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https://github.com/Mangio621/Mangio-RVC-Fork.git
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Apply Code Formatter Change
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kalomaze
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github-actions[bot]
parent
593b740709
commit
9ff9503b43
56
config.py
56
config.py
@@ -7,6 +7,7 @@ from multiprocessing import cpu_count
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global usefp16
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usefp16 = False
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def use_fp32_config():
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usefp16 = False
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device_capability = 0
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@@ -24,15 +25,19 @@ def use_fp32_config():
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with open(f"configs/{config_file}", "w") as d:
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json.dump(data, d, indent=4)
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print(f"Set fp16_run to true in {config_file}")
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with open("trainset_preprocess_pipeline_print.py", "r", encoding="utf-8") as f:
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with open(
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"trainset_preprocess_pipeline_print.py", "r", encoding="utf-8"
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) as f:
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strr = f.read()
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strr = strr.replace("3.0", "3.7")
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with open("trainset_preprocess_pipeline_print.py", "w", encoding="utf-8") as f:
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with open(
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"trainset_preprocess_pipeline_print.py", "w", encoding="utf-8"
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) as f:
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f.write(strr)
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else:
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for config_file in ["32k.json", "40k.json", "48k.json"]:
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@@ -41,23 +46,30 @@ def use_fp32_config():
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if "train" in data and "fp16_run" in data["train"]:
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data["train"]["fp16_run"] = False
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with open(f"configs/{config_file}", "w") as d:
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json.dump(data, d, indent=4)
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print(f"Set fp16_run to false in {config_file}")
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with open("trainset_preprocess_pipeline_print.py", "r", encoding="utf-8") as f:
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with open(
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"trainset_preprocess_pipeline_print.py", "r", encoding="utf-8"
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) as f:
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strr = f.read()
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strr = strr.replace("3.7", "3.0")
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with open("trainset_preprocess_pipeline_print.py", "w", encoding="utf-8") as f:
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with open(
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"trainset_preprocess_pipeline_print.py", "w", encoding="utf-8"
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) as f:
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f.write(strr)
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else:
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print("CUDA is not available. Make sure you have an NVIDIA GPU and CUDA installed.")
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print(
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"CUDA is not available. Make sure you have an NVIDIA GPU and CUDA installed."
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)
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return (usefp16, device_capability)
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class Config:
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def __init__(self):
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self.device = "cuda:0"
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@@ -74,7 +86,7 @@ class Config:
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self.paperspace,
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self.is_cli,
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) = self.arg_parse()
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self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
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@staticmethod
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@@ -92,11 +104,15 @@ class Config:
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action="store_true",
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help="Do not open in browser automatically",
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)
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parser.add_argument( # Fork Feature. Paperspace integration for web UI
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"--paperspace", action="store_true", help="Note that this argument just shares a gradio link for the web UI. Thus can be used on other non-local CLI systems."
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parser.add_argument( # Fork Feature. Paperspace integration for web UI
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"--paperspace",
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action="store_true",
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help="Note that this argument just shares a gradio link for the web UI. Thus can be used on other non-local CLI systems.",
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)
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parser.add_argument( # Fork Feature. Embed a CLI into the infer-web.py
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"--is_cli", action="store_true", help="Use the CLI instead of setting up a gradio UI. This flag will launch an RVC text interface where you can execute functions from infer-web.py!"
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parser.add_argument( # Fork Feature. Embed a CLI into the infer-web.py
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"--is_cli",
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action="store_true",
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help="Use the CLI instead of setting up a gradio UI. This flag will launch an RVC text interface where you can execute functions from infer-web.py!",
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)
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cmd_opts = parser.parse_args()
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@@ -185,4 +201,4 @@ class Config:
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x_center = 30
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x_max = 32
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return x_pad, x_query, x_center, x_max
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return x_pad, x_query, x_center, x_max
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@@ -5,10 +5,10 @@ sys.path.append(now_dir)
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from my_utils import load_audio
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import pyworld
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import numpy as np, logging
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import torchcrepe # Fork Feature. Crepe algo for training and preprocess
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import torchcrepe # Fork Feature. Crepe algo for training and preprocess
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import torch
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from torch import Tensor # Fork Feature. Used for pitch prediction for torch crepe.
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import scipy.signal as signal # Fork Feature hybrid inference
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from torch import Tensor # Fork Feature. Used for pitch prediction for torch crepe.
