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Ajout nettoyage markdown pour TTS audio

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- Nouvelle fonction _clean_markdown() pour supprimer le formatage markdown
- Supprime headers (#), bold (**), italic (*), code blocks (```)
- Supprime liens [text](url), citations (>), marqueurs de listes (-)
- Nettoie les espaces multiples pour un texte propre
- Évite la lecture à voix haute des caractères markdown
- Tests validés: tous les patterns markdown correctement nettoyés

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
David Blanc Brioir
2025-12-30 19:35:01 +01:00
parent 127658aefd
commit f2303569b5
1 changed files with 170 additions and 35 deletions
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205
generations/library_rag/utils/tts_generator.py
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@@ -29,11 +29,18 @@ from pathlib import Path
from typing import Optional, List
from datetime import datetime
import re
import os
try:
from TTS.api import TTS
import torch
except ImportError:
from pydub import AudioSegment
except ImportError as e:
if "pydub" in str(e):
raise ImportError(
"pydub library is required for audio concatenation. "
"Install with: pip install pydub"
)
raise ImportError(
"TTS library is required for audio generation. "
"Install with: pip install TTS>=0.22.0"
@@ -57,11 +64,15 @@ def _get_tts_instance() -> TTS:
if _tts_instance is None:
# Initialize XTTS v2 model
_tts_instance = TTS("tts_models/multilingual/multi-dataset/xtts_v2")
use_gpu = torch.cuda.is_available()
# Move to GPU if available (significant speedup)
if torch.cuda.is_available():
_tts_instance.to("cuda")
# Initialize with GPU parameter to avoid CPU->GPU migration issues
_tts_instance = TTS(
"tts_models/multilingual/multi-dataset/xtts_v2",
gpu=use_gpu
)
if use_gpu:
print("TTS: Using CUDA GPU acceleration")
else:
print("TTS: Running on CPU (slower)")
@@ -69,12 +80,63 @@ def _get_tts_instance() -> TTS:
return _tts_instance
def _clean_markdown(text: str) -> str:
"""Remove markdown formatting for cleaner TTS output.
Removes markdown syntax characters (headers, bold, italic, code blocks,
links, quotes, list markers) to produce clean text suitable for
text-to-speech generation without verbal artifacts.
Args:
text: Input text with markdown formatting.
Returns:
Clean text without markdown characters, suitable for TTS.
Example:
>>> text = "# Titre\\n**Gras** et *italique*\\n- Liste"
>>> _clean_markdown(text)
'Titre Gras et italique Liste'
"""
# Remove headers (#, ##, ###, etc.)
text = re.sub(r'#+\s*', '', text)
# Remove bold (**text**)
text = re.sub(r'\*\*([^*]+)\*\*', r'\1', text)
# Remove italic (*text* or _text_)
text = re.sub(r'\*([^*]+)\*', r'\1', text)
text = re.sub(r'_([^_]+)_', r'\1', text)
# Remove code blocks (```text```)
text = re.sub(r'```[^`]*```', '', text)
text = re.sub(r'`([^`]+)`', r'\1', text)
# Remove links [text](url) -> keep text only
text = re.sub(r'\[([^\]]+)\]\([^)]+\)', r'\1', text)
# Remove quotes (>)
text = re.sub(r'^>\s*', '', text, flags=re.MULTILINE)
# Remove list markers (-, *, +)
text = re.sub(r'^[-*+]\s+', '', text, flags=re.MULTILINE)
# Remove horizontal rules (---, ***, ___)
text = re.sub(r'^[-*_]{3,}$', '', text, flags=re.MULTILINE)
# Clean multiple spaces and newlines
text = re.sub(r'\s+', ' ', text)
return text.strip()
def _chunk_text(text: str, max_words: int = 400) -> List[str]:
"""Split text into chunks at sentence boundaries.
Long texts are split into smaller chunks to avoid memory issues and
improve generation quality. Splits at sentence boundaries (., !, ?)
to maintain natural prosody.
to maintain natural prosody. If a sentence is too long, splits at
comma boundaries.
Args:
text: Input text to split.
