def combine(manifests: Pathlike, output_manifest: Pathlike):
"""Load MANIFESTS, combine them into a single one, and write it to OUTPUT_MANIFEST."""
from lhotse import load_manifest
from lhotse.manipulation import combine as combine_manifests
data_set = combine_manifests(*[load_manifest(m) for m in manifests])
data_set.to_file(output_manifest)
def combine(manifests: Pathlike, output_manifest: Pathlike):
"""Load MANIFESTS, combine them into a single one, and write it to OUTPUT_MANIFEST."""
data_set = combine_manifests(*[load_manifest(m) for m in manifests])
data_set.to_json(output_manifest)
def copy_feats(
input_manifest: Pathlike,
output_manifest: Pathlike,
storage_path: str,
storage_type: str,
max_jobs: int,
) -> None:
"""
Load INPUT_MANIFEST of type :class:`lhotse.FeatureSet` or `lhotse.CutSet`,
read every feature matrix using ``features.load()`` or ``cut.load_features()``,
save them in STORAGE_PATH and save the updated manifest to OUTPUT_MANIFEST.
"""
from lhotse.serialization import load_manifest_lazy_or_eager
from lhotse.manipulation import combine as combine_manifests
manifests = load_manifest_lazy_or_eager(input_manifest)
if isinstance(manifests, FeatureSet):
with get_writer(storage_type)(storage_path) as w:
# FeatureSet is copied in-memory and written (TODO: make it incremental if needed)
manifests = manifests.copy_feats(writer=w)
manifests.to_file(output_manifest)
elif isinstance(manifests, CutSet):
# Group cuts by their underlying feature files.
manifests = sorted(manifests,
key=lambda cut: cut.features.storage_path)
subsets = groupby(manifests, lambda cut: cut.features.storage_path)
unique_storage_paths, subsets = zip(*[(k, CutSet.from_cuts(grp))
for k, grp in subsets])
# Create paths for new feature files and subset cutsets.
tot_items = len(unique_storage_paths)
new_storage_paths = [
f"{storage_path}/feats-{i}" for i in range(tot_items)
]
partial_manifest_paths = [
f"{storage_path}/cuts-{i}.jsonl.gz" for i in range(tot_items)
]
num_jobs = len(unique_storage_paths)
if max_jobs > 0:
num_jobs = min(num_jobs, max_jobs)
# Create directory if needed (storage_path might be an URL)
if Path(storage_path).parent.is_dir():
Path(storage_path).mkdir(exist_ok=True)
# Copy each partition in parallel and combine lazily opened manifests.
with ProcessPoolExecutor(num_jobs) as ex:
futures = []
for cs, nsp, pmp in zip(subsets, new_storage_paths,
partial_manifest_paths):
futures.append(
ex.submit(copy_feats_worker, cs, nsp, storage_type, pmp))
all_cuts = combine_manifests(
(f.result() for f in as_completed(futures)))
# Combine and save subset cutsets into the final file.
with CutSet.open_writer(output_manifest) as w:
for c in all_cuts:
w.write(c)
else:
raise ValueError(
f"Unsupported manifest type ({type(manifests)}) at: {input_manifest}"
)
