inital commit (for testing)

README.md

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+# SpeechTaxi
+
+TODO 
+
+## Usage
+
+```python
+# pip install datasets pandas
+dataset = load_dataset(
+    "LennartKeller/SpeechTaxi",
+    name="fra",
+    split="train",
+    trust_remote_code=True
+)
+```

SpeechTaxi.py

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+from pathlib import Path
+import datasets
+from datasets import (
+    Features,
+    SplitGenerator,
+)
+import pandas as pd
+
+DATA_DIR = "data"
+TABLE_DIR = "tables"
+
+NAMES = [
+    "all",
+    "asm",
+    "bgc",
+    "bht",
+    "ckb",
+    "eng",
+    "ewe",
+    "fra",
+    "guj",
+    "ibo",
+    "kan",
+    "lin",
+    "luo",
+    "mal",
+    "mar",
+    "nag",
+    "nde",
+    "nlx",
+    "pan",
+    "peg",
+    "rus",
+    "tam",
+    "tel",
+    "tw-akuapem",
+    "tw-asante",
+    "ukr",
+    "urd",
+    "vie",
+    "yor",
+    
+]
+
+DESCRIPTION = ""
+CITATION = "TODO"
+HOMPAGE = "github.com/LennartKeller"
+
+class SpeechTaxiConfig(datasets.BuilderConfig):
+    def __init__(
+        self, name, description, citation, homepage
+    ):
+        super().__init__(
+            name=name,
+            version=datasets.Version("0.0.1"),
+            description=description,
+        )
+        self.name = name
+        self.description = description
+        self.citation = citation
+        self.homepage = homepage
+
+def get_config(name):
+    return SpeechTaxiConfig(name=name, description=DESCRIPTION, citation=CITATION, homepage=HOMPAGE)
+
+class SpeechTaxi(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [get_config(name) for name in NAMES]
+    BUILDER_CONFIG_CLASS = SpeechTaxiConfig
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=DESCRIPTION,
+            features=Features(
+                {
+                    "verse_ref": datasets.features.Value("string"),
+                    "text_en": datasets.features.Value("string"),
+                    "transcription": datasets.features.Value("string"),
+                    "transcription_romanized": datasets.features.Value("string"),
+                    "label": datasets.features.ClassLabel(
+                        names=[
+                            "Recommendation",
+                            "Faith",
+                            "Description",
+                            "Sin",
+                            "Grace",
+                            "Violence",
+                        ]
+                    ),
+                    "transription_mms-zeroshot-300m": datasets.features.Value("string"),
+                    "transription_mms": datasets.features.Value("string"),
+                    "transription_whisper-large-v3": datasets.features.Value("string"),
+                    "audio": datasets.features.Audio(sampling_rate=16_000)
+                }
+            ),
+            supervised_keys=None,
+            homepage=HOMPAGE,
+            citation=CITATION,
+        )
+    def _split_generators(self, dl_manager):
+        table_dir = dl_manager.download(TABLE_DIR)
+        # Make sure data is downloaded 
+        data_dir = dl_manager.download(DATA_DIR)
+        return [
+            SplitGenerator(name="train", gen_kwargs={"split": "train", "table_dir": table_dir, "data_dir": data_dir}),
+            SplitGenerator(name="test", gen_kwargs={"split": "test", "table_dir": table_dir, "data_dir": data_dir}),
+            SplitGenerator(name="dev", gen_kwargs={"split": "dev", "table_dir": table_dir, "data_dir": data_dir}),
+            # Add more splits as necessary
+        ]
+    
+    def _generate_examples(self, split, table_dir, data_dir):
+        table_dir = Path(table_dir)
+        data_dir = Path(data_dir)
+        idx = 0
+        if self.config.name != "all":
+            table_files = [table_dir / f"{self.config.name}.tsv"]
+        else:
+            table_files = sorted(table_dir.glob("*.tsv"))
+
+        for table_file in table_files:
+            # Load the table
+            df = pd.read_table(table_file)
+            df["language"] = table_file.stem
+            df = df.query("split == @split").reset_index(drop=True)
+
+            for _, row in df.iterrows():
+                row["audio"] = str(data_dir / row["audio"])
+                yield idx, row.to_dict()
+                idx += 1
+
+    

