367 lines
13 KiB
Python
367 lines
13 KiB
Python
import hashlib
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import json
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from multiprocessing.pool import ThreadPool
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import os
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from .helper import ObjectNamespace
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import numpy as np
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import torch
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from tqdm import tqdm
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from .mdx import MDXModel
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from .uvr5_pack.constants import MDX_NET_FREQ_CUT
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from .uvr5_pack.vr_network.model_param_init import ModelParameters
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from .uvr5_pack.vr_network.nets_new import CascadedNet
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from .uvr5_pack.vr_network.nets import CascadedASPPNet
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from .audio import remix_audio
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import librosa
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from .uvr5_pack import spec_utils
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dir_path = os.path.dirname(os.path.realpath(__file__))
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class UVR5Base:
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def __init__(self, agg, model_path, device, is_half,**kwargs):
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self.model_path = model_path
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self.device = device
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self.data = {
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# Processing Options
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"postprocess": False,
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"tta": False,
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# Constants
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"window_size": 512,
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"agg": agg,
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"high_end_process": "mirroring",
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}
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mp = ModelParameters(os.path.join(dir_path,"uvr5_pack","vr_network","modelparams","4band_v2.json"))
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model = CascadedASPPNet(mp.param["bins"] * 2)
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cpk = torch.load(model_path, map_location=self.device)
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model.load_state_dict(cpk)
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model.eval()
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if is_half:
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model = model.half().to(device)
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else:
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model = model.to(device)
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self.mp = mp
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self.model = model
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@staticmethod
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def get_model_params(model_path):
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try:
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with open(model_path, 'rb') as f:
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f.seek(- 10000 * 1024, 2)
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model_hash = hashlib.md5(f.read()).hexdigest()
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except:
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model_hash = hashlib.md5(open(model_path, 'rb').read()).hexdigest()
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print(model_hash)
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model_settings_json = os.path.splitext(model_path)[0]+".json"
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model_data_json = os.path.join(os.path.dirname(model_path),"model_data.json")
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if os.path.isfile(model_settings_json):
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return json.load(open(model_settings_json))
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elif os.path.isfile(model_data_json):
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with open(model_data_json,"r") as d:
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hash_mapper = json.loads(d.read())
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for hash, settings in hash_mapper.items():
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if model_hash in hash:
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return settings
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return None
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def inference(self, X_spec, aggressiveness):
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"""
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data ： dic configs
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"""
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data = self.data
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device = self.device
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model = self.model
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def _execute(
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X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True
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):
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model.eval()
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with torch.no_grad():
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preds = []
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for i in tqdm(range(n_window)):
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start = i * roi_size
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X_mag_window = X_mag_pad[
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None, :, :, start : start + data["window_size"]
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]
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X_mag_window = torch.from_numpy(X_mag_window)
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if is_half:
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X_mag_window = X_mag_window.half()
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X_mag_window = X_mag_window.to(device)
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pred = model.predict(X_mag_window, aggressiveness)
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pred = pred.detach().cpu().numpy()
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preds.append(pred[0])
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pred = np.concatenate(preds, axis=2)
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return pred
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def preprocess(X_spec):
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X_mag = np.abs(X_spec)
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X_phase = np.angle(X_spec)
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return X_mag, X_phase
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X_mag, X_phase = preprocess(X_spec)
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coef = X_mag.max()
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X_mag_pre = X_mag / coef
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n_frame = X_mag_pre.shape[2]
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pad_l, pad_r, roi_size = spec_utils.make_padding(n_frame, data["window_size"], model.offset)
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n_window = int(np.ceil(n_frame / roi_size))
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X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
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if list(model.state_dict().values())[0].dtype == torch.float16:
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is_half = True
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else:
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is_half = False
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pred = _execute(
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X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
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)
