def test_extend_by_cut_with_supervision(
cut_start,
cut_duration,
extend_duration,
extend_direction,
supervision_start,
supervision_duration,
expected_start,
expected_end,
):
recording = dummy_recording(int(uuid4()), duration=1.0)
supervisions = SupervisionSet.from_segments([
SupervisionSegment(
id=int(uuid4()),
recording_id=recording.id,
start=supervision_start,
duration=supervision_duration,
)
])
cut = dummy_cut(int(uuid4()),
start=cut_start,
duration=cut_duration,
supervisions=supervisions)
extended_cut = cut.extend_by(duration=extend_duration,
direction=extend_direction)
assert isclose(extended_cut.supervisions[0].start, expected_start)
assert isclose(extended_cut.supervisions[0].end, expected_end)
def supervision_set():
return SupervisionSet.from_segments([
SupervisionSegment(
id="segment-1",
recording_id="recording-1",
channel=0,
start=0.1,
duration=0.3,
text="transcript of the first segment",
language="english",
speaker="Norman Dyhrentfurth",
gender="male",
alignment={
"word": [
AlignmentItem(symbol="transcript",
start=0.1,
duration=0.08),
AlignmentItem(symbol="of", start=0.18, duration=0.02),
AlignmentItem(symbol="the", start=0.2, duration=0.03),
AlignmentItem(symbol="first", start=0.23, duration=0.07),
AlignmentItem(symbol="segment", start=0.3, duration=0.1),
]
},
)
])
def supervision_set():
return SupervisionSet.from_segments([
SupervisionSegment(id='segment-1',
recording_id='recording-1',
channel=0,
start=0.1,
duration=0.3,
text='transcript of the first segment',
language='english',
speaker='Norman Dyhrentfurth',
gender='male',
alignment={
'word': [
AlignmentItem(symbol='transcript',
start=0.1,
duration=0.08),
AlignmentItem(symbol='of',
start=0.18,
duration=0.02),
AlignmentItem(symbol='the',
start=0.2,
duration=0.03),
AlignmentItem(symbol='first',
start=0.23,
duration=0.07),
AlignmentItem(symbol='segment',
start=0.3,
duration=0.1),
]
})
])
def prepare_vctk(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
) -> Dict[str, Union[RecordingSet, SupervisionSet]]:
"""
Prepares and returns the L2 Arctic manifests which consist of Recordings and Supervisions.
:param corpus_dir: Pathlike, the path of the data dir.
:param output_dir: Pathlike, the path where to write the manifests.
:return: a dict with keys "read" and "spontaneous".
Each hold another dict of {'recordings': ..., 'supervisions': ...}
"""
corpus_dir = Path(corpus_dir)
assert corpus_dir.is_dir(), f"No such directory: {corpus_dir}"
speaker_meta = _parse_speaker_description(corpus_dir)
recordings = RecordingSet.from_recordings(
Recording.from_file(wav)
for wav in (corpus_dir / "wav48").rglob("*.wav"))
supervisions = []
for path in (corpus_dir / "txt").rglob("*.txt"):
# One utterance (line) per file
text = path.read_text().strip()
speaker = path.name.split("_")[0] # p226_001.txt -> p226
seg_id = path.stem
meta = speaker_meta.get(speaker, defaultdict(lambda: None))
if meta is None:
logging.warning(f"Cannot find metadata for speaker {speaker}.")
supervisions.append(
SupervisionSegment(
id=seg_id,
recording_id=seg_id,
start=0,
duration=recordings[seg_id].duration,
text=text,
language="English",
speaker=speaker,
gender=meta["gender"],
custom={
"accent": meta["accent"],
"age": meta["age"],
"region": meta["region"],
},
))
supervisions = SupervisionSet.from_segments(supervisions)
# note(pzelasko): There were 172 recordings without supervisions when I ran it.
# I am just removing them.
recordings, supervisions = remove_missing_recordings_and_supervisions(
recordings, supervisions)
validate_recordings_and_supervisions(recordings, supervisions)
if output_dir is not None:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
recordings.to_json(output_dir / "recordings.json")
supervisions.to_json(output_dir / "supervisions.json")
return {"recordings": recordings, "supervisions": supervisions}
def prepare_tedlium(
tedlium_root: Pathlike,
output_dir: Optional[Pathlike] = None
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Prepare manifests for the TED-LIUM v3 corpus.
The manifests are created in a dict with three splits: train, dev and test.
Each split contains a RecordingSet and SupervisionSet in a dict under keys 'recordings' and 'supervisions'.
:param tedlium_root: Path to the unpacked TED-LIUM data.
:return: A dict with standard corpus splits containing the manifests.
"""
tedlium_root = Path(tedlium_root)
output_dir = Path(output_dir) if output_dir is not None else None
corpus = {}
for split in ("train", "dev", "test"):
root = tedlium_root / "legacy" / split
recordings = RecordingSet.from_recordings(
Recording.from_file(p) for p in (root / "sph").glob("*.sph"))
stms = list((root / "stm").glob("*.stm"))
assert len(stms) == len(recordings), (
f"Mismatch: found {len(recordings)} "
f"sphere files and {len(stms)} STM files. "
f"You might be missing some parts of TEDLIUM...")
segments = []
for p in stms:
with p.open() as f:
for idx, l in enumerate(f):
rec_id, _, _, start, end, _, *words = l.split()
start, end = float(start), float(end)
text = " ".join(words).replace("{NOISE}", "[NOISE]")
if text == "ignore_time_segment_in_scoring":
continue
segments.append(
SupervisionSegment(
id=f"{rec_id}-{idx}",
recording_id=rec_id,
start=start,
duration=round(end - start, ndigits=8),
channel=0,
text=text,
language="English",
speaker=rec_id,
))
supervisions = SupervisionSet.from_segments(segments)
corpus[split] = {
"recordings": recordings,
"supervisions": supervisions
}
validate_recordings_and_supervisions(**corpus[split])
if output_dir is not None:
recordings.to_file(output_dir /
f"tedlium_recordings_{split}.jsonl.gz")
supervisions.to_file(output_dir /
f"tedlium_supervisions_{split}.jsonl.gz")
return corpus
def prepare_single_partition(
raw_manifest_path: Path,
corpus_dir: Path,
speaker_id: str,
clean_or_other: str,
):
recordings = []
supervisions = []
for meta in load_jsonl(raw_manifest_path):
recording = Recording.from_file(corpus_dir / meta["audio_filepath"])
recordings.append(recording)
supervisions.append(
SupervisionSegment(
id=recording.id,
recording_id=recording.id,
start=0,
duration=recording.duration,
channel=0,
text=meta["text"],
speaker=ID2SPEAKER[speaker_id],
gender=ID2GENDER[speaker_id],
custom={
"text_punct": meta["text_normalized"],
"split": clean_or_other
},
))
recordings = RecordingSet.from_recordings(recordings)
supervisions = SupervisionSet.from_segments(supervisions)
validate_recordings_and_supervisions(recordings, supervisions)
return recordings, supervisions
def prepare_tedlium(
tedlium_root: Pathlike,
output_dir: Optional[Pathlike] = None
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Prepare manifests for the TED-LIUM v3 corpus.
The manifests are created in a dict with three splits: train, dev and test.
Each split contains a RecordingSet and SupervisionSet in a dict under keys 'recordings' and 'supervisions'.
:param tedlium_root: Path to the unpacked TED-LIUM data.
:return: A dict with standard corpus splits containing the manifests.
