Add zipformer recipe for audio tagging

egs/audioset/AT/local/generate_audioset_manifest.py

@@ -0,0 +1,159 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Xiaoyu Yang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This file generates the manifest and computes the fbank features for AudioSet
+dataset. The generated manifests and features are stored in data/fbank.
+"""
+
+import argparse
+import csv
+import glob
+import logging
+import os
+
+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):
+    # The content of the csv file shoud be something like this
+    # ------------------------------------------------------
+    # filename  label
+    # dataset/AudioSet/balanced/xxxx.wav 0;451
+    # dataset/AudioSet/balanced/xxxy.wav 375
+    # ------------------------------------------------------
+    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",
+    )
+
+    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 = min(15, os.cpu_count())
+    num_mel_bins = 80
+
+    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}.")
+            continue
+        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}_{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()