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pred = pred[:, :, :n_frame]
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if data["tta"]:
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pad_l += roi_size // 2
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pad_r += roi_size // 2
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n_window += 1
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X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
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pred_tta = _execute(
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X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
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)
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pred_tta = pred_tta[:, :, roi_size // 2 :]
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pred_tta = pred_tta[:, :, :n_frame]
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return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase)
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else:
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return pred * coef, X_mag, np.exp(1.0j * X_phase)
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def process_vocals(self,v_spec_m,input_high_end,input_high_end_h,return_dict={}):
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if self.data["high_end_process"].startswith("mirroring"):
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input_high_end_ = spec_utils.mirroring(
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self.data["high_end_process"], v_spec_m, input_high_end, self.mp
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)
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wav_vocals = spec_utils.cmb_spectrogram_to_wave(
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v_spec_m, self.mp, input_high_end_h, input_high_end_
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)
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else:
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wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
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print(f"vocals done: {wav_vocals.shape}")
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return_dict["vocals"] = remix_audio((wav_vocals,return_dict["sr"]),to_int16=True,axis=0)
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return return_dict["vocals"]
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def process_instrumental(self,y_spec_m,input_high_end,input_high_end_h,return_dict={}):
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if self.data["high_end_process"].startswith("mirroring"):
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input_high_end_ = spec_utils.mirroring(
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self.data["high_end_process"], y_spec_m, input_high_end, self.mp
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)
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wav_instrument = spec_utils.cmb_spectrogram_to_wave(
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y_spec_m, self.mp, input_high_end_h, input_high_end_
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)
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else:
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wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
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print(f"instruments done: {wav_instrument.shape}")
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return_dict["instrumentals"] = remix_audio((wav_instrument,return_dict["sr"]),to_int16=True,axis=0)
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return return_dict["instrumentals"]
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def process_audio(self,y_spec_m,v_spec_m,input_high_end,input_high_end_h):
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return_dict = {
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"sr": self.mp.param["sr"]
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}
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with ThreadPool(2) as pool:
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pool.apply(self.process_vocals, args=(v_spec_m,input_high_end,input_high_end_h,return_dict))
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pool.apply(self.process_instrumental, args=(y_spec_m,input_high_end,input_high_end_h,return_dict))
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return return_dict
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def run_inference(self, music_file):
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X_wave, X_spec_s = {}, {}
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bands_n = len(self.mp.param["band"])
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# print(bands_n)
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for d in range(bands_n, 0, -1):
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bp = self.mp.param["band"][d]
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if d == bands_n: # high-end band
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input_audio = librosa.load(music_file,sr=bp["sr"],res_type=bp["res_type"])
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X_wave[d] = input_audio[0]
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if X_wave[d].ndim == 1:
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X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
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else: # lower bands
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X_wave[d] = librosa.resample(
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X_wave[d + 1],
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orig_sr=self.mp.param["band"][d + 1]["sr"],
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target_sr=bp["sr"],
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res_type=bp["res_type"],
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)
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# Stft of wave source
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X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
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X_wave[d],
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bp["hl"],
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bp["n_fft"],
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self.mp.param["mid_side"],
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self.mp.param["mid_side_b2"],
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self.mp.param["reverse"],
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)
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# pdb.set_trace()
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if d == bands_n and self.data["high_end_process"] != "none":
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input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
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self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
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)
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input_high_end = X_spec_s[d][
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:, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
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]
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X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
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aggresive_set = float(self.data["agg"] / 100)
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aggressiveness = {
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"value": aggresive_set,
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"split_bin": self.mp.param["band"][1]["crop_stop"],
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}
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# pred, X_mag, X_phase = run_inference(X_spec_m, self.device, self.models, aggressiveness, self.data)
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with torch.no_grad():
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pred, X_mag, X_phase = self.inference(X_spec_m, aggressiveness)
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# return pred, X_mag, X_phase, X_spec_m, input_high_end,input_high_end_h
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# def run_process_audio(self, pred, X_mag, X_phase, X_spec_m, input_high_end,input_high_end_h):
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# Postprocess
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if self.data["postprocess"]:
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pred_inv = np.clip(X_mag - pred, 0, np.inf)
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pred = spec_utils.mask_silence(pred, pred_inv)
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y_spec_m = pred * X_phase
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v_spec_m = X_spec_m - y_spec_m
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return_dict = self.process_audio(y_spec_m,v_spec_m,input_high_end,input_high_end_h)
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return_dict["input_audio"] = input_audio
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return return_dict
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class UVR5New(UVR5Base):
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def __init__(self, agg, model_path, device, is_half, dereverb, **kwargs):
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self.model_path = model_path
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self.device = device
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self.data = {
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# Processing Options
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"postprocess": False,
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"tta": False,
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# Constants
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"window_size": 512,
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"agg": agg,
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"high_end_process": "mirroring",
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}
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mp = ModelParameters(os.path.join(dir_path,"uvr5_pack","vr_network","modelparams","4band_v3.json"))
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nout = 64 if dereverb else 48
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model = CascadedNet(mp.param["bins"] * 2, nout)
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cpk = torch.load(model_path, map_location=self.device)
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model.load_state_dict(cpk)
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model.eval()
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if is_half:
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model = model.half().to(device)
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else:
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model = model.to(device)
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self.mp = mp
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self.model = model
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class MDXNet:
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def __init__(self, model_path, chunks=15,denoise=False,num_threads=1,device="cpu",**kwargs):
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self.chunks = chunks
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self.sr = 44100
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self.args = ObjectNamespace(**kwargs)
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self.denoise = denoise
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self.num_threads = num_threads
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self.device = device
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self.is_mdx_ckpt = "ckpt" in model_path
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self.model = MDXModel(model_path, device=self.device,chunks=self.chunks,margin=self.sr)
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def __del__(self):
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del self.model
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def process_audio(self,primary,secondary,target_sr=None):
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target_sr = self.sr if target_sr is None else target_sr
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# foreground is processed data
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vocals,instrumental = (secondary,primary) if "instrument" in self.model.params.stem_name.lower() else (primary,secondary)
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with ThreadPool(2) as pool:
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results = pool.starmap(remix_audio, [
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((instrumental,self.sr),target_sr,False,True,self.sr!=target_sr,True),
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((vocals,self.sr),target_sr,False,True,self.sr!=target_sr,True)
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])
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return_dict = {
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"sr": target_sr,
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"instrumentals": results[0],
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"vocals": results[1]
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}
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return return_dict
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def run_inference(self, audio_path):
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mdx_net_cut = True if self.model.params.stem_name in MDX_NET_FREQ_CUT else False
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mix, raw_mix, samplerate = prepare_mix(audio_path, self.model.chunks, self.model.margin, mdx_net_cut=mdx_net_cut)
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wave_processed = self.model.demix_base(mix, is_ckpt=self.is_mdx_ckpt)[0]
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raw_mix = self.model.demix_base(raw_mix, is_match_mix=True)[0] if mdx_net_cut else raw_mix
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return_dict = self.process_audio(primary=wave_processed,secondary=(raw_mix-wave_processed),target_sr=samplerate)
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return_dict["input_audio"] = (raw_mix, samplerate)
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return return_dict
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def prepare_mix(mix, chunk_set, margin_set, mdx_net_cut=False):
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samplerate = 44100
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if not isinstance(mix, np.ndarray):
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mix, samplerate = librosa.load(mix, mono=False, sr=44100)
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else:
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mix = mix.T
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if mix.ndim == 1:
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mix = np.asfortranarray([mix,mix])
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def get_segmented_mix(chunk_set=chunk_set):
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segmented_mix = {}
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samples = mix.shape[-1]
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margin = margin_set
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chunk_size = chunk_set*44100
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assert not margin == 0, 'margin cannot be zero!'
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if margin > chunk_size:
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margin = chunk_size
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if chunk_set == 0 or samples < chunk_size:
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chunk_size = samples
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counter = -1
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for skip in range(0, samples, chunk_size):
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counter+=1
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s_margin = 0 if counter == 0 else margin
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end = min(skip+chunk_size+margin, samples)
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start = skip-s_margin
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segmented_mix[skip] = mix[:,start:end].copy()
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if end == samples:
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break
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return segmented_mix
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segmented_mix = get_segmented_mix()
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raw_mix = get_segmented_mix(chunk_set=0) if mdx_net_cut else mix
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return segmented_mix, raw_mix, samplerate |