import sys, os if sys.platform == "darwin": os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" import logging logging.getLogger("numba").setLevel(logging.WARNING) logging.getLogger("markdown_it").setLevel(logging.WARNING) logging.getLogger("urllib3").setLevel(logging.WARNING) logging.getLogger("matplotlib").setLevel(logging.WARNING) logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s") logger = logging.getLogger(__name__) import torch import argparse import commons import utils from models import SynthesizerTrn from text.symbols import symbols from text import cleaned_text_to_sequence, get_bert from text.cleaner import clean_text import gradio as gr import webbrowser net_g = None def get_text(text, language_str, hps): norm_text, phone, tone, word2ph = clean_text(text, language_str) phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) if hps.data.add_blank: phone = commons.intersperse(phone, 0) tone = commons.intersperse(tone, 0) language = commons.intersperse(language, 0) for i in range(len(word2ph)): word2ph[i] = word2ph[i] * 2 word2ph[0] += 1 bert = get_bert(norm_text, word2ph, language_str) del word2ph assert bert.shape[-1] == len(phone) phone = torch.LongTensor(phone) tone = torch.LongTensor(tone) language = torch.LongTensor(language) return bert, phone, tone, language import soundfile as sf def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid): global net_g bert, phones, tones, lang_ids = get_text(text, "ZH", hps) with torch.no_grad(): x_tst=phones.to(device).unsqueeze(0) tones=tones.to(device).unsqueeze(0) lang_ids=lang_ids.to(device).unsqueeze(0) bert = bert.to(device).unsqueeze(0) x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) del phones speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy() del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers sf.write("tmp.wav", audio, 44100) return audio def convert_wav_to_ogg(wav_file): os.makedirs('out', exist_ok=True) filename = os.path.splitext(os.path.basename(wav_file.name))[0] output_path_ogg = os.path.join('out', f"out.ogg") wav_file.close() renamed_input_path = os.path.join('in', f"in.wav") os.makedirs('in', exist_ok=True) os.rename(wav_file.name, renamed_input_path) command = ["ffmpeg", "-i", renamed_input_path, "-acodec", "libopus", "-y", output_path_ogg] os.system(" ".join(command)) return output_path_ogg def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): with torch.no_grad(): audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker) with open('tmp.wav', 'rb') as wav_file: newogg = convert_wav_to_ogg(wav_file) return "Success", (hps.data.sampling_rate, audio),newogg import re from utils import play import numpy as np from scipy.io.wavfile import write from pydub import AudioSegment import os folder_name = "out" if not os.path.exists(folder_name): os.mkdir(folder_name) def longread(input_file,sdp_ratio, noise_scale, noise_scale_w, length_scale): with open(input_file.name, "r", encoding="utf-8") as file: content = file.read() directory = "out" file_list = os.listdir(directory) # Delete each file in the directory for file_name in file_list: file_path = os.path.join(directory, file_name) try: if os.path.isfile(file_path): os.remove(file_path) print(f"Deleted {file_path}") except Exception as e: print(f"Error deleting {file_path}: {e}") if len(content) >3000: content="文本过长，请缩短字数到三千字以内。" content = re.sub(r'\s+', ' ', content).strip() content = content.replace('......', '，') paragraphs = [] sentence_delimiters = r'[^。！？；]*[。！？