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import scipy.signal as signal # Fork Feature hybrid inference
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import tqdm
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logging.getLogger("numba").setLevel(logging.WARNING)
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@@ -19,9 +19,9 @@ f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
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DoFormant = False
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with open('formanting.txt', 'r') as fvf:
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content = fvf.readlines()
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Quefrency, Timbre = content[1].split('\n')[0], content[2].split('\n')[0]
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with open("formanting.txt", "r") as fvf:
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content = fvf.readlines()
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Quefrency, Timbre = content[1].split("\n")[0], content[2].split("\n")[0]
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def printt(strr):
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@@ -32,7 +32,7 @@ def printt(strr):
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n_p = int(sys.argv[2])
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f0method = sys.argv[3]
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extraction_crepe_hop_length = 0
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extraction_crepe_hop_length = 0
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try:
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extraction_crepe_hop_length = int(sys.argv[4])
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except:
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@@ -53,11 +53,11 @@ class FeatureInput(object):
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self.f0_min = 50.0
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self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
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self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
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# EXPERIMENTAL. PROBABLY BUGGY
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def get_f0_hybrid_computation(
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self,
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methods_str,
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self,
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methods_str,
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x,
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f0_min,
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f0_max,
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@@ -67,9 +67,9 @@ class FeatureInput(object):
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):
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# Get various f0 methods from input to use in the computation stack
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s = methods_str
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s = s.split('hybrid')[1]
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s = s.replace('[', '').replace(']', '')
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methods = s.split('+')
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s = s.split("hybrid")[1]
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s = s.replace("[", "").replace("]", "")
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methods = s.split("+")
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f0_computation_stack = []
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print("Calculating f0 pitch estimations for methods: %s" % str(methods))
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@@ -99,7 +99,9 @@ class FeatureInput(object):
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torch_device_index = 0
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torch_device = None
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if torch.cuda.is_available():
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torch_device = torch.device(f"cuda:{torch_device_index % torch.cuda.device_count()}")
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torch_device = torch.device(
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f"cuda:{torch_device_index % torch.cuda.device_count()}"
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)
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elif torch.backends.mps.is_available():
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torch_device = torch.device("mps")
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else:
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@@ -123,7 +125,7 @@ class FeatureInput(object):
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f0 = torchcrepe.filter.mean(f0, 3)
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f0[pd < 0.1] = 0
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f0 = f0[0].cpu().numpy()
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f0 = f0[1:] # Get rid of extra first frame
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f0 = f0[1:] # Get rid of extra first frame
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elif method == "mangio-crepe":
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# print("Performing crepe pitch extraction. (EXPERIMENTAL)")
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# print("CREPE PITCH EXTRACTION HOP LENGTH: " + str(crepe_hop_length))
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@@ -132,7 +134,9 @@ class FeatureInput(object):
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torch_device_index = 0
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torch_device = None
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if torch.cuda.is_available():
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torch_device = torch.device(f"cuda:{torch_device_index % torch.cuda.device_count()}")
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torch_device = torch.device(
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f"cuda:{torch_device_index % torch.cuda.device_count()}"
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)
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elif torch.backends.mps.is_available():
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torch_device = torch.device("mps")
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else:
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@@ -156,7 +160,7 @@ class FeatureInput(object):
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"full",
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batch_size=crepe_hop_length * 2,
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device=torch_device,
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pad=True
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pad=True,
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)
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p_len = p_len or x.shape[0] // crepe_hop_length
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# Resize the pitch
