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https://github.com/k2-fsa/icefall.git
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915e8e399c
- Added CHiME-4 dataset integration in asr_datamodule.py - Added Hugging Face upload script - Added RIR augmentation - Added Self-Distillation Training
761 lines
28 KiB
Python
Executable File
761 lines
28 KiB
Python
Executable File
# Copyright 2021 Piotr Żelasko
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# Copyright 2022 Xiaomi Corporation (Author: Mingshuang Luo)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import inspect
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import logging
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import warnings
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from functools import lru_cache
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from pathlib import Path
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from typing import Any, Dict, Optional, List
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import torch
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from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
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from lhotse.dataset import ( # noqa F401 for PrecomputedFeatures
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CutConcatenate,
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CutMix,
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DynamicBucketingSampler,
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K2SpeechRecognitionDataset,
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PrecomputedFeatures,
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SimpleCutSampler,
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SpecAugment,
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)
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from lhotse.dataset.input_strategies import ( # noqa F401 For AudioSamples
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AudioSamples,
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OnTheFlyFeatures,
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)
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from lhotse.augmentation import ReverbWithImpulseResponse
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from lhotse.utils import fix_random_seed
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from torch.utils.data import DataLoader
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from icefall.utils import str2bool
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# Filter out RIR reverberation warnings
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class RIRWarningFilter(logging.Filter):
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def filter(self, record):
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return not ("Attempting to reverberate" in record.getMessage() and "pre-computed features" in record.getMessage())
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# Apply the filter to root logger
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logging.getLogger().addFilter(RIRWarningFilter())
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class RandomRIRTransform:
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"""
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Random RIR (Room Impulse Response) transform that applies reverberation
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to CutSet using lhotse's built-in reverb_rir method.
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"""
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def __init__(self, rir_paths, prob=0.5):
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from lhotse import Recording, RecordingSet
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# Load RIR recordings from file paths
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self.rir_recordings = []
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for i, rir_path in enumerate(rir_paths[:50]): # Limit to first 50 for memory
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try:
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rir_rec = Recording.from_file(rir_path)
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# Resample to 16kHz if needed
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if rir_rec.sampling_rate != 16000:
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rir_rec = rir_rec.resample(16000)
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self.rir_recordings.append(rir_rec)
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except Exception as e:
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continue # Skip problematic files
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# Create RecordingSet from loaded recordings
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if self.rir_recordings:
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self.rir_recording_set = RecordingSet.from_recordings(self.rir_recordings)
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else:
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self.rir_recording_set = None
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self.prob = prob
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print(f"Loaded {len(self.rir_recordings)} RIR recordings for augmentation")
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def __call__(self, cuts):
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"""Apply RIR to CutSet with specified probability."""
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import random
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if random.random() < self.prob and self.rir_recording_set is not None:
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# Apply reverb_rir to the entire CutSet
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return cuts.reverb_rir(rir_recordings=self.rir_recording_set)
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return cuts
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class _SeedWorkers:
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def __init__(self, seed: int):
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self.seed = seed
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def __call__(self, worker_id: int):
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fix_random_seed(self.seed + worker_id)
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class LibriSpeechAsrDataModule:
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"""
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DataModule for k2 ASR experiments.
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It assumes there is always one train and valid dataloader,
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but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
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and test-other).
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It contains all the common data pipeline modules used in ASR
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experiments, e.g.:
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- dynamic batch size,
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- bucketing samplers,
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- cut concatenation,
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- augmentation,
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- on-the-fly feature extraction
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This class should be derived for specific corpora used in ASR tasks.
