def prepare_callhome_english_sre(
audio_dir: Pathlike,
rttm_dir: Optional[Pathlike] = None,
output_dir: Optional[Pathlike] = None,
absolute_paths: bool = False,
) -> Dict[str, Union[RecordingSet, SupervisionSet]]:
"""
Prepare manifests for the Callhome American English portion prepartion.
We create two manifests: one with recordings, and the other one with text
supervisions.
:param audio_dir: Path to ``LDC2001S97`` package.
:param rttm_dir: Path to the transcripts directory. If not provided,
the transcripts will be downloaded.
: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'}``.
"""
if rttm_dir is None:
rttm_dir = download_callhome_metadata()
rttm_path = rttm_dir / "fullref.rttm"
supervisions = read_rttm(rttm_path)
audio_paths = check_and_rglob(audio_dir, "*.sph")
recordings = RecordingSet.from_recordings(
Recording.from_file(p,
relative_path_depth=None if absolute_paths else 4)
for p in tqdm(audio_paths))
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 / "recordings.json")
supervisions.to_json(output_dir / "supervisions.json")
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_fisher_english(
corpus_dir: Pathlike,
output_dir: Pathlike,
audio_dirs: List[str] = FISHER_AUDIO_DIRS,
transcript_dirs: List[str] = FISHER_TRANSCRIPT_DIRS,
absolute_paths: bool = False,
num_jobs: int = 1,
) -> Dict[str, Union[RecordingSet, SupervisionSet]]:
"""
Prepares manifests for Fisher English Part 1, 2.
Script assumes that audio_dirs and transcript_dirs are in the corpus_path.
We create two manifests: one with recordings, and the other one with text supervisions.
:param corpus_path: Path to Fisher corpus
:param audio_dirs: List of dirs of audio corpora.
:param transcripts_dirs: List of dirs of transcript corpora.
: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'}``.
"""
corpus_dir = Path(corpus_dir)
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
for workdir in audio_dirs + transcript_dirs:
workdir_path = corpus_dir / workdir
if not workdir_path.is_dir():
raise ValueError(
f"Could not find '{workdir}' directory inside '{corpus_dir}'.")
audio_subdir_paths = []
for audio_dir in audio_dirs:
audio_dir_path = corpus_dir / audio_dir
for audio_partition_dir in audio_dir_path.iterdir():
audio_partition_dir_path = audio_dir_path / audio_partition_dir / "audio"
audio_subdir_paths += [
audio_partition_dir_path / audio_subdir
for audio_subdir in audio_partition_dir_path.iterdir()
]
transcript_subdir_paths = []
for transcript_dir in transcript_dirs:
transcript_dir_path = corpus_dir / transcript_dir / "data" / "trans"
transcript_subdir_paths += [
transcript_dir_path / transcript_subdir
for transcript_subdir in transcript_dir_path.iterdir()
]
audio_paths = walk_dirs_parallel(audio_subdir_paths, "*.sph",
"Parsing audio sub-dirs")
transcript_paths = walk_dirs_parallel(transcript_subdir_paths, "*.txt",
"Parsing transcript sub-dirs")
sessions = {}
for transcript_dir in transcript_dirs:
sessions_data_path = check_and_rglob(
corpus_dir / transcript_dir / "doc", "*_calldata.tbl")[0]
with codecs.open(sessions_data_path, "r", "utf8") as sessions_data_f:
tmp_sessions = [
l.rstrip("\n").split(",") for l in sessions_data_f.readlines()
][1:]
sessions.update(
{l[0]: {
"A": l[5],
"B": l[10]
}
for l in tmp_sessions})
assert len(transcript_paths) == len(
audio_paths), f"{len(transcript_paths)} == {len(audio_paths)}"
if len(transcript_paths) != len(sessions):
warnings.warn(
f"Fisher's *_calldata.tbl files indicate there should be {len(sessions)} sessions, "
f"but our scanning of audio and transcript files indicates there are only {len(transcript_paths)}."
)
recs_path = output_dir / "recordings_notfixed.jsonl.gz"
if recs_path.is_file():
logging.info(f"Using existing recording manifest at {recs_path}")
recordings = RecordingSet.from_jsonl_lazy(recs_path)
else:
logging.info(f"Building fresh recording manifest")
create_recordings_input = [(p, None if absolute_paths else 5)
for p in audio_paths]
err_recos = 0
with ProcessPoolExecutor(
num_jobs) as executor, RecordingSet.open_writer(
recs_path) as writer:
with tqdm(total=len(create_recordings_input),
desc="Collect recordings") as pbar:
for reco in executor.map(create_recording,
create_recordings_input):
if reco is not None:
writer.write(reco, flush=True)
else:
err_recos += 1
pbar.update()
if err_recos:
warnings.warn(f"Out of {len(create_recordings_input)} recordings, "
f"{err_recos} had errors and were omitted.")
