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 import time from datetime import datetime, timedelta 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 callback(text): if text == "reboot": os._exit(0) one_hour_later = datetime.now() + timedelta(hours=1) else: global start_time if time.time() - start_time >= 3600: os._exit(0) one_hour_later = datetime.now() + timedelta(hours=1) else: return text 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: start_time = time.time() one_hour_later = datetime.now() + timedelta(hours=1) gr.Markdown('

SVC歌声转换全流程体验（伴奏分离，转换，混音）

') with gr.Tabs() as tabs: with gr.TabItem("人声伴奏分离"): gr.Markdown('

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

') 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) with gr.TabItem("设置"): output = gr.Textbox(label="输出",placeholder=f"距离下一次允许重启时间为{one_hour_later}") btn_reboot = gr.Button("重启",variant="primary") 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_compose.click(compose_fn,[vocal_input2,instrumental_input1,mixing_ratio],[text_output3,song_output]) btn_reboot.click(callback,output) 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()