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"""
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def __init__(self, args: argparse.Namespace):
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self.args = args
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@classmethod
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def add_arguments(cls, parser: argparse.ArgumentParser):
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group = parser.add_argument_group(
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title="ASR data related options",
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description="These options are used for the preparation of "
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"PyTorch DataLoaders from Lhotse CutSet's -- they control the "
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"effective batch sizes, sampling strategies, applied data "
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"augmentations, etc.",
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)
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group.add_argument(
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"--full-libri",
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type=str2bool,
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default=True,
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help="""Used only when --mini-libri is False.When enabled,
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use 960h LibriSpeech. Otherwise, use 100h subset.""",
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)
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group.add_argument(
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"--mini-libri",
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type=str2bool,
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default=False,
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help="True for mini librispeech",
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)
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group.add_argument(
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"--manifest-dir",
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type=Path,
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default=Path("data/fbank"),
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help="Path to directory with train/valid/test cuts.",
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)
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group.add_argument(
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"--max-duration",
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type=int,
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default=200.0,
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help="Maximum pooled recordings duration (seconds) in a "
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"single batch. You can reduce it if it causes CUDA OOM.",
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)
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group.add_argument(
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"--valid-max-duration",
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type=int,
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default=None,
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help="Maximum pooled recordings duration (seconds) in a "
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"single validation batch. If None, uses --max-duration. "
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"You should reduce this if validation causes CUDA OOM.",
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)
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group.add_argument(
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"--bucketing-sampler",
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type=str2bool,
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default=True,
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help="When enabled, the batches will come from buckets of "
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"similar duration (saves padding frames).",
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)
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group.add_argument(
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"--num-buckets",
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type=int,
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default=30,
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help="The number of buckets for the DynamicBucketingSampler"
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"(you might want to increase it for larger datasets).",
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)
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group.add_argument(
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"--concatenate-cuts",
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type=str2bool,
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default=False,
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help="When enabled, utterances (cuts) will be concatenated "
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"to minimize the amount of padding.",
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)
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group.add_argument(
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"--duration-factor",
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type=float,
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default=1.0,
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help="Determines the maximum duration of a concatenated cut "
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"relative to the duration of the longest cut in a batch.",
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)
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group.add_argument(
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"--gap",
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type=float,
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default=1.0,
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help="The amount of padding (in seconds) inserted between "
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"concatenated cuts. This padding is filled with noise when "
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"noise augmentation is used.",
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)
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group.add_argument(
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"--on-the-fly-feats",
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type=str2bool,
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default=False,
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help="When enabled, use on-the-fly cut mixing and feature "
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"extraction. Will drop existing precomputed feature manifests "
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"if available.",
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)
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group.add_argument(
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"--shuffle",
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type=str2bool,
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default=True,
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help="When enabled (=default), the examples will be "
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"shuffled for each epoch.",
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)
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group.add_argument(
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"--drop-last",
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type=str2bool,
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default=True,
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help="Whether to drop last batch. Used by sampler.",
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)
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group.add_argument(
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"--return-cuts",
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type=str2bool,
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default=True,
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help="When enabled, each batch will have the "
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"field: batch['supervisions']['cut'] with the cuts that "
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"were used to construct it.",
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)
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group.add_argument(
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"--num-workers",
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type=int,
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default=2,
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help="The number of training dataloader workers that "
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"collect the batches.",
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)
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group.add_argument(
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"--enable-spec-aug",
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type=str2bool,
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default=False,
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help="When enabled, use SpecAugment for training dataset.",
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)
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group.add_argument(
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"--spec-aug-time-warp-factor",
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type=int,
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default=80,
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help="Used only when --enable-spec-aug is True. "
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"It specifies the factor for time warping in SpecAugment. "
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"Larger values mean more warping. "
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"A value less than 1 means to disable time warp.",
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)
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group.add_argument(
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"--enable-musan",
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type=str2bool,
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default=False,
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help="When enabled, select noise from MUSAN and mix it"
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"with training dataset. ",
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)
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group.add_argument(
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"--enable-rir",
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type=str2bool,
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default=False,
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help="When enabled, convolve training data with RIR "
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"(Room Impulse Response) for data augmentation.",
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)
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group.add_argument(
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"--rir-cuts-path",
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type=Path,
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default=None,
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help="Path to RIR cuts manifest file (e.g., data/rir/rir_cuts.jsonl.gz). "
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"Required when --enable-rir is True.",
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)
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group.add_argument(
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"--rir-prob",
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type=float,
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default=0.5,
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help="Probability of applying RIR augmentation to each utterance.",
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)
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group.add_argument(
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"--input-strategy",
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type=str,
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default="PrecomputedFeatures",
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help="AudioSamples or PrecomputedFeatures",
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)
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def train_dataloaders(
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self,
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cuts_train: CutSet,
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sampler_state_dict: Optional[Dict[str, Any]] = None,
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) -> DataLoader:
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"""
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Args:
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cuts_train:
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CutSet for training.
