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.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+test.jsonl filter=lfs diff=lfs merge=lfs -text

convert_dialekt.py

@@ -0,0 +1,123 @@
+import os
+import re
+from typing import Dict
+
+import jsonlines
+from tqdm import tqdm
+
+TARGET = ".data/dialekt_v2_ort.vert"
+
+
+def process_vert_format(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="([^"]*)".+?rok="([^"]*)".+?misto="([^"]*)" sidlotyp="([^"]*)".+?tema="([^"]*)" pocetml="([^"]*)"')
+
+    # Pattern to match speaker turns
+    sp_pattern = re.compile(r'<sp[^>]*id="([^"]*)"[^>]*prezdivka="([^"]*)"\s*[^>]*>(.*?)</sp>', 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:
+            # r'<doc id="([^"]*)".+?rok="([^"]*)".+?misto="([^"]*)" sidlotyp="([^"]*)".+?tema="([^"]*)" pocetml="([^"]*)"')
+
+            doc_id = metadata_match.group(1)
+            year = metadata_match.group(2)
+            place = metadata_match.group(3)
+            settlement_type = metadata_match.group(4)
+            topic = metadata_match.group(5)
+            speakers = metadata_match.group(6)
+
+            metadata_str = (f"Rok: {year}, "
+                            f"Typ sídla: {settlement_type}, "
+                            f"Místo: {place}, "
+                            f"Téma: {topic}, "
+                            f"Počet mluvčích: {speakers}")
+        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]
+            speaker_nickname = sp_match[1]
+            sp_content = sp_match[2]
+
+            # if speaker is Y, rename him as Jiný zvuk
+            if speaker_id == "Y":
+                speaker_id = "Zvuk"
+
+            # remove symbols ---, ...:,
+            sp_content = sp_content.replace("---", "")
+            sp_content = sp_content.replace("...:", "")
+            sp_content = sp_content.replace("...", "")
+            sp_content = sp_content.replace("..", "")
+            sp_content = sp_content.replace("?.", "?")
+
+            # remove tags from each line, and join text
+            tokens = [line.split("\t")[0].strip() for line in sp_content.split("\n") if line.strip() != ""]
+            speaker_text = " ".join(tokens)
+
+            # replace more than one space with one space
+            speaker_text = re.sub(r'\s+', ' ', speaker_text).strip()
+
+            # 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
+            # If the turn is not finished by ., ?, !, add .
+            if speaker_text[-1] not in [".", "!", "?"]:
+                speaker_text += "."
+            # Capitalize the first letter of the speaker turn
+            speaker_text = speaker_text[0].upper() + speaker_text[1:]
+
+            # sometimes there is @ in the text, remove it
+            speaker_text = speaker_text.replace("@", "")
+
+            # capitalize first (non whitespace) letter after ., !, ?
+            speaker_text = re.sub(r'([.!?]\s*)(\w)', lambda m: m.group(1) + m.group(2).upper(), speaker_text)
+            speaker_text = speaker_text.replace("overlap", "překryv mluvčích")
+
+            # Format the speaker turn and add to the processed document list
+            if speakers == "1":
+                processed_document.append(speaker_text)
+            else:
+                processed_document.append(f"[mluvčí: {speaker_nickname}] {speaker_text}")
+
+        # Join all speaker turns into a single string for the document
+        if speakers == "1":
+            final_text = processed_document[0] + "\n" + " ".join(processed_document[1:])
+        else:
+            final_text = '\n'.join(processed_document)
+        processed_documents[doc_id] = final_text
+
+    return processed_documents
+
+
+# Read the content from the file
+with open(TARGET, "r") as f:
+    vert_content = f.read()
+
+# Process the content
+processed_documents = process_vert_format(vert_content)
+
+# write all splits into same json file in  .data/hf_dataset/cnc_fictree/test.jsonl
+OF = ".data/hf_dataset/cnc_dialekt/test.jsonl"
+os.makedirs(os.path.dirname(OF), exist_ok=True)
+with jsonlines.open(OF, "w") as writer:
+    for split, docs in list(processed_documents.items()):
+        for doc in docs:
+            writer.write({"text": doc})

test.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a8830468997bc5a62e0a890383cf201dad01b8f5ec6aa7fd74ca42fee4d34a4
+size 39884457