create_release.py

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+# This scripts generates a self-contained dataset relase that can be uploaded to the HF-Hub or distributed as an archive file
+
+# Apart from pip-installable packages this file should be self-contained for reproducibility
+import json
+import re
+import shutil
+from pathlib import Path
+import jinja2
+import pandas as pd
+import uroman as ur
+from unidecode import unidecode
+
+# Define some variables
+NAME = "SpeechTaxi"
+VERSION = "0.0.1"
+HOMEPAGE = "github.com/LennartKeller"
+CITATION = "TODO"
+DESCRIPTION = "TODO"
+SAVE_DIR = Path("/hdd/lennart/datasets")
+
+# Define some arguments and 'constants'
+# TODO think about ways to avoid having to do this
+REWRITE_PATHS = (
+    "/Volumes/SanDiskData/bible-audio-scraper-data/processed",
+    "/hdd/lennart/bible-audio-scraper-data/processed",
+)
+REWRITE_MASS_PATHS = ("/Volumes/SanDiskData/", "/hdd/lennart/")
+ALIGNED_TABLES_DIR = Path("aligned_tables")
+INCLUDE_MASS_LANGUAGES = True
+MASS_LANGUAGES = ("eng", "rus", "spa", "ron", "eus", "fra")
+SKIP_LANGUAGES = ("ron", "eus", "spa", "hau", "bdv", "nep", "ben")
+SCRIPT_TEMPLATE = Path("SpeechTaxi.py.jinja")
+
+
+def rewrite_path(p: str | Path) -> str | Path:
+    if REWRITE_PATHS:
+        if isinstance(p, str):
+            p = p.replace(*REWRITE_PATHS)
+        p = Path(str(p).replace(*REWRITE_PATHS))
+    return p
+
+
+def rewrite_mass_path(p: str | Path) -> str | Path:
+    if REWRITE_MASS_PATHS:
+        if isinstance(p, str):
+            p = p.replace(*REWRITE_MASS_PATHS)
+        p = Path(str(p).replace(*REWRITE_MASS_PATHS))
+    return p
+
+
+# Define a 'filter_instances' function that holds the selection logic
+# for selecting instances that shold end up in the final dataset
+def filter_instances(
+    df: pd.DataFrame, table_file_path: str | Path = None
+) -> pd.DataFrame:
+    df = df.copy()  # Avoid warnings
+    # Some mass tables do not have them...
+    if "alignment_score" not in df.columns:
+        df["alignment_score"] = 1.0
+    df = df.query("score == 1.0 and alignment_score >= 0.8")
+    return df
+
+
+def render_script_template(**kwargs):
+    templateLoader = jinja2.FileSystemLoader(
+        searchpath=SCRIPT_TEMPLATE.parent.as_posix()
+    )
+    templateEnv = jinja2.Environment(loader=templateLoader)
+    template = templateEnv.get_template(SCRIPT_TEMPLATE.name)
+    return template.render(**kwargs)
+
+
+# BEGIN LIB:
+# Define libs that are used to create the dataset.
+# Do not change stuff here unless you really want to.
+
+BOOK_ABBREVIATIONS = {
+    "Matthew": "MAT",
+    "Mark": "MRK",
+    "Luke": "LUK",
+    "John": "JHN",
+    "Acts": "ACT",
+    "Romans": "ROM",
+    "Corinthians": "COR",  # Can be used as "1COR" or "2COR" based on context
+    "Galatians": "GAL",
+    "Ephesians": "EPH",
+    "Philippians": "PHP",
+    "Colossians": "COL",
+    "Thess": "THS",  # Can be used as "1THS" or "2THS" based on context
+    "Timothy": "TIM",  # Can be used as "1TIM" or "2TIM" based on context
+    "Titus": "TIT",
+    "Philemon": "PHM",
+    "Hebrews": "HEB",
+    "James": "JAS",
+    "Peter": "PET",  # Can be used as "1PET" or "2PET" based on context
+    "Jude": "JUD",
+    "Revelation": "REV",
+}
+BOOK_ABBREVIATIONS_FR = {
+    "Matthieu": "MAT",
+    "Marc": "MRK",
+    "Luc": "LUC",
+    "Jean": "JHN",
+    "Actes": "ACT",
+    "Romains": "ROM",
+    "Corinthiens": "COR",  # Can be used as "1COR" or "2COR" based on context
+    "Galates": "GAL",
+    "Éphésiens": "EPH",
+    "Philippiens": "PHP",
+    "Colossiens": "COL",
+    "Thessaloniciens": "THS",  # Can be used as "1THS" or "2THS" based on context
+    "Timothée": "TIM",  # Can be used as "1TIM" or "2TIM" based on context
+    "Tite": "TIT",
+    "Philémon": "PHM",
+    "Hébreux": "HEB",
+    "Jacques": "JAC",
+    "Pierre": "PIE",  # Can be used as "1PIE" or "2PIE" based on context
+    "Jude": "JUD",
+    "Apocalypse": "APO",
+}
+
+BOOK_ABBREVIATIONS |= {
+    unidecode(k): unidecode(v) for k, v in BOOK_ABBREVIATIONS_FR.items()
+}
+
+MASS_LANGUAGE_IDS = [
+    "FRNTLSN2DA",
+    "ENGESVN1DA",
+    "RUSS76N2DA",
+    "SPNBDAN1DA",
+    "EUSEABN1DA",
+    "RONDCVN1DA",
+]
+
+
+def parse_reference_from_mass_file(filename: str | Path, language_id: str = ""):
+    filename = Path(filename).name
+    book_num, rest = filename.split("___", 1)
+    book_num = int(book_num.replace("B", ""))
+    chapter_num, rest = rest.split("_", 1)
