def prepare_hifitts(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
num_jobs: int = 1,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Prepare manifests for the HiFiTTS dataset.
:param corpus_dir: Path or str, the path to the downloaded corpus main directory.
:param output_dir: Path or str, the path where to write the manifests.
:param num_jobs: How many concurrent workers to use for preparing each dataset partition.
:return: a dict with manifests for all the partitions
(example query: ``manifests['92_clean_train']['recordings']``).
"""
corpus_dir = Path(corpus_dir)
assert corpus_dir.is_dir(), f"No such directory: {corpus_dir}"
manifests = {}
json_manifests = list(corpus_dir.glob("*.json"))
dataset_partitions = [to_partition_id(p) for p in json_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_partitions,
output_dir=output_dir,
prefix="hifitts")
with ProcessPoolExecutor(num_jobs) as ex:
futures = []
partition_ids = []
for raw_manifest_path in json_manifests:
speaker_id, _, clean_or_other, part = raw_manifest_path.stem.split(
"_")
partition_id = to_partition_id(raw_manifest_path)
if manifests_exist(part=partition_id,
output_dir=output_dir,
prefix="hifitts"):
logging.info(
f"HiFiTTS subset: {part} already prepared - skipping.")
continue
futures.append(
ex.submit(
prepare_single_partition,
raw_manifest_path,
corpus_dir,
speaker_id,
clean_or_other,
))
partition_ids.append(partition_id)
for future, partition_id in tqdm(
zip(as_completed(futures), partition_ids),
desc="Preparing HiFiTTS parts",
total=len(futures),
):
recordings, supervisions = future.result()
if output_dir is not None:
supervisions.to_json(
output_dir / f"hifitts_supervisions_{partition_id}.json")
recordings.to_json(output_dir /
f"hifitts_recordings_{partition_id}.json")
manifests[partition_id] = {
"recordings": recordings,
"supervisions": supervisions,
}
return manifests
def prepare_librispeech(
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.
: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 == "mini_librispeech":
dataset_parts = set(MINI_LIBRISPEECH).intersection(
path.name for path in corpus_dir.glob("*"))
elif dataset_parts == "auto":
dataset_parts = (set(LIBRISPEECH).union(MINI_LIBRISPEECH).intersection(
path.name for path in corpus_dir.glob("*")))
if not dataset_parts:
raise ValueError(
f"Could not find any of librispeech or mini_librispeech splits in: {corpus_dir}"
)
elif isinstance(dataset_parts, str):
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)
with ThreadPoolExecutor(num_jobs) as ex:
for part in tqdm(dataset_parts, desc="Dataset parts"):
logging.info(f"Processing LibriSpeech subset: {part}")
if manifests_exist(part=part, output_dir=output_dir):
logging.info(
f"LibriSpeech subset: {part} already prepared - skipping.")
continue
recordings = []
supervisions = []
part_path = corpus_dir / part
futures = []
for trans_path in tqdm(part_path.rglob("*.trans.txt"),
desc="Distributing tasks",
leave=False):
alignments = {}
ali_path = trans_path.parent / (trans_path.stem.split(".")[0] +
".alignment.txt")
if ali_path.exists():
alignments = parse_alignments(ali_path)
# "trans_path" file contains lines like:
#
# 121-121726-0000 ALSO A POPULAR CONTRIVANCE
# 121-121726-0001 HARANGUE THE TIRESOME PRODUCT OF A TIRELESS TONGUE
# 121-121726-0002 ANGOR PAIN PAINFUL TO HEAR
#
# We will create a separate Recording and SupervisionSegment for those.
with open(trans_path) as f:
for line in f:
futures.append(
ex.submit(parse_utterance, part_path, line,
alignments))
for future in tqdm(futures, desc="Processing", leave=False):
result = future.result()
if result is None:
continue
recording, segment = result
recordings.append(recording)
supervisions.append(segment)
recording_set = RecordingSet.from_recordings(recordings)
supervision_set = SupervisionSet.from_segments(supervisions)
validate_recordings_and_supervisions(recording_set,
supervision_set)
if output_dir is not None:
supervision_set.to_file(output_dir /
f"supervisions_{part}.json")
recording_set.to_file(output_dir / f"recordings_{part}.json")
manifests[part] = {
"recordings": recording_set,
"supervisions": supervision_set,
}
return manifests
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_spgispeech(
corpus_dir: Pathlike,
output_dir: Pathlike,
normalize_text: bool = True,
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 output_dir: Pathlike, the path where to write the manifests.
