Upload ./RepCodec/examples/feature_utils.py with huggingface_hub

RepCodec/examples/feature_utils.py

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+# Copyright (c) ByteDance, Inc. and its affiliates.
+# Copyright (c) Chutong Meng
+# 
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+# Based on fairseq (https://github.com/facebookresearch/fairseq)
+
+# ref: https://github.com/facebookresearch/fairseq/blob/main/examples/hubert/simple_kmeans/feature_utils.py
+
+import logging
+import os
+import sys
+
+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("feature_utils")
+
+
+def get_shard_range(tot, nshard, rank):
+    assert rank < nshard and rank >= 0, f"invaid rank/nshard {rank}/{nshard}"
+    start = round(tot / nshard * rank)
+    end = round(tot / nshard * (rank + 1))
+    assert start < end, f"start={start}, end={end}"
+    logger.info(
+        f"rank {rank} of {nshard}, process {end-start} "
+        f"({start}-{end}) out of {tot}"
+    )
+    return start, end
+
+
+def get_path_iterator(tsv, nshard, rank):
+    with open(tsv, "r") as f:
+        root = f.readline().rstrip()
+        lines = [line.rstrip() for line in f]
+        start, end = get_shard_range(len(lines), nshard, rank)
+        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(reader, generator, num, nshard, rank, feat_dir):
+    iterator = generator()
+
+    feat_path = f"{feat_dir}/{rank}_{nshard}.npy"
+    leng_path = f"{feat_dir}/{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")
+
+