metadata

language:
  - yue
license: cc0-1.0
size_categories:
  - 10K<n<100K
task_categories:
  - automatic-speech-recognition
  - text-to-speech
  - text-generation
  - feature-extraction
  - audio-to-audio
  - audio-classification
  - text-to-audio
pretty_name: c
configs:
  - config_name: default
    data_files:
      - split: saamgwokjinji
        path: data/saamgwokjinji-*
      - split: seoiwuzyun
        path: data/seoiwuzyun-*
tags:
  - cantonese
  - audio
  - art
dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: id
      dtype: string
    - name: episode_id
      dtype: int64
    - name: audio_duration
      dtype: float64
    - name: transcription
      dtype: string
  splits:
    - name: saamgwokjinji
      num_bytes: 2398591354.589
      num_examples: 39173
    - name: seoiwuzyun
      num_bytes: 1629539808
      num_examples: 24744
  download_size: 4046904982
  dataset_size: 4028131162.589

張悦楷講《三國演義》《水滸傳》語音數據集

English

Dataset Description

Homepage: 張悦楷講古語音數據集 The Zoeng Jyut Gaai Story-telling Speech Dataset
License: CC0 1.0 Universal

呢個係張悦楷講《三國演義》同《水滸傳》語音數據集。張悦楷係廣州最出名嘅講古佬 / 粵語説書藝人。佢從上世紀七十年代開始就喺廣東各個收音電台度講古，佢把聲係好多廣州人嘅共同回憶。本數據集《三國演義》係佢最知名嘅作品一。

數據集用途：

TTS（語音合成）訓練集
ASR（語音識別）訓練集或測試集
各種語言學、文學研究
直接聽嚟欣賞藝術！

TTS 效果演示：https://huggingface.co/spaces/laubonghaudoi/zoengjyutgaai_tts

説明

所有文本都根據 https://jyutping.org/blog/typo/ 同 https://jyutping.org/blog/particles/ 規範用字。
所有文本都使用全角標點，冇半角標點。
所有文本都用漢字轉寫，無阿拉伯數字無英文字母
所有音頻源都存放喺/source，為方便直接用作訓練數據，切分後嘅音頻都放喺 opus/
所有 opus 音頻皆為 48000 Hz 採樣率。
所有源字幕 SRT 文件都存放喺 srt/ 路經下，搭配 source/ 下嘅音源可以直接作為帶字幕嘅錄音直接欣賞。
cut.py 係切分腳本，將對應嘅音源根據 srt 切分成短句並生成一個文本轉寫 csv。
stats.py 係統計腳本，運行佢就會顯示成個數據集嘅各項統計數據。

下載使用

要下載使用呢個數據集，可以喺 Python 入面直接跑：

from datasets import load_dataset

ds = load_dataset("CanCLID/zoengjyutgaai")

如果想單純將 opus/ 入面所有嘢下載落嚟，可以跑下面嘅 Python 代碼，注意要安裝 pip install --upgrade huggingface_hub 先：

from huggingface_hub import snapshot_download

# 如果你淨係想下載啲字幕或者源音頻，噉就將下面嘅 `wav/*` 改成 `srt/*` 或者 `webm/*`
snapshot_download(repo_id="CanCLID/zoengjyutgaai",allow_patterns="opus/*",local_dir="./",repo_type="dataset")

如果唔想用 python，你亦都可以用命令行叫 git 針對克隆個opus/或者其他路經，避免將成個 repo 都克隆落嚟浪費空間同下載時間：

mkdir zoengjyutgaai
cd zoengjyutgaai
git init

git remote add origin https://huggingface.co/datasets/CanCLID/zoengjyutgaai
git sparse-checkout init --cone

# 指定凈係下載個別路徑
git sparse-checkout set opus

# 開始下載
git pull origin main

數據集構建流程

本數據集嘅收集、構建過程係：

從 YouTube 或者國內評書網站度下載錄音源文件，一般都係每集半個鐘長嘅 .webm 或者 .mp3。
用加字幕工具幫呢啲錄音加字幕，得到對應嘅 .srt 文件。
將啲源錄音用下面嘅命令儘可能無壓縮噉轉換成 .opus 格式。
運行cut.py，將每一集 .opus 按照 .srt 入面嘅時間點切分成一句一個 .opus，然後對應嘅文本寫入本數據集嘅 xxx.csv。
然後打開一個 IPython，逐句跑下面嘅命令，將啲數據推上 HuggingFace。

from datasets import load_dataset, DatasetDict
from huggingface_hub import login

sg = load_dataset('audiofolder', data_dir='./opus/saamgwokjinji')
sw = load_dataset('audiofolder', data_dir='./opus/seoiwuzyun')
dataset = DatasetDict({
    "saamgwokjinji": sg["train"],
    "seoiwuzyun": sw["train"],
})

