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# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""LibriSpeech-PC dataset module refered from LibriSpeech dataset module."""
import os
import datasets
import json
_CITATION = {
"librispeech":
"""\
@inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--5210},
year={2015},
organization={IEEE}
}""",
"librispeech_pc":
"""\
@article{meister2023librispeechpc,
title={LibriSpeech-PC: Benchmark for Evaluation of Punctuation and Capitalization Capabilities of end-to-end ASR Models},
author={A. Meister and M. Novikov and N. Karpov and E. Bakhturina and V. Lavrukhin and B. Ginsburg},
journal={arXiv preprint arXiv:2310.02943},
year={2023},
}
"""
}
_DESCRIPTION = """\
Merge Librispeech audio files with punctuation and captalization restored transcripts from LibriSpeech-PC.
I refered to the original LibriSpeech dataset module script from HuggingFace Datasets (https://huggingface.co/datasets/openslr/librispeech_asr).
If you already have downloaded the LibriSpeech dataset via `load_dataset('openslr/librispeech_asr')`, the script will use the extracted audio files from the local directory and not download them twice. (only tested in my local environment though)
"""
_URL = "http://www.openslr.org/12"
_DL_URL = "http://www.openslr.org/resources/12/"
_URL_PC = "https://www.openslr.org/145"
_DL_URL_PC = "https://www.openslr.org/resources/145/"
_DL_URLS = {
"clean": {
"dev": _DL_URL + "dev-clean.tar.gz",
"test": _DL_URL + "test-clean.tar.gz",
"train.100": _DL_URL + "train-clean-100.tar.gz",
"train.360": _DL_URL + "train-clean-360.tar.gz",
"transcript_pc": _DL_URL_PC + "manifests.tar.gz",
},
"other": {
"test": _DL_URL + "test-other.tar.gz",
"dev": _DL_URL + "dev-other.tar.gz",
"train.500": _DL_URL + "train-other-500.tar.gz",
"transcript_pc": _DL_URL_PC + "manifests.tar.gz",
},
"all": {
"dev.clean": _DL_URL + "dev-clean.tar.gz",
"dev.other": _DL_URL + "dev-other.tar.gz",
"test.clean": _DL_URL + "test-clean.tar.gz",
"test.other": _DL_URL + "test-other.tar.gz",
"train.clean.100": _DL_URL + "train-clean-100.tar.gz",
"train.clean.360": _DL_URL + "train-clean-360.tar.gz",
"train.other.500": _DL_URL + "train-other-500.tar.gz",
"transcript_pc": _DL_URL_PC + "manifests.tar.gz",
},
}
class LibrispeechASRConfig(datasets.BuilderConfig):
"""BuilderConfig for LibriSpeechASR."""
def __init__(self, **kwargs):
"""
Args:
data_dir: `string`, the path to the folder containing the files in the
downloaded .tar
citation: `string`, citation for the data set
url: `string`, url for information about the data set
**kwargs: keyword arguments forwarded to super.
"""
super(LibrispeechASRConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs)
class LibrispeechASR(datasets.GeneratorBasedBuilder):
"""Librispeech dataset."""
DEFAULT_WRITER_BATCH_SIZE = 256
DEFAULT_CONFIG_NAME = "all"
BUILDER_CONFIGS = [
LibrispeechASRConfig(name="clean", description="'Clean' speech."),
LibrispeechASRConfig(name="other", description="'Other', more challenging, speech."),
LibrispeechASRConfig(name="all", description="Combined clean and other dataset."),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"text": datasets.Value("string"),
"text_raw": datasets.Value("string"),
"text_normalized": datasets.Value("string"),
"speaker_id": datasets.Value("int64"),
"chapter_id": datasets.Value("int64"),
"id": datasets.Value("string"),
"duration": datasets.Value("float"),
}
),
supervised_keys=("file", "text"),
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
# (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
# print(local_extracted_archive)
# print(list(dl_manager.iter_archive(archive_path["transcript_pc"])))
transcript_pc_dir = local_extracted_archive.get("transcript_pc")
if self.config.name == "clean":
train_splits = [
datasets.SplitGenerator(
name="train.100",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("train.100"),
"files": dl_manager.iter_archive(archive_path["train.100"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "train-clean-100.json"),
},
),
datasets.SplitGenerator(
name="train.360",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("train.360"),
"files": dl_manager.iter_archive(archive_path["train.360"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "train-clean-360.json"),
},
),
]
dev_splits = [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("dev"),
"files": dl_manager.iter_archive(archive_path["dev"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "dev-clean.json"),
},
)
]
test_splits = [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("test"),
"files": dl_manager.iter_archive(archive_path["test"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "test-clean.json"),
