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import multiprocessing as mp
import os
import traceback
from concurrent.futures import ProcessPoolExecutor
from typing import *
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
from fairseq import checkpoint_utils
from tqdm import tqdm
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
MODELS_DIR = os.path.join(ROOT_DIR, "models")
EMBEDDINGS_LIST = {
"hubert-base-japanese": (
"rinna_hubert_base_jp.pt",
"hubert-base-japanese",
"local",
),
"contentvec": ("checkpoint_best_legacy_500.pt", "contentvec", "local"),
}
def get_embedder(embedder_name):
if embedder_name in EMBEDDINGS_LIST:
return EMBEDDINGS_LIST[embedder_name]
return None
def load_embedder(embedder_path: str, device):
try:
models, cfg, _ = checkpoint_utils.load_model_ensemble_and_task(
[embedder_path],
suffix="",
)
embedder_model = models[0]
embedder_model = embedder_model.to(device)
if device != "cpu":
embedder_model = embedder_model.half()
else:
embedder_model = embedder_model.float()
embedder_model.eval()
except Exception as e:
print(f"Error: {e} {embedder_path}")
traceback.print_exc()
return embedder_model, cfg
# wave must be 16k, hop_size=320
def readwave(wav_path, normalize=False):
wav, sr = sf.read(wav_path)
assert sr == 16000
feats = torch.from_numpy(wav).float()
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
if normalize:
with torch.no_grad():
feats = F.layer_norm(feats, feats.shape)
feats = feats.view(1, -1)
return feats
def processor(
todo: List[str],
device: torch.device,
embedder_path: str,
embedder_load_from: str,
embedding_channel: bool,
embedding_output_layer: int,
wav_dir: str,
out_dir: str,
process_id: int,
):
half_support = (
device.type == "cuda" and torch.cuda.get_device_capability(device)[0] >= 5.3
)
is_feats_dim_768 = embedding_channel == 768
if embedder_load_from == "local" and not os.path.exists(embedder_path):
return f"Embedder not found: {embedder_path}"
model, cfg = load_embedder(embedder_path, device)
for file in tqdm(todo, position=1 + process_id):
try:
if file.endswith(".wav"):
wav_filepath = os.path.join(wav_dir, file)
out_filepath = os.path.join(out_dir, file.replace("wav", "npy"))
if os.path.exists(out_filepath):
continue
os.makedirs(os.path.dirname(out_filepath), exist_ok=True)
is_normalize = False if cfg is None else cfg.task.normalize
feats = readwave(wav_filepath, normalize=is_normalize)
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
if isinstance(model, tuple):
feats = model[0](
feats.squeeze(0).squeeze(0).to(device),
return_tensors="pt",
sampling_rate=16000,
)
if half_support:
feats = feats.input_values.to(device).half()
else:
feats = feats.input_values.to(device).float()
with torch.no_grad():
if half_support:
if is_feats_dim_768:
feats = model[1](feats).last_hidden_state
else:
feats = model[1](feats).extract_features
else:
if is_feats_dim_768:
feats = model[1].float()(feats).last_hidden_state
else:
feats = model[1].float()(feats).extract_features
else:
inputs = {
"source": feats.half().to(device)
if half_support
else feats.to(device),
"padding_mask": padding_mask.to(device),
"output_layer": embedding_output_layer,
}
# なんかまだこの時点でfloat16なので改めて変換
if not half_support:
model = model.float()
inputs["source"] = inputs["source"].float()
with torch.no_grad():
logits = model.extract_features(**inputs)
if is_feats_dim_768:
feats = logits[0]
else:
feats = model.final_proj(logits[0])
feats = feats.squeeze(0).float().cpu().numpy()
if np.isnan(feats).sum() == 0:
np.save(out_filepath, feats, allow_pickle=False)
else:
print(f"{file} contains nan")
except Exception as e:
print(f"Error: {e} {file}")
traceback.print_exc()
def run(
training_dir: str,
embedder_path: str,
embedder_load_from: str,
embedding_channel: int,
embedding_output_layer: int,
gpu_ids: List[int],
device: Optional[Union[torch.device, str]] = None,
):
wav_dir = os.path.join(training_dir, "1_16k_wavs")
out_dir = os.path.join(training_dir, "3_feature256")
num_gpus = len(gpu_ids)
for gpu_id in gpu_ids:
if num_gpus < gpu_id + 1:
print(f"GPU {gpu_id} is not available")
return
if os.path.exists(out_dir):
return
os.makedirs(out_dir, exist_ok=True)
todo = [
os.path.join(dir, f)
for dir in sorted(list(os.listdir(wav_dir)))
if os.path.isdir(os.path.join(wav_dir, dir))
for f in sorted(list(os.listdir(os.path.join(wav_dir, dir))))
]
if device is not None:
if type(device) == str:
device = torch.device(device)
if device.type == "mps":
device = torch.device(
"cpu"
) # Mac(MPS) crashes when multiprocess, so change to CPU.
processor(
todo,
device,
embedder_path,
embedder_load_from,
embedding_channel,
embedding_output_layer,
wav_dir,
out_dir,
process_id=0,
)
else:
with ProcessPoolExecutor(mp_context=mp.get_context("spawn")) as executor:
for i, id in enumerate(gpu_ids):
executor.submit(
processor,
todo[i::num_gpus],
torch.device(f"cuda:{id}"),
embedder_path,
embedder_load_from,
embedding_channel,
embedding_output_layer,
wav_dir,
out_dir,
process_id=i,
)
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