return tts_input1 |
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elif language == "日文": |
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tts_input1 = "[JA]" + text + "[JA]" |
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return tts_input1 |
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elif language == "英文": |
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tts_input1 = "[EN]" + text + "[EN]" |
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return tts_input1 |
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elif language == "手动": |
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return text |
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|
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def get_text(text,hps_ms): |
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text_norm = text_to_sequence(text,hps_ms.symbols,hps_ms.data.text_cleaners) |
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if hps_ms.data.add_blank: |
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text_norm = commons.intersperse(text_norm, 0) |
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text_norm = torch.LongTensor(text_norm) |
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return text_norm |
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|
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def create_tts_fn(net_g,hps,speaker_id): |
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speaker_id = int(speaker_id) |
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def tts_fn(is_transfer,original_speaker, target_speaker,history,is_gpt,api_key,is_audio,audiopath,repeat_time,text, language, extract, n_scale= 0.667,n_scale_w = 0.8, l_scale = 1 ): |
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text = check_text(text) |
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repeat_time = int(repeat_time) |
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original_speaker_id = selection(original_speaker) |
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target_speaker_id = selection(target_speaker) |
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if is_gpt: |
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openai.api_key = api_key |
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text = chatgpt(text) |
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history[-1][1] = text |
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if not extract: |
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print(text) |
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t1 = time.time() |
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stn_tst = get_text(sle(language,text),hps) |
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with torch.no_grad(): |
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x_tst = stn_tst.unsqueeze(0).to(dev) |
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(dev) |
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sid = torch.LongTensor([speaker_id]).to(dev) |
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audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=n_scale, noise_scale_w=n_scale_w, length_scale=l_scale)[0][0,0].data.cpu().float().numpy() |
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t2 = time.time() |
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spending_time = "推理时间为："+str(t2-t1)+"s" |
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print(spending_time) |
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file_path = "subtitles.srt" |
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try: |
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write(audiopath + '.wav',22050,audio) |
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if is_audio: |
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for i in range(repeat_time): |
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cmd = 'ffmpeg -y -i ' + audiopath + '.wav' + ' -ar 44100 '+ audiopath.replace('temp','temp'+str(i)) |
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os.system(cmd) |
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except: |
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pass |
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return history,file_path,(hps.data.sampling_rate,audio) |
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else: |
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a = ['【','[','(','（'] |
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b = ['】',']',')','）'] |
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for i in a: |
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text = text.replace(i,'<') |
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for i in b: |
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text = text.replace(i,'>') |
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final_list = extrac(text.replace('“','').replace('”','')) |
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split_list = [] |
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while len(final_list) > 0: |
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split_list.append(final_list[:500]) |
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final_list = final_list[500:] |
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c0 = 0 |
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for lists in split_list: |
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audio_fin = [] |
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t = datetime.timedelta(seconds=0) |
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c = 0 |
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f1 = open(audiopath.replace('.wav',str(c0)+".srt"),'w',encoding='utf-8') |
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for sentence in lists: |
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try: |
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c +=1 |
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stn_tst = get_text(sle(language,sentence),hps) |
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with torch.no_grad(): |
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x_tst = stn_tst.unsqueeze(0).to(dev) |
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(dev) |
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sid = torch.LongTensor([original_speaker_id]).to(dev) |
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t1 = time.time() |
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audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=n_scale, noise_scale_w=n_scale_w, length_scale=l_scale)[0][0,0].data.cpu().float().numpy() |
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t2 = time.time() |
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spending_time = "第"+str(c)+"句的推理时间为："+str(t2-t1)+"s" |
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print(spending_time) |
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time_start = str(t).split(".")[0] + "," + str(t.microseconds)[:3] |
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last_time = datetime.timedelta(seconds=len(audio)/float(22050)) |
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t+=last_time |
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time_end = str(t).split(".")[0] + "," + str(t.microseconds)[:3] |
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print(time_end) |