:param normalize_text: Bool, if True, normalize the text (similar to ESPNet recipe).
:param num_jobs: int, the number of jobs to use for parallel processing.
: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 `normalize_text` option removes all punctuation and converts all upper case to lower case.
This includes removing possibly important punctuations such as dashes and apostrophes.
"""
corpus_dir = Path(corpus_dir)
assert corpus_dir.is_dir(), f"No such directory: {corpus_dir}"
audio_dir = (corpus_dir if
(corpus_dir /
"train").is_dir() else corpus_dir / "spgispeech")
dataset_parts = ["train", "val"]
manifests = {}
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="spgispeech",
suffix="jsonl.gz",
lazy=True,
)
for part in dataset_parts:
logging.info(f"Processing SPGISpeech subset: {part}")
if manifests_exist(part=part,
output_dir=output_dir,
prefix="spgispeech",
suffix="jsonl.gz"):
logging.info(
f"SPGISpeech 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.
global audio_read_worker
durations = {}
def audio_read_worker(p: Path) -> Recording:
r = Recording.from_file(p,
recording_id=f"{p.parent.stem}_{p.stem}")
durations[r.id] = r.duration
return r
with RecordingSet.open_writer(
output_dir /
f"spgispeech_recordings_{part}.jsonl.gz") as rec_writer:
for recording in tqdm(
parallel_map(
audio_read_worker,
(audio_dir / part).rglob("*.wav"),
num_jobs=num_jobs,
),
desc="Processing SPGISpeech recordings",
):
rec_writer.write(recording)
# Read supervisions and write them to manifest
with SupervisionSet.open_writer(
output_dir / f"spgispeech_supervisions_{part}.jsonl.gz"
) as sup_writer, open(corpus_dir / f"{part}.csv", "r") as f:
# Skip the header
next(f)
for line in tqdm(f, desc="Processing utterances"):
parts = line.strip().split("|")
# 07a785e9237c389c1354bb60abca42d5/1.wav -> 07a785e9237c389c1354bb60abca42d5_1
recording_id = parts[0].replace("/", "_").replace(".wav", "")
text = parts[2]
if normalize_text:
text = normalize(text)
spkid = recording_id.split("_")[0]
segment = SupervisionSegment(
id=recording_id,
recording_id=recording_id,
text=text,
speaker=spkid,
start=0,
duration=durations[recording_id],
language="English",
)
sup_writer.write(segment)
manifests[part] = {
"recordings": RecordingSet.from_jsonl_lazy(rec_writer.path),
"supervisions": SupervisionSet.from_jsonl_lazy(sup_writer.path),
}
return manifests
def prepare_gigaspeech(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike],
dataset_parts: Union[str, Sequence[str]] = "auto",
num_jobs: int = 1,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
if is_module_available("speechcolab"):
from speechcolab.datasets.gigaspeech import GigaSpeech
else:
raise ImportError(
"To process the GigaSpeech corpus, please install optional dependency: pip install speechcolab"
)
subsets = ("XL", "DEV", "TEST") if dataset_parts == "auto" else dataset_parts
if isinstance(subsets, str):
subsets = [subsets]
corpus_dir = Path(corpus_dir)
gigaspeech = GigaSpeech(corpus_dir)
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# Maybe some 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="gigaspeech",
suffix="jsonl.gz",
lazy=True,
)
for part in subsets:
logging.info(f"Processing GigaSpeech subset: {part}")
if manifests_exist(
part=part, output_dir=output_dir, prefix="gigaspeech", suffix="jsonl.gz"
):
logging.info(f"GigaSpeech subset: {part} already prepared - skipping.")
continue
with RecordingSet.open_writer(
output_dir / f"gigaspeech_recordings_{part}.jsonl.gz"
) as rec_writer, SupervisionSet.open_writer(
output_dir / f"gigaspeech_supervisions_{part}.jsonl.gz"
) as sup_writer, CutSet.open_writer(
output_dir / f"gigaspeech_cuts_{part}.jsonl.gz"
) as cut_writer:
for recording, segments in tqdm(
parallel_map(
parse_utterance,
gigaspeech.audios("{" + part + "}"),
repeat(gigaspeech.gigaspeech_dataset_dir),
num_jobs=num_jobs,
),
desc="Processing GigaSpeech JSON entries",
):
# Fix and validate the recording + supervisions
recordings, segments = fix_manifests(
recordings=RecordingSet.from_recordings([recording]),
supervisions=SupervisionSet.from_segments(segments),
)
validate_recordings_and_supervisions(
recordings=recordings, supervisions=segments
)
