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from local
This commit is contained in:
dohe0342
parent
f87fb7e0fe
commit
9536f6e4f7
179
egs/librispeech/ASR/local/prepare_LJSpeech.py
Executable file
179
egs/librispeech/ASR/local/prepare_LJSpeech.py
Executable file
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import logging
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import sys
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import os
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import re
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import shutil
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import tarfile
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import zipfile
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from concurrent.futures.thread import ThreadPoolExecutor
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from pathlib import Path
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from typing import Dict, List, Optional, Sequence, Tuple, Union
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from tqdm.auto import tqdm
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from lhotse import validate_recordings_and_supervisions
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from lhotse.audio import Recording, RecordingSet
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from lhotse.recipes.utils import manifests_exist, read_manifests_if_cached
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from lhotse.supervision import AlignmentItem, SupervisionSegment, SupervisionSet
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from lhotse.utils import (
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Pathlike,
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is_module_available,
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safe_extract,
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urlretrieve_progress,
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)
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# LIBRISPEECH_ALIGNMENTS_URL = (
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# "https://drive.google.com/uc?id=1WYfgr31T-PPwMcxuAq09XZfHQO5Mw8fE"
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# )
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def prepare_LJSpeech(
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corpus_dir: str,
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dataset_parts: str = "auto",
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output_dir: str = None,
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num_jobs: int = 1,
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) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
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"""
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Returns the manifests which consist of the Recordings and Supervisions.
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When all the manifests are available in the ``output_dir``, it will simply read and return them.
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:param corpus_dir: Pathlike, the path of the data dir.
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:param dataset_parts: string or sequence of strings representing dataset part names, e.g. 'train-clean-100', 'train-clean-5', 'dev-clean'.
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By default we will infer which parts are available in ``corpus_dir``.
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:param output_dir: Pathlike, the path where to write the manifests.
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:return: a Dict whose key is the dataset part, and the value is Dicts with the keys 'audio' and 'supervisions'.
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"""
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assert os.path.exists(corpus_dir), f"{corpus_dir} does not exist"
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# wav_dir = Path(corpus_dir + "/wavs")
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# wavs = os.listdir(wav_dir)
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# text_dir = Path(corpus_dir + "/wavs")
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# texts = os.listdir(text_dir)
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# wavs_parts = (
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# set(wavs)
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# )
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# books_parts = (
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# set(texts)
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# )
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manifests = {}
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dataset_parts = ["train", "dev", "test"]
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if output_dir is not None:
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output_dir = Path(output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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import glob
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futures = []
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for part in tqdm(dataset_parts, desc="Dataset parts"):
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logging.info(f"Processing LJSpeech subset: {part}")
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if manifests_exist(part=part, output_dir=output_dir):
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logging.info(f"LJSpeech subset: {part} already prepared - skipping.")
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continue
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recordings = []
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supervisions = []
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part_path = Path(os.path.join(corpus_dir, "wavs", part))
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part_file_names = list(map(lambda x: x.strip('.wav'),os.listdir(part_path)))
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txt_path = os.path.join(corpus_dir, "texts")
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futures = []
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for trans_path in tqdm(
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glob.iglob(str(txt_path) + "/*.txt"), desc="Distributing tasks", leave=False
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):
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alignments = {}
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with open(trans_path) as f:
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cur_file_name = trans_path.split('/')[-1].replace('.txt', '')
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if cur_file_name not in part_file_names:
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continue
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for line in f:
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futures.append(
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parse_utterance(part_path, trans_path + ' ' + line, alignments)
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)
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for future in tqdm(futures, desc="Processing", leave=False):
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result = future
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if result is None:
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continue
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recording, segment = result
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recordings.append(recording)
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supervisions.append(segment)
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recording_set = RecordingSet.from_recordings(recordings)
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supervision_set = SupervisionSet.from_segments(supervisions)
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validate_recordings_and_supervisions(recording_set, supervision_set)
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if output_dir is not None:
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supervision_set.to_file(
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output_dir / f"LJSpeech_supervisions_{part}.jsonl.gz"
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)
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recording_set.to_file(
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output_dir / f"LJSpeech_recordings_{part}.jsonl.gz"
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)
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manifests[part] = {
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"recordings": recording_set,
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"supervisions": supervision_set,
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}
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return manifests
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def parse_utterance(
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dataset_split_path: Path,
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line: str,
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alignments: Dict[str, List[AlignmentItem]],