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@@ -165,7 +169,7 @@ class FeatureInput(object):
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target = np.interp(
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np.arange(0, len(source) * p_len, len(source)) / p_len,
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np.arange(0, len(source)),
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source
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source,
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)
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f0 = np.nan_to_num(target)
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elif method == "harvest":
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@@ -191,12 +195,12 @@ class FeatureInput(object):
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f0 = signal.medfilt(f0, 3)
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f0 = f0[1:]
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f0_computation_stack.append(f0)
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for fc in f0_computation_stack:
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print(len(fc))
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# print("Calculating hybrid median f0 from the stack of: %s" % str(methods))
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f0_median_hybrid = None
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if len(f0_computation_stack) == 1:
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f0_median_hybrid = f0_computation_stack[0]
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@@ -236,10 +240,9 @@ class FeatureInput(object):
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elif f0_method == "rmvpe":
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if hasattr(self, "model_rmvpe") == False:
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from rmvpe import RMVPE
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print("loading rmvpe model")
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self.model_rmvpe = RMVPE(
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"rmvpe.pt", is_half=False, device="cuda:0"
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)
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self.model_rmvpe = RMVPE("rmvpe.pt", is_half=False, device="cuda:0")
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f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
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elif f0_method == "dio":
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f0, t = pyworld.dio(
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@@ -250,12 +253,16 @@ class FeatureInput(object):
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frame_period=1000 * self.hop / self.fs,
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)
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f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
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elif f0_method == "crepe": # Fork Feature: Added crepe f0 for f0 feature extraction
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elif (
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f0_method == "crepe"
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): # Fork Feature: Added crepe f0 for f0 feature extraction
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# Pick a batch size that doesn't cause memory errors on your gpu
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torch_device_index = 0
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torch_device = None
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if torch.cuda.is_available():
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torch_device = torch.device(f"cuda:{torch_device_index % torch.cuda.device_count()}")
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torch_device = torch.device(
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f"cuda:{torch_device_index % torch.cuda.device_count()}"
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)
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elif torch.backends.mps.is_available():
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torch_device = torch.device("mps")
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else:
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@@ -287,7 +294,9 @@ class FeatureInput(object):
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torch_device_index = 0
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torch_device = None
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if torch.cuda.is_available():
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torch_device = torch.device(f"cuda:{torch_device_index % torch.cuda.device_count()}")
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torch_device = torch.device(
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f"cuda:{torch_device_index % torch.cuda.device_count()}"
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)
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elif torch.backends.mps.is_available():
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torch_device = torch.device("mps")
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else:
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@@ -311,7 +320,7 @@ class FeatureInput(object):
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"full",
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batch_size=crepe_hop_length * 2,
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device=torch_device,
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pad=True
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pad=True,
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)
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p_len = p_len or x.shape[0] // crepe_hop_length
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# Resize the pitch
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@@ -320,20 +329,20 @@ class FeatureInput(object):
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target = np.interp(
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np.arange(0, len(source) * p_len, len(source)) / p_len,
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np.arange(0, len(source)),
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source
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source,
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)
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f0 = np.nan_to_num(target)
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elif "hybrid" in f0_method: # EXPERIMENTAL
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elif "hybrid" in f0_method: # EXPERIMENTAL
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# Perform hybrid median pitch estimation
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time_step = 160 / 16000 * 1000
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f0 = self.get_f0_hybrid_computation(
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f0_method,
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f0_method,
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x,
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self.f0_min,
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self.f0_max,