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sampler_state_dict:
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The state dict for the training sampler.
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"""
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# Setup augmentation transforms (for noisy dataset)
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transforms = []
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if self.args.enable_musan:
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logging.info("Enable MUSAN")
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logging.info("About to get Musan cuts")
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cuts_musan = load_manifest("data/fbank/musan_cuts.jsonl.gz")
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transforms.append(
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CutMix(cuts=cuts_musan, p=0.5, snr=(10, 20), preserve_id=True)
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)
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else:
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logging.info("Disable MUSAN")
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if self.args.enable_rir:
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logging.info("Enable RIR (Room Impulse Response) augmentation")
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logging.info(f"Loading RIR paths from {self.args.rir_cuts_path}")
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# Load RIR file paths from rir.scp
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rir_paths = []
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try:
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with open("data/manifests/rir.scp", "r") as f:
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rir_paths = [line.strip() for line in f if line.strip()]
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logging.info(f"Found {len(rir_paths)} RIR files")
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except FileNotFoundError:
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logging.warning("RIR file data/manifests/rir.scp not found, skipping RIR augmentation")
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rir_paths = []
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if rir_paths:
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# Use the module-level RandomRIRTransform class with audio-level processing
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transforms.append(
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RandomRIRTransform(rir_paths, prob=self.args.rir_prob)
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)
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else:
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logging.info("Disable RIR augmentation")
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if self.args.concatenate_cuts:
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logging.info(
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f"Using cut concatenation with duration factor "
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f"{self.args.duration_factor} and gap {self.args.gap}."
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)
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# Cut concatenation should be the first transform in the list,
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# so that if we e.g. mix noise in, it will fill the gaps between
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# different utterances.
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transforms = [
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CutConcatenate(
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duration_factor=self.args.duration_factor, gap=self.args.gap
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)
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] + transforms
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input_transforms = []
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if self.args.enable_spec_aug:
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logging.info("Enable SpecAugment")
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logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
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# Set the value of num_frame_masks according to Lhotse's version.
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# In different Lhotse's versions, the default of num_frame_masks is
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# different.
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num_frame_masks = 10
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num_frame_masks_parameter = inspect.signature(
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SpecAugment.__init__
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).parameters["num_frame_masks"]
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if num_frame_masks_parameter.default == 1:
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num_frame_masks = 2
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logging.info(f"Num frame mask: {num_frame_masks}")
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input_transforms.append(
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SpecAugment(
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time_warp_factor=self.args.spec_aug_time_warp_factor,
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num_frame_masks=num_frame_masks,
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features_mask_size=27,
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num_feature_masks=2,
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frames_mask_size=100,
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)
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)
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else:
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logging.info("Disable SpecAugment")
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# Create input strategy (same for both clean and noisy - only transforms differ)
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input_strategy = eval(self.args.input_strategy)()
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if self.args.on_the_fly_feats:
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input_strategy = OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
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# Create clean dataset (no augmentation)
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# Create train dataset (with augmentations)
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logging.info("About to create train dataset")
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augmentation_details = []
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if transforms:
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transform_names = [type(t).__name__ for t in transforms]
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augmentation_details.append(f"Cut transforms: {transform_names}")
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if input_transforms:
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input_transform_names = [type(t).__name__ for t in input_transforms]
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augmentation_details.append(f"Input transforms: {input_transform_names}")
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if augmentation_details:
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logging.info(f"Train dataset augmentations: {'; '.join(augmentation_details)}")
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else:
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logging.info("Train dataset: No augmentations will be applied")
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logging.info(f"Train dataset: {len(transforms)} cut transforms, {len(input_transforms)} input transforms")
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train = K2SpeechRecognitionDataset(
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input_strategy=input_strategy,
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cut_transforms=transforms, # Apply cut augmentations (MUSAN, RIR, concat)
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input_transforms=input_transforms, # Apply input augmentations (SpecAugment)
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return_cuts=self.args.return_cuts,
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)
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# Create sampler
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if self.args.bucketing_sampler:
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logging.info("Using DynamicBucketingSampler.")
