from text.symbols import symbols from text.cleaner import clean_text from text import cleaned_text_to_sequence, get_bert from modelscope import snapshot_download from models import SynthesizerTrn from tqdm import tqdm import gradio as gr import numpy as np import commons import random import utils import torch import sys import re import 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__) net_g = None debug = False 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 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 return audio 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, ) return (hps.data.sampling_rate, audio) def text_splitter(text: str): punctuation = r"[。,；,！,？,〜,\n,\r,\t,.,!,;,?,~, ]" # 使用正则表达式根据标点符号分割文本，并忽略重叠的分隔符 sentences = re.split(punctuation, text.strip()) # 过滤掉空字符串 return [sentence.strip() for sentence in sentences if sentence.strip()] def concatenate_audios(audio_samples, sample_rate=44100): half_second_silence = np.zeros(int(sample_rate / 2)) # 初始化最终的音频数组 final_audio = audio_samples[0] # 遍历音频样本列表，并将它们连接起来，每个样本之间插入半秒钟的静音 for sample in audio_samples[1:]: final_audio = np.concatenate((final_audio, half_second_silence, sample)) print("Audio pieces concatenated!") return (sample_rate, final_audio) def read_text(file_path: str): try: # 打开文件并读取内容 with open(file_path, "r", encoding="utf-8") as file: content = file.read() return content except FileNotFoundError: print(f"文件未找到: {file_path}") except IOError: print(f"读取文件时发生错误: {file_path}") except Exception as e: print(f"发生未知错误: {e}") def infer_tab1(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): try: content = read_text(text) sentences = text_splitter(content) audios = [] for sentence in tqdm(sentences, desc="TTS inferring..."): with torch.no_grad(): audios.append( infer( sentence, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, ) ) return concatenate_audios(audios, hps.data.sampling_rate), content except Exception as e: return None, f"{e}" def infer_tab2(content, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): try: sentences = text_splitter(content) audios = [] for sentence in tqdm(sentences, desc="TTS inferring..."): with torch.no_grad(): audios.append( infer( sentence, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, ) ) return concatenate_audios(audios, hps.data.sampling_rate) except Exception as e: print(f"{e}") return None if __name__ == "__main__": model_dir = snapshot_download("Genius-Society/hoyoTTS", cache_dir="./__pycache__") if debug: logger.info("Enable DEBUG-LEVEL log") logging.basicConfig(level=logging.DEBUG) hps = utils.get_hparams_from_dir(model_dir) 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(f"{model_dir}/G_78000.pth", net_g, None, skip_optimizer=True) speaker_ids = hps.data.spk2id speakers = list(speaker_ids.keys()) random.shuffle(speakers) with gr.Blocks() as app: gr.Markdown( """
欢迎使用此创空间, 此创空间基于 Bert-vits2 开源项目制作，完全免费。使用此创空间必须遵守当地相关法律法规，禁止用其从事任何违法犯罪活动。首次推理需耗时下载模型，还请耐心等待。另外，移至最底端有原理浅讲。
""" ) with gr.Tab("输入模式"): gr.Interface( fn=infer_tab2, # 使用 text_to_speech 函数 inputs=[ gr.TextArea(label="请输入简体中文文案", show_copy_button=True), gr.Dropdown(choices=speakers, value="莱依拉", label="角色"), gr.Slider( minimum=0, maximum=1, value=0.2, step=0.1, label="语调调节" ), # SDP/DP混合比 gr.Slider( minimum=0.1, maximum=2, value=0.6, step=0.1, label="感情调节" ), gr.Slider( minimum=0.1, maximum=2, value=0.8, step=0.1, label="音素长度" ), gr.Slider( minimum=0.1, maximum=2, value=1, step=0.1, label="生成时长" ), ], outputs=gr.Audio(label="输出音频"), flagging_mode="never", concurrency_limit=4, ) with gr.Tab("上传模式"): gr.Interface( fn=infer_tab1, # 使用 text_to_speech 函数 inputs=[ gr.components.File( label="请上传简体中文TXT文案", type="filepath", file_types=[".txt"], ), gr.Dropdown(choices=speakers, value="莱依拉", label="角色"), gr.Slider( minimum=0, maximum=1, value=0.2, step=0.1, label="语调调节" ), # SDP/DP混合比 gr.Slider( minimum=0.1, maximum=2, value=0.6, step=0.1, label="感情调节" ), gr.Slider( minimum=0.1, maximum=2, value=0.8, step=0.1, label="音素长度" ), gr.Slider( minimum=0.1, maximum=2, value=1, step=0.1, label="生成时长" ), ], outputs=[ gr.Audio(label="输出音频"), gr.TextArea(label="文案提取结果", show_copy_button=True), ], flagging_mode="never", concurrency_limit=4, ) gr.HTML( """ """ ) app.launch()