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) -> Optional[Tuple[Recording, SupervisionSegment]]:
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recording_id, text = line.strip().split(maxsplit=1)
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recording_id = recording_id.split('/')[-1].split('.txt')[0]
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# Create the Recording first
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audio_path = (
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dataset_split_path / f"{recording_id}.wav"
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)
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if not os.path.exists(audio_path):
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logging.warning(f"No such file: {audio_path}")
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return None
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recording = Recording.from_file(audio_path, recording_id=recording_id)
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# Then, create the corresponding supervisions
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segment = SupervisionSegment(
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id=recording_id,
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recording_id=recording_id,
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start=0.0,
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duration=recording.duration,
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channel=0,
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language="English",
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speaker=re.sub(r"-.*", r"", recording.id),
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text=text.strip(),
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alignment={"word": alignments[recording_id]}
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if recording_id in alignments
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else None,
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)
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return recording, segment
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def parse_alignments(ali_path: Pathlike) -> Dict[str, List[AlignmentItem]]:
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alignments = {}
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for line in Path(ali_path).read_text().splitlines():
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utt_id, words, timestamps = line.split()
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words = words.replace('"', "").split(",")
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timestamps = [0.0] + list(map(float, timestamps.replace('"', "").split(",")))
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alignments[utt_id] = [
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AlignmentItem(
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symbol=word, start=start, duration=round(end - start, ndigits=8)
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)
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for word, start, end in zip(words, timestamps, timestamps[1:])
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]
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return alignments
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def main(corpus_dir):
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nj = 15
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output_dir = "data/manifests"
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prepare_LJSpeech(corpus_dir, "auto", output_dir, nj)
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corpus_dir = sys.argv[1]
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main(corpus_dir)
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179
egs/librispeech/ASR/local/prepare_LJSpeech_pseudo.py
Executable file
179
egs/librispeech/ASR/local/prepare_LJSpeech_pseudo.py
Executable file
@ -0,0 +1,179 @@
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import logging
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import sys
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import os
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import re
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import shutil
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import tarfile
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import zipfile
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from concurrent.futures.thread import ThreadPoolExecutor
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from pathlib import Path
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from typing import Dict, List, Optional, Sequence, Tuple, Union
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from tqdm.auto import tqdm
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from lhotse import validate_recordings_and_supervisions
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from lhotse.audio import Recording, RecordingSet
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from lhotse.recipes.utils import manifests_exist, read_manifests_if_cached
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from lhotse.supervision import AlignmentItem, SupervisionSegment, SupervisionSet
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from lhotse.utils import (
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Pathlike,
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is_module_available,
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safe_extract,
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urlretrieve_progress,
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)
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# LIBRISPEECH_ALIGNMENTS_URL = (
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# "https://drive.google.com/uc?id=1WYfgr31T-PPwMcxuAq09XZfHQO5Mw8fE"
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# )
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def prepare_LJSpeech(
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corpus_dir: str,
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dataset_parts: str = "auto",
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output_dir: str = None,
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num_jobs: int = 1,
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) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
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"""
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Returns the manifests which consist of the Recordings and Supervisions.
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When all the manifests are available in the ``output_dir``, it will simply read and return them.
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:param corpus_dir: Pathlike, the path of the data dir.
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:param dataset_parts: string or sequence of strings representing dataset part names, e.g. 'train-clean-100', 'train-clean-5', 'dev-clean'.
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By default we will infer which parts are available in ``corpus_dir``.
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:param output_dir: Pathlike, the path where to write the manifests.
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:return: a Dict whose key is the dataset part, and the value is Dicts with the keys 'audio' and 'supervisions'.
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"""
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assert os.path.exists(corpus_dir), f"{corpus_dir} does not exist"
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# wav_dir = Path(corpus_dir + "/wavs")
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# wavs = os.listdir(wav_dir)
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# text_dir = Path(corpus_dir + "/wavs")
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# texts = os.listdir(text_dir)
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# wavs_parts = (
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# set(wavs)
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# )
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# books_parts = (
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# set(texts)
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# )
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manifests = {}
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dataset_parts = ["train", "dev", "test"]
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if output_dir is not None:
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output_dir = Path(output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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import glob
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futures = []
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for part in tqdm(dataset_parts, desc="Dataset parts"):
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logging.info(f"Processing LJSpeech subset: {part}")
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if manifests_exist(part=part, output_dir=output_dir):
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logging.info(f"LJSpeech subset: {part} already prepared - skipping.")