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p_len,
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crepe_hop_length,
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time_step
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time_step,
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)
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# Mangio-RVC-Fork Feature: Add hybrid f0 inference to feature extraction. EXPERIMENTAL...
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@@ -362,14 +371,19 @@ class FeatureInput(object):
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with tqdm.tqdm(total=len(paths), leave=True, position=thread_n) as pbar:
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for idx, (inp_path, opt_path1, opt_path2) in enumerate(paths):
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try:
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pbar.set_description("thread:%s, f0ing, Hop-Length:%s" % (thread_n, crepe_hop_length))
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pbar.set_description(
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"thread:%s, f0ing, Hop-Length:%s"
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% (thread_n, crepe_hop_length)
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)
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pbar.update(1)
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if (
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os.path.exists(opt_path1 + ".npy") == True
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and os.path.exists(opt_path2 + ".npy") == True
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):
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continue
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featur_pit = self.compute_f0(inp_path, f0_method, crepe_hop_length)
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featur_pit = self.compute_f0(
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inp_path, f0_method, crepe_hop_length
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)
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np.save(
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opt_path2,
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featur_pit,
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@@ -382,7 +396,9 @@ class FeatureInput(object):
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allow_pickle=False,
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) # ori
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except:
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printt("f0fail-%s-%s-%s" % (idx, inp_path, traceback.format_exc()))
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printt(
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"f0fail-%s-%s-%s" % (idx, inp_path, traceback.format_exc())
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)
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if __name__ == "__main__":
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@@ -411,12 +427,7 @@ if __name__ == "__main__":
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for i in range(n_p):
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p = Process(
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target=featureInput.go,
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args=(
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paths[i::n_p],
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f0method,
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extraction_crepe_hop_length,
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i
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),
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args=(paths[i::n_p], f0method, extraction_crepe_hop_length, i),
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)
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ps.append(p)
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p.start()
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43
gui_v0.py
43
gui_v0.py
@@ -51,8 +51,10 @@ class RVC:
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self.window = 160
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# Get Torch Device
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if(torch.cuda.is_available()):
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self.torch_device = torch.device(f"cuda:{0 % torch.cuda.device_count()}")
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if torch.cuda.is_available():
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self.torch_device = torch.device(
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f"cuda:{0 % torch.cuda.device_count()}"
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)
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elif torch.backends.mps.is_available():
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self.torch_device = torch.device("mps")
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else:
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@@ -141,7 +143,7 @@ class RVC:
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def get_f0(self, x, f0_up_key, inp_f0=None):
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# Calculate Padding and f0 details here
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p_len = x.shape[0] // 512 # For Now This probs doesn't work
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p_len = x.shape[0] // 512 # For Now This probs doesn't work
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x_pad = 1
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f0_min = 50
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f0_max = 1100
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@@ -150,11 +152,11 @@ class RVC:
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f0 = 0
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# Here, check f0_methods and get their computations
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if(self.f0_method == 'harvest'):
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if self.f0_method == "harvest":
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f0 = self.get_harvest_computation(x, f0_min, f0_max)
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elif(self.f0_method == 'reg-crepe'):
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elif self.f0_method == "reg-crepe":
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f0 = self.get_regular_crepe_computation(x, f0_min, f0_max)
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elif(self.f0_method == 'reg-crepe-tiny'):
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elif self.f0_method == "reg-crepe-tiny":
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f0 = self.get_regular_crepe_computation(x, f0_min, f0_max, "tiny")
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# Calculate f0_course and f0_bak here
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@@ -300,7 +302,7 @@ class GUI:
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with open("values1.json", "r") as j:
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data = json.load(j)
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except:
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# Injecting f0_method into the json data
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# Injecting f0_method into the json data
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with open("values1.json", "w") as j:
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data = {
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"pth_path": "",
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@@ -328,11 +330,7 @@ class GUI:
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[
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sg.Frame(
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title="Proudly forked by Mangio621",
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layout=[
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[
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sg.Image('./mangio_utils/lol.png')
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]
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]
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layout=[[sg.Image("./mangio_utils/lol.png")]],
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),
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sg.Frame(
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title=i18n("加载模型"),
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||||
@@ -384,14 +382,16 @@ class GUI:
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||||
),
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||||
],
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||||
],
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||||
)
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),
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],
|
||||
[
|
||||
# Mangio f0 Selection frame Here
|
||||
sg.Frame(
|
||||
layout=[
|
||||
[
|
||||
sg.Radio("Harvest", "f0_method", key="harvest", default=True),
|
||||
sg.Radio(
|
||||
"Harvest", "f0_method", key="harvest", default=True
|
||||
),
|
||||
sg.Radio("Crepe", "f0_method", key="reg-crepe"),
|
||||
sg.Radio("Crepe Tiny", "f0_method", key="reg-crepe-tiny"),
|
||||
]
|
||||
@@ -536,20 +536,21 @@ class GUI:
|
||||
if event == "stop_vc" and self.flag_vc == True:
|
||||
self.flag_vc = False
|
||||
|
||||
# Function that returns the used f0 method in string format "harvest"
|
||||
# Function that returns the used f0 method in string format "harvest"
|
||||
def get_f0_method_from_radios(self, values):
|
||||
f0_array = [
|
||||
{"name": "harvest", "val": values['harvest']},
|
||||
{"name": "reg-crepe", "val": values['reg-crepe']},
|
||||
{"name": "reg-crepe-tiny", "val": values['reg-crepe-tiny']},
|
||||
{"name": "harvest", "val": values["harvest"]},
|
||||
{"name": "reg-crepe", "val": values["reg-crepe"]},
|
||||
{"name": "reg-crepe-tiny", "val": values["reg-crepe-tiny"]},
|
||||
]
|
||||
# Filter through to find a true value
|
||||
used_f0 = ""
|
||||
for f0 in f0_array:
|
||||
if(f0['val'] == True):
|
||||
used_f0 = f0['name']
|
||||
if f0["val"] == True:
|
||||
used_f0 = f0["name"]
|
||||
break
|
||||
if(used_f0 == ""): used_f0 = "harvest" # Default Harvest if used_f0 is empty somehow
|
||||
if used_f0 == "":
|
||||
used_f0 = "harvest" # Default Harvest if used_f0 is empty somehow
|
||||
return used_f0
|
||||
|
||||
def set_values(self, values):
|
||||
|
||||
687
infer-web.py
687
infer-web.py
File diff suppressed because it is too large
Load Diff
61
my_utils.py
61
my_utils.py
@@ -1,11 +1,14 @@
|
||||
import ffmpeg
|
||||
import numpy as np
|
||||
#import praatio
|
||||
#import praatio.praat_scripts
|
||||
import os
|
||||
#from os.path import join
|
||||
|
||||
#praatEXE = join('.',os.path.abspath(os.getcwd()) + r"\Praat.exe")
|
||||
# import praatio
|
||||
# import praatio.praat_scripts
|
||||
import os
|
||||
|
||||
# from os.path import join
|
||||
|
||||
# praatEXE = join('.',os.path.abspath(os.getcwd()) + r"\Praat.exe")
|
||||
|
||||
|
||||
def load_audio(file, sr, DoFormant, Quefrency, Timbre):
|
||||
try:
|
||||
@@ -15,43 +18,47 @@ def load_audio(file, sr, DoFormant, Quefrency, Timbre):
|
||||
file = (
|
||||
file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
|
||||
) # 防止小白拷路径头尾带了空格和"和回车
|
||||
file_formanted = (
|
||||
file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
|
||||
)
|
||||
with open('formanting.txt', 'r') as fvf:
|
||||
file_formanted = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
|
||||
with open("formanting.txt", "r") as fvf:
|
||||
content = fvf.readlines()
|
||||
if 'True' in content[0].split('\n')[0]:
|
||||
#print("true")
|
||||
if "True" in content[0].split("\n")[0]:
|
||||
# print("true")
|
||||
DoFormant = True
|
||||
Quefrency, Timbre = content[1].split('\n')[0], content[2].split('\n')[0]
|
||||
|
||||
Quefrency, Timbre = content[1].split("\n")[0], content[2].split("\n")[0]
|
||||
|
||||
else:
|
||||
#print("not true")
|
||||
# print("not true")
|
||||
DoFormant = False
|
||||
|
||||
|
||||
if DoFormant:
|
||||
#os.system(f"stftpitchshift -i {file} -q {Quefrency} -t {Timbre} -o {file_formanted}")
|
||||
#print('stftpitchshift -i "%s" -p 1.0 --rms -w 128 -v 8 -q %s -t %s -o "%s"' % (file, Quefrency, Timbre, file_formanted))
|
||||
# os.system(f"stftpitchshift -i {file} -q {Quefrency} -t {Timbre} -o {file_formanted}")
|
||||
# print('stftpitchshift -i "%s" -p 1.0 --rms -w 128 -v 8 -q %s -t %s -o "%s"' % (file, Quefrency, Timbre, file_formanted))
|
||||
print("formanting...")
|
||||
|
||||
os.system('stftpitchshift -i "%s" -q %s -t %s -o "%sFORMANTED"' % (file, Quefrency, Timbre, file_formanted))
|
||||
|
||||
os.system(
|
||||
'stftpitchshift -i "%s" -q %s -t %s -o "%sFORMANTED"'
|
||||
% (file, Quefrency, Timbre, file_formanted)
|
||||
)
|
||||
print("formanted!")
|
||||
#filepraat = (os.path.abspath(os.getcwd()) + '\\' + file).replace('/','\\')
|
||||
#file_formantedpraat = ('"' + os.path.abspath(os.getcwd()) + '/' + 'formanted'.join(file_formanted) + '"').replace('/','\\')
|
||||
# filepraat = (os.path.abspath(os.getcwd()) + '\\' + file).replace('/','\\')
|
||||
# file_formantedpraat = ('"' + os.path.abspath(os.getcwd()) + '/' + 'formanted'.join(file_formanted) + '"').replace('/','\\')
|
||||
|
||||
out, _ = (
|
||||
ffmpeg.input('%sFORMANTED%s' % (file_formanted, '.wav'), threads=0)
|
||||
ffmpeg.input("%sFORMANTED%s" % (file_formanted, ".wav"), threads=0)
|
||||
.output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
|
||||
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
|
||||
.run(
|
||||
cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True
|
||||
)
|
||||
)
|
||||
|
||||
os.remove('%sFORMANTED%s' % (file_formanted, '.wav'))
|
||||
|
||||
os.remove("%sFORMANTED%s" % (file_formanted, ".wav"))
|
||||
else:
|
||||
|
||||
out, _ = (
|
||||
ffmpeg.input(file, threads=0)
|
||||
.output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
|
||||
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
|
||||
.run(
|
||||
cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to load audio: {e}")
|
||||
|
||||
@@ -568,10 +568,10 @@ def train_and_evaluate(
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
with open("stop.txt", "r+") as tostop:
|
||||
content = tostop.read()
|
||||
if 'stop' in content:
|
||||
if "stop" in content:
|
||||
logger.info("Stop Button was pressed. The program is closed.")
