diff --git a/datasets/loquacious.py b/datasets/loquacious.py
new file mode 100644
index 00000000..5a25cb54
--- /dev/null
+++ b/datasets/loquacious.py
@@ -0,0 +1,309 @@
+import re
+from typing import Dict, Any
+import os
+from tqdm import tqdm
+
+from sisyphus import Job, Task, Path
+
+from i6_core.lib.corpus import Corpus, Recording, Segment
+
+import subprocess as sp
+from io import BytesIO
+
+filters = {
+    "voxpopuli": re.compile("PLENARY"),
+    "commonvoice": re.compile("common_voice"),
+    "librispeech": re.compile("^[0-9-]*$"),
+    "yodas": re.compile(".wav$"),
+}
+
+
+def extract_audio_from_text(entry, output_path):
+    sp.run(
+        [
+            "ffmpeg",
+            "-y",
+            "-hide_banner",
+            "-loglevel",
+            "error",
+            "-threads",
+            "2",
+            "-i",
+            "pipe:0",
+            "-ar",
+            "16000",
+            "-ac",
+            "1",
+            "-c:a",
+            "libvorbis",
+            "-b:a",
+            "16k",
+            os.path.join(output_path, entry["ID"] + ".ogg"),
+        ],
+        input=BytesIO(entry["wav"]["bytes"]).read(),
+        check=True,
+    )
+    entry.pop("wav")
+    return entry
+
+
+def remove_audio_from_entry(entry):
+    entry.pop("wav")
+    return entry
+
+
+def create_recording_from_entry(entry, out_dir: str):
+    recording = Recording()
+    recording.name = entry["ID"]
+    recording.audio = os.path.join(out_dir, entry["ID"] + ".ogg")
+
+    segment = Segment()
+    segment.name = "0"
+    segment.start = 0.0
+    segment.end = entry["duration"]
+    segment.orth = entry["text"]
+
+    recording.add_segment(segment)
+    return recording
+
+
+class PrepareLoquaciousDatasetJob(Job):
+    @classmethod
+    def hash(cls, parsed_args: Dict[str, Any]) -> str:
+        d = {}
+        return super().hash(d)
+
+
+class PrepareLoquaciousTrainSmallDatasetJob(PrepareLoquaciousDatasetJob):
+    """
+    Prepare the Loquacious dataset from HuggingFace.
+    """
+
+    def __init__(
+        self,
+        hf_home_dir: Path,
+    ):
+        self.hf_home_dir = hf_home_dir
+
+        self.out_corpus = self.output_path("loquacious_train_small.xml.gz")
+        self.out_dir = self.output_path("audio", directory=True)
+
+    def tasks(self):
+        yield Task("run", rqmt={"cpu": 16, "mem": 32, "time": 4})
+
+    def run(self):
+        os.environ["HF_HOME"] = self.hf_home_dir.get_path()
+        from datasets import load_dataset
+
+        dataset = load_dataset(
+            path="speechbrain/LoquaciousSet",
+            name="small",
+            split="train",
+            num_proc=8,
+        )
+        print("extract audio")
+        text_only_dataset = dataset.map(
+            extract_audio_from_text,
+            fn_kwargs={"output_path": self.out_dir.get_path()},
+            num_proc=8,
+            load_from_cache_file=False,
+        )
+        print("start corpus creation")
+        corpus = Corpus()
+        corpus.name = "loquacious-train-small"
+        progress = tqdm(text_only_dataset, file=open("/dev/null", "w"))
+        for i, entry in enumerate(progress):
+            if i % 100 == 0:
+                # do not bloat log files, so print manually
+                print(progress)
+            recording = create_recording_from_entry(entry, self.out_dir.get_path())
+            corpus.add_recording(recording)
+
+        corpus.dump(self.out_corpus.get_path())
+
+
+class PrepareLoquaciousTrainMediumDatasetJob(PrepareLoquaciousDatasetJob):
+    """
+    Prepare the Loquacious dataset from HuggingFace.
+    """
+
+    def __init__(
+        self,
+        hf_home_dir: Path,
+    ):
+        self.hf_home_dir = hf_home_dir
+
+        self.out_corpus = self.output_path("loquacious_train_medium.xml.gz")
+        self.out_corpus_wo_small = self.output_path("loquacious_train_medium_wo_small.xml.gz")
+        self.out_dir = self.output_path("audio", directory=True)
+
+    def tasks(self):
+        yield Task("run", rqmt={"cpu": 8, "mem": 32, "time": 40})
+
+    def run(self):
+        os.environ["HF_HOME"] = self.hf_home_dir.get_path()
+        from datasets import load_dataset
+
+        print("load small and medium datasets using HF")
+        dataset_medium = load_dataset(
+            path="speechbrain/LoquaciousSet",
+            name="medium",
+            split="train",
+            num_proc=8,
+        )
+        dataset_small = load_dataset(
+            path="speechbrain/LoquaciousSet",
+            name="small",
+            split="train",
+            num_proc=8,
+        )
+
+        print("remove audio from small set")
+        text_only_dataset_small = dataset_small.map(remove_audio_from_entry, num_proc=8, load_from_cache_file=False)
+
+        print("extract audio from medium set")
+        text_only_dataset_medium = dataset_medium.map(
+            extract_audio_from_text,
+            fn_kwargs={"output_path": self.out_dir.get_path()},
+            num_proc=8,
+            load_from_cache_file=False,
+        )
+
+        print("get sequence IDs of small set")
+        small_ids = set()
+        progress = tqdm(text_only_dataset_small, file=("/dev/null", "w"))
+        for i, entry in enumerate(progress):
+            if i % 100 == 0:
+                print(progress)
+            small_ids.add(entry["ID"])
+
+        print("start corpus creation")
+        corpus = Corpus()
+        corpus.name = "loquacious-train-medium"
+        corpus_wo_small = Corpus()
+        corpus_wo_small.name = "loquacious-train-medium-wo-small"
+        progress = tqdm(text_only_dataset_medium, file=open("/dev/null", "w"))
+        for i, entry in enumerate(progress):
+            if i % 1000 == 0:
+                print(progress)
+            recording = create_recording_from_entry(entry, self.out_dir.get_path())
+            corpus.add_recording(recording)
+            if entry["ID"] not in small_ids:
+                corpus_wo_small.add_recording(recording)
+
+        corpus.dump(self.out_corpus.get_path())
+        corpus_wo_small.dump(self.out_corpus_wo_small.get_path())
+
+
+class PrepareLoquaciousTestDatasetsJob(PrepareLoquaciousDatasetJob):
+    """
+    Prepare the Loquacious dataset from HuggingFace.