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train_sampler = DynamicBucketingSampler(
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cuts_train,
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max_duration=self.args.max_duration,
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shuffle=self.args.shuffle,
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num_buckets=self.args.num_buckets,
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buffer_size=self.args.num_buckets * 2000,
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shuffle_buffer_size=self.args.num_buckets * 5000,
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drop_last=self.args.drop_last,
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)
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else:
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logging.info("Using SimpleCutSampler.")
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train_sampler = SimpleCutSampler(
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cuts_train,
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max_duration=self.args.max_duration,
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shuffle=self.args.shuffle,
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)
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logging.info("About to create train dataloader")
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if sampler_state_dict is not None:
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logging.info("Loading sampler state dict")
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train_sampler.load_state_dict(sampler_state_dict)
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# 'seed' is derived from the current random state, which will have
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# previously been set in the main process.
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seed = torch.randint(0, 100000, ()).item()
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worker_init_fn = _SeedWorkers(seed)
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train_dl = DataLoader(
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train,
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sampler=train_sampler,
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batch_size=None,
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num_workers=self.args.num_workers,
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persistent_workers=False,
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worker_init_fn=worker_init_fn,
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)
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return train_dl
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def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
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transforms = []
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if self.args.concatenate_cuts:
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transforms = [
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CutConcatenate(
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duration_factor=self.args.duration_factor, gap=self.args.gap
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)
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] + transforms
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# Determine the max_duration for validation
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valid_max_duration = self.args.valid_max_duration if self.args.valid_max_duration is not None else self.args.max_duration
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logging.info(f"Validation max_duration: {valid_max_duration} seconds")
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logging.info("About to create dev dataset")
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if self.args.on_the_fly_feats:
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validate = K2SpeechRecognitionDataset(
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cut_transforms=transforms,
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input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
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return_cuts=self.args.return_cuts,
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)
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else:
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validate = K2SpeechRecognitionDataset(
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cut_transforms=transforms,
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return_cuts=self.args.return_cuts,
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)
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valid_sampler = DynamicBucketingSampler(
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cuts_valid,
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max_duration=valid_max_duration,
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shuffle=False,
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)
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logging.info("About to create dev dataloader")
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valid_dl = DataLoader(
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validate,
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sampler=valid_sampler,
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batch_size=None,
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num_workers=2,
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persistent_workers=False,
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)
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return valid_dl
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def test_dataloaders(self, cuts: CutSet) -> DataLoader:
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logging.debug("About to create test dataset")
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test = K2SpeechRecognitionDataset(
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input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
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if self.args.on_the_fly_feats
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else eval(self.args.input_strategy)(),
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return_cuts=self.args.return_cuts,
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)
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sampler = DynamicBucketingSampler(
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cuts,
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max_duration=self.args.max_duration,
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shuffle=False,
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)
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logging.debug("About to create test dataloader")
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test_dl = DataLoader(
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test,
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batch_size=None,
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sampler=sampler,
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num_workers=self.args.num_workers,
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)
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return test_dl
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|
|
|
def all_test_dataloaders(self) -> Dict[str, DataLoader]:
|
|
"""
|
|
Returns all test dataloaders including LibriSpeech and CHiME-4.
