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| import io | |
| import os | |
| import gradio as gr | |
| import librosa | |
| import numpy as np | |
| import soundfile | |
| from inference.infer_tool import Svc | |
| import logging | |
| import os | |
| import paddle | |
| import requests | |
| import utils | |
| from spleeter import Separator | |
| build_dir=os.getcwd() | |
| if build_dir == "/home/aistudio": | |
| build_dir += "/build" | |
| model_dir=build_dir+'/trained_models' | |
| model_list_path = model_dir + "/model_list.txt" | |
| # 筛选出文件夹 | |
| models = [] | |
| for filename in os.listdir(model_dir): | |
| # 判断文件名是否以 '.pdparams' 结尾，并且不包含后缀部分 | |
| if filename.endswith('.pdparams') and os.path.splitext(filename)[0].isalpha(): | |
| models.append(os.path.splitext(filename)[0]) | |
| cache_model = {} | |
| def reboot(): | |
| os._exit(0) | |
| def separate_fn(song_input): | |
| try: | |
| if song_input is None: | |
| return "请上传歌曲",None,None,None,None | |
| params_2stems = { | |
| 'sample_rate': 44100, | |
| 'frame_length': 4096, | |
| 'frame_step': 1024, | |
| 'T': 512, | |
| 'F': 1024, | |
| 'num_instruments': ['vocals', 'instrumental'], | |
| 'output_dir': build_dir+'/output_2stems', | |
| 'checkpoint_path': build_dir+'/spleeter', | |
| 'use_elu': False} | |
| sampling_rate, song = song_input | |
| soundfile.write("temp.wav", song, sampling_rate, format="wav") | |
| # 初始化分离器 | |
| sep = Separator(params_2stems) | |
| sep.separate('temp.wav') | |
| vocal_path = params_2stems["output_dir"]+"/temp-vocals.wav" | |
| instrumental_path = params_2stems["output_dir"]+"/temp-instrumental.wav" | |
| return "分离成功，请继续前往体验【转换】和【混音】",vocal_path,instrumental_path,vocal_path,instrumental_path | |
| except Exception as e: | |
| import traceback | |
| return traceback.format_exc() , None,None,None,None | |
| def convert_fn(model_name, input_audio,input_audio_micro, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale): | |
| try: | |
| if model_name in cache_model: | |
| model = cache_model[model_name] | |
| else: | |
| if paddle.device.is_compiled_with_cuda()==False and len(cache_model)!=0: | |
| return f"目前运行环境为CPU，受制于平台算力，每次启动本项目只允许加载1个模型，当前已加载{next(iter(cache_model))}",None,None | |
| config_path = f"{build_dir}/trained_models/config.json" | |
| model = Svc(f"{build_dir}/trained_models/{model_name}.pdparams", config_path,mode="test") | |
| cache_model[model_name] = model | |
| if input_audio is None and input_audio_micro is None: | |
| return "请上传音频", None,None | |
| if input_audio_micro is not None: | |
| input_audio = input_audio_micro | |
| sampling_rate, audio = input_audio | |
| duration = audio.shape[0] / sampling_rate | |
| audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) | |
| if len(audio.shape) > 1: | |
| audio = librosa.to_mono(audio.transpose(1, 0)) | |
| if sampling_rate != 16000: | |
| audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) | |
| print(audio.shape) | |
| out_wav_path = "temp.wav" | |
| soundfile.write(out_wav_path, audio, 16000, format="wav") | |
| print(cluster_ratio, auto_f0, noise_scale) | |
| _audio = model.slice_inference(out_wav_path, model_name, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale) | |
| del model | |
| return "转换成功，请继续前往体验【混音】", (44100, _audio),(44100, _audio) | |
| except Exception as e: | |
| import traceback | |
| return traceback.format_exc() , None,None | |
| def compose_fn(input_vocal,input_instrumental,mixing_ratio=0.5): | |
| try: | |
| outlog = "混音成功" | |
| if input_vocal is None: | |
| return "请上传人声", None | |
| if input_instrumental is None: | |
| return "请上传伴奏", None | |
| vocal_sampling_rate, vocal = input_vocal | |
| vocal_duration = vocal.shape[0] / vocal_sampling_rate | |
| vocal = (vocal / np.iinfo(vocal.dtype).max).astype(np.float32) | |
| if len(vocal.shape) > 1: | |
| vocal = librosa.to_mono(vocal.transpose(1, 0)) | |
| if vocal_sampling_rate != 44100: | |
| vocal = librosa.resample(vocal, orig_sr=vocal_sampling_rate, target_sr=44100) | |
| instrumental_sampling_rate, instrumental = input_instrumental | |
