Delete app.py

app.py

@@ -1,183 +0,0 @@
-import os
-import torch
-import librosa
-import gradio as gr
-from scipy.io.wavfile import write
-from huggingface_hub import hf_hub_download, snapshot_download
-import utils
-from models import SynthesizerTrn
-from mel_processing import mel_spectrogram_torch
-from speaker_encoder.voice_encoder import SpeakerEncoder
-import logging
-from transformers import Wav2Vec2FeatureExtractor, HubertModel
-
-# 设置日志级别
-logging.getLogger('numba').setLevel(logging.WARNING)
-
-# 模型配置
-MODEL_CONFIG = {
-    "freevc": {
-        "repo_id": "guetLzy/Chinese-FreeVC-Model",  # FreeVC模型仓库
-        "files": ["G_17000.pth", "G_35000.pth"]  # 需要下载的模型文件
-    },
-    "hubert": {
-        "repo_id": "guetLzy/chinese-hubert-large-fariseq-ckpt",  # 中文HuBERT官方仓库
-    }
-}
-
-# 设备设置
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-# 可用的模型选项
-MODEL_OPTIONS = {
-    "Model_17000": "model/G_17000.pth",
-    "Model_35000": "model/G_35000.pth",  # 新增 G_35000.pth 选项
-}
-
-def download_models():
-    """下载所有需要的模型文件"""
-    os.makedirs("model", exist_ok=True)
-    os.makedirs("hubert/chinese-hubert-large-fairseq-ckpt", exist_ok=True)
-
-    # 下载FreeVC模型
-    freevc_paths = {}
-    for model_name, model_path in MODEL_OPTIONS.items():
-        path = hf_hub_download(
-            repo_id=MODEL_CONFIG["freevc"]["repo_id"],
-            filename=os.path.basename(model_path),
-            local_dir="model",
-            resume_download=True
-        )
-        freevc_paths[model_name] = path
-
-    # 下载整个HuBERT仓库
-    hubert_dir = "hubert/chinese-hubert-large-fairseq-ckpt"
-    snapshot_download(
-        repo_id=MODEL_CONFIG["hubert"]["repo_id"],
-        local_dir=hubert_dir,
-        repo_type="model",
-        resume_download=True
-    )
-    hubert_paths = {"snapshot": hubert_dir}  # 返回整个目录路径
-
-    return {
-        "freevc": freevc_paths,
-        "hubert": hubert_paths
-    }
-
-def load_hubert(hubert_dir):
-    """加载HuBERT模型（使用fairseq格式的检查点）"""
-    print("正在加载 HuBERT 模型...")
-
-    # 加载标准HuBERT模型
-    model = HubertModel.from_pretrained(hubert_dir)
-    feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(hubert_dir)
-
-    return model.to(device).float().eval(), feature_extractor
-
-def load_freevc(model_path):
-    """加载FreeVC模型（使用本地配置文件）"""
-    print(f"正在从 {model_path} 加载 FreeVC 模型...")
-    hps = utils.get_hparams_from_file("configs/freevc.json")  # 本地配置文件
-
-    net_g = SynthesizerTrn(
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        **hps.model
-    ).to(device)
-
-    utils.load_checkpoint(model_path, net_g, None, True)
-    net_g.eval()
-
-    # 加载本地说话人编码器
-    smodel = SpeakerEncoder("speaker_encoder/ckpt/pretrained_bak_5805000.pt") if hps.model.use_spk else None
-    return net_g, smodel, hps
-
-# 预加载模型
-print("正在下载模型...")
-model_paths = download_models()
-print(model_paths)
-print("正在初始化 HuBERT...")
-hubert_dir = "hubert/chinese-hubert-large-fairseq-ckpt"
-hubert_model, hubert_feature_extractor = load_hubert(hubert_dir)
-
-def voice_conversion(src_audio, tgt_audio, output_name, model_selection):
-    """执行语音转换"""
-    try:
-        # 加载选中的FreeVC模型
-        freevc_model, speaker_model, hps = load_freevc(MODEL_OPTIONS[model_selection])
-
-        with torch.no_grad():
-            # 处理目标音频
-            wav_tgt, _ = librosa.load(tgt_audio, sr=hps.data.sampling_rate)
-            wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
-            
-            if hps.model.use_spk:
-                g_tgt = speaker_model.embed_utterance(wav_tgt)
-                g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device)
-            else:
-                wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(device)
-                mel_tgt = mel_spectrogram_torch(
-                    wav_tgt, 
-                    hps.data.filter_length,
-                    hps.data.n_mel_channels,
-                    hps.data.sampling_rate,
-                    hps.data.hop_length,
-                    hps.data.win_length,
-                    hps.data.mel_fmin,
-                    hps.data.mel_fmax
-                )
-
-            # 处理源音频（HuBERT需要16kHz）
-            wav_src, _ = librosa.load(src_audio, sr=16000)
-            inputs = hubert_feature_extractor(
-                wav_src, 
-                return_tensors="pt",
-                sampling_rate=16000
-            ).input_values.to(device)
-            
-            c = hubert_model(inputs.float()).last_hidden_state.transpose(1, 2)
-
-            # 执行转换
-            audio = freevc_model.infer(c, g=g_tgt) if hps.model.use_spk else freevc_model.infer(c, mel=mel_tgt)
-            
-            # 保存结果
-            os.makedirs("output", exist_ok=True)
-            output_path = f"output/{output_name}.wav"
-            write(output_path, hps.data.sampling_rate, audio[0][0].data.cpu().float().numpy())
-            
-            return output_path
-        
-    except Exception as e:
-        print(f"转换错误: {str(e)}")
-        return None
-
-# Gradio界面
-with gr.Blocks(title="Chinese-FreeVC 语音转换") as app:
-    gr.Markdown("## Chinese-FreeVC 语音转换系统")
-
-    with gr.Row():
-        with gr.Column():
-            src_input = gr.Audio(label="源语音", type="filepath")
-            tgt_input = gr.Audio(label="目标音色", type="filepath")
-            model_dropdown = gr.Dropdown(
-                choices=list(MODEL_OPTIONS.keys()),
-                label="选择模型",
-                value="Model_17000"
-            )
-            output_name = gr.Textbox(label="输出文件名", value="converted")
-            convert_btn = gr.Button("开始转换", variant="primary")
-        
-        with gr.Column():
-            output_audio = gr.Audio(label="转换结果", interactive=False)
-            status = gr.Textbox(label="状态")
-
-    convert_btn.click(
-        fn=voice_conversion,
-        inputs=[src_input, tgt_input, output_name, model_dropdown],
-        outputs=[output_audio],
-        api_name="convert"
-    )
-
-if __name__ == "__main__":
-    app.launch(server_name="0.0.0.0", server_port=7860)