recordings = writer.open_manifest()
sups_path = output_dir / "supervisions_notfixed.jsonl.gz"
if sups_path.is_file():
logging.info(f"Using existing supervision manifest at {recs_path}")
supervisions = SupervisionSet.from_jsonl_lazy(sups_path)
else:
logging.info(f"Building fresh supervision manifest")
create_supervisions_input = [(sessions, p) for p in transcript_paths]
err_sups = 0
with ThreadPoolExecutor(os.cpu_count() *
4) as executor, SupervisionSet.open_writer(
sups_path) as writer:
with tqdm(total=len(create_supervisions_input),
desc="Create supervisions") as pbar:
for tmp_supervisions in executor.map(
create_supervision, create_supervisions_input):
if not tmp_supervisions:
err_sups += 1
for s in tmp_supervisions:
writer.write(s)
pbar.update()
supervisions = writer.open_manifest()
if err_recos:
warnings.warn(
f"Out of {len(create_supervisions_input)} transcript files, "
f"{err_sups} had errors and were omitted.")
recordings, supervisions = fix_manifests(recordings, supervisions)
validate_recordings_and_supervisions(recordings, supervisions)
# Write the fixed and validated version to files with standard names.
recordings.to_file(recs_path.parent / "recordings.jsonl.gz")
supervisions.to_file(sups_path.parent / "supervisions.jsonl.gz")
return {"recordings": recordings, "supervisions": supervisions}
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_ami(
data_dir: Pathlike,
annotations_dir: Optional[Pathlike] = None,
output_dir: Optional[Pathlike] = None,
mic: Optional[str] = "ihm",
partition: Optional[str] = "full-corpus",
normalize_text: str = "kaldi",
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Returns the manifests which consist of the Recordings and Supervisions
:param data_dir: Pathlike, the path of the data dir.
:param annotations: Pathlike, the path of the annotations dir or zip file.
:param output_dir: Pathlike, the path where to write the manifests.
:param mic: str {'ihm','ihm-mix','sdm','mdm'}, type of mic to use.
:param partition: str {'full-corpus','full-corpus-asr','scenario-only'}, AMI official data split
:param normalize_text: str {'none', 'upper', 'kaldi'} normalization of text
:return: a Dict whose key is ('train', 'dev', 'eval'), and the values are dicts of manifests under keys
'recordings' and 'supervisions'.
Example usage:
1. Prepare IHM-Mix data for ASR:
>>> manifests = prepare_ami('/path/to/ami-corpus', mic='ihm-mix', partition='full-corpus-asr')
2. Prepare SDM data:
>>> manifests = prepare_ami('/path/to/ami-corpus', mic='sdm', partition='full-corpus')
"""
data_dir = Path(data_dir)
assert data_dir.is_dir(), f"No such directory: {data_dir}"
assert mic in MICS, f"Mic {mic} not supported"
assert partition in PARTITIONS, f"Partition {partition} not supported"
if output_dir is not None:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
logging.info("Parsing AMI annotations")
if not annotations_dir:
if (data_dir / "ami_public_manual_1.6.2").is_dir():
annotations_dir = data_dir / "ami_public_manual_1.6.2"
elif (data_dir / "ami_public_manual_1.6.2.zip").is_file():
annotations_dir = data_dir / "ami_public_manual_1.6.2.zip"
else:
raise ValueError(
f"No annotations directory specified and no zip file found in {data_dir}"
)
# Prepare annotations which is a list of segment-level transcriptions
annotations = parse_ami_annotations(annotations_dir,
normalize=normalize_text)
# Audio
logging.info("Preparing recording manifests")
wav_dir = data_dir
if mic in ["ihm", "mdm"]:
audio_paths = (wav_dir.rglob("*Headset-?.wav")
if mic == "ihm" else wav_dir.rglob("*Array?-0?.wav"))
audio = prepare_audio_grouped(list(audio_paths))
elif mic in ["ihm-mix", "sdm"]:
audio_paths = (wav_dir.rglob("*Mix-Headset.wav") if mic == "ihm-mix"
else wav_dir.rglob("*Array1-01.wav"))
audio = prepare_audio_single(list(audio_paths))
# Supervisions
logging.info("Preparing supervision manifests")
supervision = (prepare_supervision_ihm(audio, annotations) if mic == "ihm"
else prepare_supervision_other(audio, annotations))
manifests = defaultdict(dict)
dataset_parts = PARTITIONS[partition]
for part in ["train", "dev", "test"]:
# Get recordings for current data split
audio_part = audio.filter(lambda x: x.id in dataset_parts[part])
supervision_part = supervision.filter(
lambda x: x.recording_id in dataset_parts[part])
# Write to output directory if a path is provided
if output_dir is not None:
audio_part.to_file(output_dir / f"recordings_{part}.jsonl")
supervision_part.to_file(output_dir / f"supervisions_{part}.jsonl")
audio_part, supervision_part = fix_manifests(audio_part,
supervision_part)
validate_recordings_and_supervisions(audio_part, supervision_part)
# Combine all manifests into one dictionary
manifests[part] = {
"recordings": audio_part,
"supervisions": supervision_part
}
return dict(manifests)
def prepare_fisher_spanish(
audio_dir_path: Pathlike,
transcript_dir_path: Pathlike,
output_dir: Optional[Pathlike] = None,
absolute_paths: bool = False,
) -> Dict[str, Union[RecordingSet, SupervisionSet]]:
"""
Prepares manifests for Fisher Spanish.