；]' matches = re.finditer(sentence_delimiters, content) current_paragraph = '' for match in matches: sentence_with_delimiter = match.group(0) if len(current_paragraph) + len(sentence_with_delimiter) < 60: current_paragraph += sentence_with_delimiter elif len(current_paragraph) + len(sentence_with_delimiter) <= 110: current_paragraph += sentence_with_delimiter paragraphs.append(current_paragraph.strip()) current_paragraph = '' else: paragraphs.append(current_paragraph.strip()) current_paragraph = sentence_with_delimiter if current_paragraph: paragraphs.append(current_paragraph.strip()) output_audio = AudioSegment.empty() for cnt, paragraph in enumerate(paragraphs): text=paragraph audio_output = infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, "ign") output_filename = f"out/{cnt}.wav" write(output_filename, 44100, audio_output.astype(np.float32)) input_file = f"out/{cnt}.wav" pause_duration = 15 try: audio_segment = AudioSegment.from_wav(input_file) if len(output_audio) > 0: pause = AudioSegment.silent(duration=pause_duration) output_audio += pause output_audio += audio_segment except FileNotFoundError: continue output_file = "output.wav" output_audio.export(output_file, format="wav") return output_file if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--model_dir", default="./logs/ign/ign.pth", help="path of your model") parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file") parser.add_argument("--share", default=False, help="make link public") parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log") args = parser.parse_args() if args.debug: logger.info("Enable DEBUG-LEVEL log") logging.basicConfig(level=logging.DEBUG) hps = utils.get_hparams_from_file(args.config_dir) device = "cuda:0" if torch.cuda.is_available() else "cpu" ''' device = ( "cuda:0" if torch.cuda.is_available() else ( "mps" if sys.platform == "darwin" and torch.backends.mps.is_available() else "cpu" ) ) ''' net_g = SynthesizerTrn( len(symbols), hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, **hps.model).to(device) _ = net_g.eval() _ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True) speaker_ids = hps.data.spk2id speakers = list(speaker_ids.keys()) with gr.Blocks() as app: with gr.Row(): with gr.Column(): gr.Markdown(value=""" IGN 中国 长文本专用 Bert-Vits2在线语音生成\n 0、必看！！！重要！！！ 当显示“Warning There is a long queue of requests pending. Duplicate this Space to skip.”，或显示“queue”字样时，表示此时系统拥挤。请点击右上角 “Community” 旁的三个点，选择 “Duplicate this space” 然后确定，大约等待五分钟，就可将该空间克隆到自己的空间上。\n 1、请确保上传的txt文件是UTF-8格式（请自行百度如何txt保存为UTF-8格式）。由于CPU版本生成缓慢，每次上传文本字数请确保少于3000字。\n 2、模型作者：数字星瞳企划 https://t.me/xingtong25680 有问题请在telegram与我联系。 \n 3、原项目地址：https://github.com/Stardust-minus/Bert-VITS2\n 4、使用此模型进行二创请注明AI生成，以及该项目地址。\n 5、素材来自散文朗读比赛，严禁将此项目用于一切违反《中华人民共和国宪法》，《中华人民共和国刑法》，《中华人民共和国治安管理处罚法》和《中华人民共和国民法典》之用途。严禁用于任何政治相关用途。 \n """) # speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker') input_file=gr.inputs.File(label="在这里上传TXT文件") sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label='语调变化') noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.6, step=0.01, label='感情变化') noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.8, step=0.01, label='音节发音长度变化') length_scale = gr.Slider(minimum=0.1, maximum=2, value=0.97, step=0.01, label='语速') btn = gr.Button("开启AI语音之旅吧!", variant="primary") with gr.Column(): output_file=gr.outputs.File(label="下载生成的WAV文件") gr.Markdown(value=""" 模型汇总：\n 星瞳整合 https://huggingface.co/spaces/digitalxingtong/Xingtong-All-in-One\n 男声朗读 https://huggingface.co/spaces/digitalxingtong/Kanghui-Read-Bert-VITS2 \n 男声朗读（长文本） https://huggingface.co/spaces/digitalxingtong/Kanghui-Longread-Bert-VITS2\n IGN 中国 https://huggingface.co/spaces/digitalxingtong/Ign-Read-Bert-VITS2 \n IGN 中国（长文本）https://huggingface.co/spaces/digitalxingtong/Ign-Longread-Bert-VITS2 \n """) btn.click(longread, inputs=[input_file,sdp_ratio,noise_scale,noise_scale_w,length_scale], outputs=[output_file]) app.launch(show_error=True)