"""
tedlium_root = Path(tedlium_root)
output_dir = Path(output_dir) if output_dir is not None else None
corpus = {}
for split in ('train', 'dev', 'test'):
root = tedlium_root / 'legacy' / split
recordings = RecordingSet.from_recordings(
Recording.from_file(p) for p in (root / 'sph').glob('*.sph')
)
stms = list((root / 'stm').glob('*.stm'))
assert len(stms) == len(recordings), f'Mismatch: found {len(recordings)} ' \
f'sphere files and {len(stms)} STM files. ' \
f'You might be missing some parts of TEDLIUM...'
segments = []
for p in stms:
with p.open() as f:
for idx, l in enumerate(f):
rec_id, _, _, start, end, _, *words = l.split()
start, end = float(start), float(end)
text = ' '.join(words).replace('{NOISE}', '[NOISE]')
if text == 'ignore_time_segment_in_scoring':
continue
segments.append(
SupervisionSegment(
id=f'{rec_id}-{idx}',
recording_id=rec_id,
start=start,
duration=round(end - start, ndigits=8),
channel=0,
text=text,
language='English',
speaker=rec_id,
)
)
supervisions = SupervisionSet.from_segments(segments)
corpus[split] = {
'recordings': recordings,
'supervisions': supervisions
}
validate_recordings_and_supervisions(**corpus[split])
if output_dir is not None:
recordings.to_json(output_dir / f'{split}_recordings.json')
supervisions.to_json(output_dir / f'{split}_supervisions.json')
return corpus
def prepare_single_babel_language(corpus_dir: Pathlike, output_dir: Optional[Pathlike] = None):
manifests = defaultdict(dict)
for split in ('dev', 'eval', 'training'):
audio_dir = corpus_dir / f'conversational/{split}/audio'
recordings = RecordingSet.from_recordings(Recording.from_sphere(p) for p in audio_dir.glob('*.sph'))
if len(recordings) == 0:
logging.warning(f"No SPHERE files found in {audio_dir}")
manifests[split]['recordings'] = recordings
supervisions = []
text_dir = corpus_dir / f'conversational/{split}/transcription'
for p in text_dir.glob('*'):
# p.stem -> BABEL_BP_101_10033_20111024_205740_inLine
# parts:
# 0 -> BABEL
# 1 -> BP
# 2 -> <language-code> (101)
# 3 -> <speaker-id> (10033)
# 4 -> <date> (20111024)
# 5 -> <hour> (205740)
# 6 -> channel (inLine) ; inLine <=> A ; outLine <=> B ; "scripted" <=> A
p0, p1, lang_code, speaker, date, hour, channel, *_ = p.stem.split('_')
channel = {'inLine': 'A', 'outLine': 'B'}.get(channel, 'A')
# Add a None at the end so that the last timestamp is only used as "next_timestamp"
# and ends the iretation (otherwise we'd lose the last segment).
lines = p.read_text().splitlines() + [None]
for (timestamp, text), (next_timestamp, _) in sliding_window(2, zip(lines[::2], lines[1::2])):
start = float(timestamp[1:-1])
end = float(next_timestamp[1:-1])
supervisions.append(
SupervisionSegment(
id=f'{lang_code}_{speaker}_{channel}_{date}_{hour}_{int(100 * start):06}',
recording_id=p.stem,
start=start,
duration=round(end - start, ndigits=8),
channel=0,
text=normalize_text(text),
language=BABELCODE2LANG[lang_code],
speaker=speaker,
)
)
if len(supervisions) == 0:
logging.warning(f"No supervisions found in {text_dir}")
manifests[split]['supervisions'] = SupervisionSet.from_segments(supervisions)
validate_recordings_and_supervisions(
manifests[split]['recordings'],
manifests[split]['superevisions']
)
if output_dir is not None:
language = BABELCODE2LANG[lang_code]
if split == 'training':
split = 'train'
manifests[split]['recordings'].to_json(f'recordings_{language}_{split}.json')
manifests[split]['supervisions'].to_json(f'supervisions_{language}_{split}.json')
return manifests
def prepare_cmu_arctic(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
) -> Dict[str, Union[RecordingSet, SupervisionSet]]:
"""
Prepares and returns the CMU Arctic manifests,
which consist of Recordings and Supervisions.
:param corpus_dir: Pathlike, the path of the data dir.
:param output_dir: Pathlike, the path where to write the manifests.
:return: a dict of {'recordings': ..., 'supervisions': ...}
"""
corpus_dir = Path(corpus_dir)
assert corpus_dir.is_dir(), f"No such directory: {corpus_dir}"
recordings = RecordingSet.from_recordings(
# Example ID: cmu_us_sup_arctic-arctic_a0001
Recording.from_file(
wav, recording_id=f"{_get_speaker(wav.parent.parent.name)}-{wav.stem}"
)
for wav in corpus_dir.rglob("*.wav")
)
supervisions = []
for path in corpus_dir.rglob("txt.done.data"):
lines = path.read_text().splitlines()
speaker = _get_speaker(path.parent.parent.name)
for l in lines:
l = l[2:-2] # get rid of parentheses and whitespaces on the edges
seg_id, text = l.split(maxsplit=1)
seg_id = f"{speaker}-{seg_id}"
supervisions.append(
SupervisionSegment(
id=seg_id,
recording_id=seg_id,
start=0,
duration=recordings[seg_id].duration,
text=text.replace('"', ""), # get rid of quotation marks,
language="English",
speaker=speaker,
gender=GENDER_MAP.get(speaker),
custom={"accent": ACCENT_MAP.get(speaker)},
)
)
supervisions = SupervisionSet.from_segments(supervisions)
# There seem to be 20 recordings missing; remove the before validation
recordings, supervisions = remove_missing_recordings_and_supervisions(
recordings, supervisions
)
validate_recordings_and_supervisions(recordings, supervisions)
if output_dir is not None:
output_dir = Path(output_dir)
recordings.to_json(output_dir / "cmu_arctic_recordings.json")
supervisions.to_json(output_dir / "cmu_arctic_supervisions.json")
return {"recordings": recordings, "supervisions": supervisions}
def prepare_norm_cn(
corpus_dir: Pathlike,
output_dir: Pathlike,
num_jobs: int = 15,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Prepare manifests for the VoxCeleb1 corpus. The manifests are created in a dict with
"""
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)
manifests = defaultdict(dict)
dataset_parts = ["dev", "test", "train"]
for part in dataset_parts:
transcript_path = corpus_dir / f"{part}/text.txt"
transcript_dict = {}
with open(transcript_path, "r", encoding="utf-8") as f:
for line in f.readlines():
idx_transcript = line.split()
if len(idx_transcript) < 2 :
logging.info(f"get transcript err: {line}")
continue
transcript_dict[idx_transcript[0]] = " ".join(idx_transcript[1:])
file_path = corpus_dir / f"{part}/wav.scp"
file_paths = []
with open(file_path, "r", encoding="utf-8") as f:
file_paths = [line.strip() for line in f]
recordings = []
supervisions = []
with ThreadPoolExecutor(num_jobs) as ex:
for recording, supervision in tqdm(
ex.map(
process_file,
file_paths,
repeat(transcript_dict),
),
desc="Processing NormcnSpeech JSON entries",
leave=False,
):
#for p in file_paths:
# recording, supervision = process_file(p, transcript_dict)
if recording is not None :
recordings.append(recording)
supervisions.append(supervision)
supervision_set = SupervisionSet.from_segments(supervisions)
recording_set = RecordingSet.from_recordings(recordings)
if output_dir is not None:
supervision_set.to_json(output_dir / f"supervisions_{part}.json")
recording_set.to_json(output_dir / f"recordings_{part}.json")
manifests[part] = {"recordings": recording_set, "supervisions": supervision_set}
return manifests
def supervision_set():
return SupervisionSet.from_segments([
SupervisionSegment(id='segment-1',
recording_id='recording-1',
channel=0,
start=0.1,
duration=0.3,
text='transcript of the first segment',
language='english',
speaker='Norman Dyhrentfurth',
gender='male')
])
def prepare_same_close_mic(part3_path):
check_dependencies()
from textgrids import TextGrid
recordings = []
supervisions = []
for audio_path in tqdm(
(part3_path / "AudioSameCloseMic").glob("*.wav"),
desc="Creating manifests for SameCloseMic",
):
try:
recording_id = audio_path.stem
recording = Recording.from_file(audio_path)
tg = TextGrid(
part3_path / f"ScriptsSame/{recording_id}.TextGrid", coding="utf-16"
)
segments = [
s
for s in (
SupervisionSegment(
id=f"{recording_id}-{idx}",
recording_id=recording_id,
start=segment.xmin,
# We're trimming the last segment's duration as it exceeds the actual duration of the recording.
# This is safe because if we end up with a zero/negative duration, the validation will catch it.
duration=min(
round(segment.xmax - segment.xmin, ndigits=8),
recording.duration - segment.xmin,
),
text=segment.text,
language="Singaporean English",
speaker=recording_id,
)
for idx, segment in enumerate(tg[recording_id])
if segment.text not in ("<S>", "<Z>") # skip silences
)
if s.duration > 0 # NSC has some bad segments
]
recordings.append(recording)
supervisions.extend(segments)
except:
print(f"Error when processing {audio_path} - skipping...")
