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import asyncio
import os
import re
from pathlib import Path
from uuid import uuid4
import random
from langchain_community.callbacks import get_openai_callback
from pydub import AudioSegment
from src.lc_callbacks import LCMessageLoggerAsync
from src.tts import tts_astream, sound_generation_astream
from src.utils import consume_aiter
from src.emotions.generation import EffectGeneratorAsync
from src.emotions.utils import add_overlay_for_audio
from src.config import AI_ML_API_KEY, ELEVENLABS_MAX_PARALLEL, logger
from src.text_split_chain import SplitTextOutput
class AudioGeneratorSimple:
async def generate_audio(
self,
text_split: SplitTextOutput,
character_to_voice: dict[str, str],
) -> Path:
semaphore = asyncio.Semaphore(ELEVENLABS_MAX_PARALLEL)
async def tts_astream_with_semaphore(voice_id: str, text: str):
async with semaphore:
iter_ = tts_astream(voice_id=voice_id, text=text)
bytes_ = await consume_aiter(iter_)
return bytes_
tasks = []
for character_phrase in text_split.phrases:
voice_id = character_to_voice[character_phrase.character]
task = tts_astream_with_semaphore(
voice_id=voice_id, text=character_phrase.text
)
tasks.append(task)
results = await asyncio.gather(*tasks)
save_dir = Path("data") / "books"
save_dir.mkdir(exist_ok=True)
audio_combined_fp = save_dir / f"{uuid4()}.wav"
logger.info(f'saving generated audio book to: "{audio_combined_fp}"')
with open(audio_combined_fp, "wb") as ab:
for result in results:
for chunk in result:
ab.write(chunk)
return audio_combined_fp
class AudioGeneratorWithEffects:
def __init__(self):
self.effect_generator = EffectGeneratorAsync(AI_ML_API_KEY)
self.semaphore = asyncio.Semaphore(ELEVENLABS_MAX_PARALLEL)
self.temp_files = []
async def generate_audio(
self,
text_split: SplitTextOutput,
character_to_voice: dict[str, str],
) -> Path:
"""Main method to generate the audiobook with TTS, emotion, and sound effects."""
num_lines = len(text_split.phrases)
lines_for_sound_effect = self._select_lines_for_sound_effect(num_lines)
# Step 1: Process and modify text
modified_texts, sound_emotion_results = await self._process_and_modify_text(
text_split, lines_for_sound_effect
)
# Step 2: Generate TTS audio for modified text
tts_results, self.temp_files = await self._generate_tts_audio(
text_split, modified_texts, character_to_voice
)
# Step 3: Add sound effects to selected lines
audio_chunks = await self._add_sound_effects(
tts_results, lines_for_sound_effect, sound_emotion_results, self.temp_files
)
# Step 4: Merge audio files
normalized_audio_chunks = self._normalize_audio_chunks(audio_chunks, self.temp_files)
final_output = self._merge_audio_files(normalized_audio_chunks)
# Clean up temporary files
self._cleanup_temp_files(self.temp_files)
return final_output
def _select_lines_for_sound_effect(self, num_lines: int) -> list[int]:
"""Select 20% of the lines randomly for sound effect generation."""
return random.sample(range(num_lines), k=int(0.0 * num_lines))
async def _process_and_modify_text(
self, text_split: SplitTextOutput, lines_for_sound_effect: list[int]
) -> tuple[list[dict], list[dict]]:
"""Process the text by modifying it and generating tasks for sound effects."""
tasks_for_text_modification = []
sound_emotion_tasks = []
for idx, character_phrase in enumerate(text_split.phrases):
character_text = character_phrase.text.strip().lower()
# Add text emotion modification task
tasks_for_text_modification.append(
self.effect_generator.add_emotion_to_text(character_text)
)
# If this line needs sound effects, generate parameters
if idx in lines_for_sound_effect:
sound_emotion_tasks.append(
self.effect_generator.generate_parameters_for_sound_effect(
character_text
)
)
# Await tasks for text modification and sound effects
modified_texts = await asyncio.gather(*tasks_for_text_modification)
sound_emotion_results = await asyncio.gather(*sound_emotion_tasks)
return modified_texts, sound_emotion_results
async def _generate_tts_audio(
self,
text_split: SplitTextOutput,
modified_texts: list[dict],
character_to_voice: dict[str, str],
) -> tuple[list[str], list[str]]:
"""Generate TTS audio for modified text."""