|
||||
if hasattr(net_g, "module"):
|
||||
ckpt = net_g.module.state_dict()
|
||||
@@ -581,15 +581,21 @@ def train_and_evaluate(
|
||||
"saving final ckpt:%s"
|
||||
% (
|
||||
savee(
|
||||
ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch, hps.version, hps
|
||||
ckpt,
|
||||
hps.sample_rate,
|
||||
hps.if_f0,
|
||||
hps.name,
|
||||
epoch,
|
||||
hps.version,
|
||||
hps,
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
tostop.truncate(0)
|
||||
tostop.writelines("not")
|
||||
os._exit(2333333)
|
||||
|
||||
|
||||
if rank == 0:
|
||||
logger.info("====> Epoch: {} {}".format(epoch, epoch_recorder.record()))
|
||||
if epoch >= hps.total_epoch and rank == 0:
|
||||
|
||||
@@ -24,9 +24,10 @@ Timbre = 0.0
|
||||
mutex = multiprocessing.Lock()
|
||||
f = open("%s/preprocess.log" % exp_dir, "a+")
|
||||
|
||||
with open('formanting.txt', 'r') as fvf:
|
||||
content = fvf.readlines()
|
||||
Quefrency, Timbre = content[1].split('\n')[0], content[2].split('\n')[0]
|
||||
with open("formanting.txt", "r") as fvf:
|
||||
content = fvf.readlines()
|
||||
Quefrency, Timbre = content[1].split("\n")[0], content[2].split("\n")[0]
|
||||
|
||||
|
||||
def println(strr):
|
||||
mutex.acquire()
|
||||
@@ -104,12 +105,14 @@ class PreProcess:
|
||||
idx1 += 1
|
||||
break
|
||||
self.norm_write(tmp_audio, idx0, idx1)
|
||||
#println("%s->Suc." % path)
|
||||
# println("%s->Suc." % path)
|
||||
except:
|
||||
println("%s->%s" % (path, traceback.format_exc()))
|
||||
|
||||
def pipeline_mp(self, infos, thread_n):
|
||||
for path, idx0 in tqdm.tqdm(infos, position=thread_n, leave=True, desc="thread:%s" % thread_n):
|
||||
for path, idx0 in tqdm.tqdm(
|
||||
infos, position=thread_n, leave=True, desc="thread:%s" % thread_n
|
||||
):
|
||||
self.pipeline(path, idx0)
|
||||
|
||||
def pipeline_mp_inp_dir(self, inp_root, n_p):
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import numpy as np, parselmouth, torch, pdb, sys, os
|
||||
from time import time as ttime
|
||||
import torch.nn.functional as F
|
||||
import torchcrepe # Fork feature. Use the crepe f0 algorithm. New dependency (pip install torchcrepe)
|
||||
import torchcrepe # Fork feature. Use the crepe f0 algorithm. New dependency (pip install torchcrepe)
|
||||
from torch import Tensor
|
||||
import scipy.signal as signal
|
||||
import pyworld, os, traceback, faiss, librosa, torchcrepe
|
||||
@@ -15,6 +15,7 @@ bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
|
||||
|
||||
input_audio_path2wav = {}
|
||||
|
||||
|
||||
@lru_cache
|
||||
def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
|
||||
audio = input_audio_path2wav[input_audio_path]
|
||||
@@ -74,24 +75,28 @@ class VC(object):
|
||||
def get_optimal_torch_device(self, index: int = 0) -> torch.device:
|
||||
# Get cuda device
|
||||
if torch.cuda.is_available():
|
||||
return torch.device(f"cuda:{index % torch.cuda.device_count()}") # Very fast
|
||||
return torch.device(
|
||||
f"cuda:{index % torch.cuda.device_count()}"
|
||||
) # Very fast
|
||||
elif torch.backends.mps.is_available():
|
||||
return torch.device("mps")
|
||||
# Insert an else here to grab "xla" devices if available. TO DO later. Requires the torch_xla.core.xla_model library
|
||||
# Else wise return the "cpu" as a torch device,
|
||||
# Else wise return the "cpu" as a torch device,
|
||||
return torch.device("cpu")
|
||||
|
||||
# Fork Feature: Compute f0 with the crepe method
|
||||
def get_f0_crepe_computation(
|
||||
self,
|
||||
x,
|
||||
f0_min,
|
||||
f0_max,
|
||||
p_len,
|
||||
hop_length=160, # 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time.
