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import argparse
import logging
import os
import random
from concurrent.futures import ProcessPoolExecutor
from glob import glob
from random import shuffle
import librosa
import numpy as np
import torch
import torch.multiprocessing as mp
from loguru import logger
from tqdm import tqdm
import diffusion.logger.utils as du
import utils
from diffusion.vocoder import Vocoder
from modules.mel_processing import spectrogram_torch
logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)
hps = utils.get_hparams_from_file("configs/config.json")
dconfig = du.load_config("configs/diffusion.yaml")
sampling_rate = hps.data.sampling_rate
hop_length = hps.data.hop_length
speech_encoder = hps["model"]["speech_encoder"]
def process_one(filename, hmodel, f0p, device, diff=False, mel_extractor=None):
wav, sr = librosa.load(filename, sr=sampling_rate)
audio_norm = torch.FloatTensor(wav)
audio_norm = audio_norm.unsqueeze(0)
soft_path = filename + ".soft.pt"
if not os.path.exists(soft_path):
wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
wav16k = torch.from_numpy(wav16k).to(device)
c = hmodel.encoder(wav16k)
torch.save(c.cpu(), soft_path)
f0_path = filename + ".f0.npy"
if not os.path.exists(f0_path):
f0_predictor = utils.get_f0_predictor(f0p,sampling_rate=sampling_rate, hop_length=hop_length,device=None,threshold=0.05)
f0,uv = f0_predictor.compute_f0_uv(
wav
)
np.save(f0_path, np.asanyarray((f0,uv),dtype=object))
spec_path = filename.replace(".wav", ".spec.pt")
if not os.path.exists(spec_path):
# Process spectrogram
# The following code can't be replaced by torch.FloatTensor(wav)
# because load_wav_to_torch return a tensor that need to be normalized
if sr != hps.data.sampling_rate:
raise ValueError(
"{} SR doesn't match target {} SR".format(
sr, hps.data.sampling_rate
)
)
#audio_norm = audio / hps.data.max_wav_value
spec = spectrogram_torch(
audio_norm,
hps.data.filter_length,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_path)
if diff or hps.model.vol_embedding:
volume_path = filename + ".vol.npy"
volume_extractor = utils.Volume_Extractor(hop_length)
if not os.path.exists(volume_path):
volume = volume_extractor.extract(audio_norm)
np.save(volume_path, volume.to('cpu').numpy())
if diff:
mel_path = filename + ".mel.npy"
if not os.path.exists(mel_path) and mel_extractor is not None:
mel_t = mel_extractor.extract(audio_norm.to(device), sampling_rate)
mel = mel_t.squeeze().to('cpu').numpy()
np.save(mel_path, mel)
aug_mel_path = filename + ".aug_mel.npy"
aug_vol_path = filename + ".aug_vol.npy"
max_amp = float(torch.max(torch.abs(audio_norm))) + 1e-5
max_shift = min(1, np.log10(1/max_amp))
log10_vol_shift = random.uniform(-1, max_shift)
keyshift = random.uniform(-5, 5)
if mel_extractor is not None:
aug_mel_t = mel_extractor.extract(audio_norm * (10 ** log10_vol_shift), sampling_rate, keyshift = keyshift)
aug_mel = aug_mel_t.squeeze().to('cpu').numpy()
aug_vol = volume_extractor.extract(audio_norm * (10 ** log10_vol_shift))
if not os.path.exists(aug_mel_path):
np.save(aug_mel_path,np.asanyarray((aug_mel,keyshift),dtype=object))
if not os.path.exists(aug_vol_path):
np.save(aug_vol_path,aug_vol.to('cpu').numpy())
def process_batch(file_chunk, f0p, diff=False, mel_extractor=None, device="cpu"):
logger.info("Loading speech encoder for content...")
rank = mp.current_process()._identity
rank = rank[0] if len(rank) > 0 else 0
if torch.cuda.is_available():
gpu_id = rank % torch.cuda.device_count()
device = torch.device(f"cuda:{gpu_id}")
logger.info(f"Rank {rank} uses device {device}")
hmodel = utils.get_speech_encoder(speech_encoder, device=device)
logger.info(f"Loaded speech encoder for rank {rank}")
for filename in tqdm(file_chunk, position = rank):
process_one(filename, hmodel, f0p, device, diff, mel_extractor)
def parallel_process(filenames, num_processes, f0p, diff, mel_extractor, device):
with ProcessPoolExecutor(max_workers=num_processes) as executor:
tasks = []
for i in range(num_processes):
start = int(i * len(filenames) / num_processes)
end = int((i + 1) * len(filenames) / num_processes)
file_chunk = filenames[start:end]
tasks.append(executor.submit(process_batch, file_chunk, f0p, diff, mel_extractor, device=device))
for task in tqdm(tasks, position = 0):
task.result()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--device', type=str, default=None)
parser.add_argument(
"--in_dir", type=str, default="dataset/44k", help="path to input dir"
)
parser.add_argument(
'--use_diff',action='store_true', help='Whether to use the diffusion model'
)
parser.add_argument(
'--f0_predictor', type=str, default="rmvpe", help='Select F0 predictor, can select crepe,pm,dio,harvest,rmvpe,fcpe|default: pm(note: crepe is original F0 using mean filter)'
)
parser.add_argument(
'--num_processes', type=int, default=1, help='You are advised to set the number of processes to the same as the number of CPU cores'
)
args = parser.parse_args()
f0p = args.f0_predictor
device = args.device
if device is None:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(speech_encoder)
logger.info("Using device: " + str(device))
logger.info("Using SpeechEncoder: " + speech_encoder)
logger.info("Using extractor: " + f0p)
logger.info("Using diff Mode: " + str(args.use_diff))
if args.use_diff:
print("use_diff")
print("Loading Mel Extractor...")
mel_extractor = Vocoder(dconfig.vocoder.type, dconfig.vocoder.ckpt, device=device)
print("Loaded Mel Extractor.")
else:
mel_extractor = None
filenames = glob(f"{args.in_dir}/*/*.wav", recursive=True) # [:10]
shuffle(filenames)
mp.set_start_method("spawn", force=True)
num_processes = args.num_processes
if num_processes == 0:
num_processes = os.cpu_count()
parallel_process(filenames, num_processes, f0p, args.use_diff, mel_extractor, device)
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