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f1.write(str(c-1)+'\n'+time_start+' --> '+time_end+'\n'+sentence+'\n\n') |
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if is_transfer: |
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with torch.no_grad(): |
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y = torch.FloatTensor(audio) |
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y = y / max(-y.min(), y.max()) / 0.99 |
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y = y.to(dev) |
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y = y.unsqueeze(0) |
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spec = spectrogram_torch(y, hps.data.filter_length, |
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hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length, |
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center=False).to(dev) |
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spec_lengths = torch.LongTensor([spec.size(-1)]).to(dev) |
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sid_src = torch.LongTensor([original_speaker_id]).to(dev) |
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sid_tgt = torch.LongTensor([target_speaker_id]).to(dev) |
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audio = net_g.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][ |
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0, 0].data.cpu().float().numpy() |
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del y, spec, spec_lengths, sid_src, sid_tgt |
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audio_fin.append(audio) |
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except: |
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pass |
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write(audiopath.replace('.wav',str(c0)+'.wav'),22050,np.concatenate(audio_fin)) |
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c0 += 1 |
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file_path = audiopath.replace('.wav',str(c0)+".srt") |
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return history,file_path,(hps.data.sampling_rate, np.concatenate(audio_fin)) |
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return tts_fn |
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|
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def create_vc_fn(net_g,hps): |
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def vc_fn(text,language,n_scale,n_scale_w,l_scale,original_speaker, target_speaker, record_audio, upload_audio): |
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input_audio = record_audio if record_audio is not None else upload_audio |
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original_speaker_id = selection(original_speaker) |
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target_speaker_id = selection(target_speaker) |
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if input_audio is None: |
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stn_tst = get_text(sle(language,text),hps) |
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with torch.no_grad(): |
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x_tst = stn_tst.unsqueeze(0).to(dev) |
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(dev) |
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sid = torch.LongTensor([original_speaker_id]).to(dev) |
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audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=n_scale, noise_scale_w=n_scale_w, length_scale=l_scale)[0][0,0].data.cpu().float().numpy() |
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sampling_rate = hps.data.sampling_rate |
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else: |
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sampling_rate, audio = input_audio |
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audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) |
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if len(audio.shape) > 1: |
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audio = librosa.to_mono(audio.transpose(1, 0)) |
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if sampling_rate != hps.data.sampling_rate: |
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audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate) |
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with torch.no_grad(): |
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y = torch.FloatTensor(audio) |
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y = y / max(-y.min(), y.max()) / 0.99 |
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y = y.to(dev) |
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y = y.unsqueeze(0) |
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spec = spectrogram_torch(y, hps.data.filter_length, |
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hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length, |
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center=False).to(dev) |
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spec_lengths = torch.LongTensor([spec.size(-1)]).to(dev) |
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sid_src = torch.LongTensor([original_speaker_id]).to(dev) |
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sid_tgt = torch.LongTensor([target_speaker_id]).to(dev) |
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audio = net_g.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][ |
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0, 0].data.cpu().float().numpy() |
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del y, spec, spec_lengths, sid_src, sid_tgt |
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return "Success", (hps.data.sampling_rate, audio) |
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return vc_fn |
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|
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def bot(history,user_message): |
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return history + [[check_text(user_message), None]] |
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|
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def selection(speaker): |
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if speaker == "高咲侑": |
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spk = 0 |
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return spk |
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|
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elif speaker == "歩夢": |
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spk = 1 |
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return spk |
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|
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elif speaker == "かすみ": |
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spk = 2 |
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return spk |
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|
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elif speaker == "しずく": |
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spk = 3 |
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return spk |
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|
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elif speaker == "果林": |
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spk = 4 |
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return spk |
