Add zipformer recipe for audio tagging

egs/audioset/AT/local/generate_audioset_manifest.py

@@ -0,0 +1,134 @@
+import argparse
+import csv
+import glob
+import logging
+
+import torch
+from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
+from lhotse.audio import Recording
+from lhotse.cut import MonoCut
+from lhotse.supervision import SupervisionSegment
+
+from icefall.utils import get_executor
+
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+
+def parse_csv(csv_file="downloads/audioset/full_train_asedata_with_duration.csv"):
+
+ mapping = {}
+ with open(csv_file, "r") as fin:
+ reader = csv.reader(fin, delimiter="\t")
+ for i, row in enumerate(reader):
+ if i == 0:
+ continue
+ key = "/".join(row[0].split("/")[-2:])
+ mapping[key] = row[1]
+ return mapping
+
+
+def get_parser():
+ parser = argparse.ArgumentParser(
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter
+ )
+
+ parser.add_argument("--dataset-dir", type=str, default="downloads/audioset")
+
+ parser.add_argument(
+ "--split",
+ type=str,
+ default="balanced",
+ choices=["balanced", "unbalanced", "eval", "eval_all"],
+ )
+
+ parser.add_argument(
+ "--feat-output-dir",
+ type=str,
+ default="data/fbank_audioset",
+ )
+
+ return parser
+
+
+def main():
+ parser = get_parser()
+ args = parser.parse_args()
+
+ dataset_dir = args.dataset_dir
+ split = args.split
+ feat_output_dir = args.feat_output_dir
+
+ num_jobs = 15
+ num_mel_bins = 80
+
+ import pdb
+
+ pdb.set_trace()
+ if split in ["balanced", "unbalanced"]:
+ csv_file = "downloads/audioset/full_train_asedata_with_duration.csv"
+ elif split == "eval":
+ csv_file = "downloads/audioset/eval.csv"
+ elif split == "eval_all":
+ csv_file = "downloads/audioset/eval_all.csv"
+ else:
+ raise ValueError()
+
+ labels = parse_csv(csv_file)
+
+ audio_files = glob.glob(f"{dataset_dir}/eval/wav_all/*.wav")
+
+ new_cuts = []
+ for i, audio in enumerate(audio_files):
+ cut_id = "/".join(audio.split("/")[-2:])
+ recording = Recording.from_file(audio, cut_id)
+ cut = MonoCut(
+ id=cut_id,
+ start=0.0,
+ duration=recording.duration,
+ channel=0,
+ recording=recording,
+ )
+ supervision = SupervisionSegment(
+ id=cut_id,
+ recording_id=cut.recording.id,
+ start=0.0,
+ channel=0,
+ duration=cut.duration,
+ )
+ try:
+ supervision.audio_event = labels[cut_id]
+ except KeyError:
+ logging.info(f"No labels found for {cut_id}.")
+ supervision.audio_event = ""
+ cut.supervisions = [supervision]
+ new_cuts.append(cut)
+
+ if i % 100 == 0 and i:
+ logging.info(f"Processed {i} cuts until now.")
+
+ cuts = CutSet.from_cuts(new_cuts)
+
+ extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
+
+ logging.info(f"Computing fbank features for {split}")
+ with get_executor() as ex:
+ cuts = cuts.compute_and_store_features(
+ extractor=extractor,
+ storage_path=f"{feat_output_dir}/{split}_{args.split}_feats",
+ num_jobs=num_jobs if ex is None else 80,
+ executor=ex,
+ storage_type=LilcomChunkyWriter,
+ )
+
+ manifest_output_dir = feat_output_dir + "/" + f"cuts_audioset_{split}.jsonl.gz"
+
+ logging.info(f"Storing the manifest to {manifest_output_dir}")
+ cuts.to_jsonl(manifest_output_dir)
+
+
+if __name__ == "__main__":
+ formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+ logging.basicConfig(format=formatter, level=logging.INFO)
+ main()