return {
"recordings": RecordingSet.from_recordings(recordings),
"supervisions": SupervisionSet.from_segments(supervisions),
}
def _prepare_voxceleb_v1(
corpus_path: Pathlike,
num_jobs: int,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Prepare manifests for the VoxCeleb1 corpus. The manifests are created in a dict with
2 splits: train ("dev") and test.
"""
speaker_metadata = {}
with open(corpus_path / "vox1_meta.csv", "r") as f:
next(f)
for line in f:
spkid, name, gender, nationality, split = line.strip().split("\t")
speaker_metadata[spkid] = SpeakerMetadata(
id=spkid, name=name, gender=gender, nationality=nationality, split=split
)
with ProcessPoolExecutor(num_jobs) as ex:
recordings = []
supervisions = []
futures = []
for p in (corpus_path / "wav").rglob("*.wav"):
futures.append(ex.submit(_process_file, p, speaker_metadata))
for future in tqdm(
as_completed(futures),
total=len(futures),
desc="Processing VoxCeleb1",
leave=False,
):
recording, supervision = future.result()
recordings.append(recording)
supervisions.append(supervision)
recording_set = RecordingSet.from_recordings(recordings)
supervision_set = SupervisionSet.from_segments(supervisions)
manifests = defaultdict(dict)
# Split into dev and test sets based on the split of the speakers.
for split in ("dev", "test"):
manifests[split]["supervisions"] = supervision_set.filter(
lambda s: s.custom["split"] == split
)
split_ids = [s.recording_id for s in manifests[split]["supervisions"]]
manifests[split]["recordings"] = recording_set.filter(
lambda r: r.id in split_ids
)
manifests["train"] = manifests.pop("dev")
return manifests
def _prepare_voxceleb_v2(
corpus_path: Pathlike,
num_jobs: int,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Prepare manifests for the VoxCeleb2 corpus. The manifests are created the same dict
without any splits since the whole data is used in the final "train" split.
"""
# Read the speaker metadata.
speaker_metadata = {}
with open(corpus_path / "vox2_meta.csv", "r") as f:
next(f)
for line in f:
spkid, _, gender, split = map(str.strip, line.split(","))
speaker_metadata[spkid] = SpeakerMetadata(id=spkid,
name="",
gender=gender,
nationality="",
split=split)
# Read the wav files and prepare manifests
with ProcessPoolExecutor(num_jobs) as ex:
recordings = []
supervisions = []
futures = []
for p in (corpus_path / split).glob("*.wav"):
futures.append(
ex.submit(_process_file, p, speaker_metadata, type="command"))
for future in tqdm(
futures,
total=len(futures),
desc=f"Processing VoxCeleb2 {split} split...",
leave=False,
):
recording, supervision = future.result()
recordings.append(recording)
supervisions.append(supervision)
recording_set = RecordingSet.from_recordings(recordings)
supervision_set = SupervisionSet.from_segments(supervisions)
manifests = {
"recordings": recording_set,
"supervisions": supervision_set,
}
return manifests
def prepare_music(
corpus_dir: Path, use_vocals: bool = True
) -> Dict[str, Union[RecordingSet, SupervisionSet]]:
music_dir = corpus_dir / "music"
recordings = scan_recordings(music_dir)
supervisions = SupervisionSet.from_segments(
SupervisionSegment(
id=utt,
recording_id=utt,
start=0,
duration=recordings.duration(utt),
speaker=musician,
custom={"genres": genres.split(","), "vocals": vocals == "Y"},
)
for file in music_dir.rglob("ANNOTATIONS")
for utt, genres, vocals, musician in read_annotations(file, max_fields=4)
)
if not use_vocals:
supervisions = supervisions.filter(lambda s: s.custom["vocals"] is False)
return {"recordings": recordings, "supervisions": supervisions}
def prepare_separate_phone_mic(part3_path):
check_dependencies()
from textgrids import TextGrid
recordings = []
supervisions = []
for audio_path in tqdm(
(part3_path / 'AudioSeparateIVR').rglob('**/*.wav'),
desc='Creating manifests for SeparateIVR'
):
try:
recording_id = f'{audio_path.parent.name}_{audio_path.stem}'
recording = Recording.from_file(audio_path)
tg = TextGrid(part3_path / f'ScriptsSeparate/{recording_id}.TextGrid', coding='utf-16')
segments = [
s for s in (
SupervisionSegment(
id=f'{recording_id}-{idx}',
recording_id=recording_id,
start=segment.xmin,
# We're trimming the last segment's duration as it exceeds the actual duration of the recording.
# This is safe because if we end up with a zero/negative duration, the validation will catch it.
duration=min(round(segment.xmax - segment.xmin, ndigits=8), recording.duration - segment.xmin),
text=segment.text,
language='Singaporean English',
speaker=recording_id,
)
for idx, segment in enumerate(tg[recording_id])
if segment.text not in ('<S>', '<Z>') # skip silences
)
if s.duration > 0 # NSC has some bad segments
]
supervisions.extend(segments)
recordings.append(recording)
except:
print(f'Error when processing {audio_path} - skipping...')
return {
'recordings': RecordingSet.from_recordings(recordings),
'supervisions': SupervisionSet.from_segments(supervisions)
}
def read_rttm(path: Pathlike) -> SupervisionSet:
lines = Path(path).read_text().splitlines()
sups = []
rec_cntr = Counter()
for line in lines:
_, recording_id, channel, start, duration, _, _, speaker, _, _ = line.split(
)
start, duration, channel = float(start), float(duration), int(channel)
if duration == 0.0:
continue
rec_cntr[recording_id] += 1
sups.append(
SupervisionSegment(
id=f"{recording_id}_{rec_cntr[recording_id]}",
recording_id=recording_id,
start=start,
duration=duration,
channel=channel,
speaker=f"{recording_id}_{speaker}",
language="English",
))
return SupervisionSet.from_segments(sups)
def prepare_music(
corpus_dir: Path,
use_vocals: bool = True
) -> Dict[str, Union[RecordingSet, SupervisionSet]]:
music_dir = corpus_dir / 'music'
recordings = scan_recordings(music_dir)
supervisions = SupervisionSet.from_segments(
SupervisionSegment(id=utt,
recording_id=utt,
start=0,
duration=recordings.duration(utt),
speaker=musician,
custom={
'genres': genres.split(','),
'vocals': vocals == 'Y'
}) for file in music_dir.rglob('ANNOTATIONS')
for utt, genres, vocals, musician in read_annotations(file,
max_fields=4))
if not use_vocals:
supervisions = supervisions.filter(
lambda s: s.custom['vocals'] is False)
return {'recordings': recordings, 'supervisions': supervisions}
def prepare_same_close_mic(part3_path):
check_dependencies()
from textgrids import TextGrid
recordings = []
supervisions = []
for audio_path in tqdm(
(part3_path / 'AudioSameCloseMic').glob('*.wav'),
desc='Creating manifests for SameCloseMic'
):
try:
recording_id = audio_path.stem
recording = Recording.from_wav(audio_path)
tg = TextGrid(part3_path / f'ScriptsSame/{recording_id}.TextGrid', coding='utf-16')
segments = [
s for s in (
SupervisionSegment(
id=f'{recording_id}-{idx}',
recording_id=recording_id,
start=segment.xmin,
duration=round(segment.xmax - segment.xmin, ndigits=8),
text=segment.text,
language='Singaporean English',
speaker=recording_id,
)
for idx, segment in enumerate(tg[recording_id])
if segment.text not in ('<S>', '<Z>') # skip silences
)
if s.duration > 0 # NSC has some bad segments
]
recordings.append(recording)
supervisions.extend(segments)
except:
print(f'Error when processing {audio_path} - skipping...')