|
||||
model="full", # Either use crepe-tiny "tiny" or crepe "full". Default is full
|
||||
self,
|
||||
x,
|
||||
f0_min,
|
||||
f0_max,
|
||||
p_len,
|
||||
hop_length=160, # 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time.
|
||||
model="full", # Either use crepe-tiny "tiny" or crepe "full". Default is full
|
||||
):
|
||||
x = x.astype(np.float32) # fixes the F.conv2D exception. We needed to convert double to float.
|
||||
x = x.astype(
|
||||
np.float32
|
||||
) # fixes the F.conv2D exception. We needed to convert double to float.
|
||||
x /= np.quantile(np.abs(x), 0.999)
|
||||
torch_device = self.get_optimal_torch_device()
|
||||
audio = torch.from_numpy(x).to(torch_device, copy=True)
|
||||
@@ -109,7 +114,7 @@ class VC(object):
|
||||
model,
|
||||
batch_size=hop_length * 2,
|
||||
device=torch_device,
|
||||
pad=True
|
||||
pad=True,
|
||||
)
|
||||
p_len = p_len or x.shape[0] // hop_length
|
||||
# Resize the pitch for final f0
|
||||
@@ -118,17 +123,17 @@ class VC(object):
|
||||
target = np.interp(
|
||||
np.arange(0, len(source) * p_len, len(source)) / p_len,
|
||||
np.arange(0, len(source)),
|
||||
source
|
||||
source,
|
||||
)
|
||||
f0 = np.nan_to_num(target)
|
||||
return f0 # Resized f0
|
||||
|
||||
return f0 # Resized f0
|
||||
|
||||
def get_f0_official_crepe_computation(
|
||||
self,
|
||||
x,
|
||||
f0_min,
|
||||
f0_max,
|
||||
model="full",
|
||||
self,
|
||||
x,
|
||||
f0_min,
|
||||
f0_max,
|
||||
model="full",
|
||||
):
|
||||
# Pick a batch size that doesn't cause memory errors on your gpu
|
||||
batch_size = 512
|
||||
@@ -153,15 +158,15 @@ class VC(object):
|
||||
|
||||
# Fork Feature: Compute pYIN f0 method
|
||||
def get_f0_pyin_computation(self, x, f0_min, f0_max):
|
||||
y, sr = librosa.load('saudio/Sidney.wav', self.sr, mono=True)
|
||||
y, sr = librosa.load("saudio/Sidney.wav", self.sr, mono=True)
|
||||
f0, _, _ = librosa.pyin(y, sr=self.sr, fmin=f0_min, fmax=f0_max)
|
||||
f0 = f0[1:] # Get rid of extra first frame
|
||||
f0 = f0[1:] # Get rid of extra first frame
|
||||
return f0
|
||||
|
||||
# Fork Feature: Acquire median hybrid f0 estimation calculation
|
||||
def get_f0_hybrid_computation(
|
||||
self,
|
||||
methods_str,
|
||||
self,
|
||||
methods_str,
|
||||
input_audio_path,
|
||||
x,
|
||||
f0_min,
|
||||
@@ -173,9 +178,9 @@ class VC(object):
|
||||
):
|
||||
# Get various f0 methods from input to use in the computation stack
|
||||
s = methods_str
|
||||
s = s.split('hybrid')[1]
|
||||
s = s.replace('[', '').replace(']', '')
|
||||
methods = s.split('+')
|
||||
s = s.split("hybrid")[1]
|
||||
s = s.replace("[", "").replace("]", "")
|
||||
methods = s.split("+")
|
||||
f0_computation_stack = []
|
||||
|
||||
print("Calculating f0 pitch estimations for methods: %s" % str(methods))
|
||||
@@ -202,35 +207,39 @@ class VC(object):
|
||||
)
|
||||
elif method == "crepe":
|
||||
f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
|
||||
f0 = f0[1:] # Get rid of extra first frame
|
||||
f0 = f0[1:] # Get rid of extra first frame
|
||||
elif method == "crepe-tiny":
|
||||
f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
|
||||
f0 = f0[1:] # Get rid of extra first frame
|
||||
f0 = f0[1:] # Get rid of extra first frame
|
||||
elif method == "mangio-crepe":
|
||||
f0 = self.get_f0_crepe_computation(x, f0_min, f0_max, p_len, crepe_hop_length)
|
||||
f0 = self.get_f0_crepe_computation(
|
||||
x, f0_min, f0_max, p_len, crepe_hop_length
|
||||
)
|
||||
elif method == "mangio-crepe-tiny":
|
||||
f0 = self.get_f0_crepe_computation(x, f0_min, f0_max, p_len, crepe_hop_length, "tiny")
|
||||
f0 = self.get_f0_crepe_computation(
|
||||
x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
|
||||
)
|
||||
elif method == "harvest":
|
||||
f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
|
||||
if filter_radius > 2:
|
||||
f0 = signal.medfilt(f0, 3)
|
||||
f0 = f0[1:] # Get rid of first frame.