|
|
|
|
Returns:
|
|
Dict[str, DataLoader]: Dictionary with test set names as keys and DataLoaders as values
|
|
"""
|
|
test_dataloaders = {}
|
|
|
|
# LibriSpeech test sets
|
|
test_clean_cuts = self.test_clean_cuts()
|
|
test_other_cuts = self.test_other_cuts()
|
|
|
|
test_dataloaders["test-clean"] = self.test_dataloaders(test_clean_cuts)
|
|
test_dataloaders["test-other"] = self.test_dataloaders(test_other_cuts)
|
|
|
|
# CHiME-4 test sets
|
|
chime4_dls = self.chime4_test_dataloaders()
|
|
for test_set_name, dl in chime4_dls.items():
|
|
test_dataloaders[f"chime4-{test_set_name}"] = dl
|
|
|
|
return test_dataloaders
|
|
|
|
@lru_cache()
|
|
def train_clean_5_cuts(self) -> CutSet:
|
|
logging.info("mini_librispeech: About to get train-clean-5 cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_train-clean-5.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def train_clean_100_cuts(self) -> CutSet:
|
|
logging.info("About to get train-clean-100 cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_train-clean-100.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def train_clean_360_cuts(self) -> CutSet:
|
|
logging.info("About to get train-clean-360 cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_train-clean-360.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def train_other_500_cuts(self) -> CutSet:
|
|
logging.info("About to get train-other-500 cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_train-other-500.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def train_all_shuf_cuts(self) -> CutSet:
|
|
logging.info(
|
|
"About to get the shuffled train-clean-100, \
|
|
train-clean-360 and train-other-500 cuts"
|
|
)
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_train-all-shuf.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def dev_clean_2_cuts(self) -> CutSet:
|
|
logging.info("mini_librispeech: About to get dev-clean-2 cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_dev-clean-2.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def dev_clean_cuts(self) -> CutSet:
|
|
logging.info("About to get dev-clean cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_dev-clean.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def dev_other_cuts(self) -> CutSet:
|
|
logging.info("About to get dev-other cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_dev-other.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def test_clean_cuts(self) -> CutSet:
|
|
logging.info("About to get test-clean cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_test-clean.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def test_other_cuts(self) -> CutSet:
|
|
logging.info("About to get test-other cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_test-other.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def gigaspeech_subset_small_cuts(self) -> CutSet:
|
|
logging.info("About to get Gigaspeech subset-S cuts")
|
|
return load_manifest_lazy(self.args.manifest_dir / "cuts_S.jsonl.gz")
|
|
|
|
@lru_cache()
|
|
def gigaspeech_dev_cuts(self) -> CutSet:
|
|
logging.info("About to get Gigaspeech dev cuts")
|
|
return load_manifest_lazy(self.args.manifest_dir / "cuts_DEV.jsonl.gz")
|
|
|
|
@lru_cache()
|
|
def gigaspeech_test_cuts(self) -> CutSet:
|
|
logging.info("About to get Gigaspeech test cuts")
|
|
return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST.jsonl.gz")
|
|
|
|
def chime4_test_dataloaders(self) -> Dict[str, DataLoader]:
|
|
"""Create CHiME-4 test dataloaders for different conditions."""
