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| # -*- coding: utf-8 -*- | |
| import traceback | |
| import torch | |
| from scipy.io import wavfile | |
| import edge_tts | |
| import subprocess | |
| import gradio as gr | |
| import gradio.processing_utils as gr_pu | |
| import io | |
| import os | |
| import logging | |
| import time | |
| from pathlib import Path | |
| import re | |
| import json | |
| import argparse | |
| import librosa | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import soundfile | |
| from inference import infer_tool | |
| from inference import slicer | |
| from inference.infer_tool import Svc | |
| from huggingface_hub import hf_hub_download | |
| logging.getLogger('numba').setLevel(logging.WARNING) | |
| chunks_dict = infer_tool.read_temp("inference/chunks_temp.json") | |
| logging.getLogger('numba').setLevel(logging.WARNING) | |
| logging.getLogger('markdown_it').setLevel(logging.WARNING) | |
| logging.getLogger('urllib3').setLevel(logging.WARNING) | |
| logging.getLogger('matplotlib').setLevel(logging.WARNING) | |
| logging.getLogger('multipart').setLevel(logging.WARNING) | |
| model = None | |
| spk = None | |
| debug = False | |
| MODEL_REPO = 'Asankilp/gochiusa-sovits4.0' | |
| MODEL_NAME = 'G_74400.pth' | |
| DOWNLOAD_DIR = './downloaded' | |
| CHECKPOINT_REPO = 'innnky/contentvec' | |
| if not os.path.exists(os.path.join(DOWNLOAD_DIR,MODEL_NAME)): | |
| print('下载模型文件...') | |
| model_pth = hf_hub_download(repo_id=MODEL_REPO,filename=MODEL_NAME,local_dir=DOWNLOAD_DIR) | |
| if not os.path.exists(os.path.join(DOWNLOAD_DIR,'config.json')): | |
| print('下载配置文件...') | |
| model_configfile = hf_hub_download(repo_id=MODEL_REPO,filename='config.json',local_dir=DOWNLOAD_DIR) | |
| if not os.path.exists(os.path.join('./pretrain/checkpoint_best_legacy_500.pt')): | |
| print('下载预训练模型文件...') | |
| checkpoint_pt = hf_hub_download(repo_id=CHECKPOINT_REPO,filename='checkpoint_best_legacy_500.pt',local_dir='./pretrain') | |
| class HParams(): | |
| def __init__(self, **kwargs): | |
| for k, v in kwargs.items(): | |
| if type(v) == dict: | |
| v = HParams(**v) | |
| self[k] = v | |
| def keys(self): | |
| return self.__dict__.keys() | |
| def items(self): | |
| return self.__dict__.items() | |
| def values(self): | |
| return self.__dict__.values() | |
| def __len__(self): | |
| return len(self.__dict__) | |
| def __getitem__(self, key): | |
| return getattr(self, key) | |
| def __setitem__(self, key, value): | |
| return setattr(self, key, value) | |
| def __contains__(self, key): | |
| return key in self.__dict__ | |
| def __repr__(self): | |
| return self.__dict__.__repr__() | |
| def get_hparams_from_file(config_path): | |
| with open(config_path, "r", encoding="utf-8") as f: | |
| data = f.read() | |
| config = json.loads(data) | |
| hparams = HParams(**config) | |
| return hparams | |
| def vc_fn(sid, input_audio, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold): | |
| try: | |
| if input_audio is None: | |
| raise gr.Error("你需要上传音频") | |
| if model is None: | |
| raise gr.Error("你需要指定模型") | |
| sampling_rate, audio = input_audio | |
| # print(audio.shape,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)) | |
| temp_path = "temp.wav" | |
| soundfile.write(temp_path, audio, sampling_rate, format="wav") | |
| _audio = model.slice_inference(temp_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale, | |
| pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold) | |