We create two manifests: one with recordings, and the other one with text supervisions.
:param audio_dir_path: Path to audio directory (usually LDC2010S01).
:param transcript_dir_path: Path to transcript directory (usually LDC2010T04).
: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, transcript_dir_path = Path(audio_dir_path), Path(
transcript_dir_path
)
audio_paths = check_and_rglob(audio_dir_path, "*.sph")
transcript_paths = check_and_rglob(transcript_dir_path, "*.tdf")
sessions_data_path = check_and_rglob(transcript_dir_path, "*_call.tbl")[0]
with codecs.open(sessions_data_path, "r", "utf8") as sessions_data_f:
session_lines = [
l.rstrip("\n").split(",") for l in sessions_data_f.readlines()
][1:]
sessions = {l[0]: {0: l[2], 1: l[8]} for l in session_lines}
assert len(transcript_paths) == len(sessions) == len(audio_paths)
create_recordings_input = [(p, None if absolute_paths else 4) for p in audio_paths]
recordings = [None] * len(audio_paths)
with ThreadPoolExecutor(os.cpu_count() * 4) as executor:
with tqdm(total=len(audio_paths), desc="Collect recordings") as pbar:
for i, reco in enumerate(
executor.map(create_recording, create_recordings_input)
):
recordings[i] = reco
pbar.update()
recordings = RecordingSet.from_recordings(recordings)
create_supervisions_input = [(sessions, p) for p in transcript_paths]
supervisions = [None] * len(create_supervisions_input)
with ThreadPoolExecutor(os.cpu_count() * 4) as executor:
with tqdm(
total=len(create_supervisions_input), desc="Create supervisions"
) as pbar:
for i, tmp_supervisions in enumerate(
executor.map(create_supervision, create_supervisions_input)
):
supervisions[i] = tmp_supervisions
pbar.update()
supervisions = list(it.chain.from_iterable(supervisions))
supervisions = SupervisionSet.from_segments(supervisions).filter(
lambda s: s.duration > 0.0
)
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 / "recordings.json")
supervisions.to_json(output_dir / "supervisions.json")
return {"recordings": recordings, "supervisions": supervisions}
def prepare_icsi(
audio_dir: Pathlike,
transcripts_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
mic: Optional[str] = "ihm",
normalize_text: str = "kaldi",
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Returns the manifests which consist of the Recordings and Supervisions
:param audio_dir: Pathlike, the path which holds the audio data
:param transcripts_dir: Pathlike, the path which holds the transcripts data
:param output_dir: Pathlike, the path where to write the manifests - `None` means manifests aren't stored on disk.
:param mic: str {'ihm','ihm-mix','sdm','mdm'}, type of mic to use.
:param normalize_text: str {'none', 'upper', 'kaldi'} normalization of text
:return: a Dict whose key is ('train', 'dev', 'test'), and the values are dicts of manifests under keys
'recordings' and 'supervisions'.
"""
audio_dir = Path(audio_dir)
transcripts_dir = Path(transcripts_dir)
assert audio_dir.is_dir(), f"No such directory: {audio_dir}"
assert transcripts_dir.is_dir(), f"No such directory: {transcripts_dir}"
assert mic in MIC_TO_CHANNELS.keys(), f"Mic {mic} not supported"
if output_dir is not None:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
logging.info("Parsing ICSI transcripts")
annotations, channel_to_idx_map = parse_icsi_annotations(
transcripts_dir, normalize=normalize_text)
# Audio
logging.info("Preparing recording manifests")
channels = "".join(MIC_TO_CHANNELS[mic])
if mic == "ihm" or mic == "mdm":
audio_paths = audio_dir.rglob(f"chan[{channels}].sph")
audio = prepare_audio_grouped(
list(audio_paths), channel_to_idx_map if mic == "ihm" else None)
elif mic == "sdm" or mic == "ihm-mix":
audio_paths = (audio_dir.rglob(f"chan[{channels}].sph")
if len(channels) else audio_dir.rglob("*.wav"))
audio = prepare_audio_single(list(audio_paths))
# Supervisions
logging.info("Preparing supervision manifests")
supervision = (prepare_supervision_ihm(
audio, annotations, channel_to_idx_map) if mic == "ihm" else
prepare_supervision_other(audio, annotations))
manifests = defaultdict(dict)
for part in ["train", "dev", "test"]:
# Get recordings for current data split
audio_part = audio.filter(lambda x: x.id in PARTITIONS[part])
supervision_part = supervision.filter(
lambda x: x.recording_id in PARTITIONS[part])
# Write to output directory if a path is provided
if output_dir is not None:
audio_part.to_file(output_dir / f"recordings_{part}.jsonl")
supervision_part.to_file(output_dir / f"supervisions_{part}.jsonl")
audio_part, supervision_part = fix_manifests(audio_part,
supervision_part)
validate_recordings_and_supervisions(audio_part, supervision_part)
# Combine all manifests into one dictionary
manifests[part] = {
"recordings": audio_part,
"supervisions": supervision_part
}
return dict(manifests)
