mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-05 17:20:25 +08:00
1e22d468ea
* feat(audio): use PyAV instead of ffmpeg replaced usage of ffmpeg in favor of PyAV (`av`) * refactor(audio): store all of the audio related functions in the `infer.lib.audio` refactors previous commit to have singular functions for each task, all located in `infer.lib.audio` * fix(audio): remove downsample_audio from mdxnet.py it is no longer needed, since it's imported from infer.lib.audio * docs: remove every ffmpeg mention in the documentation to avoid confusion * chore(requirements): remove ffmpeg-python and ffmpy from all requirements * fix(audio): fix loading for UVR wrapped gathering of META info from the stream into a function fixes loading for UVR * fix(audio): use np.frombuffer() instead of direct conversion of the resampled frames this fixes traceback on preprocessing * feat(audio): pre-allocate decoded_audio array in the load_audio function this should improve performance, even if just a little * Revert "docs: remove every ffmpeg mention in the documentation to avoid confusion" This reverts commit1e05bbce03. * chore(format): run black on dev * fix(requirements): revert removal of ffmpeg in unitest.yml and Dockerfile * Revert "fix(requirements): revert removal of ffmpeg in unitest.yml and Dockerfile" This reverts commite28a0eebb2. * feat(audio): pre-allocate numpy array to store the AudioFrame data in ndarray of dtype float32 * chore(format): run black on dev * fix(audio): fix the decoded_audio size estimation in estimated_total_samples we multiply by `sr` instead of `container.streams.audio[0].rate` since we want to estimate size of the OUTPUT file, not the input one. - Added dynamic resizing, in case something goes wrong and the size of decoded_audio is estimated incorrectly Fixed function `load_audio` when the input audio's samplerate does not match the desired samplerate (`sr`) * chore(format): run black on dev * refactor(audio): remove `clean_path()` function as it serves no purpose anymore * docs: remove everything related to ffmpeg this includes everything except for formats support specification in the training_tips docs, since it has nothing to do with what ffmpeg does/did but rather what audio formats are supported (all the ones that ffmpeg supports!) * docs: fix order of the steps in preparation in the READMEs --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
349 lines
14 KiB
Python
349 lines
14 KiB
Python
import os
|
||
import logging
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
import librosa
|
||
import numpy as np
|
||
import soundfile as sf
|
||
from infer.lib.audio import downsample_audio
|
||
import torch
|
||
|
||
from infer.lib.uvr5_pack.lib_v5 import nets_123821KB as Nets
|
||
from infer.lib.uvr5_pack.lib_v5 import spec_utils
|
||
from infer.lib.uvr5_pack.lib_v5.model_param_init import ModelParameters
|
||
from infer.lib.uvr5_pack.lib_v5.nets import CascadedNet
|
||
from infer.lib.uvr5_pack.utils import inference
|
||
|
||
|
||
class AudioPre:
|
||
def __init__(self, agg, model_path, device, is_half, tta=False):
|
||
self.model_path = model_path
|
||
self.device = device
|
||
self.data = {
|
||
# Processing Options
|
||
"postprocess": False,
|
||
"tta": tta,
|
||
# Constants
|
||
"window_size": 512,
|
||
"agg": agg,
|
||
"high_end_process": "mirroring",
|
||
}
|
||
mp = ModelParameters("infer/lib/uvr5_pack/lib_v5/modelparams/4band_v2.json")
|
||
model = Nets.CascadedASPPNet(mp.param["bins"] * 2)
|
||
cpk = torch.load(model_path, map_location="cpu")
|
||
model.load_state_dict(cpk)
|
||
model.eval()
|
||
if is_half:
|
||
model = model.half().to(device)
|
||
else:
|
||
model = model.to(device)
|
||
|
||
self.mp = mp
|
||
self.model = model
|
||
|
||
def _path_audio_(
|
||
self, music_file, ins_root=None, vocal_root=None, format="flac", is_hp3=False
|
||
):
|
||
if ins_root is None and vocal_root is None:
|
||
return "No save root."
