Update README.md

README.md

@@ -23,3 +23,195 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+# Dataset Card for code-switching yodas
+
+<!-- Provide a quick summary of the dataset. -->
+
+This dataset is derived from espnet/yodas, more details can be found here: https://huggingface.co/datasets/espnet/yodas
+
+This is a subset of the zh000 subset of espnet/yodas dataset, which focuses on videos with Mandarin-English code-switching phenomenon.
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+- **Language(s):** Chinese, English
+- **License:** CC-BY-3.0
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** https://huggingface.co/datasets/espnet/yodas
+
+## Dataset Creation
+
+#### Data Collection and Processing
+
+<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+
+1. Read the text content of clips of espnet/yodas
+```python
+import glob
+import re
+import pandas as pd
+from pathlib import Path
+from tqdm.auto import tqdm
+from collections import defaultdict
+from dataclasses import dataclass, asdict
+
+@dataclass
+class Video:
+    name: str = ""
+    shard: str = ""
+    duration: float = 0
+    content: str = ""
+
+data = defaultdict(Video)
+trange = tqdm(glob.glob("yodas/data/zh000/text/*.txt"))
+for file in trange:
+    shard = Path(file).stem
+    with open(file, "r", encoding="utf8") as f:
+        for m in re.finditer(r"(.{11})-\d{5}-\d{8}-(\d{8})\s+(.*)", f.read()):
+            name = m.group(1)
+            assert data[name].shard in ["", shard]
+            data[name].shard = shard
+            data[name].name = name
+            data[name].duration = int(m.group(2)) / 100
+            data[name].content += " " + m.group(3)
+    trange.set_postfix(vids=len(data))
+
+data_df = pd.DataFrame(map(asdict, data.values()))
+```
+2. Retain videos with chinese symbols
+```python
+import re
+cjk_pattern = re.compile(
+    # puncs \uff00-\uffef \u3000-\u303f
+    r"[\u3400-\u4db5\u4e00-\u9fa5\u9fa6-\u9fbb\uf900-\ufa2d\ufa30-\ufa6a\ufa70-\ufad9\u2e80-\u2eff\u31c0-\u31ef\u2f00-\u2fdf\u2ff0-\u2fff\u3100-\u312f\u31a0-\u31bf\ufe10-\ufe1f\ufe30-\ufe4f\u2600-\u26ff\u2700-\u27bf\u3200-\u32ff\u3300-\u33ff]"
+)
+chinese_df = data_df[data_df['content'].apply(lambda x: cjk_pattern.search(x) is not None)]
+```
+3. Filter out videos with Pingyin's
+```python
+pinyin_pattern = re.compile(
+    r'[üÜāáǎàōóǒòēéěèīíǐìūúǔùǖǘǚǜ]'
+)
+chinese_pin_df = chinese_df[chinese_df['content'].apply(lambda x: pinyin_pattern.search(x) is None)]
+```
+4. Retain videos with latin scripts
+```python
+az_pattern = re.compile(
+    r"[a-zA-Z]+"
+)
+mixed_df = chinese_pin_df[chinese_pin_df['content'].apply(lambda x: az_pattern.search(x) is not None)]
+```
+5. Retain videos with punctuations
+```python
+punc_pattern = re.compile(
+    r'[！？。，、·.,?!]'
+)
+mixed_punc_df = mixed_df[mixed_df['content'].apply(lambda x: punc_pattern.search(x) is not None)]
+```
+6. Sort by increasing proportion of chinese characters
+```python
+def func(x):
+    return x.apply(lambda z: len(cjk_pattern.findall(z)) / len(z))
+mixed_punc_df = mixed_punc_df.sort_values(by='content', key=func)
+```
+> This gives around 1000 videos left.
+
+7. Save to csv to for manual inspection
+```python
+mixed_punc_df.to_csv('sanity.csv')
+```
+8. Manually inspect 0-500
+  - NwRTR8mY-7A: mostly english
+  - ASL3yEYC1IE, etc.: contains English translation for each line
+  - Recurring creators whose content is not good code-switching: "天天開心","日向蓝子","笑花兒","关于麻将的职人","大濕：","朋友sisi","please my hero","金玲老師"
+  - Manually pick exceptions to previous rule to add to accepted list
+  - Recurring creators whose content is good code-switching: "我是小夫","久德電子","GL_TECH"
+  - Most videos about: "U.S. stock market", "tech reviews" are accepted.
+
+9. Quickly skim through 501-1000 (only 10 were picked)
+
+> A total of 176 videos were picked in step 8 & 9
+
+10. Extract selected video clips' audio
+```python
+from tqdm.auto import tqdm
+from pathlib import path
+import tarfile
+
+with open("codeswitch.txt", "r") as f: # list of 176 picked video_ids
+    codeswitch = set(map(str.strip, f.readlines()))
+code_switch_data = data_df[data_df['name'].apply(lambda x: x in codeswitch)]
+
+shard_names = {}
+for name, shard in zip(
+    code_switch_data['name'].tolist(),
+    code_switch_data['shard'].tolist()
+):
+    if shard not in shard_names:
+        shard_names[shard] = set()
+    shard_names[shard].add(name)
+
+def extract_wav_files(shard, output_dir):
+    # Create the output directory if it doesn't exist
+    tar_file_path = f"yodas/data/zh000/audio/{shard}.tar.gz"
+    names = shard_names[shard]
+
+    # Open the tar.gz file
+    with tarfile.open(tar_file_path, 'r:gz') as tar:
+        # Iterate through the contents of the tar file
+        for member in tar.getmembers():
+            # Check if the member is a WAV file
+            video_id = re.search(r"(.{11})-\d{5}-\d{8}-\d{8}", member.name)
+            if video_id and video_id.group(1) in names:
+                # Extract the WAV file contents into the output directory
+                output_path = Path(output_dir, Path(member.name).name)
+                with open(output_path, 'wb') as output_file:
+                    output_file.write(tar.extractfile(member).read())
+
+output_dir = "./code_switch_yodas"
+Path(output_dir).mkdir(exist_ok=True, parents=True)
+for shard in tqdm(shard_names):
+    extract_wav_files(shard, output_dir)
+```
+11. Publish the subset
+```python
+import datasets
+from datasets import Dataset
+
+audio_dataset = Dataset.from_dict({
+    "audio": [
+        f"{output_dir}/{clip_id}.wav"
+        for clip_id in clip_ids
+    ],
+    "text": texts,
+    "id": clip_ids,
+    "session_id": [x[:11] for x in clip_ids]
+})
+audio_dataset = audio_dataset.cast_column("audio", datasets.features.Audio(sampling_rate=16000))
+audio_dataset = audio_dataset.sort("id")
+audio_dataset.push_to_hub(
+    "georgechang8/code_switch_yodas_zh",
+    commit_message="Initial commit",
+    embed_external_files=True
+)
+```
+
+## Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+1. The filtering & hand-picking process might left out useful videos.
+2. The transcriptions is not processed in any way, so might need further cleansing.
+
+## Dataset Card Contact
+
+Original dataset: https://huggingface.co/datasets/espnet/yodas
+CS processing: Chih-Chiang Chang (cc.chang0828@gmail.com)