return {
'recordings': RecordingSet.from_recordings(recordings),
'supervisions': SupervisionSet.from_segments(supervisions)
}
def prepare_cmu_indic(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
) -> Dict[str, Union[RecordingSet, SupervisionSet]]:
"""
Prepares and returns the CMU Indic manifests,
which consist of Recordings and Supervisions.
:param corpus_dir: Pathlike, the path of the data dir.
:param output_dir: Pathlike, the path where to write the manifests.
:return: a dict of {'recordings': ..., 'supervisions': ...}
"""
corpus_dir = Path(corpus_dir)
assert corpus_dir.is_dir(), f"No such directory: {corpus_dir}"
recordings = RecordingSet.from_recordings(
# Example ID: cmu_indic_ben_rm_bn_00001
Recording.from_file(
wav, recording_id=f"{_get_speaker(wav.parent.parent.name)}-{wav.stem}"
)
for wav in corpus_dir.rglob("*.wav")
)
supervisions = []
for path in corpus_dir.rglob("txt.done.data"):
lines = path.read_text().splitlines()
speaker = _get_speaker(path.parent.parent.name)
lang_code = speaker.split("_")[0] # example: 'ben_rm' -> 'ben' (Bengali)
try:
# Example contents of voice.feats file:
# variant guj
# age 28
# gender male
# description Built with build_cg_rfs_voice, 3 rf and 3 dur
# gujarati_data h2r_prompts
# prompt_dur 59.27min
age = int(
(path.parent / "voice.feats")
.read_text()
.splitlines()[1]
.replace("age ", "")
.strip()
)
except:
age = None
for l in lines:
l = l[2:-2] # get rid of parentheses and whitespaces on the edges
seg_id, text = l.split(maxsplit=1)
seg_id = f"{speaker}-{seg_id}"
language = LANGUAGE_MAP[lang_code]
is_english = "arctic" in seg_id
# Determine available custom meta-data to attach.
custom = None
if is_english or age is not None:
custom = {}
if is_english:
custom["accent"] = language
if age is not None:
custom["age"] = age
supervisions.append(
SupervisionSegment(
id=seg_id,
recording_id=seg_id,
start=0,
duration=recordings[seg_id].duration,
text=text.replace('"', ""), # get rid of quotation marks,
language="English" if is_english else language,
speaker=speaker,
gender=GENDER_MAP.get(speaker),
custom=custom,
)
)
supervisions = SupervisionSet.from_segments(supervisions)
# There seem to be 20 recordings missing; remove the before validation
recordings, supervisions = remove_missing_recordings_and_supervisions(
recordings, supervisions
)
validate_recordings_and_supervisions(recordings, supervisions)
if output_dir is not None:
output_dir = Path(output_dir)
recordings.to_file(output_dir / "cmu-indic_recordings_all.jsonl.gz")
supervisions.to_file(output_dir / "cmu-indic_supervisions_all.jsonl.gz")
return {"recordings": recordings, "supervisions": supervisions}
def prepare_single_mtedx_language(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
language: str = "language",
num_jobs: int = 1,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Prepares manifests using a single MTEDx language.
This function works as follows:
- First it looks for the audio directory in the data/wav where the .flac
files are stored.
- Then, it looks for the vtt directory in data/{train,dev,test}/vtt
which contains the segmentation and transcripts for the audio.
- The transcripts undergo some basic text normalization
:param corpus_dir: Path to the root of the MTEDx download
:param output_dir: Path where the manifests are stored as .json files
:param language: The two-letter language code.
:param num_jobs: Number of threads to use when preparing data.
:return:
"""
if isinstance(corpus_dir, str):
corpus_dir = Path(corpus_dir)
manifests = defaultdict(dict)
with ThreadPoolExecutor(num_jobs) as ex:
for split in ("train", "valid", "test"):
audio_dir = corpus_dir / f"data/{split}/wav"
recordings = RecordingSet.from_recordings(
Recording.from_file(p) for p in audio_dir.glob("*.flac")
)
if len(recordings) == 0:
logging.warning(f"No .flac files found in {audio_dir}")
supervisions = []
text_dir = corpus_dir / f"data/{split}/vtt"
futures = []
for p in text_dir.glob("*"):
futures.append(ex.submit(_filename_to_supervisions, p, language))
for future in tqdm(futures, desc="Processing", leave=False):
result = future.result()
if result is None:
continue
for sup in result:
supervisions.append(sup)
if len(supervisions) == 0:
logging.warning(f"No supervisions found in {text_dir}")
supervisions = SupervisionSet.from_segments(supervisions)
recordings, supervisions = remove_missing_recordings_and_supervisions(
recordings, supervisions
)
supervisions = trim_supervisions_to_recordings(recordings, supervisions)
validate_recordings_and_supervisions(recordings, supervisions)
manifests[split] = {
"recordings": recordings,
"supervisions": supervisions,
}
if output_dir is not None:
if isinstance(output_dir, str):
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
save_split = "dev" if split == "valid" else split
recordings.to_file(output_dir / f"recordings_{language}_{split}.json")
supervisions.to_file(
output_dir / f"supervisions_{language}_{split}.json"
)
return dict(manifests)
def prepare_l2_arctic(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Prepares and returns the L2 Arctic manifests which consist of Recordings and Supervisions.
:param corpus_dir: Pathlike, the path of the data dir.
:param output_dir: Pathlike, the path where to write the manifests.
:return: a dict with keys "read" and "spontaneous".
Each hold another dict of {'recordings': ..., 'supervisions': ...}
"""
corpus_dir = Path(corpus_dir)
assert corpus_dir.is_dir(), f'No such directory: {corpus_dir}'
speaker_meta = _parse_speaker_description()
recordings = RecordingSet.from_recordings(
# Example ID: zhaa-arctic_b0126
Recording.from_file(
wav, recording_id=f'{wav.parent.parent.name.lower()}-{wav.stem}')
for wav in corpus_dir.rglob('*.wav'))
supervisions = []
for path in corpus_dir.rglob('*.txt'):
# One utterance (line) per file
text = path.read_text().strip()
is_suitcase_corpus = 'suitcase_corpus' in path.parts
speaker = path.parent.parent.name.lower(
) # <root>/ABA/transcript/arctic_a0051.txt -> aba
if is_suitcase_corpus:
speaker = path.stem # <root>/suitcase_corpus/transcript/aba.txt -> aba
seg_id = f'suitcase_corpus-{speaker}' if is_suitcase_corpus else f'{speaker}-{path.stem}'
supervisions.append(
SupervisionSegment(
id=seg_id,
recording_id=seg_id,
start=0,
duration=recordings[seg_id].duration,
text=text,
language='English',
speaker=speaker,
gender=speaker_meta[speaker]['gender'],
custom={'accent': speaker_meta[speaker]['native_lang']}))
supervisions = SupervisionSet.from_segments(supervisions)
validate_recordings_and_supervisions(recordings, supervisions)
splits = {
'read': {
'recordings':
recordings.filter(lambda r: 'suitcase_corpus' not in r.id),
'supervisions':
supervisions.filter(
lambda s: 'suitcase_corpus' not in s.recording_id)
},
'suitcase': {
'recordings':
recordings.filter(lambda r: 'suitcase_corpus' in r.id),
'supervisions':
supervisions.filter(lambda s: 'suitcase_corpus' in s.recording_id)
}
}
if output_dir is not None:
output_dir = Path(output_dir)
makedirs(output_dir, exist_ok=True)
for key, manifests in splits.items():
manifests['recordings'].to_json(output_dir /
f'recordings-{key}.json')
manifests['supervisions'].to_json(output_dir /
f'supervisions-{key}.json')
return splits
def prepare_libricss(
corpus_dir: Pathlike,
output_dir: Pathlike = None,
type: str = "mdm",
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Returns the manifests which consist of the Recordings and Supervisions.
