Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from scipy.io.wavfile import write
+import os
+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", "config.json"]  # 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)
+    os.makedirs("./models", exist_ok=True)
+
+    # 下载 RoBERTa 模型
+    roberta_paths = {}
+    for file in MODEL_CONFIG["roberta"]["files"]:
+        path = hf_hub_download(
+            repo_id=MODEL_CONFIG["roberta"]["repo_id"],
+            filename=file,
+            local_dir="./bert/chinese-roberta-wwm-ext-large",
+            resume_download=True
+        )
+        roberta_paths[file] = path
+
+    # 下载 VITS 模型
+    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
+        )
+        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")
+
+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()
+
+# 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"):
+    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
+    
+    if hps.data.use_bert:
+        bert = get_bert(norm_text, word2ph, language_str, device)
+        del word2ph
+        assert bert.shape[-1] == len(phone)
+        if language_str == "ZH":
+            bert = bert
+        else:
+            bert = torch.zeros(1024, len(phone))
+    else:
+        bert = torch.zeros(1024, len(phone))
+    
+    phone = torch.LongTensor(phone)
+    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):
+    bert, phones, tones, language_id = get_text(text, hps)
+    
+    with torch.no_grad():
+        x_tst = phones.to(device).unsqueeze(0)
+        tones = tones.to(device).unsqueeze(0)
+        language_id = language_id.to(device).unsqueeze(0)
+        bert = bert.to(device).unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
+        sid = torch.LongTensor([hps.data.spk2id["single"]]).to(device)
+        
+        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()
+            .float()
+            .numpy()
+        )
+    
+    output_path = "output.wav"
+    write(output_path, 22050, (audio * 32767.0).astype(np.int16))
+    return output_path
+
+# 创建 Gradio 界面
+interface = gr.Interface(
+    fn=generate_audio,
+    inputs=[
+        gr.Textbox(label="输入文本", placeholder="请输入中文文本..."),
+        gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=1.0, label="Noise Scale"),
+        gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=0.8, label="Noise Scale W"),
+        gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.0, label="Length Scale")
+    ],
+    outputs=gr.Audio(label="生成的音频"),
+    title="中文文本转语音",
+    description="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。",
+    theme="default"
+)
+
+# 启动界面
+interface.launch()