| model.clear_empty() | |
| os.remove(temp_path) | |
| # 构建保存文件的路径，并保存到results文件夹内 | |
| try: | |
| timestamp = str(int(time.time())) | |
| filename = sid + "_" + timestamp + ".wav" | |
| # output_file = os.path.join("./results", filename) | |
| # soundfile.write(output_file, _audio, model.target_sample, format="wav") | |
| soundfile.write('/tmp/'+filename, _audio, | |
| model.target_sample, format="wav") | |
| # return f"推理成功，音频文件保存为results/{filename}", (model.target_sample, _audio) | |
| return f"推理成功，音频文件保存为{filename}", (model.target_sample, _audio) | |
| except Exception as e: | |
| if debug: | |
| traceback.print_exc() | |
| return f"文件保存失败，请手动保存", (model.target_sample, _audio) | |
| except Exception as e: | |
| if debug: | |
| traceback.print_exc() | |
| raise gr.Error(e) | |
| def tts_func(_text, _rate, _voice): | |
| # 使用edge-tts把文字转成音频 | |
| # voice = "zh-CN-XiaoyiNeural"#女性，较高音 | |
| # voice = "zh-CN-YunxiNeural"#男性 | |
| voice = "zh-CN-YunxiNeural" # 男性 | |
| if (_voice == "女"): | |
| voice = "zh-CN-XiaoyiNeural" | |
| output_file = "/tmp/"+_text[0:10]+".wav" | |
| # communicate = edge_tts.Communicate(_text, voice) | |
| # await communicate.save(output_file) | |
| if _rate >= 0: | |
| ratestr = "+{:.0%}".format(_rate) | |
| elif _rate < 0: | |
| ratestr = "{:.0%}".format(_rate) # 减号自带 | |
| p = subprocess.Popen("edge-tts " + | |
| " --text "+_text + | |
| " --write-media "+output_file + | |
| " --voice "+voice + | |
| " --rate="+ratestr, shell=True, | |
| stdout=subprocess.PIPE, | |
| stdin=subprocess.PIPE) | |
| p.wait() | |
| return output_file | |
| def text_clear(text): | |
| return re.sub(r"[\n\,\(\) ]", "", text) | |
| def vc_fn2(sid, input_audio, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, text2tts, tts_rate, tts_voice, f0_predictor, enhancer_adaptive_key, cr_threshold): | |
| # 使用edge-tts把文字转成音频 | |
| text2tts = text_clear(text2tts) | |
| output_file = tts_func(text2tts, tts_rate, tts_voice) | |
| # 调整采样率 | |
| sr2 = 44100 | |
| wav, sr = librosa.load(output_file) | |
| wav2 = librosa.resample(wav, orig_sr=sr, target_sr=sr2) | |
| save_path2 = text2tts[0:10]+"_44k"+".wav" | |
| wavfile.write(save_path2, sr2, | |
| (wav2 * np.iinfo(np.int16).max).astype(np.int16) | |
| ) | |
| # 读取音频 | |
| sample_rate, data = gr_pu.audio_from_file(save_path2) | |
| vc_input = (sample_rate, data) | |
| a, b = vc_fn(sid, vc_input, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, | |
| pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold) | |
| os.remove(output_file) | |
| os.remove(save_path2) | |
| return a, b | |
| models_info = [ | |
| { | |
| "description": """ | |
| 请问你今天要来点so-vits-svc 4.0模型吗？\n\n | |
| * 模型尚处于半成品（ | |
| * 此 Space 使用基本 CPU，只能用于**极短**的音频的转换。 | |
| * 若要转换长音频，请在本地或 GPU 实例使用[此模型](https://huggingface.co/Asankilp/gochiusa-sovits4.0)。 | |
| * **禁止商用以及用于生成对个人或组织造成侵害的内容。** | |
| """, | |
| "model_path": os.path.join(DOWNLOAD_DIR,MODEL_NAME), | |
| "config_path": os.path.join(DOWNLOAD_DIR,'config.json'), | |
| } | |
| ] | |
| model_inferall = [] | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--share", action="store_true", | |
| default=False, help="share gradio app") | |
| # 一定要设置的部分 | |
| parser.add_argument('-cl', '--clip', type=float, | |
| default=0, help='音频强制切片，默认0为自动切片，单位为秒/s') | |
| parser.add_argument('-n', '--clean_names', type=str, nargs='+', | |
| default=["君の知らない物語-src.wav"], help='wav文件名列表，放在raw文件夹下') | |
| parser.add_argument('-t', '--trans', type=int, nargs='+', | |