When all the manifests are available in the ``output_dir``, it will simply read and return them.
NOTE: The recordings contain all 7 channels. If you want to use only one channel, you can
use either ``recording.load_audio(channel=0)`` or ``MonoCut(id=...,recording=recording,channel=0)``
while creating the CutSet.
:param corpus_dir: Pathlike, the path to the extracted corpus.
:param output_dir: Pathlike, the path where to write the manifests.
:param type: str, the type of data to prepare ('mdm', 'sdm', 'ihm-mix', or 'ihm'). These settings
are similar to the ones in AMI and ICSI recipes.
:return: a Dict whose key is the dataset part, and the value is Dicts with the keys 'audio' and 'supervisions'.
"""
assert type in ["mdm", "ihm-mix", "ihm"]
manifests = {}
corpus_dir = Path(corpus_dir)
corpus_dir = (corpus_dir / "for_release"
if corpus_dir.stem != "for_release" else corpus_dir)
recordings = []
segments = []
for ov in OVERLAP_RATIOS:
for session in (corpus_dir / ov).iterdir():
_, _, _, _, _, name, actual_ov = session.name.split("_")
actual_ov = float(actual_ov.split("actual")[1])
recording_id = f"{ov}_{name}"
audio_path = (session / "clean" /
"mix.wav" if type == "ihm-mix" else session /
"clean" /
"each_spk.wav" if type == "ihm" else session /
"record" / "raw_recording.wav")
recordings.append(
Recording.from_file(audio_path, recording_id=recording_id))
for idx, seg in enumerate(
parse_transcript(session / "transcription" /
"meeting_info.txt")):
segments.append(
SupervisionSegment(
id=f"{recording_id}-{idx}",
recording_id=recording_id,
start=seg[0],
duration=seg[1] - seg[0],
text=seg[4],
language="English",
speaker=seg[2],
channel=SPK_TO_CHANNEL_MAP[session.name][seg[2]]
if type == "ihm" else 0,
))
supervisions = SupervisionSet.from_segments(segments)
recordings = RecordingSet.from_recordings(recordings)
validate_recordings_and_supervisions(recordings, supervisions)
if output_dir is not None:
output_dir = Path(output_dir)
output_dir.mkdir(exist_ok=True, parents=True)
recordings.to_jsonl(output_dir / "recordings.jsonl")
supervisions.to_jsonl(output_dir / "supervisions.jsonl")
return {"recordings": recordings, "supervisions": supervisions}
def prepare_callhome_egyptian(
audio_dir: Pathlike,
transcript_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
absolute_paths: bool = False,
) -> Dict[str, Union[RecordingSet, SupervisionSet]]:
"""
Prepare manifests for the Callhome Egyptian Arabic Corpus
We create two manifests: one with recordings, and the other one with text
supervisions.
:param audio_dir: Path to ``LDC97S45`` package.
:param transcript_dir: Path to the ``LDC97T19`` content
:param output_dir: Directory where the manifests should be written. Can be omitted
to avoid writing.
:param absolute_paths: Whether to return absolute or relative (to the corpus dir)
paths for recordings.
:return: A dict with manifests. The keys are: ``{'recordings', 'supervisions'}``.
"""
audio_dir = Path(audio_dir)
transcript_dir = Path(transcript_dir)
manifests = {}
for split in ["train", "devtest", "evaltest"]:
audio_paths = check_and_rglob(
# The LDC distribution has a typo.
audio_dir / "callhome/arabic" /
split.replace("evaltest", "evltest"),
"*.sph",
)
recordings = RecordingSet.from_recordings(
Recording.from_file(
p, relative_path_depth=None if absolute_paths else 4)
for p in tqdm(audio_paths))
transcript_paths = check_and_rglob(
transcript_dir /
f"callhome_arabic_trans_970711/transcrp/{split}/roman",
"*.txt",
)
# TODO: Add text normalization like in Kaldi recipe.
# Not doing this right now as it's not needed for VAD/diarization...
supervisions = []
for p in transcript_paths:
idx = 0
for line in p.read_text().splitlines():
line = line.strip()
if not line:
continue
recording_id = p.stem
# example line:
# 19.33 21.18 B: %ah Tayyib
start, end, spk, text = line.split(maxsplit=3)
spk = spk.replace(":", "")
duration = float(Decimal(end) - Decimal(start))
if duration <= 0:
continue
start = float(start)
supervisions.append(
SupervisionSegment(
id=f"{recording_id}_{idx}",
recording_id=recording_id,
start=start,
duration=duration,
speaker=f"{recording_id}_{spk}",
text=text,
))
idx += 1
supervisions = SupervisionSet.from_segments(supervisions)
recordings, supervisions = fix_manifests(recordings, supervisions)
validate_recordings_and_supervisions(recordings, supervisions)
if output_dir is not None:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
recordings.to_json(output_dir / f"recordings_{split}.json")
supervisions.to_json(output_dir / f"supervisions_{split}.json")
manifests[split] = {
"recordings": recordings,
"supervisions": supervisions
}
return manifests
def prepare_libritts(
corpus_dir: Pathlike,
dataset_parts: Union[str, Sequence[str]] = 'auto',
output_dir: Optional[Pathlike] = None,
num_jobs: int = 1
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Returns the manifests which consist of the Recordings and Supervisions.
When all the manifests are available in the ``output_dir``, it will simply read and return them.
:param corpus_dir: Pathlike, the path of the data dir.
:param dataset_parts: string or sequence of strings representing dataset part names, e.g. 'train-clean-100', 'train-clean-5', 'dev-clean'.
By default we will infer which parts are available in ``corpus_dir``.
:param output_dir: Pathlike, the path where to write the manifests.
:param num_jobs: the number of parallel workers parsing the data.
:return: a Dict whose key is the dataset part, and the value is Dicts with the keys 'audio' and 'supervisions'.
"""
corpus_dir = Path(corpus_dir)
assert corpus_dir.is_dir(), f'No such directory: {corpus_dir}'
if dataset_parts == 'auto':
dataset_parts = LIBRITTS
elif isinstance(dataset_parts, str):
assert dataset_parts in LIBRITTS
dataset_parts = [dataset_parts]
if output_dir is not None:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# Maybe the manifests already exist: we can read them and save a bit of preparation time.
maybe_manifests = read_manifests_if_cached(dataset_parts=dataset_parts,
output_dir=output_dir,
prefix='libritts')
if maybe_manifests is not None:
return maybe_manifests
# Contents of the file
# ;ID |SEX| SUBSET |MINUTES| NAME
# 14 | F | train-clean-360 | 25.03 | ...
# 16 | F | train-clean-360 | 25.11 | ...
# 17 | M | train-clean-360 | 25.04 | ...
spk2gender = {
spk_id.strip(): gender.strip()
for spk_id, gender, *_ in (line.split('|') for line in (
corpus_dir / 'SPEAKERS.txt').read_text().splitlines()
if not line.startswith(';'))
}
manifests = defaultdict(dict)
for part in tqdm(dataset_parts, desc='Preparing LibriTTS parts'):
part_path = corpus_dir / part
recordings = RecordingSet.from_dir(part_path,
'*.wav',
num_jobs=num_jobs)
supervisions = []
for trans_path in tqdm(
part_path.rglob('*.trans.tsv'),
desc='Scanning transcript files (progbar per speaker)',
leave=False):
# The trans.tsv files contain only the recordings that were kept for LibriTTS.
# Example path to a file:
# /export/corpora5/LibriTTS/dev-clean/84/121123/84_121123.trans.tsv
#
# Example content:
# 84_121123_000007_000001 Maximilian. Maximilian.
# 84_121123_000008_000000 Villefort rose, half ashamed of being surprised in such a paroxysm of grief. Villefort rose, half ashamed of being surprised in such a paroxysm of grief.
