Run Coqui XTTS and NeMo STT together: avoiding transformers clashes in a single asyncio chatbot

Learn how to resolve Coqui XTTS and NeMo STT transformers conflicts in one asyncio chatbot via the coqui-ai-TTS fork, isolation, or a safe monkey-patch.

When you try to run text-to-speech and speech-to-text stacks side by side inside one asyncio-driven chatbot, the runtime friction often isn’t about CPU or IO at all. It’s packaging. Coqui XTTS on one side and NeMo STT models on the other can pull incompatible versions of transformers into the same interpreter, which derails an otherwise clean event loop with import-time breakage.

The core issue, in one place

The failure surfaces because parts of the TTS stack interact with transformers using an argument shape that newer releases no longer accept. A representative fragment looks like this inside the XTTS stream generation path:

if call_args.get("attention_mask", None) is None and needs_mask and supports_mask:
    call_args["attention_mask"] = self._make_attention_mask_for_gen(
        in_tensor,
        gen_cfg.pad_token_id,
        gen_cfg.eos_token_id,
    )

This mirrors the logic used in TTS/TTS/tts/layers/xtts/stream_generator.py around preparing attention masks. The interaction with transformers becomes brittle when the downstream function signature or expected types shift, which is what you see after upgrading transformers beyond what the legacy XTTS code was written against.

Why a single environment won’t save you

Python packaging does not provide a straightforward way to install and import two different versions of the same distribution into one interpreter. If one library pins transformers to an older release and another one requires a newer API, the single-process, single-environment approach hits a hard limit. The universal escape hatch is to split the workloads into separate processes that run separate interpreters backed by separate environments. Virtual environments and Docker are the right tools when you truly must keep conflicting dependency graphs alive at the same time.

Practical ways out that don’t require a full redesign

There are two less disruptive avenues in this specific TTS/STT mix. The first is to switch from the unmaintained Coqui repository to the actively maintained fork coqui-ai-TTS at https://github.com/idiap/coqui-ai-TTS. You shouldn’t run into the same transformers deadlock there. The second is a surgical workaround: monkey-patch the part of the generation path that forwards arguments to transformers so that it passes the types your installed transformers expects. The fragility is real and you should treat it as a stopgap, but for a localized incompatibility in how arguments are shaped, a patch can unblock you without re-architecting the app.

A minimal look at the fragile spot

The mask-preparation branch is small but happens at a critical boundary. Rewritten for clarity with different identifiers but identical behavior, it boils down to the following:

if call_args.get("attention_mask", None) is None and needs_mask and supports_mask:
    call_args["attention_mask"] = self._make_attention_mask_for_gen(
        in_tensor,
        gen_cfg.pad_token_id,
        gen_cfg.eos_token_id,
    )

The problem arises when the receiving transformers utility expects different argument shapes than what this branch provides. That’s why merely upgrading transformers in place can break XTTS inference paths unless you pin back to an older version.

Solution paths

If you need a durable fix without splitting the runtime, adopt the maintained coqui-ai-TTS fork. It’s actively cared for and is not tied to an old transformers pin for the same path. If you must stick with the legacy XTTS codebase for now, a monkey-patch targeted at the generation call site is a viable short-term tactic. You replace the generation routine with an equivalent one that forwards the correct argument types to the internal mask-preparation function. When done carefully, the rest of the pipeline remains untouched, and you can continue iterating on your chatbot.

An optional monkey-patch pattern

The following shows the structure for swapping in a patched generation method at runtime while preserving existing behavior. The wrapper delegates to the original implementation; the adaptation step is where you would align argument types for your installed transformers version.

# Illustrative pattern only; align the class and import to your runtime
from some_module import NewGenerationMixin as _GenShim
_original_generate = _GenShim.generate
def _generate_patched(self, *pos_args, **kw_args):
    # This is the place to adapt argument types if needed before
    # the underlying generation logic runs.
    return _original_generate(self, *pos_args, **kw_args)
_GenShim.generate = _generate_patched

If the incompatibility is limited to a narrow call site, this pattern can carry you until you migrate fully to the maintained fork or refactor the TTS boundary.

Why this matters

ASR and TTS stacks evolve independently and move quickly. Binding them to a single interpreter means you inherit the tightest constraint in the set, which slows upgrades and complicates operations. Knowing when to isolate environments, when to switch to a maintained fork, and when a tactical patch is appropriate keeps your event loop responsive and your deployment surface small.

Takeaways

Within one Python process you can’t rely on running multiple versions of the same package cleanly, so plan for isolation when dependencies conflict. For Coqui XTTS specifically, consider the maintained coqui-ai-TTS fork to avoid legacy pins. If you are locked to the old code, a carefully scoped monkey-patch of the generation path can bridge a transformers API mismatch, with the understanding that it’s a short-term measure. The more changes you need, the more sense it makes to fork and maintain the fixes in source rather than carry a runtime patch.