320 lines
11 KiB
Python
320 lines
11 KiB
Python
import os
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import torch
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# os.system("wget -P cvec/ https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt")
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import gradio as gr
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import librosa
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import numpy as np
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import logging
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from fairseq import checkpoint_utils
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from vc_infer_pipeline import VC
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import traceback
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from config import Config
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from lib.infer_pack.models import (
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SynthesizerTrnMs256NSFsid,
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SynthesizerTrnMs256NSFsid_nono,
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SynthesizerTrnMs768NSFsid,
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SynthesizerTrnMs768NSFsid_nono,
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)
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from i18n import I18nAuto
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logging.getLogger("numba").setLevel(logging.WARNING)
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logging.getLogger("markdown_it").setLevel(logging.WARNING)
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logging.getLogger("urllib3").setLevel(logging.WARNING)
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logging.getLogger("matplotlib").setLevel(logging.WARNING)
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i18n = I18nAuto()
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i18n.print()
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config = Config()
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weight_root = "weights"
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weight_uvr5_root = "uvr5_weights"
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index_root = "logs"
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names = []
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hubert_model = None
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for name in os.listdir(weight_root):
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if name.endswith(".pth"):
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names.append(name)
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index_paths = []
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for root, dirs, files in os.walk(index_root, topdown=False):
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for name in files:
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if name.endswith(".index") and "trained" not in name:
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index_paths.append("%s/%s" % (root, name))
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def get_vc(sid):
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global n_spk, tgt_sr, net_g, vc, cpt, version
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if sid == "" or sid == []:
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global hubert_model
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if hubert_model != None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
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print("clean_empty_cache")
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del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt
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hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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###楼下不这么折腾清理不干净
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if_f0 = cpt.get("f0", 1)
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version = cpt.get("version", "v1")
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if version == "v1":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(
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*cpt["config"], is_half=config.is_half
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)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif version == "v2":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs768NSFsid(
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*cpt["config"], is_half=config.is_half
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)
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else:
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net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del net_g, cpt
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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cpt = None
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return {"visible": False, "__type__": "update"}
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person = "%s/%s" % (weight_root, sid)
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print("loading %s" % person)
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cpt = torch.load(person, map_location="cpu")
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tgt_sr = cpt["config"][-1]
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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if_f0 = cpt.get("f0", 1)
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version = cpt.get("version", "v1")
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if version == "v1":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif version == "v2":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
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else:
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net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del net_g.enc_q
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print(net_g.load_state_dict(cpt["weight"], strict=False))
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net_g.eval().to(config.device)
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if config.is_half:
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net_g = net_g.half()
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else:
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net_g = net_g.float()
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vc = VC(tgt_sr, config)
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n_spk = cpt["config"][-3]
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return {"visible": True, "maximum": n_spk, "__type__": "update"}
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def load_hubert():
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global hubert_model
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models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
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["hubert_base.pt"],
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suffix="",
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)
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hubert_model = models[0]
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hubert_model = hubert_model.to(config.device)
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if config.is_half:
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hubert_model = hubert_model.half()
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else:
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hubert_model = hubert_model.float()
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hubert_model.eval()
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def vc_single(
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sid,
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input_audio_path,
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f0_up_key,
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f0_file,
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f0_method,
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file_index,
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file_index2,
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# file_big_npy,
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index_rate,
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filter_radius,
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resample_sr,
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rms_mix_rate,
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protect,
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): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
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global tgt_sr, net_g, vc, hubert_model, version
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if input_audio_path is None:
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return "You need to upload an audio", None
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f0_up_key = int(f0_up_key)
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try:
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audio = input_audio_path[1] / 32768.0
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if len(audio.shape) == 2:
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audio = np.mean(audio, -1)
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audio = librosa.resample(audio, orig_sr=input_audio_path[0], target_sr=16000)
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audio_max = np.abs(audio).max() / 0.95
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if audio_max > 1:
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audio /= audio_max
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times = [0, 0, 0]
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if hubert_model == None:
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load_hubert()
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if_f0 = cpt.get("f0", 1)
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file_index = (
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(
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file_index.strip(" ")
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.strip('"')
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.strip("\n")
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.strip('"')
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.strip(" ")
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.replace("trained", "added")
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)
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if file_index != ""
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else file_index2
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) # 防止小白写错，自动帮他替换掉
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# file_big_npy = (
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# file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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# )
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audio_opt = vc.pipeline(
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hubert_model,
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net_g,
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sid,
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audio,
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input_audio_path,
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times,
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f0_up_key,
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f0_method,
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file_index,
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# file_big_npy,
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index_rate,
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if_f0,
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filter_radius,
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tgt_sr,
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resample_sr,
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rms_mix_rate,
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version,
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protect,
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f0_file=f0_file,
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)
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if resample_sr >= 16000 and tgt_sr != resample_sr:
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tgt_sr = resample_sr
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index_info = (
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"Using index:%s." % file_index
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if os.path.exists(file_index)
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else "Index not used."
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)
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return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
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index_info,
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times[0],
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times[1],
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times[2],
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), (tgt_sr, audio_opt)
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except:
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info = traceback.format_exc()
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print(info)
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return info, (None, None)
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app = gr.Blocks()
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with app:
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with gr.Tabs():
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with gr.TabItem("在线demo"):
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gr.Markdown(
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value="""
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RVC 在线demo
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"""
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)
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sid = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
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with gr.Column():
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spk_item = gr.Slider(
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minimum=0,
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maximum=2333,
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step=1,
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label=i18n("请选择说话人id"),
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value=0,
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visible=False,
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interactive=True,
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)
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sid.change(
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fn=get_vc,
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inputs=[sid],
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outputs=[spk_item],
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)
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gr.Markdown(
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value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ")
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)
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vc_input3 = gr.Audio(label="上传音频（长度小于90秒）")
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vc_transform0 = gr.Number(label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0)
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f0method0 = gr.Radio(
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label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"),
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choices=["pm", "harvest", "crepe"],
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value="pm",
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interactive=True,
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)
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filter_radius0 = gr.Slider(
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minimum=0,
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maximum=7,
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label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
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value=3,
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step=1,
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interactive=True,
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)
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with gr.Column():
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file_index1 = gr.Textbox(
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label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
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value="",
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interactive=False,
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visible=False,
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)
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file_index2 = gr.Dropdown(
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label=i18n("自动检测index路径,下拉式选择(dropdown)"),
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choices=sorted(index_paths),
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interactive=True,
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)
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index_rate1 = gr.Slider(
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minimum=0,
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maximum=1,
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label=i18n("检索特征占比"),
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value=0.88,
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interactive=True,
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)
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resample_sr0 = gr.Slider(
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minimum=0,
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maximum=48000,
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label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
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value=0,
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step=1,
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interactive=True,
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)
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rms_mix_rate0 = gr.Slider(
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minimum=0,
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maximum=1,
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label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
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value=1,
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interactive=True,
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)
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protect0 = gr.Slider(
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minimum=0,
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maximum=0.5,
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label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
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value=0.33,
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step=0.01,
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interactive=True,
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)
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f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"))
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but0 = gr.Button(i18n("转换"), variant="primary")
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vc_output1 = gr.Textbox(label=i18n("输出信息"))
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vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)"))
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but0.click(
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vc_single,
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[
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spk_item,
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vc_input3,
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vc_transform0,
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f0_file,
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f0method0,
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file_index1,
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file_index2,
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# file_big_npy1,
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index_rate1,
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filter_radius0,
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resample_sr0,
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rms_mix_rate0,
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protect0,
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],
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[vc_output1, vc_output2],
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)
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app.launch()
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