import io import shlex from io import BytesIO import gradio as gr import librosa import numpy as np import soundfile from inference import slicer from inference.infer_tool import Svc import logging from logmmse import logmmse from typing import Tuple import time import requests import os,json from subprocess import getoutput from urllib.parse import quote logging.getLogger('numba').setLevel(logging.WARNING) model_sing = "./G_2000.pth" #model_talk = "logs/32k/talk1.pth" config_name = "./config.json" sid_map ={"chiya":"./chiya.pth","koyomi":"./koyomi.pth","yuki":"./yuki.pth","plw":"./plw.pth","vik":"./vik.pth"} os.system('chmod +x ./pget') class YukieGradio: def __init__(self): self.UI = gr.Blocks() with self.UI: with gr.Tabs(): with gr.TabItem("Basic"): gr.Markdown(value=""" 偷的界面，参考LICENSE """) self.sid = gr.Dropdown(label="音色", choices=["chiya","koyomi","yuki","plw","vik"], value="yuki", interactive=True) self.dev = gr.Dropdown(label="设备（云端一般请勿切换，使用默认值即可）", choices=[ "cuda", "cpu"], value="cpu", interactive=True) self.inMic = gr.Textbox(label='url(@start)') self.inAudio = gr.Audio(label="or 上传音频") self.needLogmmse = gr.Checkbox(label="是否使用自带降噪") self.slice_db = gr.Slider(label="切片阈值(较嘈杂时-30，保留呼吸声时-50，一般默认-40)", maximum=0, minimum=-60, step=1, value=-40) self.vcTransform = gr.Number( label="升降调（整数，可以正负，半音数量，升高八度就是12）", value=0) self.vcSubmit = gr.Button("转换", variant="primary") self.outVcText = gr.Textbox( label="音高平均偏差半音数量，体现转换音频的跑调情况（一般小于0.5）") self.outAudio = gr.Audio( type="numpy", label="Output Audio") self.f0_image = gr.Image( label="f0曲线，蓝色为输入音高，橙色为合成音频的音高（代码有误差）") gr.Markdown(value=""" ## 注意 如果要在本地使用该demo，请使用 `git lfs clone https://huggingface.co/spaces/yukie/yukie-sovits3`克隆该仓库([简单教程](https://huggingface.co/spaces/yukie/yukie-sovits3/edit/main/local.md)) """) self.vcSubmit.click(infer, inputs=[self.inMic, self.inAudio, self.vcTransform, self.slice_db, self.needLogmmse, self.sid, self.dev], outputs=[ self.outVcText, self.outAudio, self.f0_image],api_name="go") def download_audio(url): # 下载音频数据 response = requests.get(url) audio_bytes = BytesIO(response.content) # 转换音频格式为wav y, sr = librosa.load(audio_bytes, sr=None) with BytesIO() as wav_bytes: soundfile.write(wav_bytes, y, sr, format='wav') wav_bytes.seek(0) # 读取wav文件 data, sr = soundfile.read(wav_bytes) # 转换数据类型为int16 data = np.asarray(data * 32767, dtype=np.int16) return sr, data def downloadTubeUpload(query): pquery=shlex.quote(query.split('@')[0]) proxy=os.environ['proxy'] os.system('chmod +x ./yt-dlp') os.system(f'./yt-dlp -f worstaudio* -o "temp.mp4" --force-overwrites --no-playlist --concurrent-fragments 4 --proxy "{proxy}" {pquery}') upload_url = "https://lalal.ai/api/upload/" headers = { "Content-Disposition": f"attachment; filename=video_id.mp4" } result = os.popen('ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 temp.mp4') duration = float(result.read().strip()) # 计算需要截取的时间区间 start_time = max(0, (duration) / 2) if len(query.split('@'))==2: start_time=int(query.split('@')[-1]) end_time = start_time + 60 # 使用ffmpeg进行截取 os.system(f'ffmpeg -i temp.mp4 -ss {start_time} -t 60 -c copy output.mp4') command= f'curl --url https://www.lalal.ai/api/upload/ --data-binary @output.mp4 --header "Content-Disposition: attachment; filename=output.mp4" -s' moutput=getoutput(command) print(moutput) upload_response=json.loads(moutput) return upload_response.get("id") def split_file(file_id): command = f'rm temp.mp4' os.system(command) command = f'rm output.mp4' os.system(command) url_for_split = "https://www.lalal.ai/api/preview/" headers = { 