LiteLLM Audio Speech Fallback: Resolve 'Model ID' Errors

Hey there, fellow AI enthusiasts and developers! Ever hit a snag with your awesome LiteLLM setup, especially when trying to get those smooth audio speech fallbacks working? You're not alone, and it can feel like you're talking to a brick wall when your carefully crafted fallback logic just… doesn't kick in. We've all been there, scratching our heads, wondering why our proxy isn't doing its magic. Today, we're diving deep into a specific issue where LiteLLM's /audio/speech fallback mechanism seems to be playing hard to get, particularly when it yells about not being able to find real modelID for kokoro. We're going to break down this problem, figure out why it's happening, and, most importantly, lay out a clear path to get your audio speech fallbacks rock-solid and reliable. So, buckle up, because we're about to make your LiteLLM proxy sing!

This article isn't just about fixing a bug; it's about understanding the nuances of how LiteLLM handles different API types, especially the often-overlooked audio speech endpoints. We'll explore the critical difference between setting up fallbacks for chat models versus speech models and uncover why a simple openai/dummy placeholder might be tripping things up for your audio requests. Our goal is to empower you with the knowledge to troubleshoot similar issues efficiently and ensure your AI applications run smoothly, leveraging LiteLLM's incredible power for routing, load balancing, and, of course, robust fallbacks. By the end of this read, you'll have a much clearer picture of how to architect your LiteLLM configurations for optimal performance and resilience, making sure your users always get a response, even if a primary service decides to take a coffee break. Let's conquer this fallback challenge together and enhance your AI infrastructure!

Cracking the Code: LiteLLM Audio Speech Fallback Issues

Alright, guys, let's get right into the heart of the matter: you've set up LiteLLM, you're leveraging its fantastic proxy capabilities, and you've got this brilliant idea for an audio speech model named kokoro. You want it to be super resilient, so you've configured fallbacks to server1/kokoro and server2/kokoro. The logic seems flawless on paper, right? You even have a placeholder kokoro model pointing to an openai/dummy endpoint, intending for it to always fail so LiteLLM can gracefully switch to your working servers. But then, BAM! You hit the /v1/audio/speech endpoint, and instead of a smooth fallback, you get a nasty litellm.BadRequestError: OpenAIException - could not find real modelID for kokoro LiteLLM Retried: 3 times. It fails three times on the plain kokoro and doesn't even touch your fallback servers. What gives?

This particular scenario highlights a crucial distinction in how LiteLLM, and indeed many AI services, handle different types of requests. While fallbacks might work perfectly for your chat/completions requests, the /audio/speech endpoint has its own set of expectations. The error message, could not find real modelID for kokoro, is a huge clue. It's telling us that the initial kokoro model you've defined, the one pointing to openai/dummy with api_base: http://localhost/v1, isn't just failing to connect; it's failing at a more fundamental level. LiteLLM, when processing an audio speech request, needs to internally recognize the model_name as something that could potentially be an audio speech model. If it can't even get past this initial recognition, the fallback mechanism, which typically kicks in after a connection or response error, won't even have a chance to engage. Think of it like trying to play a video game on a console that doesn't recognize the game cartridge. The console isn't going to try a different game; it's just going to tell you it can't identify this game. The problem isn't that openai/dummy isn't responding; it's that LiteLLM doesn't see kokoro (as defined by openai/dummy) as a valid type of model for speech. This means we need to adjust our initial kokoro model definition to satisfy LiteLLM's internal checks for audio speech compatibility, or at least provide a more robust initial point of failure that allows the fallback mechanism to actually engage. This foundational understanding is key to unlocking a truly resilient audio speech setup with LiteLLM.

Understanding Your kokoro Setup and the Fallback Logic

Let's really dig into your specific setup, because understanding the configuration is half the battle when debugging these kinds of issues. You've got kokoro defined as a model like this:

- model_name: kokoro
  litellm_params:
    model: openai/dummy
    api_base: http://localhost/v1
    api_key: sk-llama

And then, your router_settings include this fallback:

router_settings:
  fallbacks:
    - "kokoro":
      - "server1/kokoro"
      - "server2/kokoro"

Your intention here is crystal clear: kokoro is designed to be a placeholder that always fails, forcing LiteLLM to try server1/kokoro and then server2/kokoro. This is a smart strategy for general chat models, where LiteLLM's retry and fallback logic is incredibly robust. However, the error litellm.BadRequestError: OpenAIException - could not find real modelID for kokoro for an /audio/speech request tells us something very specific. It's not a timeout, or a connection refused error, which would typically trigger a fallback. Instead, it's an OpenAIException originating from within the openai client (which LiteLLM uses under the hood for openai/* models) saying it could not find real modelID for kokoro. This suggests that the problem isn't the network connection or the server's response; it's how LiteLLM (or the underlying OpenAI client library) interprets the kokoro model itself when it's asked to perform a speech operation. The openai/dummy model, while useful for testing connection-level fallbacks, might not be designed to pass the initial validation checks for specific API types like audio speech. For speech synthesis, models often have predefined structures or names that the client library expects. When kokoro is defined as openai/dummy, it might not expose the necessary internal metadata or conform to the expected