|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
import logging |
|
|
import math |
|
|
import os |
|
|
import sys |
|
|
|
|
|
import fairseq |
|
|
import soundfile as sf |
|
|
import torch |
|
|
import torch.nn.functional as F |
|
|
import tqdm |
|
|
from npy_append_array import NpyAppendArray |
|
|
|
|
|
logging.basicConfig( |
|
|
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", |
|
|
datefmt="%Y-%m-%d %H:%M:%S", |
|
|
level=os.environ.get("LOGLEVEL", "INFO").upper(), |
|
|
stream=sys.stdout, |
|
|
) |
|
|
logger = logging.getLogger("dump_hubert_feature") |
|
|
|
|
|
|
|
|
class HubertFeatureReader(object): |
|
|
def __init__(self, ckpt_path, layer, max_chunk=1600000): |
|
|
( |
|
|
model, |
|
|
cfg, |
|
|
task, |
|
|
) = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path]) |
|
|
self.model = model[0].eval().cuda() |
|
|
self.task = task |
|
|
self.layer = layer |
|
|
self.max_chunk = max_chunk |
|
|
logger.info(f"TASK CONFIG:\n{self.task.cfg}") |
|
|
logger.info(f" max_chunk = {self.max_chunk}") |
|
|
|
|
|
def read_audio(self, path, ref_len=None): |
|
|
wav, sr = sf.read(path) |
|
|
assert sr == self.task.cfg.sample_rate, sr |
|
|
if wav.ndim == 2: |
|
|
wav = wav.mean(-1) |
|
|
assert wav.ndim == 1, wav.ndim |
|
|
if ref_len is not None and abs(ref_len - len(wav)) > 160: |
|
|
logging.warning(f"ref {ref_len} != read {len(wav)} ({path})") |
|
|
return wav |
|
|
|
|
|
def get_feats(self, path, ref_len=None): |
|
|
x = self.read_audio(path, ref_len) |
|
|
with torch.no_grad(): |
|
|
x = torch.from_numpy(x).float().cuda() |
|
|
if self.task.cfg.normalize: |
|
|
x = F.layer_norm(x, x.shape) |
|
|
x = x.view(1, -1) |
|
|
|
|
|
feat = [] |
|
|
for start in range(0, x.size(1), self.max_chunk): |
|
|
x_chunk = x[:, start: start + self.max_chunk] |
|
|
feat_chunk, _ = self.model.extract_features( |
|
|
source=x_chunk, |
|
|
padding_mask=None, |
|
|
mask=False, |
|
|
output_layer=self.layer, |
|
|
) |
|
|
feat.append(feat_chunk) |
|
|
return torch.cat(feat, 1).squeeze(0) |
|
|
|
|
|
|
|
|
def get_path_iterator(tsv, nshard, rank): |
|
|
with open(tsv, "r") as f: |
|
|
root = f.readline().rstrip() |
|
|
lines = [line.rstrip() for line in f] |
|
|
tot = len(lines) |
|
|
shard_size = math.ceil(tot / nshard) |
|
|
start, end = rank * shard_size, min((rank + 1) * shard_size, tot) |
|
|
assert start < end, "start={start}, end={end}" |
|
|
logger.info( |
|
|
f"rank {rank} of {nshard}, process {end-start} " |
|
|
f"({start}-{end}) out of {tot}" |
|
|
) |
|
|
|
|
|
lines = lines[start:end] |
|
|
|
|
|
def iterate(): |
|
|
for line in lines: |
|
|
subpath, nsample = line.split("\t") |
|
|
yield f"{root}/{subpath}", int(nsample) |
|
|
|
|
|
return iterate, len(lines) |
|
|
|
|
|
|
|
|
def dump_feature( |
|
|
tsv_dir, split, ckpt_path, layer, nshard, rank, feat_dir, max_chunk |
|
|
): |
|
|
reader = HubertFeatureReader(ckpt_path, layer, max_chunk) |
|
|
generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank) |
|
|
iterator = generator() |
|
|
|
|
|
feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy" |
|
|
leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len" |
|
|
|
|
|
os.makedirs(feat_dir, exist_ok=True) |
|
|
if os.path.exists(feat_path): |
|
|
os.remove(feat_path) |
|
|
|
|
|
feat_f = NpyAppendArray(feat_path) |
|
|
with open(leng_path, "w") as leng_f: |
|
|
for path, nsample in tqdm.tqdm(iterator, total=num): |
|
|
feat = reader.get_feats(path, nsample) |
|
|
feat_f.append(feat.cpu().numpy()) |
|
|
leng_f.write(f"{len(feat)}\n") |
|
|
logger.info("finished successfully") |
|
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
import argparse |
|
|
|
|
|
parser = argparse.ArgumentParser() |
|
|
parser.add_argument("tsv_dir") |
|
|
parser.add_argument("split") |
|
|
parser.add_argument("ckpt_path") |
|
|
parser.add_argument("layer", type=int) |
|
|
parser.add_argument("nshard", type=int) |
|
|
parser.add_argument("rank", type=int) |
|
|
parser.add_argument("feat_dir") |
|
|
parser.add_argument("--max_chunk", type=int, default=1600000) |
|
|
args = parser.parse_args() |
|
|
logger.info(args) |
|
|
|
|
|
dump_feature(**vars(args)) |
|
|
|
