First version of KsponSpeech

README.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- kr
+language_creators:
+- crowdsourced
+license: []
+multilinguality:
+- monolingual
+pretty_name: KsponSpeech
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+tags: []
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [AIHub](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=123)
+- **Repository:**
+- **Paper:** [KsponSpeech](https://www.mdpi.com/2076-3417/10/19/6936)
+- **Leaderboard:**
+- **Point of Contact:** 
+
+### Dataset Summary
+
+This corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. This paper also presents the baseline performance of an end-to-end speech recognition model trained with KsponSpeech. In addition, we investigated the performance of standard end-to-end architectures and the number of sub-word units suitable for Korean. We investigated issues that should be considered in spontaneous speech recognition in Korean. KsponSpeech is publicly available on an open data hub site of the Korea government.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Korean
+
+## Dataset Structure
+
+### Data Instances
+
+```json
+{
+  'id': 'KsponSpeech_E00001',
+  'audio': {'path': None,
+  'array': array([0.0010376 , 0.00085449, 0.00097656, ..., 0.00250244, 0.0022583 ,
+          0.00253296]),
+  'sampling_rate': 16000},
+  'text': '어 일단은 억지로 과장해서 이렇게 하는 것보다 진실된 마음으로 이걸 어떻게 전달할 수 있을까 공감을 시킬 수 있을까 해서 좀'
+}
+```
+
+### Data Fields
+
+- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
+- text: the transcription of the audio file.
+- id: unique id of the data sample.
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+@Article{app10196936,
+AUTHOR = {Bang, Jeong-Uk and Yun, Seung and Kim, Seung-Hi and Choi, Mu-Yeol and Lee, Min-Kyu and Kim, Yeo-Jeong and Kim, Dong-Hyun and Park, Jun and Lee, Young-Jik and Kim, Sang-Hun},
+TITLE = {KsponSpeech: Korean Spontaneous Speech Corpus for Automatic Speech Recognition},
+JOURNAL = {Applied Sciences},
+VOLUME = {10},
+YEAR = {2020},
+NUMBER = {19},
+ARTICLE-NUMBER = {6936},
+URL = {https://www.mdpi.com/2076-3417/10/19/6936},
+ISSN = {2076-3417},
+ABSTRACT = {This paper introduces a large-scale spontaneous speech corpus of Korean, named KsponSpeech. This corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. This paper also presents the baseline performance of an end-to-end speech recognition model trained with KsponSpeech. In addition, we investigated the performance of standard end-to-end architectures and the number of sub-word units suitable for Korean. We investigated issues that should be considered in spontaneous speech recognition in Korean. KsponSpeech is publicly available on an open data hub site of the Korea government.},
+DOI = {10.3390/app10196936}
+}

ksponspeech.py

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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
+"""KsponSpeech: Korean Spontaneous Speech Corpus for Automatic Speech Recognition."""
+
+
+import os
+import re
+from pathlib import Path
+
+import datasets
+import numpy as np
+import librosa
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_CITATION = """\
+@Article{app10196936,
+AUTHOR = {Bang, Jeong-Uk and Yun, Seung and Kim, Seung-Hi and Choi, Mu-Yeol and Lee, Min-Kyu and Kim, Yeo-Jeong and Kim, Dong-Hyun and Park, Jun and Lee, Young-Jik and Kim, Sang-Hun},
+TITLE = {KsponSpeech: Korean Spontaneous Speech Corpus for Automatic Speech Recognition},
+JOURNAL = {Applied Sciences},
+VOLUME = {10},
+YEAR = {2020},
+NUMBER = {19},
+ARTICLE-NUMBER = {6936},
+URL = {https://www.mdpi.com/2076-3417/10/19/6936},
+ISSN = {2076-3417},
+DOI = {10.3390/app10196936}
+}
+"""
+
+_DESCRIPTION = """\
+This paper introduces a large-scale spontaneous speech corpus of Korean, named KsponSpeech. This corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. This paper also presents the baseline performance of an end-to-end speech recognition model trained with KsponSpeech. In addition, we investigated the performance of standard end-to-end architectures and the number of sub-word units suitable for Korean. We investigated issues that should be considered in spontaneous speech recognition in Korean. KsponSpeech is publicly available on an open data hub site of the Korea government.