# book.tsv contains additional metadata
utt2snr = {
rec_id: float(snr)
for rec_id, *_, snr in map(str.split, (
trans_path.parent /
trans_path.name.replace('.trans.tsv', '.book.tsv')
).read_text().splitlines())
}
for line in trans_path.read_text().splitlines():
rec_id, orig_text, norm_text = line.split('\t')
spk_id = rec_id.split('_')[0]
supervisions.append(
SupervisionSegment(id=rec_id,
recording_id=rec_id,
start=0.0,
duration=recordings[rec_id].duration,
channel=0,
text=norm_text,
language='English',
speaker=spk_id,
gender=spk2gender[spk_id],
custom={
'orig_text': orig_text,
'snr': utt2snr[rec_id]
}))
supervisions = SupervisionSet.from_segments(supervisions)
validate_recordings_and_supervisions(recordings, supervisions)
if output_dir is not None:
supervisions.to_json(output_dir /
f'libritts_supervisions_{part}.json')
recordings.to_json(output_dir / f'libritts_recordings_{part}.json')
manifests[part] = {
'recordings': recordings,
'supervisions': supervisions
}
return dict(manifests) # Convert to normal dict
def prepare_mgb2(
corpus_dir: Pathlike,
output_dir: Pathlike,
text_cleaning: bool = True,
buck_walter: bool = False,
num_jobs: int = 1,
mer_thresh: int = 80,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Returns the manifests which consist of the Recordings and Supervisions.
When all the manifests are available in the ``output_dir``, it will simply read and return them.
:param corpus_dir: Pathlike, the path of the data dir.
:param output_dir: Pathlike, the path where to write the manifests.
:param text_cleaning: Bool, if True, basic text cleaning is performed (similar to ESPNet recipe).
:param buck_walter: Bool, use BuckWalter transliteration
:param num_jobs: int, the number of jobs to use for parallel processing.
:param mer_thresh: int, filter out segments based on mer (Match Error Rate)
:return: a Dict whose key is the dataset part, and the value is Dicts with the keys 'audio' and 'supervisions'.
.. note::
Unlike other recipes, output_dir is not Optional here because we write the manifests
to the output directory while processing to avoid OOM issues, since it is a large dataset.
.. caution::
The `text_cleaning` option removes all punctuation and diacritics.
"""
corpus_dir = Path(corpus_dir)
assert corpus_dir.is_dir(), f"No such directory: {corpus_dir}"
dataset_parts = ["dev", "train", "test"]
manifests = {}
if output_dir is not None:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# Maybe the manifests already exist: we can read them and save a bit of preparation time.
manifests = read_manifests_if_cached(
dataset_parts=dataset_parts,
output_dir=output_dir,
prefix="mgb2",
suffix="jsonl.gz",
lazy=True,
)
for part in dataset_parts:
info(f"Processing MGB2 subset: {part}")
if manifests_exist(
part=part, output_dir=output_dir, prefix="mgb2", suffix="jsonl.gz"
):
info(f"MGB2 subset: {part} already prepared - skipping.")
continue
# Read the recordings and write them into manifest. We additionally store the
# duration of the recordings in a dict which will be used later to create the
# supervisions.
output_dir = Path(output_dir)
corpus_dir = Path(corpus_dir)
if part == "test" or part == "dev":
(output_dir / part).mkdir(parents=True, exist_ok=True)
copy(
corpus_dir / part / "text.non_overlap_speech",
output_dir / part / "text",
)
copy(
corpus_dir / part / "segments.non_overlap_speech",
output_dir / part / "segments",
)
with open(corpus_dir / part / "wav.scp", "r") as f_in, open(
output_dir / part / "wav.scp", "w"
) as f_out:
for line in f_in:
f_out.write(line.replace("wav/", f"{corpus_dir}/{part}/wav/"))
f_out.write("\n")
recordings, supervisions, _ = load_kaldi_data_dir(
(output_dir / part), 16000
)
if buck_walter is False:
supervisions = supervisions.transform_text(from_buck_walter)
if part == "test":
assert (
len(supervisions) == 5365
), f"Expected 5365 supervisions for test, found {len(supervisions)}"
elif part == "dev":
assert (
len(supervisions) == 5002
), f"Expected 5002 supervisions for dev, found {len(supervisions)}"
elif part == "train":
recordings = RecordingSet.from_dir(
(corpus_dir / part / "wav"), pattern="*.wav", num_jobs=num_jobs
)
xml_paths = check_and_rglob(
path.join(corpus_dir, part, "xml/utf8"), "*.xml"
)
# Read supervisions and write them to manifest
with recursion_limit(5000):
supervisions_list = list(
chain.from_iterable(
[make_supervisions(p, mer_thresh) for p in xml_paths]
)
)
supervisions = SupervisionSet.from_segments(supervisions_list)
assert (
len(supervisions) == 375103
), f"Expected 375103 supervisions for train, found {len(supervisions)}"
if text_cleaning is True:
supervisions = supervisions.transform_text(cleaning)
recordings, supervisions = fix_manifests(recordings, supervisions)
validate_recordings_and_supervisions(recordings, supervisions)
# saving recordings and supervisions
recordings.to_file((output_dir / f"mgb2_recordings_{part}.jsonl.gz"))
supervisions.to_file((output_dir / f"mgb2_supervisions_{part}.jsonl.gz"))
manifests[part] = {
"recordings": recordings,
"supervisions": supervisions,
}
return manifests
def prepare_single_babel_language(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
no_eval_ok: bool = False,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Prepares manifests using a single BABEL LDC package.
This function works like the following:
- first, it will scan `corpus_dir` for a directory named `conversational`;
if there is more than once, it picks the first one (and emits a warning)
- then, it will try to find `dev`, `eval`, and `training` splits inside
(if any of them is not present, it will skip it with a warning)
- finally, it scans the selected location for SPHERE audio files and transcripts.
:param corpus_dir: Path to the root of the LDC package with a BABEL language.
:param output_dir: Path where the manifests are stored.json
:param no_eval_ok: When set to True, this function won't emit a warning
that the eval set was not found.
:return:
"""
manifests = defaultdict(dict)
# Auto-detect the location of the "conversational" directory
orig_corpus_dir = corpus_dir
corpus_dir = Path(corpus_dir)
corpus_dir = [d for d in corpus_dir.rglob("conversational") if d.is_dir()]
if not corpus_dir:
raise ValueError(
f"Could not find 'conversational' directory anywhere inside '{orig_corpus_dir}' "
f"- please check your path.")
if len(corpus_dir) > 1:
# People have very messy data distributions, the best we can do is warn them.
logging.warning(
f"It seems there are multiple 'conversational' directories in '{orig_corpus_dir}' - "
f"we are selecting the first one only ({corpus_dir[0]}). Please ensure that you provided "
f"the path to a single language's dir, and the root dir for all BABEL languages."
