Can you give me a strategy for implementing TTS ca...
# ask-aib
bumpy-florist-37019
09/15/2025, 10:35 PMCan you give me a strategy for implementing TTS caching for my livekit agent
t
tall-airline-16756
09/15/2025, 10:35 PMThanks for reaching out, Patrick. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
tall-airline-16756
09/15/2025, 10:36 PMTo help you implement TTS caching for your LiveKit agent, a great approach is to create a wrapper around the TTS instance. This way, you can easily intercept synthesis requests and return cached audio whenever it's available. Here’s a simple overview to guide you:
• Cache Key Design: Consider using a unique key that combines the input text, TTS model, voice settings, and audio format to keep things organized.
• Cache Storage: You can store cached audio in memory, on disk, or even in a distributed store like Redis, depending on what fits your needs best.
• Integration: The
before_tts_cbb
bumpy-florist-37019
09/15/2025, 10:37 PMThat would be great thank you
t
tall-airline-16756
09/15/2025, 10:37 PMHere’s a simple example of a minimal caching wrapper for TTS using an in-memory dictionary that I hope you'll find helpful:
Copy code
class CachingTTS(TTS):
def __init__(self, tts: TTS):
super().__init__(
capabilities=tts.capabilities,
sample_rate=tts.sample_rate,
num_channels=tts.num_channels
)
self._tts = tts
self._cache = {}
def _generate_key(self, text: str, **kwargs) -> str:
# Create a unique key based on text and TTS settings
import hashlib
key_data = f"{text}|{kwargs}"
return hashlib.sha256(key_data.encode()).hexdigest()
def synthesize(self, text: str, **kwargs):
# Generate cache key
key = self._generate_key(text, **kwargs)
# Check if audio is already cached
if key in self._cache:
return self._cache[key]
# If not cached, synthesize and store in cache
audio = self._tts.synthesize(text, **kwargs)
self._cache[key] = audio
return audio_generate_keysynthesize3 Views