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import os
import sys
import glob
import time
import tqdm
import torch
import torchcrepe
import numpy as np
import concurrent.futures
import multiprocessing as mp
import json
now_dir = os.getcwd()
sys.path.append(os.path.join(now_dir))
# Zluda hijack
import rvc.lib.zluda
from rvc.lib.utils import load_audio, load_embedding
from rvc.train.extract.preparing_files import generate_config, generate_filelist
from rvc.lib.predictors.RMVPE import RMVPE0Predictor
from rvc.configs.config import Config
# Load config
config = Config()
mp.set_start_method("spawn", force=True)
class FeatureInput:
def __init__(self, sample_rate=16000, hop_size=160, device="cpu"):
self.fs = sample_rate
self.hop = hop_size
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.device = device
self.model_rmvpe = None
def compute_f0(self, audio_array, method, hop_length):
if method == "crepe":
return self._get_crepe(audio_array, hop_length, type="full")
elif method == "crepe-tiny":
return self._get_crepe(audio_array, hop_length, type="tiny")
elif method == "rmvpe":
return self.model_rmvpe.infer_from_audio(audio_array, thred=0.03)
def _get_crepe(self, x, hop_length, type):
audio = torch.from_numpy(x.astype(np.float32)).to(self.device)
audio /= torch.quantile(torch.abs(audio), 0.999)
audio = audio.unsqueeze(0)
pitch = torchcrepe.predict(
audio,
self.fs,
hop_length,
self.f0_min,
self.f0_max,
type,
batch_size=hop_length * 2,
device=audio.device,
pad=True,
)
source = pitch.squeeze(0).cpu().float().numpy()
source[source < 0.001] = np.nan
return np.nan_to_num(
np.interp(
np.arange(0, len(source) * (x.size // self.hop), len(source))
/ (x.size // self.hop),
np.arange(0, len(source)),
source,
)
)
def coarse_f0(self, f0):
f0_mel = 1127.0 * np.log(1.0 + f0 / 700.0)
f0_mel = np.clip(
(f0_mel - self.f0_mel_min)
* (self.f0_bin - 2)
/ (self.f0_mel_max - self.f0_mel_min)
+ 1,
1,
self.f0_bin - 1,
)
return np.rint(f0_mel).astype(int)
def process_file(self, file_info, f0_method, hop_length):
inp_path, opt_path_coarse, opt_path_full, _ = file_info
if os.path.exists(opt_path_coarse) and os.path.exists(opt_path_full):
return
try:
np_arr = load_audio(inp_path, self.fs)
feature_pit = self.compute_f0(np_arr, f0_method, hop_length)
np.save(opt_path_full, feature_pit, allow_pickle=False)
coarse_pit = self.coarse_f0(feature_pit)
np.save(opt_path_coarse, coarse_pit, allow_pickle=False)
except Exception as error:
print(
f"An error occurred extracting file {inp_path} on {self.device}: {error}"
)
def process_files(self, files, f0_method, hop_length, device, threads):
self.device = device
if f0_method == "rmvpe":
self.model_rmvpe = RMVPE0Predictor(
os.path.join("rvc", "models", "predictors", "rmvpe.pt"),
device=device,
)
def worker(file_info):
self.process_file(file_info, f0_method, hop_length)
with tqdm.tqdm(total=len(files), leave=True) as pbar:
with concurrent.futures.ThreadPoolExecutor(max_workers=threads) as executor:
futures = [executor.submit(worker, f) for f in files]
for _ in concurrent.futures.as_completed(futures):
pbar.update(1)
def run_pitch_extraction(files, devices, f0_method, hop_length, threads):
devices_str = ", ".join(devices)
print(
f"Starting pitch extraction with {num_processes} cores on {devices_str} using {f0_method}..."