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|
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elif speaker == "愛": |
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spk = 5 |
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return spk |
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|
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elif speaker == "彼方": |
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spk = 6 |
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return spk |
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|
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elif speaker == "せつ菜": |
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spk = 7 |
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return spk |
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|
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elif speaker == "エマ": |
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spk = 8 |
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return spk |
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|
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elif speaker == "璃奈": |
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spk = 9 |
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return spk |
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|
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elif speaker == "栞子": |
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spk = 10 |
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return spk |
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|
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elif speaker == "ランジュ": |
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spk = 11 |
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return spk |
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|
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elif speaker == "ミア": |
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spk = 12 |
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return spk |
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|
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elif speaker == "派蒙": |
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spk = 16 |
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return spk |
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|
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elif speaker == "c1": |
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spk = 18 |
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return spk |
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|
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elif speaker == "c2": |
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spk = 19 |
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return spk |
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|
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elif speaker == "華恋": |
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spk = 21 |
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return spk |
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|
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elif speaker == "まひる": |
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spk = 22 |
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return spk |
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|
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elif speaker == "なな": |
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spk = 23 |
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return spk |
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|
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elif speaker == "クロディーヌ": |
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spk = 24 |
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return spk |
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|
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elif speaker == "ひかり": |
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spk = 25 |
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return spk |
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|
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elif speaker == "純那": |
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spk = 26 |
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return spk |
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|
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elif speaker == "香子": |
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spk = 27 |
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return spk |
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|
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elif speaker == "真矢": |
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spk = 28 |
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return spk |
|
|
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elif speaker == "双葉": |
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spk = 29 |
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return spk |
|
|
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elif speaker == "ミチル": |
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spk = 30 |
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return spk |
|
|
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elif speaker == "メイファン": |
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spk = 31 |
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return spk |
|
|
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elif speaker == "やちよ": |
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spk = 32 |
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return spk |
|
|
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elif speaker == "晶": |
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spk = 33 |
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return spk |
|
|
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elif speaker == "いちえ": |
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spk = 34 |
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return spk |
|
|
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elif speaker == "ゆゆ子": |
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spk = 35 |
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return spk |
|
|
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elif speaker == "塁": |
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spk = 36 |
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return spk |
|
|
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elif speaker == "珠緒": |
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spk = 37 |
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return spk |
|
|
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elif speaker == "あるる": |
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spk = 38 |
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return spk |
|
|
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elif speaker == "ララフィン": |
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spk = 39 |
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return spk |
|
|
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elif speaker == "美空": |
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spk = 40 |
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return spk |