|
||
name = os.path.basename(music_file)
|
||
if ins_root is not None:
|
||
os.makedirs(ins_root, exist_ok=True)
|
||
if vocal_root is not None:
|
||
os.makedirs(vocal_root, exist_ok=True)
|
||
X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
|
||
bands_n = len(self.mp.param["band"])
|
||
# print(bands_n)
|
||
for d in range(bands_n, 0, -1):
|
||
bp = self.mp.param["band"][d]
|
||
if d == bands_n: # high-end band
|
||
(
|
||
X_wave[d],
|
||
_,
|
||
) = librosa.core.load( # 理论上librosa读取可能对某些音频有bug，应该上av读取，但是太麻烦了弃坑
|
||
music_file,
|
||
bp["sr"],
|
||
False,
|
||
dtype=np.float32,
|
||
res_type=bp["res_type"],
|
||
)
|
||
if X_wave[d].ndim == 1:
|
||
X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
|
||
else: # lower bands
|
||
X_wave[d] = librosa.core.resample(
|
||
X_wave[d + 1],
|
||
self.mp.param["band"][d + 1]["sr"],
|
||
bp["sr"],
|
||
res_type=bp["res_type"],
|
||
)
|
||
# Stft of wave source
|
||
X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
|
||
X_wave[d],
|
||
bp["hl"],
|
||
bp["n_fft"],
|
||
self.mp.param["mid_side"],
|
||
self.mp.param["mid_side_b2"],
|
||
self.mp.param["reverse"],
|
||
)
|
||
# pdb.set_trace()
|
||
if d == bands_n and self.data["high_end_process"] != "none":
|
||
input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
|
||
self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
|
||
)
|
||
input_high_end = X_spec_s[d][
|
||
:, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
|
||
]
|
||
|
||
X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
|
||
aggresive_set = float(self.data["agg"] / 100)
|
||
aggressiveness = {
|
||
"value": aggresive_set,
|
||
"split_bin": self.mp.param["band"][1]["crop_stop"],
|
||
}
|
||
with torch.no_grad():
|
||
pred, X_mag, X_phase = inference(
|
||
X_spec_m, self.device, self.model, aggressiveness, self.data
|
||
)
|
||
# Postprocess
|
||
if self.data["postprocess"]:
|
||
pred_inv = np.clip(X_mag - pred, 0, np.inf)
|
||
pred = spec_utils.mask_silence(pred, pred_inv)
|
||
y_spec_m = pred * X_phase
|
||
v_spec_m = X_spec_m - y_spec_m
|
||
|
||
if ins_root is not None:
|
||
if self.data["high_end_process"].startswith("mirroring"):
|
||
input_high_end_ = spec_utils.mirroring(
|
||
self.data["high_end_process"], y_spec_m, input_high_end, self.mp
|
||
)
|
||
wav_instrument = spec_utils.cmb_spectrogram_to_wave(
|
||
y_spec_m, self.mp, input_high_end_h, input_high_end_
|
||
)
|
||
else:
|
||
wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
|
||
logger.info("%s instruments done" % name)
|
||
if is_hp3 == True:
|
||
head = "vocal_"
|
||
else:
|
||
head = "instrument_"
|
||
if format in ["wav", "flac"]:
|
||
sf.write(
|
||
os.path.join(
|
||
ins_root,
|
||
head + "{}_{}.{}".format(name, self.data["agg"], format),
|
||
),
|
||
(np.array(wav_instrument) * 32768).astype("int16"),
|
||
self.mp.param["sr"],
|
||
) #
|
||
else:
|
||
path = os.path.join(
|
||
ins_root, head + "{}_{}.wav".format(name, self.data["agg"])
|
||
)
|
||
sf.write(
|
||
path,
|
||
(np.array(wav_instrument) * 32768).astype("int16"),
|
||
self.mp.param["sr"],
|
||
)
|
||
if os.path.exists(path):
|
||
opt_format_path = path[:-4] + ".%s" % format
|
||
