[MagpieTTS][TTS] Streaming Algorithm for MagpieTTS to 2508

[subhankar-ghosh]

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What does this PR do ?

Add streaming algorithm to magpietts

Collection: [Note which collection this PR will affect]

TTS

• Add specific line by line info of high level changes in this PR.

Usage

• You can potentially add a usage example below

python scripts/magpietts/infer_and_evaluate_streaming.py \
--checkpoint_files ${CKPT} \
--hparams_files ${HPARAM} \
--codecmodel_path ${CODEC} \
--out_dir ${OUT_DIR} \
--datasets ${DATASET} \
--use_cfg \
--disable_fcd \
--apply_attention_prior

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[Copilot]

Pull Request Overview

This PR adds streaming inference functionality to MagpieTTS, enabling real-time text-to-speech generation by processing text input incrementally rather than all at once. The streaming algorithm maintains sliding windows for both text and audio history while managing attention priors to ensure coherent audio generation across text chunks.

Key Changes:

• Implementation of streaming inference algorithm with windowing mechanisms for text and audio tokens
• Addition of specialized attention prior handling for streaming mode with exponential weight support
• Extraction of common argument parsing functionality to support both streaming and non-streaming inference scripts

Reviewed Changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 6 comments.

File	Description
scripts/magpietts/infer_and_evaluate_streaming.py	New streaming inference script with chunked text processing and windowed generation
nemo/collections/tts/models/magpietts.py	Core streaming methods including windowed text processing and streaming-specific attention prior construction
scripts/magpietts/infer_and_evaluate.py	Refactored to extract common argument parsing logic and removed combined violin plot functionality
scripts/magpietts/README.md	Added documentation and usage example for the new streaming inference capability

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[rfejgin]

Hi Subhankar, nice work!

I have not dived into all the details but here are a few things that come to mind:

1. There seems to be some code duplication between the streaming and non-streaming version. I wonder if it will become hard to maintain over time. Specifically, in:

• infer_and_evaluate_streaming.py vs infer_and_evaluate.py - things like setting up checkpoint name, logging of metrics, etc. I do see that there is reuse certain functions from infer_and_evaluate.py but maybe there is more commonality to extract?
• in magpietts.py does construct_streaming_inference_prior() have major differences (that we actively use) from construct_streaming_prior() aside from including the offset?

I know that there is a tradeoff between eliminating code duplication vs making unified code overly complex, but maybe the above are worth another look?

2. A README or pointer to documentation on the design of the streaming algorithm would be of interest since it's non trivial.
3. More of a minor point, but I wonder if logic that needs to know model-specific details like creating a BOS token would be better to put inside the MagpieTTSModel class (accessible to the external script via some API). That way it would also be easier for it to be reused when folks use infer_batch() directly (not through infer_and_evaluate_streaming.py), which is what I believe they do in Riva.

[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