# Create the cut since most users will need it anyway.
# There will be exactly one cut since there's exactly one recording.
cuts = CutSet.from_manifests(
recordings=recordings, supervisions=segments
)
# Write the manifests
rec_writer.write(recordings[0])
for s in segments:
sup_writer.write(s)
cut_writer.write(cuts[0])
manifests[part] = {
"recordings": RecordingSet.from_jsonl_lazy(rec_writer.path),
"supervisions": SupervisionSet.from_jsonl_lazy(sup_writer.path),
"cuts": CutSet.from_jsonl_lazy(cut_writer.path),
}
return dict(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]
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())
}
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 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
def prepare_mobvoihotwords(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike] = None
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Returns the manifests which consist of the 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 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}'
dataset_parts = ['train', 'dev', '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)
for part in dataset_parts:
logging.info(f'Preparing MobvoiHotwords subset: {part}')
if manifests_exist(part=part, output_dir=output_dir):
logging.info(
f'MobvoiHotwords subset: {part} already prepared - skipping.')
continue
# Generate a mapping: utt_id -> (audio_path, audio_info, speaker, text)
recordings = []
supervisions = []
for prefix in ['p_', 'n_']:
prefixed_part = prefix + part
json_path = corpus_dir / 'mobvoi_hotword_dataset_resources' / f'{prefixed_part}.json'
with open(json_path, 'r', encoding='utf-8') as f:
json_data = json.load(f)
for entry in json_data:
idx = entry['utt_id']
speaker = idx if entry['speaker_id'] is None else entry[
'speaker_id']
audio_path = corpus_dir / 'mobvoi_hotword_dataset' / f'{idx}.wav'
text = 'FREETEXT'
if entry['keyword_id'] == 0:
text = 'HiXiaowen'
elif entry['keyword_id'] == 1:
text = 'NihaoWenwen'
else:
assert entry['keyword_id'] == -1
if not audio_path.is_file():
logging.warning(f'No such file: {audio_path}')
continue
recording = Recording.from_file(audio_path)
recordings.append(recording)
segment = SupervisionSegment(id=idx,
recording_id=idx,
start=0.0,
duration=recording.duration,
channel=0,
language='Chinese',
speaker=speaker,
text=text.strip())
supervisions.append(segment)
recording_set = RecordingSet.from_recordings(recordings)
supervision_set = SupervisionSet.from_segments(supervisions)
validate_recordings_and_supervisions(recording_set, supervision_set)
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 prepare_mobvoihotwords(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike] = None
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
"""
Returns the manifests which consist of the 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 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}"
dataset_parts = ["train", "dev", "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)
for part in dataset_parts:
logging.info(f"Preparing MobvoiHotwords subset: {part}")
if manifests_exist(part=part, output_dir=output_dir):
logging.info(
f"MobvoiHotwords subset: {part} already prepared - skipping.")
continue
# Generate a mapping: utt_id -> (audio_path, audio_info, speaker, text)
recordings = []
supervisions = []
for prefix in ["p_", "n_"]:
prefixed_part = prefix + part
json_path = (corpus_dir / "mobvoi_hotword_dataset_resources" /
f"{prefixed_part}.json")
with open(json_path, "r", encoding="utf-8") as f:
json_data = json.load(f)
for entry in json_data:
idx = entry["utt_id"]
speaker = (idx if entry["speaker_id"] is None else
entry["speaker_id"])
audio_path = corpus_dir / "mobvoi_hotword_dataset" / f"{idx}.wav"
text = "FREETEXT"
if entry["keyword_id"] == 0:
text = "HiXiaowen"
elif entry["keyword_id"] == 1:
text = "NihaoWenwen"
else:
assert entry["keyword_id"] == -1
if not audio_path.is_file():
logging.warning(f"No such file: {audio_path}")
continue
recording = Recording.from_file(audio_path)
recordings.append(recording)
segment = SupervisionSegment(
id=idx,
recording_id=idx,
start=0.0,
duration=recording.duration,
channel=0,
language="Chinese",
speaker=speaker,
text=text.strip(),
)
supervisions.append(segment)
recording_set = RecordingSet.from_recordings(recordings)
supervision_set = SupervisionSet.from_segments(supervisions)
validate_recordings_and_supervisions(recording_set, supervision_set)
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