)
corpus_dir = corpus_dir[0].parent
for split in ("dev", "eval", "training"):
audio_dir = corpus_dir / f"conversational/{split}/audio"
sph_recordings = RecordingSet.from_recordings(
Recording.from_file(p) for p in audio_dir.glob("*.sph"))
wav_recordings = RecordingSet.from_recordings(
Recording.from_file(p) for p in audio_dir.glob("*.wav"))
recordings = combine(sph_recordings, wav_recordings)
if len(recordings) == 0:
if split == "eval" and no_eval_ok:
continue
logging.warning(f"No SPHERE or WAV files found in {audio_dir}")
supervisions = []
text_dir = corpus_dir / f"conversational/{split}/transcription"
for p in tqdm.tqdm(text_dir.glob("*")):
# p.stem -> BABEL_BP_101_10033_20111024_205740_inLine
# parts:
# 0 -> BABEL
# 1 -> BP
# 2 -> <language-code> (101)
# 3 -> <speaker-id> (10033)
# 4 -> <date> (20111024)
# 5 -> <hour> (205740)
# 6 -> channel (inLine) ; inLine <=> A ; outLine <=> B ; "scripted" <=> A
p0, p1, lang_code, speaker, date, hour, channel, *_ = p.stem.split(
"_")
channel = {"inLine": "A", "outLine": "B"}.get(channel, "A")
# Fix problematic segments that have two consecutive timestamp lines with no transcript in between
lines = p.read_text().splitlines() + [""]
lines = [
prev_l for prev_l, l in sliding_window(2, lines)
if not (prev_l.startswith("[") and l.startswith("["))
]
# Add a None at the end so that the last timestamp is only used as "next_timestamp"
# and ends the iretation (otherwise we'd lose the last segment).
lines += [None]
for (timestamp,
text), (next_timestamp,
_) in sliding_window(2, zip(lines[::2], lines[1::2])):
try:
start = float(timestamp[1:-1])
end = float(next_timestamp[1:-1])
# Create supervision
supervisions.append(
SupervisionSegment(
id=
f"{lang_code}_{speaker}_{channel}_{date}_{hour}_{int(100 * start):06}",
recording_id=p.stem,
start=start,
duration=round(end - start, ndigits=8),
channel=0,
text=normalize_text(text),
language=BABELCODE2LANG[lang_code],
speaker=f"{lang_code}_{speaker}_{channel}",
))
except Exception as e:
logging.warning(
f"Error while parsing segment. Message: {str(e)}")
raise ValueError(
f"Too many errors while parsing segments (file: '{p}'). "
f"Please check your data or increase the threshold.")
supervisions = deduplicate_supervisions(supervisions)
if len(supervisions) == 0:
logging.warning(f"No supervisions found in {text_dir}")
supervisions = SupervisionSet.from_segments(supervisions)
# Fixing and validation of manifests
if split == "eval" and len(supervisions) == 0:
# We won't remove missing recordings for the "eval" split in cases where
# the user does not have its corresponding transcripts (very likely).
pass
else:
recordings, supervisions = remove_missing_recordings_and_supervisions(
recordings, supervisions)
supervisions = trim_supervisions_to_recordings(
recordings, supervisions)
validate_recordings_and_supervisions(recordings, supervisions)
manifests[split] = {
"recordings": recordings,
"supervisions": supervisions
}
if output_dir is not None:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
language = BABELCODE2LANG[lang_code]
save_split = "train" if split == "training" else split
recordings.to_file(output_dir /
f"recordings_{language}_{save_split}.json")
supervisions.to_file(output_dir /
f"supervisions_{language}_{save_split}.json")
return dict(manifests)
def prepare_callhome_english_asr(
audio_dir: Pathlike,
transcript_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
absolute_paths: bool = False,
) -> Dict[str, Union[RecordingSet, SupervisionSet]]:
"""
Prepare manifests for the CallHome American English corpus.
We create two manifests: one with recordings, and the other one with text
supervisions.
:param audio_dir: Path to ``LDC97S42`` content
:param transcript_dir: Path to the ``LDC97T14`` content
:param output_dir: Directory where the manifests should be written.
Can be omitted to avoid writing.
:param absolute_paths: Whether to return absolute or relative
(to the corpus dir) paths for recordings.
:return: A dict with manifests. The keys are:
``{'recordings', 'supervisions'}``.
"""
audio_dir = Path(audio_dir)
transcript_dir = Path(transcript_dir)
manifests = {}
for split in ["evaltest", "train", "devtest"]:
audio_paths = check_and_rglob(
# The LDC distribution has a typo.
audio_dir / "data" / split.replace("evaltest", "evltest"),
"*.sph",
)
recordings = RecordingSet.from_recordings(
Recording.from_file(
p, relative_path_depth=None if absolute_paths else 4)
for p in tqdm(audio_paths))
transcript_paths = check_and_rglob(
transcript_dir / "transcrpt" / split,
"*.txt",
)
# TODO: Add text normalization like in Kaldi recipe.
# Not doing this right now as it's not needed for VAD/diarization...
supervisions = []
for p in transcript_paths:
idx = 0
postprocessed_lines = list()
for line in p.read_text().splitlines():
line = line.strip()
if not line:
continue
if line.startswith("#"):
continue
try:
start, end, spk, text = line.split(maxsplit=3)
duration = float(Decimal(end) - Decimal(start))
if duration <= 0:
continue
postprocessed_lines.append(line)
except InvalidOperation:
postprocessed_lines[
-1] = postprocessed_lines[-1] + " " + line
except ValueError:
postprocessed_lines[
-1] = postprocessed_lines[-1] + " " + line
for line in postprocessed_lines:
recording_id = p.stem
# example line:
# 19.33 21.18 B: %ah Tayyib
start, end, spk, text = line.split(maxsplit=3)
spk = spk.replace(":", "")
duration = float(Decimal(end) - Decimal(start))
if duration <= 0:
continue
start = float(start)
supervisions.append(
SupervisionSegment(
recording_id=recording_id,
start=start,
duration=duration,
channel=ord(spk[0]) - ord("A"),
speaker=f"{recording_id}_{spk:0>2s}",
id=f"{recording_id}_{spk:0>2s}_{idx:0>5d}",
text=text,
))
idx += 1
supervisions = SupervisionSet.from_segments(supervisions)
recordings, supervisions = fix_manifests(recordings, supervisions)
validate_recordings_and_supervisions(recordings, supervisions)
if output_dir is not None:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
recordings.to_file(output_dir /
f"callhome-english_recordings_{split}.jsonl.gz")
supervisions.to_file(
output_dir / f"callhome-english_supervisions_{split}.jsonl.gz")
manifests[split] = {
"recordings": recordings,
"supervisions": supervisions
}
return manifests
def prepare_bvcc(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
num_jobs: int = 1,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
corpus_dir = Path(corpus_dir)
phase1_main = (corpus_dir / "phase1-main").resolve()
assert phase1_main.exists(), f"Main track dir is missing {phase1_main}"
main1_sets = phase1_main / "DATA" / "sets"
main1_wav = phase1_main / "DATA" / "wav"
assert (main1_sets.exists() and main1_wav.exists()
), f"Have you run data preparation in {phase1_main}?"
main1_devp = main1_sets / "DEVSET"
assert main1_devp.exists(), main1_devp
main1_trainp = main1_sets / "TRAINSET"
assert main1_trainp.exists(), main1_trainp
phase1_ood = (corpus_dir / "phase1-ood").resolve()
assert phase1_ood.exists(
), f"Out of domain track dir is missing {phase1_ood}"
ood1_sets = phase1_ood / "DATA" / "sets"
ood1_wav = phase1_ood / "DATA" / "wav"
assert (ood1_sets.exists() and ood1_wav.exists()
), f"Have you run data preparation in {phase1_ood}?"