+    """
+
+    def __init__(
+        self,
+        hf_home_dir: Path,
+    ):
+        self.hf_home_dir = hf_home_dir
+
+        self.out_dev_all = self.output_path("loquacious_dev_all.xml.gz")
+        self.out_test_all = self.output_path("loquacious_test_all.xml.gz")
+        self.out_dev_librispeech = self.output_path("loquacious_dev_librispeech.xml.gz")
+        self.out_test_librispeech = self.output_path("loquacious_test_librispeech.xml.gz")
+        self.out_dev_commonvoice = self.output_path("loquacious_dev_commonvoice.xml.gz")
+        self.out_test_commonvoice = self.output_path("loquacious_test_commonvoice.xml.gz")
+        self.out_dev_voxpopuli = self.output_path("loquacious_dev_voxpopuli.xml.gz")
+        self.out_test_voxpopuli = self.output_path("loquacious_test_voxpopuli.xml.gz")
+        self.out_dev_yodas = self.output_path("loquacious_dev_yodas.xml.gz")
+        self.out_test_yodas = self.output_path("loquacious_test_yodas.xml.gz")
+
+        self.out_dev_corpora = {
+            "all": self.out_dev_all,
+            "librispeech": self.out_dev_librispeech,
+            "commonvoice": self.out_dev_commonvoice,
+            "voxpopuli": self.out_dev_voxpopuli,
+            "yodas": self.out_dev_yodas,
+        }
+
+        self.out_test_corpora = {
+            "all": self.out_test_all,
+            "librispeech": self.out_test_librispeech,
+            "commonvoice": self.out_test_commonvoice,
+            "voxpopuli": self.out_test_voxpopuli,
+            "yodas": self.out_test_yodas,
+        }
+
+        self.out_dir = self.output_path("audio", directory=True)
+
+    def tasks(self):
+        yield Task("run", rqmt={"cpu": 16, "mem": 32, "time": 4})
+
+    def run(self):
+        os.environ["HF_HOME"] = self.hf_home_dir.get_path()
+        from datasets import load_dataset
+
+        dataset_dev = load_dataset(
+            path="speechbrain/LoquaciousSet",
+            name="small",
+            split="dev",
+            num_proc=8,
+        )
+        dataset_test = load_dataset(
+            path="speechbrain/LoquaciousSet",
+            name="small",
+            split="test",
+            num_proc=8,
+        )
+        print("extract audio")
+        text_only_dataset_dev = dataset_dev.map(
+            extract_audio_from_text,
+            fn_kwargs={"output_path": self.out_dir.get_path()},
+            num_proc=8,
+            load_from_cache_file=False,
+        )
+        text_only_dataset_test = dataset_test.map(
+            extract_audio_from_text,
+            fn_kwargs={"output_path": self.out_dir.get_path()},
+            num_proc=8,
+            load_from_cache_file=False,
+        )
+
+        print("start corpus creation")
+        corpus_keys = [
+            "all",
+            "librispeech",
+            "commonvoice",
+            "voxpopuli",
+            "yodas",
+        ]
+        dev_corpora = {key: Corpus() for key in corpus_keys}
+        test_corpora = {key: Corpus() for key in corpus_keys}
+        for key, corpus in dev_corpora.items():
+            corpus.name = f"loquacious-dev-{key}"
+        for key, corpus in test_corpora.items():
+            corpus.name = f"loquacious-test-{key}"
+
+        for entry in tqdm(text_only_dataset_dev):
+            recording = create_recording_from_entry(entry, self.out_dir.get_path())
+            dev_corpora["all"].add_recording(recording)
+            matched = False
+            for key, filter in filters.items():
+                if filter.search(entry["ID"]):
+                    matched = True
+                    dev_corpora[key].add_recording(recording)
+            assert matched, f"no filter matched for {entry['ID']}"
+
+        for entry in tqdm(text_only_dataset_test):
+            recording = create_recording_from_entry(entry, self.out_dir.get_path())
+            test_corpora["all"].add_recording(recording)
+            matched = False
+            for key, filter in filters.items():
+                if filter.search(entry["ID"]):
+                    matched = True
+                    test_corpora[key].add_recording(recording)
+            assert matched, f"no filter matched for {entry['ID']}"
+
+        for key, dev_corpus in dev_corpora.items():
+            dev_corpus.dump(self.out_dev_corpora[key].get_path())
+        for key, test_corpus in test_corpora.items():
+            test_corpus.dump(self.out_test_corpora[key].get_path())