infer/modules/vc/modules.py

import traceback
import logging

logger = logging.getLogger(__name__)

import numpy as np
import soundfile as sf
import torch
from io import BytesIO

from infer.lib.audio import load_audio, wav2
from infer.lib.infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono,
)
from infer.modules.vc.pipeline import Pipeline
from infer.modules.vc.utils import *

# 导入彩色日志
try:
    from lib.logger import log
except ImportError:
    log = None


class VC:
    def __init__(self, config):
        self.n_spk = None
        self.tgt_sr = None
        self.net_g = None
        self.pipeline = None
        self.cpt = None
        self.version = None
        self.if_f0 = None
        self.version = None
        self.hubert_model = None

        self.config = config

    def get_vc(self, sid, *to_return_protect):
        logger.info("Get sid: " + sid)
        if log:
            log.model(f"获取模型: {sid}")

        to_return_protect0 = {
            "visible": self.if_f0 != 0,
            "value": (
                to_return_protect[0] if self.if_f0 != 0 and to_return_protect else 0.5
            ),
            "__type__": "update",
        }
        to_return_protect1 = {
            "visible": self.if_f0 != 0,
            "value": (
                to_return_protect[1] if self.if_f0 != 0 and to_return_protect else 0.33
            ),
            "__type__": "update",
        }

        if sid == "" or sid == []:
            if (
                self.hubert_model is not 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
                logger.info("Clean model cache")
                if log:
                    log.detail("清理模型缓存...")
                del (self.net_g, self.n_spk, self.hubert_model, self.tgt_sr)  # ,cpt
                self.hubert_model = self.net_g = self.n_spk = self.hubert_model = (
                    self.tgt_sr
                ) = None
                if torch.cuda.is_available():
                    torch.cuda.empty_cache()
                    if log:
                        log.detail("已清理CUDA缓存")
                ###楼下不这么折腾清理不干净
                self.if_f0 = self.cpt.get("f0", 1)
                self.version = self.cpt.get("version", "v1")
                if self.version == "v1":
                    if self.if_f0 == 1:
                        self.net_g = SynthesizerTrnMs256NSFsid(
                            *self.cpt["config"], is_half=self.config.is_half
                        )
                    else:
                        self.net_g = SynthesizerTrnMs256NSFsid_nono(*self.cpt["config"])
                elif self.version == "v2":
                    if self.if_f0 == 1:
                        self.net_g = SynthesizerTrnMs768NSFsid(
                            *self.cpt["config"], is_half=self.config.is_half
                        )
                    else:
                        self.net_g = SynthesizerTrnMs768NSFsid_nono(*self.cpt["config"])
                del self.net_g, self.cpt
                if torch.cuda.is_available():
                    torch.cuda.empty_cache()
            return (
                {"visible": False, "__type__": "update"},
                {
                    "visible": True,
                    "value": to_return_protect0,
                    "__type__": "update",
                },
                {
                    "visible": True,
                    "value": to_return_protect1,
                    "__type__": "update",
                },
                "",
                "",
            )
        person = f'{os.getenv("weight_root")}/{sid}'
        logger.info(f"Loading: {person}")
        if log:
            log.model(f"加载模型文件: {person}")

        self.cpt = torch.load(person, map_location="cpu", weights_only=False)
        self.tgt_sr = self.cpt["config"][-1]
        self.cpt["config"][-3] = self.cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
        self.if_f0 = self.cpt.get("f0", 1)
        self.version = self.cpt.get("version", "v1")

        if log:
            log.config(f"模型版本: {self.version}")
            log.config(f"目标采样率: {self.tgt_sr} Hz")
            log.config(f"F0支持: {'是' if self.if_f0 else '否'}")

        synthesizer_class = {
            ("v1", 1): SynthesizerTrnMs256NSFsid,
            ("v1", 0): SynthesizerTrnMs256NSFsid_nono,
            ("v2", 1): SynthesizerTrnMs768NSFsid,
            ("v2", 0): SynthesizerTrnMs768NSFsid_nono,
        }

        if log:
            log.detail(f"选择合成器: {synthesizer_class.get((self.version, self.if_f0), SynthesizerTrnMs256NSFsid).__name__}")

        self.net_g = synthesizer_class.get(
            (self.version, self.if_f0), SynthesizerTrnMs256NSFsid
        )(*self.cpt["config"], is_half=self.config.is_half)

        del self.net_g.enc_q

        self.net_g.load_state_dict(self.cpt["weight"], strict=False)
        self.net_g.eval().to(self.config.device)
        if self.config.is_half:
            self.net_g = self.net_g.half()
            if log:
                log.detail("使用半精度模式")
        else:
            self.net_g = self.net_g.float()
            if log:
                log.detail("使用全精度模式")

        if log:
            log.progress("初始化推理管道...")
        self.pipeline = Pipeline(self.tgt_sr, self.config)
        n_spk = self.cpt["config"][-3]
        if log:
            log.config(f"说话人数量: {n_spk}")

        index = {"value": get_index_path_from_model(sid), "__type__": "update"}
        logger.info("Select index: " + index["value"])
        if log:
            log.model(f"选择索引: {index['value']}")
            log.success("模型加载完成")

        return (
            (
                {"visible": True, "maximum": n_spk, "__type__": "update"},
                to_return_protect0,
                to_return_protect1,
                index,
                index,
            )
            if to_return_protect
            else {"visible": True, "maximum": n_spk, "__type__": "update"}
        )