},
)
]
elif self.config.name == "other":
train_splits = [
datasets.SplitGenerator(
name="train.500",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("train.500"),
"files": dl_manager.iter_archive(archive_path["train.500"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "train-other-500.json"),
},
)
]
dev_splits = [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("dev"),
"files": dl_manager.iter_archive(archive_path["dev"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "dev-other.json"),
},
)
]
test_splits = [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("test"),
"files": dl_manager.iter_archive(archive_path["test"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "test-other.json"),
},
)
]
elif self.config.name == "all":
train_splits = [
datasets.SplitGenerator(
name="train.clean.100",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("train.clean.100"),
"files": dl_manager.iter_archive(archive_path["train.clean.100"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "train-clean-100.json"),
},
),
datasets.SplitGenerator(
name="train.clean.360",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("train.clean.360"),
"files": dl_manager.iter_archive(archive_path["train.clean.360"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "train-clean-360.json"),
},
),
datasets.SplitGenerator(
name="train.other.500",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("train.other.500"),
"files": dl_manager.iter_archive(archive_path["train.other.500"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "train-other-500.json"),
},
),
]
dev_splits = [
datasets.SplitGenerator(
name="validation.clean",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("dev.clean"),
"files": dl_manager.iter_archive(archive_path["dev.clean"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "dev-clean.json"),
},
),
datasets.SplitGenerator(
name="validation.other",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("dev.other"),
"files": dl_manager.iter_archive(archive_path["dev.other"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "dev-other.json"),
},
),
]
test_splits = [
datasets.SplitGenerator(
name="test.clean",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("test.clean"),
"files": dl_manager.iter_archive(archive_path["test.clean"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "test-clean.json"),
},
),
datasets.SplitGenerator(
name="test.other",
gen_kwargs={
"local_extracted_archive": local_extracted_archive.get("test.other"),
"files": dl_manager.iter_archive(archive_path["test.other"]),
"transcript_pc_fname": os.path.join(transcript_pc_dir, "test-other.json"),
},
),
]
return train_splits + dev_splits + test_splits
def _generate_examples(self, files, local_extracted_archive, transcript_pc_fname): # original
"""Generate examples from a LibriSpeech archive_path."""
key, unseen = 0, 0
audio_data = {}
transcripts = []
# Load transcripts from LibriSpeech-PC
transcripts_pc = dict()
with open(transcript_pc_fname, mode='r') as f:
data = (f.read().splitlines())
data = [json.loads(d) for d in data]
for d in data:
_id = d['audio_filepath'].split("/")[-1][: -len(".flac")]
del d['audio_filepath']
transcripts_pc.update(
{_id: d} # keys in d : duration, text, text_raw
)
os.makedirs("./unexisting_transcripts_id", exist_ok=True)
try:
os.remove(f"./unexisting_transcripts_id/{os.path.basename(transcript_pc_fname)[:-5]}.txt")
except FileNotFoundError:
pass
for path, f in files:
if path.endswith(".flac"):
id_ = path.split("/")[-1][: -len(".flac")]
audio_data[id_] = f.read()
elif path.endswith(".trans.txt"):
for line in f:
if line:
line = line.decode("utf-8").strip()
id_, transcript = line.split(" ", 1)
audio_file = f"{id_}.flac"
speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]]
audio_file = (
os.path.join(local_extracted_archive, audio_file)
if local_extracted_archive
else audio_file
)
transcripts.append(
{
"id": id_,
"speaker_id": speaker_id,
"chapter_id": chapter_id,
"file": audio_file,
"text_normalized": transcript,
}
)
if audio_data and len(audio_data) == len(transcripts):
for transcript in transcripts:
audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]}
transcript_pc = transcripts_pc.pop(transcript["id"], {})
if transcript_pc:
yield key, {"audio": audio, **transcript, **transcript_pc}
key += 1
else:
with open(f"./unexisting_transcripts_id/{os.path.basename(transcript_pc_fname)[:-5]}.txt", mode='a') as log:
log.write(f"{transcript['id']}\n")
unseen += 1
audio_data = {}
transcripts = []
print(f"{unseen} transcripts are dropped in LibriSpeech-PC dataset {os.path.basename(transcript_pc_fname)[:-5]} compared to LibriSpeech dataset.")
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