Fluent Speech Commands dataset, SLU task (#1272)

* Implement slu data prep

* Bug fixes

* Remove commented code, add to corpora list in docs

Signed-off-by: Xinyuan Li <xli257@b17.clsp.jhu.edu>

* Move pandas import to local scope

Signed-off-by: Xinyuan Li <xli257@b17.clsp.jhu.edu>

---------

Signed-off-by: Xinyuan Li <xli257@b17.clsp.jhu.edu>
Co-authored-by: Xinyuan Li <xli257@b17.clsp.jhu.edu>
Co-authored-by: Piotr Żelasko <petezor@gmail.com>

docs/corpus.rst

@@ -107,6 +107,8 @@ a CLI tool that create the manifests given a corpus directory.
     - :func:`lhotse.recipes.prepare_fisher_english`
   * - Fisher Spanish
     - :func:`lhotse.recipes.prepare_fisher_spanish`
+  * - Fluent Speech Commands
+    - :func:`lhotse.recipes.slu`
   * - GALE Arabic Broadcast Speech
     - :func:`lhotse.recipes.prepare_gale_arabic`
   * - GALE Mandarin Broadcast Speech

lhotse/bin/modes/recipes/__init__.py

@@ -62,6 +62,7 @@
 from .peoples_speech import *
 from .primewords import *
 from .rir_noise import *
+from .slu import *
 from .speechcommands import *
 from .spgispeech import *
 from .stcmds import *

lhotse/bin/modes/recipes/slu.py

@@ -0,0 +1,17 @@
+from typing import List, Optional, Sequence, Tuple, Union
+
+import click
+
+from lhotse.bin.modes import prepare
+from lhotse.recipes.slu import prepare_slu
+from lhotse.utils import Pathlike
+
+
+@prepare.command(context_settings=dict(show_default=True))
+@click.argument("corpus_dir", type=click.Path())
+@click.argument("output_dir", type=click.Path())
+def slu(
+    corpus_dir: Pathlike,
+    output_dir: Pathlike,
+):
+    prepare_slu(corpus_dir=corpus_dir, output_dir=output_dir)

lhotse/recipes/__init__.py

@@ -63,6 +63,7 @@
 from .nsc import prepare_nsc
 from .peoples_speech import prepare_peoples_speech
 from .rir_noise import download_rir_noise, prepare_rir_noise
+from .slu import prepare_slu
 from .speechcommands import download_speechcommands, prepare_speechcommands
 from .spgispeech import download_spgispeech, prepare_spgispeech
 from .stcmds import download_stcmds, prepare_stcmds

lhotse/recipes/slu.py

@@ -0,0 +1,125 @@
+import glob
+import json
+import logging
+from collections import defaultdict
+from itertools import groupby
+from pathlib import Path
+from typing import Dict, List, NamedTuple, Optional, Tuple, Union
+
+from tqdm import tqdm
+
+from lhotse import fix_manifests, validate_recordings_and_supervisions
+from lhotse.audio import AudioSource, Recording, RecordingSet
+from lhotse.supervision import SupervisionSegment, SupervisionSet
+from lhotse.utils import Pathlike, Seconds, is_module_available
+
+
+def prepare_slu(
+    corpus_dir: Pathlike, output_dir: Optional[Pathlike] = None
+) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
+
+    import pandas
+
+    corpus_dir = Path(corpus_dir)
+    assert corpus_dir.is_dir(), f"No such directory: {corpus_dir}"
+
+    if output_dir is not None:
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+    data = {
+        "train": pandas.read_csv(
+            str(corpus_dir) + "/data/train_data.csv", index_col=0, header=0
+        ),
+        "valid": pandas.read_csv(
+            str(corpus_dir) + "/data/valid_data.csv", index_col=0, header=0
+        ),
+        "test": pandas.read_csv(
+            str(corpus_dir) + "/data/test_data.csv", index_col=0, header=0
+        ),
+    }
+    train_wavs = [
+        str(corpus_dir) + "/" + path_to_wav
+        for path_to_wav in data["train"]["path"].tolist()
+    ]
+    valid_wavs = [
+        str(corpus_dir) + "/" + path_to_wav
+        for path_to_wav in data["valid"]["path"].tolist()
+    ]
+    test_wavs = [
+        str(corpus_dir) + "/" + path_to_wav
+        for path_to_wav in data["test"]["path"].tolist()
+    ]
+
+    transcripts = {
+        "train": data["train"]["transcription"].tolist(),
+        "valid": data["valid"]["transcription"].tolist(),
+        "test": data["test"]["transcription"].tolist(),
+    }
+
+    frames = {
+        "train": list(
+            i
+            for i in zip(
+                data["train"]["action"].tolist(),
+                data["train"]["object"].tolist(),
+                data["train"]["location"].tolist(),
+            )
+        ),
+        "valid": list(
+            i
+            for i in zip(
+                data["valid"]["action"].tolist(),
+                data["valid"]["object"].tolist(),
+                data["valid"]["location"].tolist(),
+            )
+        ),
+        "test": list(
+            i
+            for i in zip(
+                data["test"]["action"].tolist(),
+                data["test"]["object"].tolist(),
+                data["test"]["location"].tolist(),
+            )
+        ),
+    }
+
+    manifests = defaultdict(dict)
+    for name, dataset in zip(
+        ["train", "valid", "test"], [train_wavs, valid_wavs, test_wavs]
+    ):
+        recordings = []
+        for wav in tqdm(dataset):
+            recording = Recording.from_file(wav)
+            recordings.append(recording)
+        recording_set = RecordingSet.from_recordings(recordings)
+
+        supervisions = []
+        for id, recording in tqdm(enumerate(recording_set)):
+            supervisions.append(
+                SupervisionSegment(
+                    id=id,
+                    recording_id=recording.id,
+                    start=0,
+                    duration=recording.duration,
+                    channel=0,
+                    text=transcripts[name][id],
+                    custom={"frames": frames[name][id]},
+                )
+            )
+        supervision_set = SupervisionSet.from_segments(supervisions)
+        recording_set, supervision_set = fix_manifests(recording_set, supervision_set)
+        validate_recordings_and_supervisions(recording_set, supervision_set)
+        manifests[name] = {
+            "recordings": recording_set,
+            "supervisions": supervision_set,
+        }
+
+    if output_dir is not None:
+        for name in ["train", "valid", "test"]:
+            manifests[name]["recordings"].to_file(
+                output_dir / ("slu_recordings_" + name + ".jsonl.gz")
+            )
+            manifests[name]["supervisions"].to_file(
+                output_dir / ("slu_supervisions_" + name + ".jsonl.gz")
+            )