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fixed audio

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This commit is contained in:
SayanoAI
2024-10-06 21:39:08 -04:00
parent 1f05cf1fff
commit 69e8d01634
6 changed files with 43 additions and 41 deletions
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custom_nodes/audio_nodes.py
+3 -6
View File
@@ -14,14 +14,11 @@ CATEGORY = "🌺RVC-Studio/audio"
input_path = folder_paths.get_input_directory()
temp_path = folder_paths.get_temp_directory()
def to_audio_dict(audio, sr):
#from https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite/blob/bf2a9402d0b2727c7170c43621d569f4d531015f/videohelpersuite/nodes.py#L706C22-L706C67
waveform = torch.from_numpy(audio).reshape((-1,audio.ndim)).transpose(0,1).unsqueeze(0)
print(f"{waveform.shape=} {waveform.ndim=} {audio.shape=} {audio.ndim=}")
def to_audio_dict(audio: "np.ndarray", sr: int):
audio = np.atleast_2d(audio)
waveform = torch.from_numpy(audio.reshape((-1,audio.shape[0]))).transpose(0,1).unsqueeze(0)
return dict(waveform=waveform,sample_rate=sr)
class LoadAudio:
@classmethod
def INPUT_TYPES(cls):
lib/audio.py
+12 -5
View File
@@ -50,21 +50,24 @@ class AudioProcessor:
return get_hash(*values)
def __call__(self, audio):
from .karafan.audio_utils import Normalize, Silent
samples,sr = get_audio(audio)
if self.threshold_silence:
from .karafan.audio_utils import Silent
samples = np.atleast_2d([samples])
samples = Silent(samples, sample_rate=sr, threshold_dB=self.silence_threshold_db)
samples = np.squeeze(samples, 0)
if self.dynamic_threshold: samples = self.dynamic_thresholding(
if self.dynamic_threshold:
samples = self.dynamic_thresholding(
samples,
multiplier=self.multiplier,
sample_size=self.sample_size,
method=self.fill_method,
kernel_size=self.kernel_size)
if self.normalize: samples = Normalize(samples,threshold_dB=self.normalize_threshold_db)
if self.normalize:
from .karafan.audio_utils import Normalize
samples = Normalize(samples,threshold_dB=self.normalize_threshold_db)
return samples, sr
@@ -297,4 +300,8 @@ def autotune_f0(f0, threshold=0.):
# Use the nearest value in array2
autotuned_f0.append(y)
# Return the result as a numpy array
return np.array(autotuned_f0, dtype="float32")
return np.array(autotuned_f0, dtype="float32")
def hz_to_mel(hz: "np.ndarray"):
# 1127 * np.log(1 + f0_min / 700)
return 2595 * np.log10(1 + hz / 700) # from wikipedia
lib/infer_pack/onnx_inference.py
+8 -9
View File
@@ -1,7 +1,8 @@
import onnxruntime
import librosa
import numpy as np
import soundfile
from ..audio import hz_to_mel
class ContentVec:
@@ -79,6 +80,7 @@ class OnnxRVC:
self.model = onnxruntime.InferenceSession(model_path, providers=providers)
self.sampling_rate = sr
self.hop_size = hop_size
self.f0_bins = 256
def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd):
onnx_input = {
@@ -102,8 +104,8 @@ class OnnxRVC:
):
f0_min = 50
f0_max = 1100
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
f0_mel_min = hz_to_mel(f0_min)
f0_mel_max = hz_to_mel(f0_max)
f0_predictor = get_f0_predictor(
f0_method,
hop_length=self.hop_size,
@@ -125,12 +127,9 @@ class OnnxRVC:
pitchf = f0_predictor.compute_f0(wav, hubert_length)
pitchf = pitchf * 2 ** (f0_up_key / 12)
pitch = pitchf.copy()
f0_mel = 1127 * np.log(1 + pitch / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
f0_mel_max - f0_mel_min
) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > 255] = 255
f0_mel = hz_to_mel(pitch)
f0_mel = (f0_mel - f0_mel_min)*(self.f0_bins-2)/(f0_mel_max - f0_mel_min) + 1
f0_mel = np.clip(f0_mel, a_min=1, a_max=self.f0_bins-1)
pitch = np.rint(f0_mel).astype(np.int64)
pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32)