    def vc_single(
        self,
        sid,
        input_audio_path,
        f0_up_key,
        f0_file,
        f0_method,
        file_index,
        file_index2,
        index_rate,
        filter_radius,
        resample_sr,
        rms_mix_rate,
        protect,
    ):
        if input_audio_path is None:
            return "You need to upload an audio", None

        if log:
            log.progress("开始单文件人声转换...")
            log.audio(f"输入音频: {input_audio_path}")
            log.config(f"音调偏移: {f0_up_key} 半音")
            log.config(f"F0方法: {f0_method}")
            log.config(f"索引率: {index_rate}")
            log.config(f"滤波半径: {filter_radius}")
            log.config(f"RMS混合率: {rms_mix_rate}")
            log.config(f"保护系数: {protect}")

        f0_up_key = int(f0_up_key)
        try:
            if log:
                log.detail("加载音频文件...")
            audio = load_audio(input_audio_path, 16000)
            audio_max = np.abs(audio).max() / 0.95
            if audio_max > 1:
                audio /= audio_max
                if log:
                    log.detail(f"音频归一化: 峰值={audio_max:.4f}")

            if log:
                log.detail(f"音频长度: {len(audio)} 样本 ({len(audio)/16000:.2f} 秒)")

            times = [0, 0, 0]

            if self.hubert_model is None:
                if log:
                    log.model("加载HuBERT模型...")
                self.hubert_model = load_hubert(self.config)
                if log:
                    log.success("HuBERT模型加载完成")

            if file_index:
                file_index = (
                    file_index.strip(" ")
                    .strip('"')
                    .strip("\n")
                    .strip('"')
                    .strip(" ")
                    .replace("trained", "added")
                )
            elif file_index2:
                file_index = file_index2
            else:
                file_index = ""  # 防止小白写错，自动帮他替换掉

            if log and file_index:
                log.model(f"使用索引文件: {file_index}")

            if log:
                log.progress("执行推理管道...")

            audio_opt = self.pipeline.pipeline(
                self.hubert_model,
                self.net_g,
                sid,
                audio,
                input_audio_path,
                times,
                f0_up_key,
                f0_method,
                file_index,
                index_rate,
                self.if_f0,
                filter_radius,
                self.tgt_sr,
                resample_sr,
                rms_mix_rate,
                self.version,
                protect,
                f0_file,
            )
            if self.tgt_sr != resample_sr >= 16000:
                tgt_sr = resample_sr
            else:
                tgt_sr = self.tgt_sr
            index_info = (
                "Index:\n%s." % file_index
                if os.path.exists(file_index)
                else "Index not used."
            )

            if log:
                log.success("推理完成")
                log.detail(f"NPY时间: {times[0]:.2f}s, F0时间: {times[1]:.2f}s, 推理时间: {times[2]:.2f}s")
                log.audio(f"输出采样率: {tgt_sr} Hz")
                log.audio(f"输出长度: {len(audio_opt)} 样本")

            return (
                "Success.\n%s\nTime:\nnpy: %.2fs, f0: %.2fs, infer: %.2fs."
                % (index_info, *times),
                (tgt_sr, audio_opt),
            )
        except:
            info = traceback.format_exc()
            logger.warning(info)
            if log:
                log.error(f"转换失败:\n{info}")
            return info, (None, None)

    def vc_multi(
        self,
        sid,
        dir_path,
        opt_root,
        paths,
        f0_up_key,
        f0_method,
        file_index,
        file_index2,
        index_rate,
        filter_radius,
        resample_sr,
        rms_mix_rate,
        protect,
        format1,
    ):
        try:
            dir_path = (
                dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
            )  # 防止小白拷路径头尾带了空格和"和回车
            opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
            os.makedirs(opt_root, exist_ok=True)
            try:
                if dir_path != "":
                    paths = [
                        os.path.join(dir_path, name) for name in os.listdir(dir_path)
                    ]
                else:
                    paths = [path.name for path in paths]
            except:
                traceback.print_exc()
                paths = [path.name for path in paths]
            infos = []
            for path in paths:
                info, opt = self.vc_single(
                    sid,
                    path,
                    f0_up_key,
                    None,
                    f0_method,
                    file_index,
                    file_index2,
                    # file_big_npy,
                    index_rate,
                    filter_radius,
                    resample_sr,
                    rms_mix_rate,
                    protect,
                )
                if "Success" in info:
                    try:
                        tgt_sr, audio_opt = opt
                        if format1 in ["wav", "flac"]:
                            sf.write(
                                "%s/%s.%s"
                                % (opt_root, os.path.basename(path), format1),
                                audio_opt,
                                tgt_sr,
                            )
                        else:
                            path = "%s/%s.%s" % (
                                opt_root,
                                os.path.basename(path),
                                format1,
                            )
                            with BytesIO() as wavf:
                                sf.write(wavf, audio_opt, tgt_sr, format="wav")
                                wavf.seek(0, 0)
                                with open(path, "wb") as outf:
                                    wav2(wavf, outf, format1)
                    except:
                        info += traceback.format_exc()
                infos.append("%s->%s" % (os.path.basename(path), info))
                yield "\n".join(infos)
            yield "\n".join(infos)
        except:
            yield traceback.format_exc()