Initial commit

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
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+*.jpg filter=lfs diff=lfs merge=lfs -text
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+*.webp filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+.venv*
+.aider*
+*.ipynb
+*.html
+.DS_Store

README.md

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+---
+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: train
+    path: data/train-*
+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: train
+    num_bytes: 38770110912.616
+    num_examples: 39173
+  download_size: 38780593424
+  dataset_size: 38770110912.616
+---
+
+# 張悦楷講《三國演義》語音數據集
+
+[主頁 Home page](https://canclid.github.io/zoengjyutgaai/)
+
+[English](#zoeng-jyut-gaai-story-telling-romance-of-the-three-kingdoms-dataset)
+
+呢個係張悦楷講《三國演義》語音數據集。[張悦楷](https://zh.wikipedia.org/wiki/%E5%BC%A0%E6%82%A6%E6%A5%B7)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`/webm`，為方便直接用作訓練數據，切分後嘅音頻都重採樣升 44100Hz 放喺 `wav/`
+- 所有源字幕 SRT 文件都存放喺 `srt/` 路經下，搭配 webm 可以直接作為帶字幕錄音賞聽。
+- `cut.py` 係切分腳本，可以將對應嘅 wav 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。
+
+## 使用
+
+要使用呢個數據集，可以喺 Python 入面直接跑：
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("CanCLID/zoengjyutgaai_saamgwokjinji")
+```
+
+如果想單純將所有 wav 文件同對應嘅轉寫複製落嚟，可以跑下面嘅命令行嚟針對克隆個`wav/`路經，避免將成個 repo 都克隆落嚟浪費空間同下載時間：
+
+```bash
+mkdir zoengjyutgaai_saamgwokjinji
+cd zoengjyutgaai_saamgwokjinji
+git init
+
+git remote add origin https://huggingface.co/datasets/CanCLID/zoengjyutgaai_saamgwokjinji
+git sparse-checkout init --cone
+
+# 指定凈係下載個別路徑
+git sparse-checkout set wav
+
+# 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：
+
+1. 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`。
+1. 用加字幕工具幫呢啲錄音加字幕，得到對應嘅 `.srt` 文件。
+1. 將啲源錄音用下面嘅命令儘可能無壓縮噉轉換成 `.wav` 格式。
+1. 運行`cut.py`，將每一集 `.wav` 按照 `.srt` 入面嘅時間點切分成一句一個 `.wav`，然後對應嘅文本寫入本數據集嘅 `xxx.csv`。
+1. 然後打開一個 IPython，逐句跑下面嘅命令，將啲數據推上 HuggingFace。
+
+```python
+from datasets import load_dataset
+from huggingface_hub import login
+dataset = load_dataset('audiofolder', data_dir='./wav')
+# 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_saamgwokjinji")
+```
+
+### 將`.webm`無損轉為`.wav`
+
+首先要安裝 [ffmpeg](https://www.ffmpeg.org/download.html)，然後運行：
+
+```bash
+ffmpeg -i "webm/001.webm" -vn -ar 44100 -c:a pcm_s16le "001.wav"
+```
+
+如果唔想指定採樣率，儘可能無損轉換，可以將上面嘅`-ar 44100`刪去。**本數據集入面所有 wav 都已經轉為 44100 採樣率**。
+
+# Zoeng Jyut Gaai story-telling _Romance of the Three Kingdoms_ voice dataset
+
+This is a speech dataset of Zoeng Jyut Gaai story-telling _Romance of the Three Kingdoms_. [Zoeng Jyut Gaai](https://zh.wikipedia.org/wiki/%E5%BC%A0%E6%82%A6%E6%A5%B7) 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 `/webm`. For the convenice of training, segmented audios are resampled into 44.1 kHz and stored in `wav/`.
+- 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 wav audios into smaller senteneces based on the srt, and generates a csv file for transcriptions.
+- `stats.py` is the script for showing stats of this datasets.
+
+## Usage
+
+To use this dataset, simply run in Python:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("CanCLID/zoengjyutgaai_saamgwokjinji")
+```
+
+To save space and downloading time and avoid clonin the entire repo, you can selectively clone only the `wav./` directory which contains all the wav files and transcriptions:
+
+```bash
+mkdir zoengjyutgaai_saamgwokjinji
+cd zoengjyutgaai_saamgwokjinji
+git init
+
+git remote add origin https://huggingface.co/datasets/CanCLID/zoengjyutgaai_saamgwokjinji
+git sparse-checkout init --cone
+
+# Tell git which directory you want
+git sparse-checkout set wav
+
+# Pull the content
+git pull origin main
+```

cut.py

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+import csv
+import os
+
+import pysrt
+from pydub import AudioSegment
+
+
+def srt_to_segments_with_metadata(srt_file, episode, audio_file, output_dir, metadata_file):
+    # Load the SRT file
+    subs = pysrt.open(srt_file)
+
+    # Load the audio file
+    audio = AudioSegment.from_wav(audio_file)
+
+    # Ensure the output directory exists
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Prepare the metadata file
+    with open(metadata_file, mode='a', newline='', encoding='utf-8') as csvfile:
+        csvwriter = csv.writer(csvfile)
+        # csvwriter.writerow(['id', 'episode_id', 'file_name', 'audio_duration',
+        #                    'transcription'])
+
+        for index, sub in enumerate(subs):
+            # Get start and end times in milliseconds
+            start_time = sub.start.ordinal
+            end_time = sub.end.ordinal
+
+            # Calculate duration in seconds
+            duration = (end_time - start_time) / 1000
+
+            # Extract the audio segment
+            segment = audio[start_time:end_time]
+
+            # Define output filenames
+            segment_filename = f"{episode}_{index + 1:03d}.wav"
+            segment_id = f"{episode}_{index + 1:03d}"
+            audio_filename = os.path.join(output_dir, segment_filename)
+
+            # Export the audio segment
+            segment.export(audio_filename, format="wav")
+
+            # Write to the metadata CSV file
+            csvwriter.writerow(
+                [segment_id, episode, f'{episode}/{segment_filename}', f'{duration:.3f}', sub.text])
+
+    print("Segmentation complete. Files saved to:", output_dir)
+    print("Metadata saved to:", metadata_file)
+
+
+if __name__ == "__main__":
+    # ffmpeg -i "webm/032.webm" -vn -ar 44100 -c:a pcm_s16le "032.wav"
+    for episode in range(32, 33):
+        episode = f'{episode:03d}'
+
+        srt_file = f'srt/{episode}.srt'
+        audio_file = f'{episode}.wav'
+        output_dir = f'./wav/{episode}'
+        srt_to_segments_with_metadata(
+            srt_file, episode, audio_file, output_dir, f'{episode}.csv')

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