nodes/SayanoAI-Comfy-RVC
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added crepe hop length param

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SayanoAI
2024-08-25 14:23:40 -04:00
parent 0a3385c2d9
commit dadc0660ec
3 changed files with 14 additions and 8 deletions
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custom_nodes/rvc_nodes.py
+10 -4
View File
@@ -66,7 +66,8 @@ class LoadPitchExtractionParams:
"min": 0., #Minimum value
"max": .5, #Maximum value
"step": .01, #Slider's step
})
}),
"crepe_hop_length": ("INT",{"default": 160, "min": 16, "max": 512, "step": 16})
},
}
@@ -252,8 +253,13 @@ class RVCProcessDatasetNode:
assert model_name, "Please provide a model name!"
assert dataset, "Please upload a dataset!"
f0_method = pitch_extraction_params.get("f0_method")
cache_name = get_hash(model_name, dataset, period, overlap, max_volume, alpha, mute_ratio, f0_method)
f0_method = pitch_extraction_params.get("f0_method", "")
cached_params = [model_name, dataset, period, overlap, max_volume, alpha, mute_ratio, sr, f0_method]
crepe_hop_length = pitch_extraction_params.get("crepe_hop_length",160)
if "crepe" in f0_method: cached_params.append(crepe_hop_length)
cache_name = get_hash(*cached_params)
model_log_dir = os.path.join(output_path,"logs",cache_name)
os.makedirs(model_log_dir,exist_ok=True)
@@ -268,7 +274,7 @@ class RVCProcessDatasetNode:
print(preprocess_trainset(dataset_dir,SR_MAP[sr],n_threads,model_log_dir,period,overlap,max_volume,alpha))
print(extract_features_trainset(hubert_model(), model_log_dir,n_p=n_threads,f0method=f0_method,device=device,if_f0=bool(f0_method),version="v2"))
print(extract_features_trainset(hubert_model(), model_log_dir,n_p=n_threads,f0method=f0_method,device=device,if_f0=bool(f0_method),version="v2",crepe_hop_length=crepe_hop_length))
gt_wavs_dir = os.path.join(model_log_dir,"0_gt_wavs")
feature_dir = os.path.join(model_log_dir,"3_feature768")
custom_nodes/utils.py
+2 -2
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@@ -22,11 +22,11 @@ def increment_filename_no_overwrite(proposed_path):
files = os.listdir(output_dir)
files = filter(lambda f: os.path.isfile(os.path.join(output_dir, f)), files)
files = filter(lambda f: f.startswith(filename), files)
files = filter(lambda f: f.endswith('.' + file_format), files)
files = filter(lambda f: f.endswith(file_format), files)
file_numbers = [re.search(r'_(\d+)\.', f) for f in files]
file_numbers = [int(f.group(1)) for f in file_numbers if f]
this_file_number = max(file_numbers) + 1 if file_numbers else 1
output_path = os.path.join(output_dir, f'{filename}_{this_file_number}.{file_format}')
output_path = os.path.join(output_dir, f'{filename}_{this_file_number}{file_format}')
return output_path
class MultipleTypeProxy(str):
preprocessing_utils.py
+2 -2
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@@ -221,9 +221,9 @@ def preprocess_trainset(inp_root, sr, n_p, exp_dir, period=3.0, overlap=.3, max_
except Exception as e:
return f"Failed to preprocess data: {e}"
def extract_features_trainset(hubert_model,exp_dir,n_p,f0method,device,version,if_f0):
def extract_features_trainset(hubert_model,exp_dir,n_p,f0method,device,version,if_f0,crepe_hop_length):
try:
featureInput = FeatureInput(f0_method=f0method,exp_dir=exp_dir,device=device,version=version,if_f0=if_f0,model=hubert_model)
featureInput = FeatureInput(f0_method=f0method,exp_dir=exp_dir,device=device,version=version,if_f0=if_f0,model=hubert_model,hop_size=crepe_hop_length)
paths = []
inp_root = os.path.join(exp_dir,"1_16k_wavs")
opt_root1 = os.path.join(exp_dir,"2a_f0")