'accept': 'application/json, text/plain, */*', 'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6', 'dnt': '1', 'origin': 'https://www.lalal.ai', 'priority': 'u=1, i', 'referer': 'https://www.lalal.ai/', 'sec-ch-ua': '"Not/A)Brand";v="8", "Chromium";v="126", "Microsoft Edge";v="126"', 'sec-ch-ua-mobile': '?0', 'sec-ch-ua-platform': '"Windows"', 'sec-fetch-dest': 'empty', 'sec-fetch-mode': 'cors', 'sec-fetch-site': 'same-origin', 'sentry-trace': 'efee9c07725645dc896a8be5ace08ba4-87568d216d25918a-0', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0', 'x-csrftoken': 'ytk6iENZ6uT71lFQ6NgAPBGvwUt6A2Xi', 'x-request-id': 'lalalai' } query_args = {'id': file_id, 'stem': "vocals",'dereverb_enabled':True} response = requests.post(url_for_split, data=query_args,headers=headers)# split_result = response.json() if split_result["status"] == "error": print(split_result["error"]) raise RuntimeError('split err') def check_file(file_id): url_for_check = "https://www.lalal.ai/api/check/" query_args = {'id': file_id} is_queueup = False while True: response = requests.get(url_for_check, params=query_args) check_result = response.json() if check_result["status"] == "error": raise RuntimeError(check_result["error"]) task_state = check_result["task"]["state"] if task_state == "error": raise RuntimeError(check_result["task"]["error"]) if task_state == "progress": progress = int(check_result["task"]["progress"]) if progress == 0 and not is_queueup: print("Queue up...") is_queueup = True elif progress > 0: print(f"Progress: {progress}%") if task_state == "success": stem_track_url = check_result["preview"]["stem_track"] back_track_url = check_result["preview"]["back_track"] return stem_track_url, back_track_url time.sleep(30) def infer(inMic, inAudio, transform, slice_db, lm, sid, dev): if inAudio != None: sampling_rate, inaudio = inAudio else: if inMic != None: id=downloadTubeUpload(inMic) split_file(id) sampling_rate, inaudio=download_audio(check_file(id)[0]) else: return "请上传一段音频后再次尝试", None print("start inference") start_time = time.time() # 预处理，重编码 inaudio = (inaudio / np.iinfo(inaudio.dtype).max).astype(np.float32) if len(inaudio.shape) > 1: inaudio = librosa.to_mono(inaudio.transpose(1, 0)) if sampling_rate != 32000: inaudio = librosa.resample( inaudio, orig_sr=sampling_rate, target_sr=32000) if lm: inaudio = logmmse(inaudio, 32000) ori_wav_path = "tmp_ori.wav" soundfile.write(ori_wav_path, inaudio, 32000, format="wav") chunks = slicer.cut(ori_wav_path, db_thresh=slice_db) audio_data, audio_sr = slicer.chunks2audio(ori_wav_path, chunks) audio = [] sid = sid_map[sid] if sid!=None: svc_model = Svc(sid, config_name, dev=dev) #sid is model path now for (slice_tag, data) in audio_data: length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample)) raw_path = io.BytesIO() soundfile.write(raw_path, data, audio_sr, format="wav") raw_path.seek(0) if slice_tag: _audio = np.zeros(length) else: out_audio, out_str = svc_model.infer(0, transform, raw_path) _audio = out_audio.cpu().numpy() audio.extend(list(_audio)) audio = (np.array(audio) * 32768.0).astype('int16') used_time = time.time() - start_time out_wav_path = "tmp.wav" soundfile.write(out_wav_path, audio, 32000, format="wav") mistake, var = svc_model.calc_error(ori_wav_path, out_wav_path, transform) out_picture = svc_model.f0_plt(ori_wav_path, out_wav_path, transform) out_str = ("Success! total use time:{}s\n半音偏差:{}\n半音方差:{}".format( used_time, mistake, var)) return out_str, (32000, audio), "temp.jpg" if __name__ == "__main__": app = YukieGradio() app.UI.launch()