# 檢查下讀入嘅數據有冇問題
dataset['train'][0]
# 準備好個 token 嚟登入
login()
# 推上 HuggingFace datasets
dataset.push_to_hub("CanCLID/zoengjyutgaai")

音頻格式轉換

首先要安裝 ffmpeg，然後運行：

# 將下載嘅音源由 webm 轉成 opus
ffmpeg -i webm/saamgwokjinji/001.webm -c:a copy source/saamgwokjinji/001.opus
# 或者轉 mp3
ffmpeg -i mp3/mouzaakdung/001.mp3 -c:a libopus -map_metadata -1 -b:a 48k -vbr on source/mouzaakdung/001.opus
# 將 opus 轉成無損 wav
ffmpeg -i source/saamgwokjinji/001.opus wav/saamgwokjinji/001.wav

如果想將所有 opus 文件全部轉換成 wav，可以直接運行to_wav.sh:

chmod +x to_wav.sh
./to_wav.sh

跟住就會生成一個 wav/ 路經，入面都係 opus/ 對應嘅音頻。注意 wav 格式非常掗埞，成個 opus/ 轉晒後會佔用至少 500GB 儲存空間，所以轉換之前記得確保有足夠空間。如果你想對音頻重採樣，亦都可以修改 to_wav.sh 入面嘅命令順便做重採樣。

The Zoeng Jyut Gaai Story-telling Speech Dataset

This is a speech dataset of Zoeng Jyut Gaai story-telling Romance of the Three Kingdoms and Water Margin. Zoeng Jyut Gaai is a famous actor, stand-up commedian and story-teller (講古佬) in 20th centry Canton. His voice remains in the memories of thousands of Cantonese people. This dataset is built from one of his most well-known story-telling piece: Romance of the Three Kingdoms.

Use case of this dataset:

TTS (Text-To-Speech) training set
ASR (Automatic Speech Recognition) training or eval set
Various linguistics / art analysis
Just listen and enjoy the art piece!

TTS demo: https://huggingface.co/spaces/laubonghaudoi/zoengjyutgaai_tts

Introduction

All transcriptions follow the prescribed orthography detailed in https://jyutping.org/blog/typo/ and https://jyutping.org/blog/particles/
All transcriptions use full-width punctuations, no half-width punctuations is used.
All transcriptions are in Chinese characters, no Arabic numbers or Latin letters.
All source audio are stored in source/. For the convenice of training, segmented audios are stored in opus/.
All opus audio are in 48000 Hz sampling rate.
All source subtitle SRT files are stored in srt/. Use them with the webm files to enjoy subtitled storytelling pieces.
cut.py is the script for cutting opus audios into senteneces based on the srt, and generates a csv file for transcriptions.
stats.py is the script for getting stats of this dataset.

Usage

To use this dataset, simply run in Python:

from datasets import load_dataset

ds = load_dataset("CanCLID/zoengjyutgaai")

If you only want to download a certain directory to save time and space from cloning the entire repo, run the Python codes below. Make sure you have pip install --upgrade huggingface_hub first：

from huggingface_hub import snapshot_download

# If you only want to download the source audio or the subtitles, change the `wav/*` below into `srt/*` or `webm/*`
snapshot_download(repo_id="CanCLID/zoengjyutgaai",allow_patterns="opus/*",local_dir="./",repo_type="dataset")

If you don't want to run python codes and want to do this via command lines, you can selectively clone only a directory of the repo:

mkdir zoengjyutgaai
cd zoengjyutgaai
git init

git remote add origin https://huggingface.co/datasets/CanCLID/zoengjyutgaai
git sparse-checkout init --cone

# Tell git which directory you want
git sparse-checkout set opus

# Pull the content
git pull origin main

Audio format conversion

Install ffmpeg first, then run：

# convert all webm into opus
ffmpeg -i webm/saamgwokjinji/001.webm -c:a copy source/saamgwokjinji/001.opus
# or into mp3
ffmpeg -i mp3/mouzaakdung/001.mp3 -c:a libopus -map_metadata -1 -b:a 48k -vbr on source/mouzaakdung/001.opus
# convert all opus into loseless wav
ffmpeg -i source/saamgwokjinji/001.opus wav/saamgwokjinji/001.wav

If you want to convert all opus to wav, run to_wav.sh:

chmod +x to_wav.sh
./to_wav.sh

It will generate a wav/ path which contains all audios converted from opus/. Be aware the wav format is very space-consuming. A full conversion will take up at least 500GB space so make sure you have enough storage. If you want to resample the audio, modify the line within to_wav.sh to resample the audio while doing the conversion.