ood1_unlabeled = ood1_sets / "unlabeled_mos_list.txt"
assert ood1_unlabeled.exists(), ood1_unlabeled
ood1_devp = ood1_sets / "DEVSET"
assert ood1_devp.exists(), ood1_devp
ood1_trainp = ood1_sets / "TRAINSET"
assert ood1_trainp.exists(), ood1_devp
manifests = {}
# ### Main track sets
main1_recs = RecordingSet.from_dir(main1_wav,
pattern="*.wav",
num_jobs=num_jobs)
logging.info("Preparing main1_dev")
main1_dev_sup = SupervisionSet.from_segments(
gen_supervision_per_utt(
sorted(open(main1_devp).readlines()),
main1_recs,
parse_main_line,
))
main1_dev_recs = main1_recs.filter(lambda rec: rec.id in main1_dev_sup)
manifests["main1_dev"] = {
"recordings": main1_dev_recs,
"supervisions": main1_dev_sup,
}
logging.info("Preparing main1_train")
main1_train_sup = SupervisionSet.from_segments(
gen_supervision_per_utt(
sorted(open(main1_trainp).readlines()),
main1_recs,
parse_main_line,
))
main1_train_recs = main1_recs.filter(lambda rec: rec.id in main1_train_sup)
manifests["main1_train"] = {
"recordings": main1_train_recs,
"supervisions": main1_train_sup,
}
# ### Out of Domain (OOD) track sets
unlabeled_wavpaths = [
ood1_wav / name.strip() for name in open(ood1_unlabeled).readlines()
]
manifests["ood1_unlabeled"] = {
"recordings":
RecordingSet.from_recordings(
Recording.from_file(p) for p in unlabeled_wavpaths)
}
ood1_recs = RecordingSet.from_dir(ood1_wav,
pattern="*.wav",
num_jobs=num_jobs)
logging.info("Preparing ood1_dev")
ood1_dev_sup = SupervisionSet.from_segments(
gen_supervision_per_utt(
sorted(open(ood1_devp).readlines()),
ood1_recs,
parse_ood_line,
))
ood1_dev_recs = ood1_recs.filter(lambda rec: rec.id in ood1_dev_sup)
manifests["ood1_dev"] = {
"recordings": ood1_dev_recs,
"supervisions": ood1_dev_sup,
}
logging.info("Preparing ood1_train")
ood1_train_sup = SupervisionSet.from_segments(
gen_supervision_per_utt(
sorted(open(ood1_trainp).readlines()),
ood1_recs,
parse_ood_line,
))
ood1_train_recs = ood1_recs.filter(lambda rec: rec.id in ood1_train_sup)
manifests["ood1_train"] = {
"recordings": ood1_train_recs,
"supervisions": ood1_train_sup,
}
# Optionally serializing to disc
if output_dir is not None:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
for part, d in manifests.items():
d["recordings"].to_file(output_dir / f"recordings_{part}.jsonl.gz")
if "supervisions" in d:
d["supervisions"].to_file(output_dir /
f"supervisions_{part}.jsonl.gz")
return manifests
def prepare_libritts(
corpus_dir: Pathlike,
dataset_parts: Union[str, Sequence[str]] = "auto",
output_dir: Optional[Pathlike] = None,
num_jobs: int = 1,
link_previous_utt: bool = False,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Returns the manifests which consist of the Recordings and Supervisions.
When all the manifests are available in the ``output_dir``, it will simply read and return them.
:param corpus_dir: Pathlike, the path of the data dir.
:param dataset_parts: string or sequence of strings representing dataset part names, e.g. 'train-clean-100', 'train-clean-5', 'dev-clean'.
By default we will infer which parts are available in ``corpus_dir``.
:param output_dir: Pathlike, the path where to write the manifests.
:param num_jobs: the number of parallel workers parsing the data.
:param link_previous_utt: If true adds previous utterance id to supervisions.
Useful for reconstructing chains of utterances as they were read.
If previous utterance was skipped from LibriTTS datasets previous_utt label is None.
:return: a Dict whose key is the dataset part, and the value is Dicts with the keys 'audio' and 'supervisions'.
"""
corpus_dir = Path(corpus_dir)
assert corpus_dir.is_dir(), f"No such directory: {corpus_dir}"
if dataset_parts == "auto":
dataset_parts = LIBRITTS
elif isinstance(dataset_parts, str):
assert dataset_parts in LIBRITTS
dataset_parts = [dataset_parts]
manifests = {}
if output_dir is not None:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# Maybe the manifests already exist: we can read them and save a bit of preparation time.
manifests = read_manifests_if_cached(dataset_parts=dataset_parts,
output_dir=output_dir,
prefix="libritts")
# Contents of the file
# ;ID |SEX| SUBSET |MINUTES| NAME
# 14 | F | train-clean-360 | 25.03 | ...
# 16 | F | train-clean-360 | 25.11 | ...
# 17 | M | train-clean-360 | 25.04 | ...
spk2gender = {
spk_id.strip(): gender.strip()
for spk_id, gender, *_ in (line.split("|") for line in (
corpus_dir / "SPEAKERS.txt").read_text().splitlines()
if not line.startswith(";"))
}
for part in tqdm(dataset_parts, desc="Preparing LibriTTS parts"):
if manifests_exist(part=part, output_dir=output_dir,
prefix="libritts"):
logging.info(
f"LibriTTS subset: {part} already prepared - skipping.")
continue
part_path = corpus_dir / part
recordings = RecordingSet.from_dir(part_path,
"*.wav",
num_jobs=num_jobs)
supervisions = []
for trans_path in tqdm(
part_path.rglob("*.trans.tsv"),
desc="Scanning transcript files (progbar per speaker)",
leave=False,
):
# The trans.tsv files contain only the recordings that were kept for LibriTTS.
# Example path to a file:
# /export/corpora5/LibriTTS/dev-clean/84/121123/84_121123.trans.tsv
#
# Example content:
# 84_121123_000007_000001 Maximilian. Maximilian.
# 84_121123_000008_000000 Villefort rose, half ashamed of being surprised in such a paroxysm of grief. Villefort rose, half ashamed of being surprised in such a paroxysm of grief.
# book.tsv contains additional metadata
utt2snr = [(rec_id, float(snr)) for rec_id, *_, snr in map(
str.split,
(trans_path.parent /
trans_path.name.replace(".trans.tsv", ".book.tsv")
).read_text().splitlines(),
)]
# keeps the order of uttids as they appear in book.tsv
uttids = [r for r, _ in utt2snr]
utt2snr = dict(utt2snr)
if link_previous_utt:
# Using the property of sorted keys to find previous utterance
# The keys has structure speaker_book_x_y e.g. 1089_134691_000004_000001
utt2prevutt = dict(zip(uttids + [None], [None] + uttids))
prev_rec_id = None
for line in trans_path.read_text().splitlines():
rec_id, orig_text, norm_text = line.split("\t")
spk_id = rec_id.split("_")[0]
customd = {"orig_text": orig_text, "snr": utt2snr[rec_id]}
if link_previous_utt:
# all recordings ids should be in the book.csv
# but they are some missing e.g. 446_123502_000030_000003
prev_utt = utt2prevutt.get(rec_id, None)
# previous utterance has to be present in trans.csv - otherwise it was skipped
prev_utt = prev_utt if prev_utt == prev_rec_id else None
customd["prev_utt"] = prev_utt
prev_rec_id = rec_id
supervisions.append(
SupervisionSegment(
id=rec_id,
recording_id=rec_id,
start=0.0,
duration=recordings[rec_id].duration,
channel=0,
text=norm_text,
language="English",
speaker=spk_id,
gender=spk2gender[spk_id],
custom=customd,
))
supervisions = SupervisionSet.from_segments(supervisions)
validate_recordings_and_supervisions(recordings, supervisions)
if output_dir is not None:
supervisions.to_file(output_dir /
f"libritts_supervisions_{part}.jsonl.gz")
recordings.to_file(output_dir /
f"libritts_recordings_{part}.jsonl.gz")
manifests[part] = {
"recordings": recordings,
"supervisions": supervisions
}
return manifests