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import sys, os |
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if sys.platform == "darwin": |
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" |
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import logging |
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logging.getLogger("numba").setLevel(logging.WARNING) |
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logging.getLogger("markdown_it").setLevel(logging.WARNING) |
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logging.getLogger("urllib3").setLevel(logging.WARNING) |
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logging.getLogger("matplotlib").setLevel(logging.WARNING) |
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logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s") |
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logger = logging.getLogger(__name__) |
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import torch |
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import argparse |
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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 cleaned_text_to_sequence, get_bert |
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from text.cleaner import clean_text |
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import gradio as gr |
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import webbrowser |
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net_g = None |
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def get_text(text, language_str, hps): |
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norm_text, phone, tone, word2ph = clean_text(text, language_str) |
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phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) |
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if hps.data.add_blank: |
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phone = commons.intersperse(phone, 0) |
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tone = commons.intersperse(tone, 0) |
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language = commons.intersperse(language, 0) |
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for i in range(len(word2ph)): |
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word2ph[i] = word2ph[i] * 2 |
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word2ph[0] += 1 |
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bert = get_bert(norm_text, word2ph, language_str) |
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del word2ph |
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assert bert.shape[-1] == len(phone) |
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phone = torch.LongTensor(phone) |
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tone = torch.LongTensor(tone) |
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language = torch.LongTensor(language) |
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return bert, phone, tone, language |
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import soundfile as sf |
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def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid): |
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global net_g |
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bert, phones, tones, lang_ids = get_text(text, "ZH", hps) |
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with torch.no_grad(): |
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x_tst=phones.to(device).unsqueeze(0) |
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tones=tones.to(device).unsqueeze(0) |
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lang_ids=lang_ids.to(device).unsqueeze(0) |
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bert = bert.to(device).unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) |
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del phones |
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speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) |
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audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio |
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, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy() |
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del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers |
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sf.write("tmp.wav", audio, 44100) |
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return audio |
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def convert_wav_to_ogg(wav_file): |
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os.makedirs('out', exist_ok=True) |
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filename = os.path.splitext(os.path.basename(wav_file.name))[0] |
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output_path_ogg = os.path.join('out', f"out.ogg") |
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wav_file.close() |
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renamed_input_path = os.path.join('in', f"in.wav") |
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os.makedirs('in', exist_ok=True) |
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os.rename(wav_file.name, renamed_input_path) |
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command = ["ffmpeg", "-i", renamed_input_path, "-acodec", "libopus", "-y", output_path_ogg] |
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os.system(" ".join(command)) |
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return output_path_ogg |
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def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): |
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with torch.no_grad(): |
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audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker) |
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with open('tmp.wav', 'rb') as wav_file: |
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newogg = convert_wav_to_ogg(wav_file) |
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return "Success", (hps.data.sampling_rate, audio),newogg |
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import re |
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from utils import play |
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import numpy as np |
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from scipy.io.wavfile import write |
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from pydub import AudioSegment |
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import os |
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folder_name = "out" |
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if not os.path.exists(folder_name): |
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os.mkdir(folder_name) |
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def longread(input_file,sdp_ratio, noise_scale, noise_scale_w, length_scale): |
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with open(input_file.name, "r", encoding="utf-8") as file: |
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content = file.read() |
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directory = "out" |
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file_list = os.listdir(directory) |
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for file_name in file_list: |
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file_path = os.path.join(directory, file_name) |
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try: |
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if os.path.isfile(file_path): |
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os.remove(file_path) |
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print(f"Deleted {file_path}") |
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except Exception as e: |
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print(f"Error deleting {file_path}: {e}") |
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if len(content) >3000: |
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content="文本过长,请缩短字数到三千字以内。" |
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content = re.sub(r'\s+', ' ', content).strip() |
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content = content.replace('......', ',') |
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paragraphs = [] |