downsample_audio(path, opt_format_path, format)
|
||
if vocal_root is not None:
|
||
if is_hp3 == True:
|
||
head = "instrument_"
|
||
else:
|
||
head = "vocal_"
|
||
if self.data["high_end_process"].startswith("mirroring"):
|
||
input_high_end_ = spec_utils.mirroring(
|
||
self.data["high_end_process"], v_spec_m, input_high_end, self.mp
|
||
)
|
||
wav_vocals = spec_utils.cmb_spectrogram_to_wave(
|
||
v_spec_m, self.mp, input_high_end_h, input_high_end_
|
||
)
|
||
else:
|
||
wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
|
||
logger.info("%s vocals done" % name)
|
||
if format in ["wav", "flac"]:
|
||
sf.write(
|
||
os.path.join(
|
||
vocal_root,
|
||
head + "{}_{}.{}".format(name, self.data["agg"], format),
|
||
),
|
||
(np.array(wav_vocals) * 32768).astype("int16"),
|
||
self.mp.param["sr"],
|
||
)
|
||
else:
|
||
path = os.path.join(
|
||
vocal_root, head + "{}_{}.wav".format(name, self.data["agg"])
|
||
)
|
||
sf.write(
|
||
path,
|
||
(np.array(wav_vocals) * 32768).astype("int16"),
|
||
self.mp.param["sr"],
|
||
)
|
||
opt_format_path = path[:-4] + ".%s" % format
|
||
downsample_audio(path, opt_format_path, format)
|
||
|
||
|
||
class AudioPreDeEcho:
|
||
def __init__(self, agg, model_path, device, is_half, tta=False):
|
||
self.model_path = model_path
|
||
self.device = device
|
||
self.data = {
|
||
# Processing Options
|
||
"postprocess": False,
|
||
"tta": tta,
|
||
# Constants
|
||
"window_size": 512,
|
||
"agg": agg,
|
||
"high_end_process": "mirroring",
|
||
}
|
||
mp = ModelParameters("infer/lib/uvr5_pack/lib_v5/modelparams/4band_v3.json")
|
||
nout = 64 if "DeReverb" in model_path else 48
|
||
model = CascadedNet(mp.param["bins"] * 2, nout)
|
||
cpk = torch.load(model_path, map_location="cpu")
|
||
model.load_state_dict(cpk)
|
||
model.eval()
|
||
if is_half:
|
||
model = model.half().to(device)
|
||
else:
|
||
model = model.to(device)
|
||
|
||
self.mp = mp
|
||
self.model = model
|
||
|
||
def _path_audio_(
|
||
self, music_file, vocal_root=None, ins_root=None, format="flac", is_hp3=False
|
||
): # 3个VR模型vocal和ins是反的
|
||
if ins_root is None and vocal_root is None:
|
||
return "No save root."
|
||
name = os.path.basename(music_file)
|
||
if ins_root is not None:
|
||
os.makedirs(ins_root, exist_ok=True)
|
||
if vocal_root is not None:
|
||
os.makedirs(vocal_root, exist_ok=True)
|
||
X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
|
||
bands_n = len(self.mp.param["band"])
|
||
# print(bands_n)
|
||
for d in range(bands_n, 0, -1):
|
||
bp = self.mp.param["band"][d]
|
||
if d == bands_n: # high-end band
|
||
(
|
||
X_wave[d],
|
||
_,
|
||
) = librosa.core.load( # 理论上librosa读取可能对某些音频有bug，应该上av读取，但是太麻烦了弃坑
|
||
music_file,
|
||
bp["sr"],
|
||
False,
|
||
dtype=np.float32,
|
||
res_type=bp["res_type"],
|
||
)
|
||
if X_wave[d].ndim == 1:
|
||
X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
|
||
else: # lower bands
|
||
X_wave[d] = librosa.core.resample(
|
||
X_wave[d + 1],
|
||
self.mp.param["band"][d + 1]["sr"],
|
||
bp["sr"],
|
||
res_type=bp["res_type"],
|
||
)
|
||
# Stft of wave source
|
||
X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
|
||
X_wave[d],
|
||
bp["hl"],
