Implementing punctuation and capitalization in recognized text Most STT engines return "flat" text without punctuation and capitalization, which complicates reading and downstream NLP processing. Automatic punctuation and capitalization make the transcript usable without manual editing. ### Built-in engine approaches — Whisper, Google STT, Azure Speech have automatic punctuation:
# Whisper — пунктуация включена по умолчанию
segments, _ = model.transcribe(audio, language="ru")
# Google STT
config = speech.RecognitionConfig(enable_automatic_punctuation=True)
```**Post-processing models** for engines without built-in punctuation:```python
from transformers import pipeline
# deepmultilingualpunctuation — поддерживает русский
punctuator = pipeline(
"token-classification",
model="kredor/punctuate-all",
aggregation_strategy="simple"
)
def add_punctuation(text: str) -> str:
result = punctuator(text)
output = ""
for token in result:
word = token["word"]
label = token["entity_group"]
output += word
if label == "COMMA":
output += ","
elif label == "PERIOD":
output += "."
elif label == "QUESTION":
output += "?"
output += " "
return output.strip()
```### Models for the Russian language - **multilingual-e5-based** models with RU support: accuracy of ~85–90% on periods and commas - **ruBERT-based** fine-tuned on Russian transcriptions: 88–92% - **Whisper large-v3**: built-in Russian punctuation — ~80–85% Timeframe: integration of the finished model — 1 day. Training a custom one on domain-specific data — 1 week.