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sentence_delimiters = r'[^。!?;]*[。!?;]' |
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matches = re.finditer(sentence_delimiters, content) |
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current_paragraph = '' |
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for match in matches: |
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sentence_with_delimiter = match.group(0) |
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if len(current_paragraph) + len(sentence_with_delimiter) < 60: |
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current_paragraph += sentence_with_delimiter |
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elif len(current_paragraph) + len(sentence_with_delimiter) <= 110: |
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current_paragraph += sentence_with_delimiter |
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paragraphs.append(current_paragraph.strip()) |
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current_paragraph = '' |
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else: |
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paragraphs.append(current_paragraph.strip()) |
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current_paragraph = sentence_with_delimiter |
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if current_paragraph: |
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paragraphs.append(current_paragraph.strip()) |
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output_audio = AudioSegment.empty() |
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for cnt, paragraph in enumerate(paragraphs): |
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text=paragraph |
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audio_output = infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, "ign") |
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output_filename = f"out/{cnt}.wav" |
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write(output_filename, 44100, audio_output.astype(np.float32)) |
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input_file = f"out/{cnt}.wav" |
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pause_duration = 15 |
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try: |
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audio_segment = AudioSegment.from_wav(input_file) |
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if len(output_audio) > 0: |
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pause = AudioSegment.silent(duration=pause_duration) |
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output_audio += pause |
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output_audio += audio_segment |
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except FileNotFoundError: |
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continue |
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output_file = "output.wav" |
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output_audio.export(output_file, format="wav") |
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return output_file |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--model_dir", default="./logs/ign/ign.pth", help="path of your model") |
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parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file") |
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parser.add_argument("--share", default=False, help="make link public") |
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parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log") |
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args = parser.parse_args() |
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if args.debug: |
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logger.info("Enable DEBUG-LEVEL log") |
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logging.basicConfig(level=logging.DEBUG) |
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hps = utils.get_hparams_from_file(args.config_dir) |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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''' |
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device = ( |
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"cuda:0" |
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if torch.cuda.is_available() |
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else ( |
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"mps" |
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if sys.platform == "darwin" and torch.backends.mps.is_available() |
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else "cpu" |
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) |
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) |
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''' |
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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, |
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n_speakers=hps.data.n_speakers, |
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**hps.model).to(device) |
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_ = net_g.eval() |
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_ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True) |
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speaker_ids = hps.data.spk2id |
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speakers = list(speaker_ids.keys()) |
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with gr.Blocks() as app: |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown(value=""" |
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IGN 中国 长文本专用 Bert-Vits2在线语音生成\n |
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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 |
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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 |
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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 |
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3、原项目地址:https://github.com/Stardust-minus/Bert-VITS2\n |
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4、使用此模型进行二创请注明AI生成,以及该项目地址。\n |
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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 |
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""") |
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input_file=gr.inputs.File(label="在这里上传TXT文件") |
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sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label='语调变化') |
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noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.6, step=0.01, label='感情变化') |
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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') |
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length_scale = gr.Slider(minimum=0.1, maximum=2, value=0.97, step=0.01, label='语速') |
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btn = gr.Button("开启AI语音之旅吧!", variant="primary") |
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with gr.Column(): |
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output_file=gr.outputs.File(label="下载生成的WAV文件") |
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gr.Markdown(value=""" |
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模型汇总:\n |
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星瞳整合 https://huggingface.co/spaces/digitalxingtong/Xingtong-All-in-One\n |
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男声朗读 https://huggingface.co/spaces/digitalxingtong/Kanghui-Read-Bert-VITS2 \n |
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男声朗读(长文本) https://huggingface.co/spaces/digitalxingtong/Kanghui-Longread-Bert-VITS2\n |
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IGN 中国 https://huggingface.co/spaces/digitalxingtong/Ign-Read-Bert-VITS2 \n |
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IGN 中国(长文本)https://huggingface.co/spaces/digitalxingtong/Ign-Longread-Bert-VITS2 \n |
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""") |
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btn.click(longread, |
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inputs=[input_file,sdp_ratio,noise_scale,noise_scale_w,length_scale], |
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outputs=[output_file]) |
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app.launch(show_error=True) |