import os import subprocess # 编译 monotonic_align def compile_monotonic_align(): # 检查是否已编译 if not os.path.exists("monotonic_align/monotonic_align/core.cpython-*.so"): print("正在编译 monotonic_align...") # 假设 monotonic_align 文件夹已存在 if not os.path.exists("monotonic_align"): raise FileNotFoundError("monotonic_align 文件夹未找到！请确保它存在于根目录中。") os.chdir("monotonic_align") os.makedirs("monotonic_align", exist_ok=True) # 创建 monotonic_align 子目录 subprocess.run(["python", "setup.py", "build_ext", "--inplace"], check=True) os.chdir("..") print("monotonic_align 编译成功！") else: print("monotonic_align 已编译，跳过...") # 在程序启动时编译 compile_monotonic_align() import gradio as gr import torch import numpy as np from scipy.io.wavfile import write import commons import utils from models import SynthesizerTrn from text.symbols import symbols from text import get_bert, cleaned_text_to_sequence from text.cleaner import clean_text from huggingface_hub import hf_hub_download, snapshot_download # 模型配置 MODEL_CONFIG = { "roberta": { "repo_id": "hfl/chinese-roberta-wwm-ext-large" }, "vits": { "repo_id": "guetLzy/BERT-ISTFT-VITS-Model", "files": ["G_1000.pth"] # 根据实际文件名调整 } } # 设备设置 device = "cuda" if torch.cuda.is_available() else "cpu" # 可用的模型选项 MODEL_OPTIONS = { "VITS_Model": "models/G_1000.pth", } def download_models(): """下载所有需要的模型文件""" os.makedirs("./bert/chinese-roberta-wwm-ext-large", exist_ok=True) # 创建 RoBERTa 模型存储目录 os.makedirs("./models", exist_ok=True) # 创建 VITS 模型存储目录 # 下载 RoBERTa 模型（所有文件） roberta_path = snapshot_download( repo_id=MODEL_CONFIG["roberta"]["repo_id"], local_dir="./bert/chinese-roberta-wwm-ext-large", resume_download=True # 支持断点续传 ) roberta_paths = {"repo_dir": roberta_path} # 返回整个文件夹路径 # 下载 VITS 模型（指定文件） vits_paths = {} for model_name, model_path in MODEL_OPTIONS.items(): path = hf_hub_download( repo_id=MODEL_CONFIG["vits"]["repo_id"], filename=os.path.basename(model_path), local_dir="./models", resume_download=True # 支持断点续传 ) vits_paths[model_name] = path return { "roberta": roberta_paths, "vits": vits_paths } # 在程序启动时下载模型 model_paths = download_models() # 加载配置和模型 hps = utils.get_hparams_from_file("configs/1.json") # 从配置文件加载超参数 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) # 初始化 SynthesizerTrn 模型并移到指定设备 _ = net_g.eval() # 设置模型为评估模式 # 加载下载的 VITS 模型权重 _ = utils.load_checkpoint(model_paths["vits"]["VITS_Model"], net_g, None) def get_text(text, hps, language_str="ZH"): """处理输入文本，生成语音所需的序列""" norm_text, phone, tone, word2ph = clean_text(text, language_str) # 清理文本 phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) # 转换为序列 if hps.data.add_blank: phone = commons.intersperse(phone, 0) # 在序列中插入空白 tone = commons.intersperse(tone, 0) language = commons.intersperse(language, 0) for i in range(len(word2ph)): word2ph[i] = word2ph[i] * 2 word2ph[0] += 1 if hps.data.use_bert: bert = get_bert(norm_text, word2ph, language_str, device) # 获取 BERT 特征 del word2ph assert bert.shape[-1] == len(phone) # 确保 BERT 特征长度与 phone 一致 if language_str == "ZH": bert = bert else: bert = torch.zeros(1024, len(phone)) # 非中文时使用零填充 else: bert = torch.zeros(1024, len(phone)) # 不使用 BERT 时填充零 phone = torch.LongTensor(phone) # 转换为张量 tone = torch.LongTensor(tone) language = torch.LongTensor(language) return bert, phone, tone, language def generate_audio(text, noise_scale=1.0, noise_scale_w=0.8, length_scale=1.0): """生成音频文件""" bert, phones, tones, language_id = get_text(text, hps) # 获取处理后的文本数据 with torch.no_grad(): # 不计算梯度 x_tst = phones.to(device).unsqueeze(0) # 输入序列 tones = tones.to(device).unsqueeze(0) # 音调 language_id = language_id.to(device).unsqueeze(0) # 语言标识 bert = bert.to(device).unsqueeze(0) # BERT 特征 x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) # 输入长度 sid = torch.LongTensor([hps.data.spk2id["SSB0005"]]).to(device) # 说话者 ID audio = ( net_g.infer( x_tst, x_tst_lengths, sid, tones, language_id, bert, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, )[0][0, 0] .data.cpu() # 将结果移到 CPU .float() .numpy() # 转换为 numpy 数组 ) output_path = "output.wav" # 输出音频文件路径 write(output_path, 22050, (audio * 32767.0).astype(np.int16)) # 保存为 WAV 文件 return output_path with gr.Blocks( title="BERT-ISTFT-VITS中文语音合成系统", theme="NoCrypt/miku" ) as interface: # 标题和描述 gr.Markdown("# BERT-ISTFT-VITS中文语音合成系统") gr.Markdown("输入中文文本并调整参数以生成语音。支持调整噪声和语速参数。") # 主布局：两列设计 with gr.Row(): # 左侧：输入区域 with gr.Column(scale=1): # 文本输入框 text_input = gr.Textbox( label="输入文本", value="桂林电子科技大学", placeholder="请输入中文文本...", lines=5, # 增加行数，便于输入长文本 ) # 参数调整分组 with gr.Group(): gr.Markdown("### 参数调整") # 分组标题 noise_scale = gr.Slider( minimum=0.1, maximum=1, step=0.1, value=0.667, label="噪声比例", info="控制生成音频的噪声水平" ) noise_scale_w = gr.Slider( minimum=0.1, maximum=1, step=0.1, value=1.0, label="噪声比例 W", info="控制音调的噪声影响" ) length_scale = gr.Slider( minimum=0.1, maximum=1, step=0.1, value=1.0, label="语速比例", info="调整语音的播放速度" ) # 右侧：输出区域 with gr.Column(scale=1): audio_output = gr.Audio( label="生成的音频", type="filepath", # 返回文件路径 interactive=False # 禁止用户编辑音频 ) # 生成按钮 generate_btn = gr.Button("生成语音", variant="primary") # 绑定生成函数 generate_btn.click( fn=generate_audio, inputs=[text_input, noise_scale, noise_scale_w, length_scale], outputs=audio_output ) # 启动界面 interface.launch()