|
||||
elif method == "dio": # Potentially buggy?
|
||||
f0 = f0[1:] # Get rid of first frame.
|
||||
elif method == "dio": # Potentially buggy?
|
||||
f0, t = pyworld.dio(
|
||||
x.astype(np.double),
|
||||
fs=self.sr,
|
||||
f0_ceil=f0_max,
|
||||
f0_floor=f0_min,
|
||||
frame_period=10
|
||||
frame_period=10,
|
||||
)
|
||||
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
|
||||
f0 = signal.medfilt(f0, 3)
|
||||
f0 = f0[1:]
|
||||
#elif method == "pyin": Not Working just yet
|
||||
# elif method == "pyin": Not Working just yet
|
||||
# f0 = self.get_f0_pyin_computation(x, f0_min, f0_max)
|
||||
# Push method to the stack
|
||||
f0_computation_stack.append(f0)
|
||||
|
||||
|
||||
for fc in f0_computation_stack:
|
||||
print(len(fc))
|
||||
|
||||
@@ -280,13 +289,13 @@ class VC(object):
|
||||
f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
|
||||
if filter_radius > 2:
|
||||
f0 = signal.medfilt(f0, 3)
|
||||
elif f0_method == "dio": # Potentially Buggy?
|
||||
elif f0_method == "dio": # Potentially Buggy?
|
||||
f0, t = pyworld.dio(
|
||||
x.astype(np.double),
|
||||
fs=self.sr,
|
||||
f0_ceil=f0_max,
|
||||
f0_floor=f0_min,
|
||||
frame_period=10
|
||||
frame_period=10,
|
||||
)
|
||||
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
|
||||
f0 = signal.medfilt(f0, 3)
|
||||
@@ -295,12 +304,17 @@ class VC(object):
|
||||
elif f0_method == "crepe-tiny":
|
||||
f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
|
||||
elif f0_method == "mangio-crepe":
|
||||
f0 = self.get_f0_crepe_computation(x, f0_min, f0_max, p_len, crepe_hop_length)
|
||||
f0 = self.get_f0_crepe_computation(
|
||||
x, f0_min, f0_max, p_len, crepe_hop_length
|
||||
)
|
||||
elif f0_method == "mangio-crepe-tiny":
|
||||
f0 = self.get_f0_crepe_computation(x, f0_min, f0_max, p_len, crepe_hop_length, "tiny")
|
||||
f0 = self.get_f0_crepe_computation(
|
||||
x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
|
||||
)
|
||||
elif f0_method == "rmvpe":
|
||||
if hasattr(self, "model_rmvpe") == False:
|
||||
from rmvpe import RMVPE
|
||||
|
||||
print("loading rmvpe model")
|
||||
self.model_rmvpe = RMVPE(
|
||||
"rmvpe.pt", is_half=self.is_half, device=self.device
|
||||
@@ -311,7 +325,7 @@ class VC(object):
|
||||
# Perform hybrid median pitch estimation
|
||||
input_audio_path2wav[input_audio_path] = x.astype(np.double)
|
||||
f0 = self.get_f0_hybrid_computation(
|
||||
f0_method,
|
||||
f0_method,
|
||||
input_audio_path,
|
||||
x,
|
||||
f0_min,
|
||||
@@ -319,7 +333,7 @@ class VC(object):
|
||||
p_len,
|
||||
filter_radius,
|
||||
crepe_hop_length,
|
||||
time_step
|
||||
time_step,
|
||||
)
|
||||
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
|
||||
Reference in New Issue
Block a user