+    chapter_num = int(chapter_num)
+    if language_id:
+        rest = rest.replace(language_id, "")
+    else:
+        # rest = "_".join(p for p in rest.split("_") if not p.endswith("DA"))
+        for language_id in MASS_LANGUAGE_IDS:
+            rest = rest.replace(language_id, "")
+    rest = rest.replace("_one_channel.wav", "")
+    book_ref, verse_ref = re.split(r"_+", rest, 1)
+    book_num = ""
+    if book_ref[0].isdigit():
+        book_num = book_ref[0]
+        book_ref = book_ref[1:]
+    try:
+        book_ref = BOOK_ABBREVIATIONS[book_ref]
+    except KeyError as e:
+        print(book_ref)
+        print(filename)
+        raise e
+    if book_num:
+        book_ref = book_num + book_ref
+    verse_num = int(verse_ref.split("_")[-1])
+    complete_ref = f"{book_ref}.{chapter_num}.{verse_num}"
+    return complete_ref
+
+
+def copy_self(dst: str | Path) -> None:
+    dst = Path(dst)
+    path_to_self = Path(__file__)
+    dst = dst / path_to_self.name
+    dst.write_text(path_to_self.read_text())
+
+
+def copy_bible_scrape_audio(file: str | Path, root: str | Path) -> Path:
+    file, root = map(Path, (file, root))
+    copied_file = (
+        root / file.parts[-4] / file.parts[-3] / file.parts[-2] / file.parts[-1]
+    )
+    copied_file.parent.mkdir(exist_ok=True, parents=True)
+    shutil.copy(file, copied_file)
+    return copied_file
+
+
+def copy_mass_audio(file: str | Path, root: str | Path) -> Path:
+    file, root = map(Path, (file, root))
+    copied_file = root / file.parts[-2] / file.parts[-1]
+    copied_file.parent.mkdir(exist_ok=True, parents=True)
+    shutil.copy(file, copied_file)
+    return copied_file
+
+
+uroman = ur.Uroman()
+
+
+def read_mass_instance(audio_file: str | Path) -> dict:
+    audio_file = Path(audio_file)
+    audio_file = rewrite_path(audio_file)
+    if not audio_file.exists():
+        raise ValueError(f"Audio file {audio_file} does not exist.")
+    verse_ref = parse_reference_from_mass_file(audio_file)
+    transcription_file = (
+        audio_file.parent / f"{audio_file.stem.removesuffix('_one_channel')}.txt"
+    )
+    transcription = transcription_file.read_text()
+    machine_generated_transcriptions = {}
+    machine_generated_transcription_files = audio_file.parent.glob(
+        f"{audio_file.stem}-*-transcript.txt"
+    )
+    for machine_generated_transcription_file in machine_generated_transcription_files:
+        # Get transcription key
+        key = machine_generated_transcription_file.stem.split("_one_channel-")[-1]
+        key = key.removesuffix("-transcript")
+        # print(audio_file, "|", machine_generated_transcription_file, "|", key)
+        machine_generated_transcriptions[f"transription_{key}"] = (
+            machine_generated_transcription_file.read_text()
+        )
+    data = {
+        "verse_ref": verse_ref,
+        "transcription": transcription,
+        "transcription_romanized": uroman.romanize_string(transcription),
+    } | machine_generated_transcriptions
+    return data
+
+
+def read_bible_scrape_instance(audio_file: str | Path) -> dict:
+    """
+    Given the file to a verse this function loads all available metadata from disk.
+    And ensure that the audio files exists...
+    """
+    audio_file = Path(audio_file)
+    audio_file = rewrite_path(audio_file)
+
+    if not audio_file.exists():
+        raise ValueError(f"Audio file {audio_file} does not exist.")
+    # 1. Load all avaiable transcriptions
+    transcription_file = audio_file.with_suffix(".json")
+    transcriptions = json.loads(transcription_file.read_text())
+    # Filter to only texts
+    try:
+        verse_ref = transcriptions.pop("verse_ref")
+        transcriptions = {
+            bible_id: content["content"] for bible_id, content in transcriptions.items()
+        }
+    except Exception as e:
+        print(transcriptions)
+        print(e)
+    # 2. Look for audio alignement scores
+    alignment_file = audio_file.parent / f"{audio_file.stem}-alignment-scores.json"
+    alignment_scores = json.loads(alignment_file.read_text())
+
+    # 3. Select the transcription with best audio alignment score
+    best_bible_id = max(alignment_scores.items(), key=lambda x: x[-1])[0]
+    transcription = transcriptions[str(best_bible_id)]
+    # 4. Load all available machine generated transcriptions
+    machine_generated_transcriptions = {}
+    machine_generated_transcription_files = audio_file.parent.glob(