)
start_time = time.time()
fe = FeatureInput()
with concurrent.futures.ProcessPoolExecutor(max_workers=len(devices)) as executor:
tasks = [
executor.submit(
fe.process_files,
files[i :: len(devices)],
f0_method,
hop_length,
devices[i],
threads // len(devices),
)
for i in range(len(devices))
]
concurrent.futures.wait(tasks)
print(f"Pitch extraction completed in {time.time() - start_time:.2f} seconds.")
def process_file_embedding(
files, embedder_model, embedder_model_custom, device_num, device, n_threads
):
model = load_embedding(embedder_model, embedder_model_custom).to(device).float()
model.eval()
n_threads = max(1, n_threads)
def worker(file_info):
wav_file_path, _, _, out_file_path = file_info
if os.path.exists(out_file_path):
return
feats = torch.from_numpy(load_audio(wav_file_path, 16000)).to(device).float()
feats = feats.view(1, -1)
with torch.no_grad():
result = model(feats)["last_hidden_state"]
feats_out = result.squeeze(0).float().cpu().numpy()
if not np.isnan(feats_out).any():
np.save(out_file_path, feats_out, allow_pickle=False)
else:
print(f"{wav_file_path} produced NaN values; skipping.")
with tqdm.tqdm(total=len(files), leave=True, position=device_num) as pbar:
with concurrent.futures.ThreadPoolExecutor(max_workers=n_threads) as executor:
futures = [executor.submit(worker, f) for f in files]
for _ in concurrent.futures.as_completed(futures):
pbar.update(1)
def run_embedding_extraction(
files, devices, embedder_model, embedder_model_custom, threads
):
devices_str = ", ".join(devices)
print(
f"Starting embedding extraction with {num_processes} cores on {devices_str}..."
)
start_time = time.time()
with concurrent.futures.ProcessPoolExecutor(max_workers=len(devices)) as executor:
tasks = [
executor.submit(
process_file_embedding,
files[i :: len(devices)],
embedder_model,
embedder_model_custom,
i,
devices[i],
threads // len(devices),
)
for i in range(len(devices))
]
concurrent.futures.wait(tasks)
print(f"Embedding extraction completed in {time.time() - start_time:.2f} seconds.")
if __name__ == "__main__":
exp_dir = sys.argv[1]
f0_method = sys.argv[2]
hop_length = int(sys.argv[3])
num_processes = int(sys.argv[4])
gpus = sys.argv[5]
sample_rate = sys.argv[6]
embedder_model = sys.argv[7]
embedder_model_custom = sys.argv[8] if len(sys.argv) > 8 else None
include_mutes = int(sys.argv[9]) if len(sys.argv) > 9 else 2
wav_path = os.path.join(exp_dir, "sliced_audios_16k")
os.makedirs(os.path.join(exp_dir, "f0"), exist_ok=True)
os.makedirs(os.path.join(exp_dir, "f0_voiced"), exist_ok=True)
os.makedirs(os.path.join(exp_dir, "extracted"), exist_ok=True)
chosen_embedder_model = (
embedder_model_custom if embedder_model == "custom" else embedder_model
)
file_path = os.path.join(exp_dir, "model_info.json")
if os.path.exists(file_path):
with open(file_path, "r") as f:
data = json.load(f)
else:
data = {}
data["embedder_model"] = chosen_embedder_model
with open(file_path, "w") as f:
json.dump(data, f, indent=4)
files = []
for file in glob.glob(os.path.join(wav_path, "*.wav")):
file_name = os.path.basename(file)
file_info = [
file,
os.path.join(exp_dir, "f0", file_name + ".npy"),
os.path.join(exp_dir, "f0_voiced", file_name + ".npy"),
os.path.join(exp_dir, "extracted", file_name.replace("wav", "npy")),
]
files.append(file_info)
devices = ["cpu"] if gpus == "-" else [f"cuda:{idx}" for idx in gpus.split("-")]
run_pitch_extraction(files, devices, f0_method, hop_length, num_processes)
run_embedding_extraction(
files, devices, embedder_model, embedder_model_custom, num_processes
)
generate_config(sample_rate, exp_dir)
generate_filelist(exp_dir, sample_rate, include_mutes)
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