+
+More info on KsponSpeech dataset can be understood from the webpage which can be found here:
+https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=123
+"""
+
+_HOMEPAGE = "https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=123"
+
+_ROOT_DIRNAME = "ksponspeech"
+_SCRIPT_DIRNAME = "KsponSpeech_scripts"
+
+_SCRIPT_SPLITS = {
+  "train": "train.trn",
+  "dev": "dev.trn",
+  "eval_clean": "eval_clean.trn",
+  "eval_other": "eval_other.trn"
+}
+
+class KsponSpeechConfig(datasets.BuilderConfig):
+  """BuilderConfig for KsponSpeech."""
+
+  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.
+    """
+    # version history
+    # 0.1.0: First release
+    super(KsponSpeechConfig, self).__init__(version=datasets.Version("0.1.0", ""), **kwargs)
+      
+
+
+class KsponSpeech(datasets.GeneratorBasedBuilder):
+  """KsponSpeech dataset."""
+
+  @property
+  def manual_download_instructions(self):
+    return (
+      "To use KsponSpeech you have to download it manually. "
+      "Please create an account and download the dataset from "
+      "https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&dataSetSn=123 \n"
+      "Then load the dataset with: "
+      "`datasets.load_dataset('ksponspeech', data_dir='path/to/folder/folder_name')`"
+    )
+
+  def _info(self):
+    return datasets.DatasetInfo(
+      description=_DESCRIPTION,
+      features=datasets.Features(
+        {
+          "id": datasets.Value("string"),
+          "audio": datasets.Audio(sampling_rate=16_000),
+          "text": datasets.Value("string")
+        }
+      ),
+      supervised_keys=("file", "text"),
+      homepage=_HOMEPAGE,
+      citation=_CITATION,
+      task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
+    )
+
+  def _split_generators(self, dl_manager):
+    # Step 1. Extract all zip files
+    # Step 2. Get scripts
+    # Step 3. Generate samples
+    data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+    data_dir = os.path.join(data_dir, _ROOT_DIRNAME)
+    if not os.path.exists(data_dir):
+      raise FileNotFoundError(
+        f"{data_dir} does not exist. Make sure you insert a manual dir via"
+        "`datasets.load_dataset('ksponspeech', data_dir=...)`"
+        "that includes files. Manual download instructions:"
+        f"{self.manual_download_instructions}"
+      )
+    archive_paths = {}
+    for fname in os.listdir(data_dir):
+      if not '.lock' in fname:
+        fname_no_ext = os.path.splitext(fname)[0]
+        archive_paths[fname_no_ext] = os.path.join(data_dir, fname)
+    local_extracted_archives = dl_manager.extract(archive_paths)
+    script_archive_path = local_extracted_archives[_SCRIPT_DIRNAME]
+    return [
+      datasets.SplitGenerator(
+        name=datasets.Split.TRAIN, 
+        gen_kwargs={
+          "script": os.path.join(script_archive_path, _SCRIPT_SPLITS['train']),
+          "local_extracted_archives": local_extracted_archives
+        }
+      ),
+      datasets.SplitGenerator(
+        name=datasets.Split.VALIDATION, 
+        gen_kwargs={
+          "script": os.path.join(script_archive_path, _SCRIPT_SPLITS['dev']),
+          "local_extracted_archives": local_extracted_archives
+        }
+      ),
+      datasets.SplitGenerator(
+        name="eval.clean", 
+        gen_kwargs={
+          "script": os.path.join(script_archive_path, _SCRIPT_SPLITS['eval_clean']),
+          "local_extracted_archives": local_extracted_archives
+        }
+      ),
+      datasets.SplitGenerator(
+        name="eval.other", 
+        gen_kwargs={
+          "script": os.path.join(script_archive_path, _SCRIPT_SPLITS['eval_other']),
+          "local_extracted_archives": local_extracted_archives
+        }
+      ),
+    ]
+
+  def _generate_examples(self, script, local_extracted_archives):
+    """Generate examples from KsponSpeech archive_path based on the test/train trn information."""
