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added audioprocessor node + migrate get_audio + broken nodes fix

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SayanoAI
2024-08-28 01:49:19 -04:00
parent c899f86a1e
commit 9a7fa5fa36
8 changed files with 222 additions and 103 deletions
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preprocessing_utils.py
+21 -14
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@@ -5,13 +5,13 @@ 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
from .lib.audio import load_input_audio, remix_audio, AudioProcessor
from .lib.utils import gc_collect
from .config import config
import torch
class Preprocess:
def __init__(self, sr, exp_dir, noparallel=True, period=3.0, overlap=.3, max_volume=.95):
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,
@@ -29,6 +29,7 @@ class Preprocess:
self.gt_wavs_dir = os.path.join(exp_dir,"0_gt_wavs")
self.wavs16k_dir = os.path.join(exp_dir,"1_16k_wavs")
self.noparallel = noparallel
self.preprocessor = preprocessor
os.makedirs(self.exp_dir, exist_ok=True)
os.makedirs(self.gt_wavs_dir, exist_ok=True)
os.makedirs(self.wavs16k_dir, exist_ok=True)
@@ -42,16 +43,19 @@ class Preprocess:
# mutex.release()
def norm_write(self, tmp_audio, idx0, idx1):
wavfile.write(os.path.join(self.gt_wavs_dir, f"{idx0}_{idx1}.wav"),self.sr,tmp_audio.astype(np.float32))
remixed_audio = remix_audio((tmp_audio, self.sr), target_sr=16000, norm=True,max_volume=self.max_volume)
wavfile.write(os.path.join(self.wavs16k_dir, f"{idx0}_{idx1}.wav"),16000,remixed_audio[0].astype(np.float32))
if len(tmp_audio) > self.overlap*self.sr*2:
wavfile.write(os.path.join(self.gt_wavs_dir, f"{idx0}_{idx1}.wav"),self.sr,tmp_audio.astype(np.float32))
remixed_audio = remix_audio((tmp_audio, self.sr), target_sr=16000, max_volume=self.max_volume)
wavfile.write(os.path.join(self.wavs16k_dir, f"{idx0}_{idx1}.wav"),16000,remixed_audio[0].astype(np.float32))
else: print(f"skipped short audio clip: {idx0}_{idx1}.wav ({len(tmp_audio)=})")
def pipeline(self, path, idx0):
try:
audio,_ = load_input_audio(path, self.sr)
input_audio = load_input_audio(path, self.sr)
if self.preprocessor is not None: input_audio = self.preprocessor(input_audio)
idx1 = 0
for audio in self.slicer.slice(audio):
for audio in self.slicer.slice(input_audio[0]):
i = 0
while 1:
start = int(self.sr * (self.per - self.overlap) * i)
@@ -188,18 +192,20 @@ class FeatureInput(FeatureExtractor):
except:
self.printt("f0fail-%s-%s-%s" % (idx, inp_path, traceback.format_exc()))
def preprocess_trainset(inp_root, sr, n_p, exp_dir, period=3.0, overlap=.3, max_volume=1.):
def preprocess_trainset(inp_root, sr, n_p, exp_dir, preprocessor=None, period=3.0, overlap=.3, max_volume=1.):
try:
pp = Preprocess(sr, exp_dir, period=period, overlap=overlap, max_volume=max_volume)
pp = Preprocess(sr, exp_dir, preprocessor=preprocessor, period=period, overlap=overlap, max_volume=max_volume)
pp.println("start preprocess")
pp.println(sys.argv)
pp.pipeline_mp_inp_dir(inp_root, n_p)
pp.println("end preprocess")
del pp
gc_collect()
return "Successfully preprocessed data"
print("Successfully preprocessed data")
return True
except Exception as e:
return f"Failed to preprocess data: {e}"
print(f"Failed to preprocess data: {e}")
return False
def extract_features_trainset(hubert_model,exp_dir,n_p,f0method,device,version,if_f0,crepe_hop_length):
try:
@@ -240,7 +246,8 @@ def extract_features_trainset(hubert_model,exp_dir,n_p,f0method,device,version,i
p.join()
except:
featureInput.printt("f0_all_fail-%s" % (traceback.format_exc()))
return f"Successfully extracted features using {f0method}"
print(f"Successfully extracted features using {f0method}")
return True
except Exception as e:
return f"Failed to extract features: {e}"
print(f"Failed to extract features: {e}")
return False