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continue
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recordings = []
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supervisions = []
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part_path = Path(os.path.join(corpus_dir, "wavs", part))
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part_file_names = list(map(lambda x: x.strip('.wav'),os.listdir(part_path)))
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txt_path = os.path.join(corpus_dir, "texts")
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futures = []
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for trans_path in tqdm(
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glob.iglob(str(txt_path) + "/*.txt"), desc="Distributing tasks", leave=False
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):
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alignments = {}
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with open(trans_path) as f:
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cur_file_name = trans_path.split('/')[-1].replace('.txt', '')
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if cur_file_name not in part_file_names:
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continue
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for line in f:
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futures.append(
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parse_utterance(part_path, trans_path + ' ' + line, alignments)
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)
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for future in tqdm(futures, desc="Processing", leave=False):
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result = future
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if result is None:
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continue
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recording, segment = result
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recordings.append(recording)
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supervisions.append(segment)
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recording_set = RecordingSet.from_recordings(recordings)
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supervision_set = SupervisionSet.from_segments(supervisions)
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validate_recordings_and_supervisions(recording_set, supervision_set)
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if output_dir is not None:
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supervision_set.to_file(
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output_dir / f"LJSpeech_pseudo_supervisions_{part}.jsonl.gz"
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)
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recording_set.to_file(
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output_dir / f"LJSpeech_pseudo_recordings_{part}.jsonl.gz"
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)
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manifests[part] = {
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"recordings": recording_set,
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"supervisions": supervision_set,
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}
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return manifests
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def parse_utterance(
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dataset_split_path: Path,
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line: str,
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alignments: Dict[str, List[AlignmentItem]],
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) -> Optional[Tuple[Recording, SupervisionSegment]]:
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recording_id, text = line.strip().split(maxsplit=1)
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recording_id = recording_id.split('/')[-1].split('.txt')[0]
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# Create the Recording first
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audio_path = (
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dataset_split_path / f"{recording_id}.wav"
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)
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if not os.path.exists(audio_path):
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logging.warning(f"No such file: {audio_path}")
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return None
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recording = Recording.from_file(audio_path, recording_id=recording_id)
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# Then, create the corresponding supervisions
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segment = SupervisionSegment(
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id=recording_id,
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recording_id=recording_id,
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start=0.0,
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duration=recording.duration,
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channel=0,
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language="English",
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speaker=re.sub(r"-.*", r"", recording.id),
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text=text.strip(),
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alignment={"word": alignments[recording_id]}
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if recording_id in alignments
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else None,
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)
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return recording, segment
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def parse_alignments(ali_path: Pathlike) -> Dict[str, List[AlignmentItem]]:
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alignments = {}
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for line in Path(ali_path).read_text().splitlines():
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utt_id, words, timestamps = line.split()
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words = words.replace('"', "").split(",")
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timestamps = [0.0] + list(map(float, timestamps.replace('"', "").split(",")))
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alignments[utt_id] = [
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AlignmentItem(
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symbol=word, start=start, duration=round(end - start, ndigits=8)
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)
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for word, start, end in zip(words, timestamps, timestamps[1:])
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]
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return alignments
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def main(corpus_dir):
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nj = 15
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output_dir = "data/manifests"
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prepare_LJSpeech(corpus_dir, "auto", output_dir, nj)
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corpus_dir = sys.argv[1]
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main(corpus_dir)
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35
egs/librispeech/ASR/local/prepare_LJSpeech_text.py
Executable file
35
egs/librispeech/ASR/local/prepare_LJSpeech_text.py
Executable file
@ -0,0 +1,35 @@
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import os
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import sys
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import re
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metafile = sys.argv[1]
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outdir = "texts"
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save_dir = "/".join(metafile.split('/')[:-1])
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save_dir = os.path.join(save_dir, outdir)
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if not os.path.exists(save_dir):
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os.makedirs(save_dir)
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with open(metafile, 'r') as f:
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strings = f.readlines()
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for string in strings:
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# Split the string into parts
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parts = string.split("|")
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# Assign the parts to variables
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filename = parts[0]
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text1 = parts[1]
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try:
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text2 = parts[2]
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except:
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text2 = text1
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text2 = text2.upper()
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text2 = re.sub(r"[^A-Z ']", "", text2)
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# Create a new text file with the filename and write text2 to it
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filename = os.path.join(save_dir, filename)
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with open(f"{filename}.txt", "w") as file:
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file.write(text2)
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