| default=[0], help='音高调整，支持正负（半音）') | |
| parser.add_argument('-s', '--spk_list', type=str, | |
| nargs='+', default=['nen'], help='合成目标说话人名称') | |
| # 可选项部分 | |
| parser.add_argument('-a', '--auto_predict_f0', action='store_true', | |
| default=False, help='语音转换自动预测音高，转换歌声时不要打开这个会严重跑调') | |
| parser.add_argument('-cm', '--cluster_model_path', type=str, | |
| default="logs/44k/kmeans_10000.pt", help='聚类模型路径，如果没有训练聚类则随便填') | |
| parser.add_argument('-cr', '--cluster_infer_ratio', type=float, | |
| default=0, help='聚类方案占比，范围0-1，若没有训练聚类模型则默认0即可') | |
| parser.add_argument('-lg', '--linear_gradient', type=float, default=0, | |
| help='两段音频切片的交叉淡入长度，如果强制切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，单位为秒') | |
| parser.add_argument('-f0p', '--f0_predictor', type=str, default="pm", | |
| help='选择F0预测器,可选择crepe,pm,dio,harvest,默认为pm(注意：crepe为原F0使用均值滤波器)') | |
| parser.add_argument('-eh', '--enhance', action='store_true', default=False, | |
| help='是否使用NSF_HIFIGAN增强器,该选项对部分训练集少的模型有一定的音质增强效果，但是对训练好的模型有反面效果，默认关闭') | |
| parser.add_argument('-shd', '--shallow_diffusion', action='store_true', | |
| default=False, help='是否使用浅层扩散，使用后可解决一部分电音问题，默认关闭，该选项打开时，NSF_HIFIGAN增强器将会被禁止') | |
| # 浅扩散设置 | |
| parser.add_argument('-dm', '--diffusion_model_path', type=str, | |
| default="logs/44k/diffusion/model_0.pt", help='扩散模型路径') | |
| parser.add_argument('-dc', '--diffusion_config_path', type=str, | |
| default="logs/44k/diffusion/config.yaml", help='扩散模型配置文件路径') | |
| parser.add_argument('-ks', '--k_step', type=int, | |
| default=100, help='扩散步数，越大越接近扩散模型的结果，默认100') | |
| parser.add_argument('-od', '--only_diffusion', action='store_true', | |
| default=False, help='纯扩散模式，该模式不会加载sovits模型，以扩散模型推理') | |
| # 不用动的部分 | |
| parser.add_argument('-sd', '--slice_db', type=int, | |
| default=-40, help='默认-40，嘈杂的音频可以-30，干声保留呼吸可以-50') | |
| parser.add_argument('-d', '--device', type=str, | |
| default=None, help='推理设备，None则为自动选择cpu和gpu') | |
| parser.add_argument('-ns', '--noice_scale', type=float, | |
| default=0.4, help='噪音级别，会影响咬字和音质，较为玄学') | |
| parser.add_argument('-p', '--pad_seconds', type=float, default=0.5, | |
| help='推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现') | |
| parser.add_argument('-wf', '--wav_format', type=str, | |
| default='flac', help='音频输出格式') | |
| parser.add_argument('-lgr', '--linear_gradient_retain', type=float, | |
| default=0.75, help='自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭') | |
| parser.add_argument('-eak', '--enhancer_adaptive_key', | |
| type=int, default=0, help='使增强器适应更高的音域(单位为半音数)|默认为0') | |
| parser.add_argument('-ft', '--f0_filter_threshold', type=float, default=0.05, | |
| help='F0过滤阈值，只有使用crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率，但会增加哑音') | |
| args = parser.parse_args() | |
| categories = ['Gochiusa'] | |
| for info in models_info: | |
| config_path = info['config_path'] | |
| model_path = info['model_path'] | |
| description = info['description'] | |
| clean_names = args.clean_names | |
| trans = args.trans | |
| spk_list = list(get_hparams_from_file(config_path).spk.keys()) | |
| slice_db = args.slice_db | |
| wav_format = args.wav_format | |
| auto_predict_f0 = args.auto_predict_f0 | |
| cluster_infer_ratio = args.cluster_infer_ratio | |
| noice_scale = args.noice_scale | |
| pad_seconds = args.pad_seconds | |
| clip = args.clip | |
| lg = args.linear_gradient | |
| lgr = args.linear_gradient_retain | |
| f0p = args.f0_predictor | |
| enhance = args.enhance | |