tasks_for_tts = []
temp_files = []
async def tts_astream_with_semaphore(voice_id: str, text: str, params: dict):
async with self.semaphore:
iter_ = tts_astream(voice_id=voice_id, text=text, params=params)
bytes_ = await consume_aiter(iter_)
return bytes_
for idx, (modified_text, character_phrase) in enumerate(
zip(modified_texts, text_split.phrases)
):
voice_id = character_to_voice[character_phrase.character]
# Use the semaphore-protected TTS function
task = tts_astream_with_semaphore(
voice_id=voice_id,
text=modified_text["modified_text"],
params=modified_text["params"],
)
tasks_for_tts.append(task)
# Gather all TTS results
tts_results = await asyncio.gather(*tasks_for_tts)
# Save the results to temporary files
tts_audio_files = []
for idx, tts_result in enumerate(tts_results):
tts_filename = f"tts_output_{idx}.wav"
with open(tts_filename, "wb") as ab:
for chunk in tts_result:
ab.write(chunk)
tts_audio_files.append(tts_filename)
temp_files.append(tts_filename)
return tts_audio_files, temp_files
async def _add_sound_effects(
self,
tts_audio_files: list[str],
lines_for_sound_effect: list[int],
sound_emotion_results: list[dict],
temp_files: list[str],
) -> list[str]:
"""Add sound effects to the selected lines."""
audio_chunks = []
for idx, tts_filename in enumerate(tts_audio_files):
# If the line has sound emotion data, generate sound effect and overlay
if idx in lines_for_sound_effect:
sound_effect_data = sound_emotion_results.pop(0) # Get next sound effect data
sound_effect_filename = f"sound_effect_{idx}.wav"
# Generate sound effect asynchronously
sound_result = await consume_aiter(sound_generation_astream(sound_effect_data))
with open(sound_effect_filename, "wb") as ab:
for chunk in sound_result:
ab.write(chunk)
# Add sound effect overlay
output_filename = add_overlay_for_audio(
main_audio_filename=tts_filename,
sound_effect_filename=sound_effect_filename,
cycling_effect=True,
decrease_effect_volume=5,
)
audio_chunks.append(output_filename)
temp_files.append(sound_effect_filename) # Track temp files
temp_files.append(output_filename)
else:
audio_chunks.append(tts_filename)
return audio_chunks
def _normalize_audio(self, audio_segment: AudioSegment, target_dBFS: float = -20.0) -> AudioSegment:
"""Normalize an audio segment to the target dBFS level."""
change_in_dBFS = target_dBFS - audio_segment.dBFS
return audio_segment.apply_gain(change_in_dBFS)
def _normalize_audio_chunks(self, audio_filenames: list[str], temp_files, target_dBFS: float = -20.0) -> list[str]:
"""Normalize all audio chunks to the target volume level."""
normalized_files = []
for audio_file in audio_filenames:
audio_segment = AudioSegment.from_file(audio_file)
normalized_audio = self._normalize_audio(audio_segment, target_dBFS)
normalized_filename = f"normalized_{Path(audio_file).stem}.wav"
normalized_audio.export(normalized_filename, format="wav")
normalized_files.append(normalized_filename)
temp_files.append(normalized_filename)
return normalized_files
def _merge_audio_files(self, audio_filenames: list[str]) -> Path:
"""Helper function to merge multiple audio files into one."""
combined = AudioSegment.from_file(audio_filenames[0])
for filename in audio_filenames[1:]:
next_audio = AudioSegment.from_file(filename)
combined += next_audio # Concatenate the audio
save_dir = Path("data") / "books"
save_dir.mkdir(exist_ok=True)
save_path = save_dir / f"{uuid4()}.wav"
combined.export(save_path, format="wav")
return Path(save_path)
def _cleanup_temp_files(self, temp_files: list[str]) -> None:
"""Helper function to delete all temporary files."""
for temp_file in temp_files:
try:
os.remove(temp_file)
except FileNotFoundError:
continue |