rvc_infer.py

import os,sys,pdb,torch
now_dir = os.getcwd()
sys.path.append(now_dir)
import argparse
import glob
import sys
import torch
import ffmpeg
import numpy as np
import json
from multiprocessing import cpu_count

# Set name of your mangio folder (common options are Mangio-RVC-Fork, Mangio-RVC-v23.7.0_INFER_TRAIN, etc.): 
MANGIO_DIR = "Mangio-RVC-Fork"


def load_audio(file, sr):
    try:
        # https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
        file = (
            file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
        )  
        out, _ = (
            ffmpeg.input(file, threads=0)
            .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
        )
    except Exception as e:
        raise RuntimeError(f"Failed to load audio: {e}")

    return np.frombuffer(out, np.float32).flatten()


class Config:
    def __init__(self,device,is_half):
        self.device = device
        self.is_half = is_half
        self.n_cpu = 0
        self.gpu_name = None
        self.gpu_mem = None
        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()

    def device_config(self) -> tuple:
        if torch.cuda.is_available():
            i_device = int(self.device.split(":")[-1])
            self.gpu_name = torch.cuda.get_device_name(i_device)
            if (
                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
                or "P40" in self.gpu_name.upper()
                or "1060" in self.gpu_name
                or "1070" in self.gpu_name
                or "1080" in self.gpu_name
            ):
                self.is_half = False
                for config_file in ["32k.json", "40k.json", "48k.json"]:
                    with open(f"configs/{config_file}", "r") as f:
                        strr = f.read().replace("true", "false")
                    with open(f"configs/{config_file}", "w") as f:
                        f.write(strr)
                with open("trainset_preprocess_pipeline_print.py", "r") as f:
                    strr = f.read().replace("3.7", "3.0")
                with open("trainset_preprocess_pipeline_print.py", "w") as f:
                    f.write(strr)
            else:
                self.gpu_name = None
            self.gpu_mem = int(
                torch.cuda.get_device_properties(i_device).total_memory
                / 1024
                / 1024
                / 1024
                + 0.4
            )
            if self.gpu_mem <= 4:
                with open("trainset_preprocess_pipeline_print.py", "r") as f:
                    strr = f.read().replace("3.7", "3.0")
                with open("trainset_preprocess_pipeline_print.py", "w") as f:
                    f.write(strr)
        elif torch.backends.mps.is_available():
            self.device = "mps"
        else:
            self.device = "cpu"
            self.is_half = True

        if self.n_cpu == 0:
            self.n_cpu = cpu_count()

        if self.is_half:
            x_pad = 3
            x_query = 10
            x_center = 60
            x_max = 65
        else:
            x_pad = 1
            x_query = 6
            x_center = 38
            x_max = 41

        if self.gpu_mem != None and self.gpu_mem <= 4:
            x_pad = 1
            x_query = 5
            x_center = 30
            x_max = 32

        return x_pad, x_query, x_center, x_max


now_dir=os.getcwd()
sys.path.append(now_dir)
sys.path.append(os.path.join(now_dir, MANGIO_DIR))
os.chdir(os.path.join(now_dir, MANGIO_DIR))
from vc_infer_pipeline import VC
from lib.infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono
from fairseq import checkpoint_utils
from scipy.io import wavfile


hubert_model=None    
def load_hubert():
    global hubert_model
    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
        ["hubert_base.pt"],
        suffix="",
    )
    hubert_model = models[0]
    hubert_model = hubert_model.to(device)
    if is_half:
        hubert_model = hubert_model.half()
    else:
        hubert_model = hubert_model.float()
    hubert_model.eval()


def vc_single(
    sid,
    input_audio,
    f0_up_key,
    f0_file,
    f0_method,
    file_index,
    index_rate,
    filter_radius,
    resample_sr,
    rms_mix_rate,
    protect,
    crepe_hop_length,
):  
    global tgt_sr, net_g, vc, hubert_model
    if input_audio is None:return "You need to upload an audio", None
    f0_up_key = int(f0_up_key)
    # audio = load_audio(input_audio,16000)
    audio = input_audio
    audio_max = np.abs(audio).max() / 0.95
    if audio_max > 1:
        audio /= audio_max
    times = [0, 0, 0]
    if not hubert_model:
        load_hubert()
    if_f0 = cpt.get("f0", 1)
    version = cpt.get("version", "v1")
    if file_index != "":
        file_index = (
            file_index.strip(" ")
            .strip('"')
            .strip("\n")
            .strip('"')
            .strip(" ")
            .replace("trained", "added")
        )
    audio_opt = vc.pipeline(
        hubert_model,
        net_g,
        sid,
        audio,
        input_audio,
        times,
        f0_up_key,
        f0_method,
        file_index,
        index_rate,
        if_f0,
        filter_radius,
        tgt_sr,
        resample_sr,
        rms_mix_rate,
        version,
        protect,
        crepe_hop_length,
        f0_file=f0_file,
    )
    if tgt_sr != resample_sr >= 16000:
        tgt_sr = resample_sr
    index_info = (
        "Using index:%s." % file_index
        if os.path.exists(file_index)
        else "Index not used."
    )
    status = "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
        index_info,
        times[0],
        times[1],
        times[2],
    )
    print(status)
    return audio_opt


def get_vc(model_path, device_="cuda:0", is_half_=False):
    global n_spk, tgt_sr, net_g, vc, cpt, device, is_half
    device = device_
    is_half = is_half_
    config = Config(device, is_half)
    cpt = torch.load(model_path, map_location="cpu")
    tgt_sr = cpt["config"][-1]
    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
    if_f0 = cpt.get("f0", 1)
    version = cpt.get("version", "v1")
    if version == "v1":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
        else:
            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
    elif version == "v2":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
        else:
            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
    del net_g.enc_q
    print(net_g.load_state_dict(cpt["weight"], strict=False))
    net_g.eval().to(device)
    if is_half:
        net_g = net_g.half()
    else:
        net_g = net_g.float()
    vc = VC(tgt_sr, config)
    n_spk = cpt["config"][-3]