Adds radio data recipe

lhotse/bin/modes/recipes/__init__.py

@@ -65,6 +65,7 @@
 from .nsc import *
 from .peoples_speech import *
 from .primewords import *
+from .radio import *
 from .reazonspeech import *
 from .rir_noise import *
 from .sbcsae import *

lhotse/bin/modes/recipes/radio.py

@@ -0,0 +1,41 @@
+from typing import List, Optional, Sequence, Tuple, Union
+
+import click
+
+from lhotse.bin.modes import prepare
+from lhotse.recipes.radio import prepare_radio
+from lhotse.utils import Pathlike
+
+__all__ = ["radio"]
+
+
+@prepare.command(context_settings=dict(show_default=True))
+@click.argument("corpus_dir", type=click.Path(dir_okay=True))
+@click.argument("output_dir", type=click.Path(dir_okay=True))
+@click.option(
+    "-d",
+    "--min-seg-dur",
+    type=float,
+    default=0.5,
+    help="The minimum segment duration",
+)
+@click.option(
+    "-j",
+    "--num-jobs",
+    type=int,
+    default=4,
+    help="The number of parallel threads to use for data preparation",
+)
+def radio(
+    corpus_dir: Pathlike,
+    output_dir: Pathlike,
+    min_seg_dur: float = 0.5,
+    num_jobs: int = 4,
+):
+    """Data preparation"""
+    prepare_radio(
+        corpus_dir,
+        output_dir=output_dir,
+        num_jobs=num_jobs,
+        min_segment_duration=min_seg_dur,
+    )

lhotse/recipes/__init__.py

@@ -66,6 +66,7 @@
 from .musan import download_musan, prepare_musan
 from .nsc import prepare_nsc
 from .peoples_speech import prepare_peoples_speech
+from .radio import prepare_radio
 from .reazonspeech import download_reazonspeech, prepare_reazonspeech
 from .rir_noise import download_rir_noise, prepare_rir_noise
 from .sbcsae import download_sbcsae, prepare_sbcsae
@@ -194,6 +195,7 @@
     "prepare_peoples_speech",
     "download_reazonspeech",
     "prepare_reazonspeech",
+    "prepare_radio",
     "download_rir_noise",
     "prepare_rir_noise",
     "prepare_slu",