| enhancer_adaptive_key = args.enhancer_adaptive_key | |
| cr_threshold = args.f0_filter_threshold | |
| diffusion_model_path = args.diffusion_model_path | |
| diffusion_config_path = args.diffusion_config_path | |
| k_step = args.k_step | |
| only_diffusion = args.only_diffusion | |
| shallow_diffusion = args.shallow_diffusion | |
| model = Svc(model_path, config_path, args.device, args.cluster_model_path, enhance, | |
| diffusion_model_path, diffusion_config_path, shallow_diffusion, only_diffusion) | |
| model_inferall.append((description, spk_list, model)) | |
| app = gr.Blocks() | |
| with app: | |
| with gr.Tabs(): | |
| for category in categories: | |
| with gr.TabItem(category): | |
| for i, (description, speakers, model) in enumerate( | |
| model_inferall): | |
| gr.Markdown(description) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(value=""" | |
| <font size=2> 推理设置</font> | |
| """) | |
| sid = gr.Dropdown( | |
| choices=speakers, value=speakers[0], label='角色选择') | |
| auto_f0 = gr.Checkbox( | |
| label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声勾选此项会究极跑调）", value=False) | |
| f0_predictor = gr.Dropdown(label="选择F0预测器,可选择crepe,pm,dio,harvest,默认为pm(注意：crepe为原F0使用均值滤波器)", choices=[ | |
| "pm", "dio", "harvest", "crepe"], value="pm") | |
| vc_transform = gr.Number( | |
| label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0) | |
| cluster_ratio = gr.Number( | |
| label="聚类模型混合比例，0-1之间，0即不启用聚类。使用聚类模型能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0) | |
| slice_db = gr.Number(label="切片阈值", value=-40) | |
| noise_scale = gr.Number( | |
| label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4) | |
| with gr.Column(): | |
| pad_seconds = gr.Number( | |
| label="推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现", value=0.5) | |
| cl_num = gr.Number( | |
| label="音频自动切片，0为不切片，单位为秒(s)", value=0) | |
| lg_num = gr.Number( | |
| label="两端音频切片的交叉淡入长度，如果自动切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，注意，该设置会影响推理速度，单位为秒/s", value=0) | |
| lgr_num = gr.Number( | |
| label="自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭", value=0.75) | |
| enhancer_adaptive_key = gr.Number( | |
| label="使增强器适应更高的音域(单位为半音数)|默认为0", value=0) | |
| cr_threshold = gr.Number( | |
| label="F0过滤阈值，只有启动crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率，但会增加哑音", value=0.05) | |
| with gr.Tabs(): | |
| with gr.TabItem("音频转音频"): | |
| vc_input3 = gr.Audio(label="选择音频") | |
| vc_submit = gr.Button( | |
| "音频转换", variant="primary") | |
| with gr.TabItem("文字转音频"): | |
| text2tts = gr.Textbox( | |
| label="在此输入要转译的文字（仅支持中文）。注意，使用该功能建议打开F0预测，不然会很怪") | |
| tts_rate = gr.Number(label="tts语速", value=0) | |
| tts_voice = gr.Radio(label="性别", choices=[ | |
| "男", "女"], value="男") | |
| vc_submit2 = gr.Button( | |
| "文字转换", variant="primary") | |
| with gr.Row(): | |
| with gr.Column(): | |
| vc_output1 = gr.Textbox(label="Output Message") | |
| with gr.Column(): | |
| vc_output2 = gr.Audio( | |
| label="Output Audio", interactive=False) | |
| vc_submit.click(vc_fn, [sid, vc_input3, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, | |
| cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold], [vc_output1, vc_output2]) | |
| vc_submit2.click(vc_fn2, [sid, vc_input3, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, | |
| lg_num, lgr_num, text2tts, tts_rate, tts_voice, f0_predictor, enhancer_adaptive_key, cr_threshold], [vc_output1, vc_output2]) | |
| app.queue(concurrency_count=3).launch(show_api=False, share=args.share) | |