+    # Iterating the contents of the data to extract the relevant information
+    with open(script) as f:
+      for key, line in enumerate(f):
+        audio_path, text = line.split(' :: ')
+        audio_subdir = audio_path.split('/')[0]
+        if os.path.basename(audio_path)[12:18] in PERCENT_FILES.keys():
+            replace = PERCENT_FILES[os.path.basename(audio_path)[12:18]]
+        else:
+            replace = None
+        text = sentence_filter(text, replace=replace).strip()
+        if 'KsponSpeech_eval/' in audio_path:
+          audio_path = audio_path.replace('KsponSpeech_eval/','')
+        audio_path = os.path.join(local_extracted_archives[audio_subdir], audio_path)
+        if os.path.exists(audio_path):
+          with open(audio_path, 'rb') as audio_file:
+            audio_data = audio_file.read() 
+            if len(audio_data) % 2 != 0:
+              # Remove unknown additional bytes in KspoonSpeech_eval
+              audio_data = audio_data[:-1]
+          audio = {
+            "path": audio_path,
+            "bytes": audio_data,
+            "sampling_rate": 16_000
+          }
+          yield key, {
+            "id": os.path.splitext(os.path.basename(audio_path))[0],
+            "audio": audio,
+            "text": text
+          }
+
+# ------------------------------------------------------------------------
+# following codes are copied from https://github.com/sooftware/ksponspeech
+
+PERCENT_FILES = {
+    '087797': '퍼센트',
+    '215401': '퍼센트',
+    '284574': '퍼센트',
+    '397184': '퍼센트',
+    '501006': '프로',
+    '502173': '프로',
+    '542363': '프로',
+    '581483': '퍼센트'
+}
+
+def bracket_filter(sentence, mode='phonetic'):
+  new_sentence = str()
+
+  if mode == 'phonetic':
+    flag = False
+
+    for ch in sentence:
+      if ch == '(' and flag is False:
+        flag = True
+        continue
+      if ch == '(' and flag is True:
+        flag = False
+        continue
+      if ch != ')' and flag is False:
+        new_sentence += ch
+
+  elif mode == 'spelling':
+    flag = True
+
+    for ch in sentence:
+      if ch == '(':
+        continue
+      if ch == ')':
+        if flag is True:
+          flag = False
+          continue
+        else:
+          flag = True
+          continue
+      if ch != ')' and flag is True:
+        new_sentence += ch
+
+  else:
+    raise ValueError("Unsupported mode : {0}".format(mode))
+
+  return new_sentence
+
+
+def special_filter(sentence, mode='phonetic', replace=None):
+  SENTENCE_MARK = ['?', '!', '.']
+  NOISE = ['o', 'n', 'u', 'b', 'l']
+  EXCEPT = ['/', '+', '*', '-', '@', '$', '^', '&', '[', ']', '=', ':', ';', ',']
+
+  new_sentence = str()
+  for idx, ch in enumerate(sentence):
+    if ch not in SENTENCE_MARK:
+      if idx + 1 < len(sentence) and ch in NOISE and sentence[idx + 1] == '/':
+        continue
+
+    if ch == '#':
+      new_sentence += '샾'
+
+    elif ch == '%':
+      if mode == 'phonetic':
+        new_sentence += replace
+      elif mode == 'spelling':
+        new_sentence += '%'
+
+    elif ch not in EXCEPT:
+      new_sentence += ch
+
+  pattern = re.compile(r'\s\s+')
+  new_sentence = re.sub(pattern, ' ', new_sentence.strip())
+  return new_sentence
+
+
+def sentence_filter(raw_sentence, mode='phonetic', replace=None):
+  return special_filter(bracket_filter(raw_sentence, mode), mode, replace)
+
+