lhotse/recipes/radio.py

@@ -0,0 +1,141 @@
+"""
+This recipe prepares data collected from radio streamed on the web. The data
+have some metadata attached to them, including the geographic location of
+broadcast, date and time of the recorded clip, as well as a unique station
+identifier.
+
+Obtaining the data
+-----------------------------------------------------------
+If you want to use this corpus please email: [email protected]
+
+As the data are collected from radio stream, they cannot be broadly
+disseminated or used for commercial purposes. In the email, include your
+affiliated academic institution and the intended use for the data and we will
+the data to you if it is indeed for non-commercial, academic purporses.
+
+Description
+------------------------------------------------------------
+The data consist of ∼4000 hours of speech collected between
+September 27, 2023 to October 1, 2023, in 9449 locations all over the world,
+from 17171 stations. 
+
+These data were used for Geolocation of speech in order to answer the question,
+Where are you from? in the paper 
+
+Where are you from? Geolocating Speech and Applications to Language
+Identification, presented at NAACL 2024. Please read for a full descrption
+and please cite as 
+
+@inproceedings{foley2024you,
+  title={Where are you from? Geolocating Speech and Applications to Language Identification},
+  author={Foley, Patrick and Wiesner, Matthew and Odoom, Bismarck and Perera, Leibny Paola Garcia and Murray, Kenton and Koehn, Philipp},
+  booktitle={Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)},
+  pages={5114--5126},
+  year={2024}
+}
+"""
+import json
+import re
+from functools import partial
+from pathlib import Path
+from typing import Dict, Optional, Union
+
+from tqdm import tqdm
+
+from lhotse.audio import Recording, RecordingSet, set_ffmpeg_torchaudio_info_enabled
+from lhotse.parallel import parallel_map
+from lhotse.supervision import SupervisionSegment, SupervisionSet
+from lhotse.utils import Pathlike
+
+set_ffmpeg_torchaudio_info_enabled(False)
+
+
+def _make_reco_and_sups_from_file(sf: str, msd: float = 0.5):
+    corpus_dir = sf.parents[2]
+    audio_dir = corpus_dir / "recos"
+    fname = sf.with_suffix(".flac").stem
+
+    # E.g. 2023_10_01_09h_02m_54s_dur30_ZnpbY9Zx_lat3.17_long113.04
+    chunk_idx = int(sf.parent.suffix.strip("."))
+    reco_file = audio_dir / f"recos.{chunk_idx}" / f"{fname}.flac"
+    reco = Recording.from_file(reco_file, recording_id=fname)
+    reco.channel_ids = [0]
+    sups = []
+    total = 0
+    with open(sf) as f:
+        segments = json.load(f)
+
+    # Parse the file format, shown in the comment above, to get:
+    # date, station, latitude, longitude, and the estimated gender
+    lat, lon = re.search(r"lat[^_]+_long[^_]+", Path(sf).stem).group(0).split("_")
+    lat = float(lat.replace("lat", ""))
+    lon = float(lon.replace("long", ""))
+    station = re.search(r"s_dur[0-9]+_(.*)_lat[^_]+_long[^_]+", fname).groups()[0]
+    fname_vals = fname.split("_")
+    date = [int(i.strip("hms")) for i in fname_vals[0:6]]  # YY MM DD hh mm ss
+    for seg in segments:
+        start, end = float(seg[1]), float(seg[2])
+        dur = end - start
+        if seg[0] in ("male", "female") and dur > msd:
+            sups.append(
+                SupervisionSegment(
+                    id=f"{fname}_{int(100*start):04}",
+                    recording_id=fname,
+                    start=start,
+                    duration=round(dur, 4),
+                    channel=0,
+                    custom={
+                        "date": date,
+                        "lat": lat,
+                        "lon": lon,
+                        "station": station,
+                        "est_gender": seg[0],
+                    },
+                )
+            )
+    return sups, reco
+
+
+def prepare_radio(
+    corpus_dir: Pathlike,
+    output_dir: Optional[Pathlike] = None,
+    min_segment_duration: float = 0.5,
+    num_jobs: int = 4,
+) -> Dict[str, Union[RecordingSet, SupervisionSet]]:
+    """
+    Return the manifests which consist of recordings and supervisions
+    :param corpus_dir: Path to the collected radio samples
+    :param output_dir: Pathlike, the path where manifests are written
+    :return: A Dict whose key is the dataset part and the value is a Dict with
+        keys 'recordings' and 'supervisions'.
+    """
+    corpus_dir = Path(corpus_dir)
+    segment_files = corpus_dir.rglob("segs/*/*.json")
+    supervisions, recordings = [], []
+    fun = partial(_make_reco_and_sups_from_file, msd=min_segment_duration)
+    output_dir = Path(output_dir) if output_dir is not None else None
+    output_dir.mkdir(mode=511, parents=True, exist_ok=True)
+    with RecordingSet.open_writer(
+        output_dir / "radio_recordings.jsonl.gz"
+    ) as rec_writer:
+        with SupervisionSet.open_writer(
+            output_dir / "radio_supervisions.jsonl.gz"
+        ) as sup_writer:
+            for sups, reco in tqdm(
+                parallel_map(
+                    fun,
+                    segment_files,
+                    num_jobs=num_jobs,
+                ),
+                desc=f"Making recordings and supervisions",
+            ):
+                rec_writer.write(reco)
+                for sup in sups:
+                    sup_writer.write(sup)
+
+            manifests = {
+                "recordings": RecordingSet.from_jsonl_lazy(rec_writer.path),
+                "supervisions": SupervisionSet.from_jsonl_lazy(sup_writer.path),
+            }
+
+    return manifests