+        f"{audio_file.stem}-*.txt"
+    )
+    for machine_generated_transcription_file in machine_generated_transcription_files:
+        # Get transcription key
+
+        key = (
+            machine_generated_transcription_file.stem.removeprefix(audio_file.stem)
+            .removesuffix("-transcript")
+            .strip("-")
+        )
+        # print(audio_file, "|", machine_generated_transcription_file, "|", key)
+        machine_generated_transcriptions[f"transription_{key}"] = (
+            machine_generated_transcription_file.read_text()
+        )
+
+    data = {
+        "verse_ref": verse_ref,
+        "transcription": transcription,
+        "transcription_romanized": uroman.romanize_string(transcription),
+    } | machine_generated_transcriptions
+    return data
+
+
+# Main logic
+if __name__ == "__main__":
+    dataset_root = SAVE_DIR / NAME
+    # Setup the output directory (-ies)
+    if dataset_root.exists():
+        raise ValueError(
+            f"Dataset-Root-Directory {dataset_root} already exists, please remove manually to continue!"
+        )
+    print(f"Creating dataset release in {dataset_root}")
+    dataset_root.mkdir(parents=True)
+    data_dir = dataset_root / "data"
+    data_dir.mkdir(parents=True)
+    table_dir = dataset_root / "tables"
+    table_dir.mkdir(parents=True)
+    
+    # Copy this script to dataset dir for reproducibility
+    copy_self(dataset_root)
+
+    # Create language datasets and copy the data to the dataset directory
+    # Load and filter alignment tables
+    language_tables = sorted(
+        [f for f in ALIGNED_TABLES_DIR.glob("*.tsv") if f.stem not in SKIP_LANGUAGES]
+    )
+
+    # Write code file
+    success_languages = []
+    for alignment_table in language_tables:
+        df = pd.read_table(alignment_table)
+        try:
+            df = filter_instances(df, alignment_table)
+        except Exception as e:
+            print(alignment_table)
+            raise e
+        if not len(df):
+            continue
+        success_languages.append(alignment_table)
+        final_table_data = []
+        for _, row in df.iterrows():
+            if alignment_table.stem not in MASS_LANGUAGES:
+                audio_file = rewrite_path(Path(row["audio_path"]))
+                copied_audio_file = copy_bible_scrape_audio(audio_file, root=data_dir)
+                english_text = row["en_text"]
+                data = read_bible_scrape_instance(audio_file)
+                # copied_audio = copy_file(audio_file, data_dir)
+                data = (
+                    {"verse_ref": data.pop("verse_ref")}
+                    | {"text_en": english_text}
+                    | {"split": row["split"], "label": row["label"]}
+                    | data
+                    | {"audio": copied_audio_file.relative_to(data_dir).as_posix()}
+                )
+                final_table_data.append(data)
+            elif INCLUDE_MASS_LANGUAGES:
+                audio_file = rewrite_mass_path(Path(row["audio_path"]))
+                copied_audio_file = copy_mass_audio(audio_file, root=data_dir)
+                data = read_mass_instance(audio_file)
+                data = data | {
+                    "split": row["split"],
+                    "label": row["label"],
+                    "text_en": row["en_text"],
+                    "audio": copied_audio_file.relative_to(data_dir).as_posix(),
+                }
+                final_table_data.append(data)
+        fina_table = pd.DataFrame.from_records(final_table_data)
+        # Reorder columns
+        # cols = [
+        #     "verse_ref",
+        #     "text_en",
+        #     "split",
+        #     "label",
+        #     "transcription",
+        #     "transcription_romanized",
+        #     "transription_mms-zeroshot-300m",
+        #     "transription_whisper-large-v3",
+        #     "transription_mms-1b-all",
+        #     "audio",
+        # ]
+        # fina_table = fina_table[cols]
+        fina_table.to_csv(table_dir / alignment_table.name, index=False, sep="\t")
+
+    # Now render dataset loading script and write to dataset dir
+    code_file = dataset_root / f"{NAME}.py"
+    code_file_content = render_script_template(
+        languages=[f.stem for f in success_languages],
+        version=VERSION,
+        homepage=HOMEPAGE,
+        citation=CITATION,
+    )
+    code_file.write_text(code_file_content)

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