convert_ORTOFON_ORAL13.py

#author: Martin Fajčík

import gzip
import os
import re
from typing import Dict
from tqdm import tqdm

FILE_PATH = ".data/ORTOFONv1/ortofon_v1_vert.gz"
with gzip.open(FILE_PATH, "rt") as f:
    data = f.read()

def process_vert_format_ortofon(vert_content: str) -> Dict[str, str]:
    # Pattern to match document boundaries and extract metadata
    doc_pattern = re.compile(r'<doc[^>]*>.*?</doc>', re.DOTALL)
    metadata_pattern = re.compile(
        r'<doc id="([^"]*)" year="([^"]*)" month="([^"]*)" location="([^"]*)" situation="([^"]*)" speakers="([^"]*)" genders="([^"]*)" generations="([^"]*)" relationship="([^"]*)"[^>]*>')

    # Pattern to match speaker turns
    sp_pattern = re.compile(r'<sp[^>]*nickname="([^"]*)"[^>]*>(.*?)</sp>', re.DOTALL)

    # Pattern to match pw tags
    pw_pattern = re.compile(r'<pw>\n(.*?)</pw>\n', re.DOTALL)

    # Pattern to remove speaker suffix
    remove_speaker_suffix = re.compile(r'_[0-9]+$')

    # Pattern to remove whitespace before punctuation
    ws_before_punct = re.compile(r'\s+([.!?])')

    # Find all documents
    documents = re.findall(doc_pattern, vert_content)
    processed_documents = {}

    for doc in tqdm(documents):
        # Extract metadata
        metadata_match = re.search(metadata_pattern, doc)
        if metadata_match:
            doc_id = metadata_match.group(1)
            location = metadata_match.group(4)
            situation = metadata_match.group(5)
            speakers = metadata_match.group(6)
            genders = metadata_match.group(7)
            generations = metadata_match.group(8)
            relationship = metadata_match.group(9)
            metadata_str = (f"Lokalita: {location}, Situace: {situation}, "
                            f"Počet mluvčích: {speakers}, Pohlaví: {genders}, "
                            f"Generace: {generations}, Vztah: {relationship}")
        else:
            raise ValueError("Metadata not found in document")

        # Initialize an empty list to hold processed document text
        processed_document = [metadata_str]

        # Find all speaker turns within the document
        for sp_match in re.findall(sp_pattern, doc):
            speaker_id = sp_match[0]
            # sometimes speaker_id ends with _1, _2, _89, etc. Remove it
            speaker_id = re.sub(remove_speaker_suffix, '', speaker_id)

            # if speaker is Y, rename him as Jiný zvuk
            if speaker_id == "Y":
                speaker_id = "Zvuk"


            sp_content = sp_match[1]

            segs = re.findall(pw_pattern, sp_content)
            if segs == []:
                segs = [sp_content]
            # remove tags from each line, and join text
            tokens = [line.split("\t")[0].strip() for seg in segs for line in seg.split("\n") if line != ""]
            speaker_text = " ".join(tokens)

            # - sometimes lines in ortofon are containing three dots only, such as [mluvčí: Miroslava] ... // REMOVE THESE LINES
            if speaker_text.strip() == "...":
                continue
            # - sometimes lines in ortofon contain @ only, e.g., [mluvčí: Radka] @ // REMOVE THESE LINES
            if speaker_text.strip() == "@":
                continue

            # remove whitespace before ., !, ?
            speaker_text = re.sub(ws_before_punct, r'\1', speaker_text)

            # Format the speaker turn and add to the processed document list
            processed_document.append(f"[mluvčí: {speaker_id}] {speaker_text}")


        # Join all speaker turns into a single string for the document
        final_text = '\n'.join(processed_document)
        processed_documents[doc_id] = final_text

    return processed_documents


ortofon_data = process_vert_format_ortofon(data)
del data


FILE_PATH = ".data/ORAL2013/oral2013_vert.gz"
with gzip.open(FILE_PATH, "rt") as f:
    data = f.read()

def process_vert_format_oral(vert_content: str) -> Dict[str, str]:
    # Pattern to match document boundaries and extract metadata
    doc_pattern = re.compile(r'<doc[^>]*>.*?</doc>', re.DOTALL)
    metadata_pattern = re.compile(
        r'<doc id="([^"]*)" temp="([^"]*)" pocet="([^"]*)" vztah="([^"]*)" situace="([^"]*)" promluva="([^"]*)"[^>]*>'
    )
    # Pattern to match speaker turns
    sp_pattern = re.compile(r'<sp[^>]*num="([^"]*)"[^>]*>(.*?)</sp>', re.DOTALL)

    # Pattern to match seg tags
    seg_pattern = re.compile(r'<seg start="[^"]*" end="[^"]*">(.*?)</seg>\n', re.DOTALL)

    # Pattern to remove whitespace before punctuation
    ws_before_punct = re.compile(r'\s+([.!?])')

    # Find all documents
    documents = re.findall(doc_pattern, vert_content)
    processed_documents = {}

    for doc in tqdm(documents):
        # Extract metadata
        metadata_match = re.search(metadata_pattern, doc)
        if metadata_match:
            doc_id = metadata_match.group(1)
            situation = metadata_match.group(5)
            speakers = metadata_match.group(3)
            relationship = metadata_match.group(4)

            metadata_str = (f"Situace: {situation}, "
                            f"Počet mluvčích: {speakers}, "
                            f"Vztah: {relationship}")
        else:
            raise ValueError("Metadata not found in document")

        # Initialize an empty list to hold processed document text
        processed_document = [metadata_str]

        # Find all speaker turns within the document
        for sp_match in re.findall(sp_pattern, doc):
            speaker_id = sp_match[0]

            # if speaker is Y, rename him as Jiný zvuk
            if speaker_id == "Y":
                speaker_id = "Zvuk"

            sp_content = sp_match[1]
            # remove symbols ---, ...:,
            sp_content = sp_content.replace("---", "")
            sp_content = sp_content.replace("...:", "")
            sp_content = sp_content.replace("...", "")
            sp_content = sp_content.replace("?.", "?")


            segs = re.findall(seg_pattern, sp_content)
            if segs == []:
                segs = [sp_content]
            # remove tags from each line, and join text
            tokens = [line.split("\t")[0].strip() for seg in segs for line in seg.split("\n") if line.strip() != ""]
            speaker_text = " ".join(tokens)

            # remove whitespace before ., !, ?
            speaker_text = re.sub(ws_before_punct, r'\1', speaker_text)

            # - sometimes lines in oral are empty? e.g. 08A009N // REMOVE THESE LINES
            if speaker_text.strip() == "":
                continue

            # Format the speaker turn and add to the processed document list
            processed_document.append(f"[mluvčí: {speaker_id}] {speaker_text}")


        # Join all speaker turns into a single string for the document
        final_text = '\n'.join(processed_document)
        processed_documents[doc_id] = final_text

    return processed_documents

oral_data = process_vert_format_oral(data)

# merge ortofon and oral data
ortofon_data.update(oral_data)

# save the merged data in jsonlines as {"text": doc, "id": doc_id}

import jsonlines
FILE_PATH = ".data/hf_dataset/ortofon_oral/test.jsonl"

os.makedirs(os.path.dirname(FILE_PATH), exist_ok=True)
with jsonlines.open(FILE_PATH, 'w') as writer:
    for doc_id, doc in ortofon_data.items():
        writer.write({"text": doc, "id": doc_id})