[TTS][Magpietts] Unify Longform and Standard Inference logic

[subhankar-ghosh]

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What does this PR do ?

This pull request refactors and unifies the text chunking and inference logic for TTS (Text-to-Speech) in the MagpieTTS pipeline. The main change is the replacement of the previous "longform" inference logic with a new, language-aware, unified chunked inference path. This affects dataset preparation, model state management, argument parsing, and the inference runner, making the codebase simpler and more robust for both short and long texts.

Key changes:

Unified Inference and Text Chunking

• Replaced the old longform inference logic with a unified, automatic text chunking approach that determines chunking based on per-sample language thresholds. Short texts are processed as single chunks, while long texts are split into sentences automatically. (examples/tts/magpietts_inference.py, nemo/collections/tts/data/text_to_speech_dataset.py, nemo/collections/tts/models/magpietts.py) [1] [2] [3]
• Removed all command-line arguments related to explicit longform control (--longform_mode, --longform_word_threshold, etc.), simplifying the inference interface. (examples/tts/magpietts_inference.py) [1] [2] [3]

Dataset and Collation Refactor

• Introduced ChunkedTTSInferenceDataset (replacing LongFormTTSInferenceDataset) with per-sample, language-aware chunking and tokenizer selection. The dataset now automatically decides chunking strategy based on language and text length. (nemo/collections/tts/data/text_to_speech_dataset.py) [1] [2] [3]
• Updated the dataset's collate_fn to handle variable-length chunked batches, padding as needed, and to generalize beyond the previous longform-specific logic. (nemo/collections/tts/data/text_to_speech_dataset.py) [1] [2]

Model and State Naming Consistency

• Renamed all "longform" classes and configs to "chunked" (e.g., LongformDecoderState → ChunkedDecoderState, LongformConfig → ChunkedInferenceConfig) throughout the model code for clarity and consistency with the new unified approach. (nemo/collections/tts/models/magpietts.py) [1] [2] [3] [4]
• Removed the _needs_longform_inference method and all language threshold logic from the model, as chunking is now handled in a unified, language-aware way. (nemo/collections/tts/models/magpietts.py)

Utility and Import Updates

• Added and updated utility imports for chunked inference and tokenizer selection to support the new pipeline. (nemo/collections/tts/models/magpietts.py, nemo/collections/tts/data/text_to_speech_dataset.py) [1] [2]

These changes make the TTS inference pipeline easier to use and maintain, while improving support for multilingual and variable-length text inputs.

Collection: TTS

Changelog

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[Copilot]

Pull request overview

Refactors MagpieTTS inference to use a single “chunked” inference path for both short and long texts, with dataset-driven automatic sentence chunking based on per-sample language thresholds.

Changes:

• Introduces language-aware thresholding + unified chunk_text_for_inference() chunking utility (replacing prior longform detection logic).
• Replaces LongFormTTSInferenceDataset with ChunkedTTSInferenceDataset and updates the inference runner to always use the unified multi/single-chunk loop.
• Updates CLI/example script to remove explicit longform args and align with the unified inference flow.

Reviewed changes

Copilot reviewed 6 out of 6 changed files in this pull request and generated 8 comments.

Show a summary per file

File	Description
nemo/collections/tts/parts/utils/tts_dataset_utils.py	Adds language-aware sentence splitting, thresholds, tokenizer mapping, and unified chunking helper.
nemo/collections/tts/data/text_to_speech_dataset.py	Replaces longform inference dataset with unified chunked inference dataset + mixed-chunk collation.
nemo/collections/tts/modules/magpietts_inference/inference.py	Removes standard/longform branching; always runs unified chunk loop via generate_speech().
nemo/collections/tts/models/magpietts.py	Renames longform state/config to chunked equivalents; updates do_tts() to the unified chunked generation path.
examples/tts/magpietts_inference.py	Removes longform CLI controls and updates messaging for unified chunking behavior.
tests/collections/tts/parts/utils/test_tts_dataset_utils.py	Adds unit tests for new thresholds and unified chunking helper.

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[blisc]

• Can you resolve conflicts?
• Can you add a unit test for magpietts.generate_speech that contains a batch of short and long texts?

[blisc]

The github UI still says that there are conflicts

To fix an unused import, we simply remove the import statement (or the specific symbol) that is not used anywhere in the file. This reduces unnecessary dependencies and cleans up the code without affecting runtime behavior.

In this case, the best fix is to delete the import line from lhotse.serialization import load_yaml at line 28 in nemo/collections/tts/models/magpietts.py. No other code changes are necessary, since we are not altering any used functionality and there are no visible references to load_yaml. Ensure that only this line is removed and that all other imports remain untouched.

[blisc]

Where does this tokenizer_names parameter come from? It seems to assume that you read it from the dataset json which doesn't seem ideal

[blisc]

And do we only support this in the non-Lhotse path? What happens if we try a Lhotse dataset?

[subhankar-ghosh]

We do inference on non-Lhotse path only.

[github-actions]

[🤖]: Hi @subhankar-ghosh 👋,

We wanted to let you know that a CICD pipeline for this PR just finished successfully.

So it might be time to merge this PR or get some approvals.

//cc @chtruong814 @ko3n1g @pablo-garay @thomasdhc

[subhankar-ghosh]

@XuesongYang Let me know if this piece of code looks good to you.

[XuesongYang]

LGTM!

@rfejgin We never reset cache for local transformer either for standard inference or longform inference before. Do we always disable the kv cache, or depends on how many frames stacked?

[rfejgin]

Thanks for being careful about this @XuesongYang and @subhankar-ghosh. Still, I think no special handling is needed for the local transformer since the LT already resets its kv cache automatically every timestep (or frame stack), since separate timesteps are completely independent for the LT. The reset step is at the start of local_transformer_sample_autoregressive() and local_transformer_sample_maskgit().

Do we always disable the kv cache, or depends on how many frames stacked?

It depends on the LT type. For Maskgit, we keep KV cache off because the that type of LT is non-causal, which makes standard KV caching impossible. For autoregressive LT it the kv cache is on and we reset it on every timestep (i.e. once per frame or stack).