|
|
from pathlib import Path
|
|
|
|
chime4_audio_root = Path("/home/nas/DB/CHiME4/data/audio/16kHz/isolated")
|
|
chime4_transcript_root = Path("/home/nas/DB/CHiME4/data/transcriptions")
|
|
|
|
test_loaders = {}
|
|
|
|
# Define test sets: dt05 (development) and et05 (evaluation)
|
|
test_sets = ["dt05_bth", "et05_bth"] # Start with booth (clean) conditions
|
|
|
|
for test_set in test_sets:
|
|
try:
|
|
audio_dir = chime4_audio_root / test_set
|
|
transcript_dir = chime4_transcript_root / test_set
|
|
|
|
if not audio_dir.exists() or not transcript_dir.exists():
|
|
logging.warning(f"CHiME-4 {test_set} not found, skipping")
|
|
continue
|
|
|
|
# Create cuts for this test set
|
|
cuts = self._create_chime4_cuts(audio_dir, transcript_dir, max_files=50)
|
|
|
|
if len(cuts) == 0:
|
|
logging.warning(f"No valid cuts for CHiME-4 {test_set}")
|
|
continue
|
|
|
|
# Create test dataset
|
|
test_dataset = K2SpeechRecognitionDataset(
|
|
input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
|
|
return_cuts=self.args.return_cuts,
|
|
)
|
|
|
|
# Create sampler
|
|
sampler = DynamicBucketingSampler(
|
|
cuts,
|
|
max_duration=self.args.max_duration,
|
|
shuffle=False,
|
|
)
|
|
|
|
# Create dataloader
|
|
test_dl = DataLoader(
|
|
test_dataset,
|
|
batch_size=None,
|
|
sampler=sampler,
|
|
num_workers=2,
|
|
)
|
|
|
|
test_loaders[test_set] = test_dl
|
|
logging.info(f"Created CHiME-4 {test_set} dataloader with {len(cuts)} cuts")
|
|
|
|
except Exception as e:
|
|
logging.warning(f"Failed to create CHiME-4 {test_set} dataloader: {e}")
|
|
|
|
return test_loaders
|
|
|
|
def _create_chime4_cuts(self, audio_dir: Path, transcript_dir: Path, max_files: int = 50) -> CutSet:
|
|
"""Helper to create CutSet from CHiME-4 audio and transcripts."""
|
|
from lhotse import CutSet, Recording, RecordingSet, SupervisionSegment, SupervisionSet
|
|
|
|
# Get audio files (limit for testing)
|
|
wav_files = sorted(list(audio_dir.glob("*.wav")))[:max_files]
|
|
|
|
# Parse transcriptions
|
|
transcriptions = {}
|
|
for trn_file in transcript_dir.glob("*.trn"):
|
|
try:
|
|
with open(trn_file, 'r', encoding='utf-8') as f:
|
|
line = f.read().strip()
|
|
if line:
|
|
parts = line.split(' ', 1)
|
|
if len(parts) == 2:
|
|
utterance_id = parts[0]
|
|
text = parts[1]
|
|
transcriptions[utterance_id] = text
|
|
except Exception as e:
|
|
logging.warning(f"Failed to read {trn_file}: {e}")
|
|
|
|
# Create recordings and supervisions
|
|
recordings = []
|
|
supervisions = []
|
|
|
|
for wav_file in wav_files:
|
|
# Extract utterance ID from filename (remove .CH0, etc.)
|
|
utterance_id = wav_file.stem
|
|
if '.CH' in utterance_id:
|
|
utterance_id = utterance_id.split('.CH')[0]
|
|
|
|
# Skip if no transcription
|
|
if utterance_id not in transcriptions:
|
|
continue
|
|
|
|
try:
|
|
# Create recording
|
|
recording = Recording.from_file(wav_file)
|
|
recording = Recording(
|
|
id=utterance_id,
|
|
sources=recording.sources,
|
|
sampling_rate=recording.sampling_rate,
|
|
num_samples=recording.num_samples,
|
|
duration=recording.duration,
|
|
channel_ids=recording.channel_ids,
|
|
transforms=recording.transforms
|
|
)
|
|
recordings.append(recording)
|
|
|
|
# Create supervision
|
|
text = transcriptions[utterance_id]
|
|
supervision = SupervisionSegment(
|
|
id=utterance_id,
|
|
recording_id=utterance_id,
|
|
start=0.0,
|
|
duration=recording.duration,
|
|
channel=0,
|
|
text=text,
|
|
language="English"
|
|
)
|
|
supervisions.append(supervision)
|
|
|
|
except Exception as e:
|
|
logging.warning(f"Failed to process {wav_file}: {e}")
|
|
continue
|
|
|
|
if not recordings:
|
|
return CutSet.from_cuts([]) # Empty CutSet
|
|
|
|
# Create manifests
|
|
recording_set = RecordingSet.from_recordings(recordings)
|
|
supervision_set = SupervisionSet.from_segments(supervisions)
|
|
cuts = CutSet.from_manifests(recordings=recording_set, supervisions=supervision_set)
|
|
|
|
return cuts
|