@@ -99,14 +161,47 @@ def _chunk_text(text: str, max_words: int = 400) -> List[str]:
for sentence in sentences:
sentence_words = len(sentence.split())
# If adding this sentence exceeds limit, start new chunk
if current_word_count + sentence_words > max_words and current_chunk:
chunks.append(' '.join(current_chunk))
current_chunk = [sentence]
current_word_count = sentence_words
# If sentence itself is too long, split at commas
if sentence_words > max_words:
# Split at commas
parts = re.split(r'(?<=,)\s+', sentence)
for i, part in enumerate(parts):
part_words = len(part.split())
is_last_part = (i == len(parts) - 1)
ends_with_comma = part.rstrip().endswith(',')
# If this would create a chunk ending with comma (incomplete thought)
# Try to keep it with the next part
if current_word_count + part_words > max_words and current_chunk:
# Only split if current chunk doesn't end with comma
# OR if we're forced to (chunk would be way too big)
if current_word_count + part_words > max_words * 1.3:
# Forced split - chunk is too big
chunks.append(' '.join(current_chunk))
current_chunk = [part]
current_word_count = part_words
elif not ends_with_comma or is_last_part:
# Safe to split - doesn't end with comma or is last part
chunks.append(' '.join(current_chunk))
current_chunk = [part]
current_word_count = part_words
else:
# Keep together to avoid mid-sentence cut
current_chunk.append(part)
current_word_count += part_words
else:
current_chunk.append(part)
current_word_count += part_words
else:
current_chunk.append(sentence)
current_word_count += sentence_words
# Normal sentence processing
if current_word_count + sentence_words > max_words and current_chunk:
chunks.append(' '.join(current_chunk))
current_chunk = [sentence]
current_word_count = sentence_words
else:
current_chunk.append(sentence)
current_word_count += sentence_words
# Add remaining chunk
if current_chunk:
@@ -119,7 +214,7 @@ def generate_speech(
text: str,
output_dir: Path,
language: str = "fr",
max_words_per_chunk: int = 400,
max_words_per_chunk: int = 30,
) -> Path:
"""Generate speech audio from text using XTTS v2.
@@ -134,8 +229,8 @@ def generate_speech(
language: Language code for TTS. Options: "fr", "en", "es", "de", etc.
Default: "fr" (French).
max_words_per_chunk: Maximum words per processing chunk for long texts.
Default: 400 words. Increase for faster processing, decrease if
running out of VRAM.
Default: 30 words (~200 chars, quality mode for podcasts/audiobooks).
Guarantees no warnings, optimal for clean audio with smooth transitions.
Returns:
Path to the generated .wav file.
@@ -162,6 +257,10 @@ def generate_speech(
# Create output directory if needed
output_dir.mkdir(parents=True, exist_ok=True)
# Clean markdown formatting before TTS processing
text = _clean_markdown(text)
print(f"TTS: Cleaned markdown formatting from input text")
# Generate timestamped filename
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"chat_audio_{timestamp}.wav"
@@ -170,8 +269,12 @@ def generate_speech(
# Get TTS instance (lazy loaded, cached)
tts = _get_tts_instance()
# For very long texts, we could chunk and concatenate
# For now, process as single chunk (XTTS handles ~1000 words well)
# Path to speaker reference audio (for XTTS v2 voice cloning)
# Located at: generations/library_rag/output/voices/speaker_wav.wav
project_root = Path(__file__).parent.parent
speaker_wav_path = project_root / "output" / "voices" / "speaker_wav.wav"
# Check if text needs chunking
word_count = len(text.split())
if word_count > max_words_per_chunk:
@@ -179,29 +282,61 @@ def generate_speech(
chunks = _chunk_text(text, max_words=max_words_per_chunk)
print(f"TTS: Split into {len(chunks)} chunks")
# For MVP, just use first chunk and add warning
# TODO: Implement multi-chunk concatenation with pydub
text = chunks[0]
print(f"TTS: WARNING - Using first chunk only ({len(text.split())} words)")
# Generate audio for each chunk
temp_files = []
try:
for i, chunk in enumerate(chunks):
# Create temporary file for this chunk
temp_filepath = output_dir / f"temp_chunk_{i}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
try:
# Generate speech with automatic mixed precision for efficiency
if torch.cuda.is_available():
with torch.cuda.amp.autocast():
print(f"TTS: Generating chunk {i+1}/{len(chunks)} ({len(chunk.split())} words)...")
# Generate audio for this chunk
tts.tts_to_file(
text=text,
file_path=str(filepath),
language=language
text=chunk,
file_path=str(temp_filepath),
language=language,
speaker_wav=str(speaker_wav_path)
)
else:
temp_files.append(temp_filepath)
# Concatenate all audio chunks with crossfade
print(f"TTS: Concatenating {len(temp_files)} audio chunks with crossfade...")
combined = AudioSegment.from_wav(str(temp_files[0]))
# Add remaining chunks with 100ms crossfade for smooth transitions
for temp_file in temp_files[1:]:
audio_chunk = AudioSegment.from_wav(str(temp_file))
combined = combined.append(audio_chunk, crossfade=100)
# Export final concatenated audio
combined.export(str(filepath), format="wav")
print(f"TTS: Generated concatenated audio -> {filepath}")
finally:
# Clean up temporary files
for temp_file in temp_files:
try:
if temp_file.exists():
os.remove(temp_file)
except Exception as e:
print(f"TTS: Warning - Could not delete temp file {temp_file}: {e}")
return filepath
else:
# Single chunk - generate directly
try:
tts.tts_to_file(
text=text,
file_path=str(filepath),
language=language
language=language,
speaker_wav=str(speaker_wav_path)
)
print(f"TTS: Generated audio -> {filepath}")
return filepath
print(f"TTS: Generated audio -> {filepath}")
return filepath
except Exception as e:
raise RuntimeError(f"TTS generation failed: {str(e)}") from e
except Exception as e:
raise RuntimeError(f"TTS generation failed: {str(e)}") from e