lib/rmvpe.py
+2 -1
View File
@@ -654,7 +654,8 @@ class RMVPE:
if self.is_half == True:
hidden = hidden.astype("float32")
f0 = self.decode(hidden, thred=thred)
f0[(f0 < f0_min) | (f0 > f0_max)] = 0
# f0[(f0 < f0_min) | (f0 > f0_max)] = 0
f0 = np.clip(f0, a_min=f0_min, a_max=f0_max)
return f0
def to_local_average_cents(self, salience, thred=0.05):
pitch_extraction.py
+14 -16
View File
@@ -6,7 +6,7 @@ from scipy import signal
import torch
from .lib.rmvpe import RMVPE
from .lib.audio import autotune_f0, pad_audio
from .lib.audio import autotune_f0, pad_audio, hz_to_mel
from .lib import BASE_MODELS_DIR
from .lib.utils import gc_collect, get_merge_func, get_optimal_threads, get_optimal_torch_device
@@ -20,14 +20,15 @@ class FeatureExtractor:
config.is_half,
)
self.sr = 16000 # hubert输入采样率
self.window = 160 # 每帧点数
self.t_pad = self.sr * self.x_pad # 每条前后pad时间
self.sr = 16000 # hubert sr
self.window = 160
self.f0_bins = 256
self.t_pad = self.sr * self.x_pad
self.t_pad_tgt = tgt_sr * self.x_pad
self.t_pad2 = self.t_pad * 2
self.t_query = self.sr * self.x_query # 查询切点前后查询时间
self.t_center = self.sr * self.x_center # 查询切点位置
self.t_max = self.sr * self.x_max # 免查询时长阈值
self.t_query = self.sr * self.x_query
self.t_center = self.sr * self.x_center
self.t_max = self.sr * self.x_max
self.device = config.device
self.onnx = onnx
self.f0_method_dict = {
@@ -193,7 +194,7 @@ class FeatureExtractor:
return self.model_rmvpe.infer_from_audio(x, thred=0.03)
def get_pitch_dependant_rmvpe(self, x, f0_min=1, f0_max=40000, *args, **kwargs):
def get_pitch_dependant_rmvpe(self, x, f0_min=0, f0_max=40000, *args, **kwargs):
if not hasattr(self,"model_rmvpe"):
self.model_rmvpe = RMVPE(os.path.join(BASE_MODELS_DIR,f"rmvpe.{'onnx' if self.onnx else 'pt'}"), is_half=self.is_half, device=self.device, onnx=self.onnx)
@@ -262,8 +263,8 @@ class FeatureExtractor:
**kwargs
):
time_step = self.window / self.sr * 1000
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
f0_mel_min = hz_to_mel(f0_min)
f0_mel_max = hz_to_mel(f0_max)
params = {'x': x, 'f0_up_key': f0_up_key, 'f0_min': f0_min,
'f0_max': f0_max, 'time_step': time_step, 'filter_radius': filter_radius,
'crepe_hop_length': crepe_hop_length, 'model': "full", 'onnx': rmvpe_onnx
@@ -295,12 +296,9 @@ class FeatureExtractor:
]
# f0bak = f0.copy()
f0_mel = 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
f0_mel_max - f0_mel_min
) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > 255] = 255
f0_mel = hz_to_mel(f0)
f0_mel = (f0_mel - f0_mel_min)*(self.f0_bins-2)/(f0_mel_max - f0_mel_min) + 1
f0_mel = np.clip(f0_mel, a_min=1, a_max=self.f0_bins-1)
f0_coarse = np.rint(f0_mel).astype(np.int16)
return f0_coarse, f0 # 1-0
preprocessing_utils.py
+4 -4
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@@ -5,7 +5,7 @@ from .lib.slicer2 import Slicer
import traceback
from scipy.io import wavfile
from .pitch_extraction import FeatureExtractor
from .lib.audio import load_input_audio, remix_audio, AudioProcessor
from .lib.audio import hz_to_mel, load_input_audio, remix_audio, AudioProcessor
from .lib.utils import gc_collect
from .config import config
import torch
@@ -14,7 +14,7 @@ class Preprocess:
def __init__(self, sr, exp_dir, preprocessor: "AudioProcessor"=None, noparallel=True, period=3.0, overlap=.3, max_volume=.95):
self.slicer = Slicer(
sr=sr,
threshold=-42,
threshold=-50,
min_length=1500,
min_interval=400,
hop_size=15,
@@ -112,8 +112,8 @@ class FeatureInput(FeatureExtractor):
self.f0_bin = 256
self.f0_max = 1100.0
self.f0_min = 50.0
self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
self.f0_mel_min = hz_to_mel(self.f0_min)
self.f0_mel_max = hz_to_mel(self.f0_max)
self.model = model