What this error means

LiteLLM Router.aspeech() bypasses async_function_with_fallbacks — TTS requests have no retry or failover is a LiteLLM failure pattern reported for developers trying to fix litellm router.aspeech no retry failover for tts requests. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Open issue on official BerriAI/litellm repo. Confirmed bug: Router.aspeech() calls litellm.aspeech directly, bypassing retry/failover. Inconsistent with routing behavior for other model types. Affects multi-deployment TTS configurations.

Common causes

  • LiteLLM's Router.aspeech() bypasses the async_function_with_fallbacks mechanism, calling litellm.aspeech directly. This means TTS requests have no retry on failure and no failover to other deployments — inconsistent with how other model types are routed. Developers with multi-deployment TTS setups get hard failures.
  • Open issue on official BerriAI/litellm repo. Confirmed bug: Router.aspeech() calls litellm.aspeech directly, bypassing retry/failover. Inconsistent with routing behavior for other model types. Affects multi-deployment TTS configurations.

Quick fixes

  1. Confirm the exact error signature matches LiteLLM Router.aspeech() bypasses async_function_with_fallbacks — TTS requests have no retry or failover.
  2. Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
  3. Check the build output, project root, and deployment platform configuration before redeploying.

Platform/tool-specific checks

  • Verify the command, editor, extension, or API client that produced the error.
  • Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
  • Avoid deleting credentials, local model data, or project settings until the failing scope is clear.

Step-by-step troubleshooting

  1. Capture the exact error message and the command, editor action, or request that triggered it.
  2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
  3. Review the source evidence below and compare it with your environment.
  4. Apply one change at a time and rerun the smallest failing action.
  5. Keep the working fix documented for the team or deployment environment.

How to prevent it

  • Keep provider/tool configuration documented.
  • Record non-secret diagnostics such as tool version, provider name, model name, and command path.
  • Add a lightweight check before CI or production workflows depend on the tool.