|
|
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elif speaker == "静羽": |
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spk = 41 |
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return spk |
|
|
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else: |
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return 0 |
|
|
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def check_text(input): |
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if isinstance(input, str): |
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return input |
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else: |
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with open(input.name, "r", encoding="utf-8") as f: |
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return f.read() |
|
|
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if __name__ == '__main__': |
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hps = utils.get_hparams_from_file('checkpoints/tmp/config.json') |
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dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
|
models = [] |
|
schools_list = ["ShojoKageki-Nijigasaki","ShojoKageki","Nijigasaki"] |
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schools = [] |
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lan = ["中文","日文","自动","手动"] |
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with open("checkpoints/info.json", "r", encoding="utf-8") as f: |
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models_info = json.load(f) |
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for i in models_info: |
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checkpoint = models_info[i]["checkpoint"] |
|
phone_dict = { |
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symbol: i for i, symbol in enumerate(symbols) |
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} |
|
net_g = SynthesizerTrn( |
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len(symbols), |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
|
n_speakers=hps.data.n_speakers, |
|
**hps.model).to(dev) |
|
_ = net_g.eval() |
|
_ = utils.load_checkpoint(checkpoint, net_g) |
|
school = models_info[i] |
|
speakers = school["speakers"] |
|
content = [] |
|
for j in speakers: |
|
sid = int(speakers[j]['sid']) |
|
title = school |
|
example = speakers[j]['speech'] |
|
name = speakers[j]["name"] |
|
content.append((sid, name, title, example, create_tts_fn(net_g,hps,sid))) |
|
models.append(content) |
|
schools.append((i,create_vc_fn(net_g,hps))) |
|
with gr.Blocks() as app: |
|
with gr.Tabs(): |
|
for (i,vc_fn) in schools: |
|
with gr.TabItem(i): |
|
idols = ["派蒙"] |
|
for (sid, name, title, example, tts_fn) in models[schools_list.index(i)]: |
|
idols.append(name) |
|
with gr.TabItem(name): |
|
with gr.Column(): |
|
with gr.Row(): |
|
with gr.Row(): |
|
gr.Markdown( |
|
'<div align="center">' |
|
f'<img style="width:auto;height:400px;" src="file/image/{name}.png">' |
|
'</div>' |
|
) |
|
chatbot = gr.Chatbot() |
|
with gr.Row(): |
|
with gr.Column(scale=0.85): |
|
input1 = gr.TextArea(label="Text", value=example,lines = 1) |
|
with gr.Column(scale=0.15, min_width=0): |
|
btnVC = gr.Button("Send") |
|
output1 = gr.Audio(label="采样率22050") |
|
with gr.Accordion(label="Setting", open=False): |
|
input2 = gr.Dropdown(label="Language", choices=lan, value="自动", interactive=True) |
|
input4 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声比例(noise scale)，以控制情感", value=0.6) |
|
input5 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声偏差(noise scale w)，以控制音素长短", value=0.668) |
|
input6 = gr.Slider(minimum=0.1, maximum=10, label="duration", value=1) |
|
with gr.Accordion(label="Advanced Setting", open=False): |
|
audio_input3 = gr.Dropdown(label="重复次数", choices=list(range(101)), value='0', interactive=True) |
|
api_input1 = gr.Checkbox(value=False, label="接入chatgpt") |
|
api_input2 = gr.TextArea(label="api-key",lines=1,value = '懂得都懂') |
|
with gr.Accordion(label="Advanced Setting", open=False): |
|
output2 = gr.outputs.File(label="字幕文件：subtitles.srt") |
|
audio_input1 = gr.Checkbox(value=False, label="保存路径") |
|
audio_input2 = gr.TextArea(label="音频路径",lines=1,value = 'D:/path/to/live2d/sounds/temp.wav') |
|
input3 = gr.Checkbox(value=False, label="长句切割(小说合成)") |
|
inputxt = gr.File(label="Text") |
|
is_transfer = gr.Checkbox(value=False, label="是否声线转化") |
|
source_speaker = gr.Dropdown(choices=idols, value=name, label="source speaker") |
|
target_speaker = gr.Dropdown(choices=idols, value=name, label="target speaker") |
|
btnbook = gr.Button("小说合成") |
|
btnVC.click(bot, inputs = [chatbot,input1], outputs = [chatbot]).then( |
|
tts_fn, inputs=[is_transfer,source_speaker,target_speaker,chatbot,api_input1,api_input2,audio_input1,audio_input2,audio_input3,input1,input2,input3,input4,input5,input6], outputs=[chatbot,output2,output1] |
|
) |
|
btnbook.click(bot, inputs = [chatbot,inputxt], outputs = [chatbot]).then( |
|
tts_fn, inputs=[is_transfer,source_speaker,target_speaker,chatbot,api_input1,api_input2,audio_input1,audio_input2,audio_input3,inputxt,input2,input3,input4,input5,input6], outputs=[chatbot,output2,output1] |
|
) |
|
with gr.Tab("Voice Conversion(类似sovits)"): |
|
gr.Markdown(""" |
|
声线转化，使用模型中的说话人作为音源时效果更佳 |
|
""") |
|
with gr.Column(): |
|
with gr.Accordion(label="方法1:录制或上传声音,可进行歌声合成", open=False): |
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record_audio = gr.Audio(label="record your voice", source="microphone") |
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upload_audio = gr.Audio(label="or upload audio here", source="upload") |
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with gr.Accordion(label="方法2:由原说话人先进行tts后套娃，适用于合成中文等特殊场景", open=True): |
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text = gr.TextArea(label="Text", value='由源说话人进行语音转化',lines = 1) |
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language = gr.Dropdown(label="Language", choices=lan, value="自动", interactive=True) |
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n_scale = gr.Slider(minimum=0, maximum=1.0, label="更改噪声比例(noise scale)，以控制情感", value=0.6) |
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n_scale_w = gr.Slider(minimum=0, maximum=1.0, label="更改噪声偏差(noise scale w)，以控制音素长短", value=0.668) |
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l_scale = gr.Slider(minimum=0.1, maximum=10, label="duration", value=1.1) |
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source_speaker = gr.Dropdown(choices=idols, value=idols[-2], label="source speaker") |
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target_speaker = gr.Dropdown(choices=idols, value=idols[-3], label="target speaker") |
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with gr.Column(): |
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message_box = gr.Textbox(label="Message") |
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converted_audio = gr.Audio(label='converted audio') |
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btn = gr.Button("Convert!") |
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btn.click(vc_fn, inputs=[text,language,n_scale,n_scale_w,l_scale,source_speaker, target_speaker, record_audio, upload_audio], |
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outputs=[message_box, converted_audio]) |
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app.launch() |
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