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"""Text processing utilities for TTS conversion."""
# : dep openai
# : dep pytest
import logging
import Omni.App as App
import Omni.Log as Log
import Omni.Test as Test
import openai
import os
import sys
logger = logging.getLogger(__name__)
Log.setup(logger)
# Configuration from environment variables
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
def prepare_text_for_tts(text: str, title: str) -> list[str]:
"""Use LLM to prepare text for TTS, returning chunks ready for speech.
First splits text mechanically, then has LLM edit each chunk.
"""
# First, split the text into manageable chunks
raw_chunks = split_text_into_chunks(text, max_chars=3000)
logger.info("Split article into %d raw chunks", len(raw_chunks))
# Prepare the first chunk with intro
edited_chunks = []
for i, chunk in enumerate(raw_chunks):
is_first = i == 0
is_last = i == len(raw_chunks) - 1
try:
edited_chunk = edit_chunk_for_speech(
chunk,
title=title if is_first else None,
is_first=is_first,
is_last=is_last,
)
edited_chunks.append(edited_chunk)
except Exception:
logger.exception("Failed to edit chunk %d", i + 1)
# Fall back to raw chunk if LLM fails
if is_first:
edited_chunks.append(
f"This is an audio version of {title}. {chunk}",
)
elif is_last:
edited_chunks.append(f"{chunk} This concludes the article.")
else:
edited_chunks.append(chunk)
return edited_chunks
def split_text_into_chunks(text: str, max_chars: int = 3000) -> list[str]:
"""Split text into chunks at sentence boundaries."""
chunks = []
current_chunk = ""
# Split into paragraphs first
paragraphs = text.split("\n\n")
for para in paragraphs:
para_stripped = para.strip()
if not para_stripped:
continue
# If paragraph itself is too long, split by sentences
if len(para_stripped) > max_chars:
sentences = para_stripped.split(". ")
for sentence in sentences:
if len(current_chunk) + len(sentence) + 2 < max_chars:
current_chunk += sentence + ". "
else:
if current_chunk:
chunks.append(current_chunk.strip())
current_chunk = sentence + ". "
# If adding this paragraph would exceed limit, start new chunk
elif len(current_chunk) + len(para_stripped) + 2 > max_chars:
if current_chunk:
chunks.append(current_chunk.strip())
current_chunk = para_stripped + " "
else:
current_chunk += para_stripped + " "
# Don't forget the last chunk
if current_chunk:
chunks.append(current_chunk.strip())
return chunks
def edit_chunk_for_speech(
chunk: str,
title: str | None = None,
*,
is_first: bool = False,
is_last: bool = False,
) -> str:
"""Use LLM to lightly edit a single chunk for speech.
Raises:
ValueError: If no content is returned from LLM.
"""
system_prompt = (
"You are a podcast script editor. Your job is to lightly edit text "
"to make it sound natural when spoken aloud.\n\n"
"Guidelines:\n"
)
system_prompt += """
- Remove URLs and email addresses, replacing with descriptive phrases
- Convert bullet points and lists into flowing sentences
- Fix any awkward phrasing for speech
- Remove references like "click here" or "see below"
- Keep edits minimal - preserve the original content and style
- Do NOT add commentary or explanations
- Return ONLY the edited text, no JSON or formatting
"""
user_prompt = chunk
# Add intro/outro if needed
if is_first and title:
user_prompt = (
f"Add a brief intro mentioning this is an audio version of "
f"'{title}', then edit this text:\n\n{chunk}"
)
elif is_last:
user_prompt = f"Edit this text and add a brief closing:\n\n{chunk}"
try:
client: openai.OpenAI = openai.OpenAI(api_key=OPENAI_API_KEY)
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
],
temperature=0.3, # Lower temperature for more consistent edits
max_tokens=4000,
)
content = response.choices[0].message.content
if not content:
msg = "No content returned from LLM"
raise ValueError(msg) # noqa: TRY301
# Ensure the chunk isn't too long
max_chunk_length = 4000
if len(content) > max_chunk_length:
# Truncate at sentence boundary
sentences = content.split(". ")
truncated = ""
for sentence in sentences:
if len(truncated) + len(sentence) + 2 < max_chunk_length:
truncated += sentence + ". "
else:
break
content = truncated.strip()
except Exception:
logger.exception("LLM chunk editing failed")
raise
else:
return content
class TestTextChunking(Test.TestCase):
"""Test text chunking edge cases."""
def test_split_text_single_long_word(self) -> None:
"""Handle text with a single word exceeding limit."""
long_word = "a" * 4000
chunks = split_text_into_chunks(long_word, max_chars=3000)
# Should keep it as one chunk or split?
# The current implementation does not split words
self.assertEqual(len(chunks), 1)
self.assertEqual(len(chunks[0]), 4000)
def test_split_text_no_sentence_boundaries(self) -> None:
"""Handle long text with no sentence boundaries."""
text = "word " * 1000 # 5000 chars
chunks = split_text_into_chunks(text, max_chars=3000)
# Should keep it as one chunk as it can't split by ". "
self.assertEqual(len(chunks), 1)
self.assertGreater(len(chunks[0]), 3000)
def test() -> None:
"""Run the tests."""
Test.run(
App.Area.Test,
[
TestTextChunking,
],
)
def main() -> None:
"""Entry point for the module."""
if "test" in sys.argv:
test()
else:
logger.info("TextProcessing module loaded")
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