|
||
bp["n_fft"],
|
||
self.mp.param["mid_side"],
|
||
self.mp.param["mid_side_b2"],
|
||
self.mp.param["reverse"],
|
||
)
|
||
# pdb.set_trace()
|
||
if d == bands_n and self.data["high_end_process"] != "none":
|
||
input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
|
||
self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
|
||
)
|
||
input_high_end = X_spec_s[d][
|
||
:, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
|
||
]
|
||
|
||
X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
|
||
aggresive_set = float(self.data["agg"] / 100)
|
||
aggressiveness = {
|
||
"value": aggresive_set,
|
||
"split_bin": self.mp.param["band"][1]["crop_stop"],
|
||
}
|
||
with torch.no_grad():
|
||
pred, X_mag, X_phase = inference(
|
||
X_spec_m, self.device, self.model, aggressiveness, self.data
|
||
)
|
||
# Postprocess
|
||
if self.data["postprocess"]:
|
||
pred_inv = np.clip(X_mag - pred, 0, np.inf)
|
||
pred = spec_utils.mask_silence(pred, pred_inv)
|
||
y_spec_m = pred * X_phase
|
||
v_spec_m = X_spec_m - y_spec_m
|
||
|
||
if ins_root is not None:
|
||
if self.data["high_end_process"].startswith("mirroring"):
|
||
input_high_end_ = spec_utils.mirroring(
|
||
self.data["high_end_process"], y_spec_m, input_high_end, self.mp
|
||
)
|
||
wav_instrument = spec_utils.cmb_spectrogram_to_wave(
|
||
y_spec_m, self.mp, input_high_end_h, input_high_end_
|
||
)
|
||
else:
|
||
wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
|
||
logger.info("%s instruments done" % name)
|
||
if format in ["wav", "flac"]:
|
||
sf.write(
|
||
os.path.join(
|
||
ins_root,
|
||
"vocal_{}_{}.{}".format(name, self.data["agg"], format),
|
||
),
|
||
(np.array(wav_instrument) * 32768).astype("int16"),
|
||
self.mp.param["sr"],
|
||
) #
|
||
else:
|
||
path = os.path.join(
|
||
ins_root, "vocal_{}_{}.wav".format(name, self.data["agg"])
|
||
)
|
||
sf.write(
|
||
path,
|
||
(np.array(wav_instrument) * 32768).astype("int16"),
|
||
self.mp.param["sr"],
|
||
)
|
||
if os.path.exists(path):
|
||
opt_format_path = path[:-4] + ".%s" % format
|
||
downsample_audio(path, opt_format_path, format)
|
||
if vocal_root is not None:
|
||
if self.data["high_end_process"].startswith("mirroring"):
|
||
input_high_end_ = spec_utils.mirroring(
|
||
self.data["high_end_process"], v_spec_m, input_high_end, self.mp
|
||
)
|
||
wav_vocals = spec_utils.cmb_spectrogram_to_wave(
|
||
v_spec_m, self.mp, input_high_end_h, input_high_end_
|
||
)
|
||
else:
|
||
wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
|
||
logger.info("%s vocals done" % name)
|
||
if format in ["wav", "flac"]:
|
||
sf.write(
|
||
os.path.join(
|
||
vocal_root,
|
||
"instrument_{}_{}.{}".format(name, self.data["agg"], format),
|
||
),
|
||
(np.array(wav_vocals) * 32768).astype("int16"),
|
||
self.mp.param["sr"],
|
||
)
|
||
else:
|
||
path = os.path.join(
|
||
vocal_root, "instrument_{}_{}.wav".format(name, self.data["agg"])
|
||
)
|
||
sf.write(
|
||
path,
|
||
(np.array(wav_vocals) * 32768).astype("int16"),
|
||
self.mp.param["sr"],
|
||
)
|
||
if os.path.exists(path):
|
||
opt_format_path = path[:-4] + ".%s" % format
|
||
downsample_audio(path, opt_format_path, format)
|