| instrumental_duration = instrumental.shape[0] / instrumental_sampling_rate | |
| instrumental = (instrumental / np.iinfo(instrumental.dtype).max).astype(np.float32) | |
| if len(instrumental.shape) > 1: | |
| instrumental = librosa.to_mono(instrumental.transpose(1, 0)) | |
| if instrumental_sampling_rate != 44100: | |
| instrumental = librosa.resample(instrumental, orig_sr=instrumental_sampling_rate, target_sr=44100) | |
| if len(vocal)!=len(instrumental): | |
| min_length = min(len(vocal),len(instrumental)) | |
| instrumental = instrumental[:min_length] | |
| vocal = vocal[:min_length] | |
| outlog = "人声伴奏长度不一致，已自动截断较长的音频" | |
| mixed_audio = (1 - mixing_ratio) * vocal + mixing_ratio * instrumental | |
| mixed_audio_data = mixed_audio.astype(np.float32) | |
| return outlog,(44100,mixed_audio_data) | |
| except Exception as e: | |
| import traceback | |
| return traceback.format_exc() , None | |
| app = gr.Blocks() | |
| with app: | |
| gr.Markdown('<h1 style="text-align: center;">SVC歌声转换全流程体验（伴奏分离，转换，混音）</h1>') | |
| btn_reboot = gr.Button("重启程序", variant="primary") | |
| with gr.Tabs() as tabs: | |
| with gr.TabItem("人声伴奏分离"): | |
| gr.Markdown('<p>该项目人声分离的效果弱于UVR5，如自备分离好的伴奏和人声可跳过该步骤</p>') | |
| song_input = gr.Audio(label="上传歌曲（tips:上传后点击右上角✏可以进行歌曲剪辑）",interactive=True) | |
| gr.Examples(examples=[build_dir+"/examples/song/blue.wav",build_dir+"/examples/song/Counter_clockwise_Clock.wav",build_dir+"/examples/song/one_last_kiss.wav"],inputs=song_input,label="歌曲样例") | |
| btn_separate = gr.Button("人声伴奏分离", variant="primary") | |
| text_output1 = gr.Textbox(label="输出信息") | |
| vocal_output1 = gr.Audio(label="输出人声",interactive=False) | |
| instrumental_output1 = gr.Audio(label="输出伴奏",interactive=False) | |
| with gr.TabItem("转换"): | |
| model_name = gr.Dropdown(label="模型", choices=models, value="纳西妲") | |
| vocal_input1 = gr.Audio(label="上传人声",interactive=True) | |
| gr.Examples(examples=[build_dir+"/examples/vocals/blue_vocal.wav",build_dir+"/examples/vocals/Counter_clockwise_Clock_vocal.wav",build_dir+"/examples/vocals/one_last_kiss_vocal.wav"],inputs=vocal_input1,label="人声样例") | |
| btn_use_separate = gr.Button("使用【人声伴奏分离】分离的人声") | |
| micro_input = gr.Audio(label="麦克风输入（优先于上传的人声）",source="microphone",interactive=True) | |
| vc_transform = gr.Number(label="变调（半音数量,升八度12降八度-12）", value=0) | |
| cluster_ratio = gr.Number(label="聚类模型混合比例", value=0,visible=False) | |
| auto_f0 = gr.Checkbox(label="自动预测音高（转换歌声时不要打开，会严重跑调）", value=False) | |
| slice_db = gr.Number(label="静音分贝阈值（嘈杂的音频可以-30，干声保留呼吸可以-50）", value=-50) | |
| noise_scale = gr.Number(label="noise_scale", value=0.2) | |
| btn_convert = gr.Button("转换", variant="primary") | |
| text_output2 = gr.Textbox(label="输出信息") | |
| vc_output2 = gr.Audio(label="输出音频",interactive=False) | |
| with gr.TabItem("混音"): | |
| vocal_input2 = gr.Audio(label="上传人声",interactive=True) | |
| btn_use_convert = gr.Button("使用【转换】输出的人声") | |
| instrumental_input1 = gr.Audio(label="上传伴奏") | |
| gr.Examples(examples=[build_dir+"/examples/instrumental/blue_instrumental.wav",build_dir+"/examples/instrumental/Counter_clockwise_Clock_instrumental.wav",build_dir+"/examples/instrumental/one_last_kiss_instrumental.wav"],inputs=instrumental_input1,label="伴奏样例") | |
| btn_use_separate2 = gr.Button("使用【人声伴奏分离】分离的伴奏") | |
| mixing_ratio = gr.Slider(0, 1, value=0.75,step=0.01,label="混音比例（人声:伴奏）", info="人声:伴奏") | |
| btn_compose = gr.Button("混音", variant="primary") | |
| text_output3 = gr.Textbox(label="输出信息") | |
| song_output = gr.Audio(label="输出歌曲",interactive=False) | |
| btn_separate.click(separate_fn, song_input, [text_output1, vocal_output1,instrumental_output1,vocal_input1,instrumental_input1]) | |
| btn_convert.click(convert_fn, [model_name, vocal_input1,micro_input,vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale], [text_output2, vc_output2,vocal_input2]) | |
| btn_reboot.click(reboot) | |
| btn_use_convert.click(lambda x:x,vc_output2,vocal_input2) | |
| btn_use_separate.click(lambda x:x,vocal_output1,vocal_input1) | |
| btn_use_separate2.click(lambda x:x,instrumental_output1,instrumental_input1) | |
| app.launch() | |