Parakeet tdt by lmaksym · Pull Request #44171 · huggingface/transformers

lmaksym · 2026-02-20T08:44:46Z

What does this PR do?

This PR adds TDT decoder support for Parakeet ASR models, extending the existing CTC-only implementation.
It incorporates the initial TDT integration work from #41545 by @hainan-xv (was not merged) and and addresses all review feedback from both #41545 and #43357.

Changes

ParakeetForTDT model with greedy TDT decoding in generate()
ParakeetTDTDecoder (LSTM prediction network) and ParakeetTDTJointNetwork as nn.Module subclasses
Per-token timestamp generation via return_timestamps=True
AutoModelForTDT auto class with pipeline, processor, and tokenizer integration
Flat ParakeetTDTConfig matching the CTC pattern (no nested decoder/joint configs)
Shared ParakeetPreTrainedModel base between CTC and TDT (no separate TDT base class)
NeMo-to-HF weight conversion script for TDT models
Documentation and tests following existing CTC patterns

Validation

278 unit tests pass, make check-repo passes
CTC model unaffected by changes
LibriSpeech test-clean: 2.09% WER (matches NVIDIA published ~2-3%)
Timestamps validated against commercial ASR (94.3% within 2 frames)
Model: MaksL/parakeet-tdt-0.6b-v3

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@ebezzam and @hainan-xv please review

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Implement Token-and-Duration Transducer (TDT) decoding for Parakeet models, extending the existing CTC-only support. This adds ParakeetForTDT with greedy TDT decoding in generate(), per-token timestamp generation, and full integration with AutoModelForTDT, processors, and ASR pipeline.

ebezzam

@lmaksym thank you putting together the PRs cleanly! I pushed a few changes for adapting to Transformers convention and added integration tests to compare with the original model from NeMo.

@hainan-xv and @nithinraok, your input could be useful for the TDT decoding, and also the loss computation.

- Use -100 label padding for training (HF convention) - Fix timestamp recording in inner blank-seeking loop - Add max_symbols_per_step guard matching NeMo - Clean up decoding loop - Add TDT training example to docs - Use setUpClass for TDT integration tests

hainan-xv

Left a comment on the loss computation part.

…arakeet-tdt

ebezzam

@lmaksym thanks for porting the TDT loss! it's nice (1) to not have to depend on torchaudio and (2) to make the TDT loss available in Transformers!

It is functional with this example (single GPU): https://gist.github.com/ebezzam/6382bdabfc64bb2541ca9f77deb7678d#file-tdt_training_snippet-py
But quite slow...

I wonder if there is a custom gradient computation in NeMo? As I noticed in the paper (Section 3.1), they say "We derive an analytical solution for the gradient of the TDT loss, since automatic differentiation for transducer loss is highly inefficient."

FYI I can test/fix on my side for multi-GPU compatibility.

lmaksym · 2026-03-03T22:30:34Z

@lmaksym thanks for porting the TDT loss! it's nice (1) to not have to depend on torchaudio and (2) to make the TDT loss available in Transformers!

It is functional with this example (single GPU): https://gist.github.com/ebezzam/6382bdabfc64bb2541ca9f77deb7678d#file-tdt_training_snippet-py But quite slow...

I wonder if there is a custom gradient computation in NeMo? As I noticed in the paper (Section 3.1), they say "We derive an analytical solution for the gradient of the TDT loss, since automatic differentiation for transducer loss is highly inefficient."

FYI I can test/fix on my side for multi-GPU compatibility.

I'll look into that

HuggingFaceDocBuilderDev · 2026-05-19T03:21:19Z

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

ebezzam · 2026-05-19T04:32:29Z

run-slow: lasr, parakeet

github-actions · 2026-05-19T04:33:46Z

Workflow Run ⚙️

This comment contains run-slow, running the specified jobs:

models: ["models/lasr", "models/parakeet"]
quantizations: []

github-actions · 2026-05-19T04:42:00Z

CI Results

Workflow Run ⚙️

Commit Info

Context	Commit	Description
RUN	5b0b54bf	workflow commit (merge commit)
PR	c2e22872	branch commit (from PR)
main	c0008d14	base commit (on `main`)

✅ No failing test specific to this PR 🎉 👏 !

github-actions · 2026-05-19T06:04:27Z

[For maintainers] Suggested jobs to run (before merge)

run-slow: auto, encodec, lasr, parakeet

github-actions · 2026-05-19T06:21:10Z

View the CircleCI Test Summary for this PR:

https://huggingface.co/spaces/transformers-community/circle-ci-viz?pr=44171&sha=f97809

@ArthurZucker

* parakeet tdt intergration * Add TDT decoder support for Parakeet ASR models Implement Token-and-Duration Transducer (TDT) decoding for Parakeet models, extending the existing CTC-only support. This adds ParakeetForTDT with greedy TDT decoding in generate(), per-token timestamp generation, and full integration with AutoModelForTDT, processors, and ASR pipeline. * Add expected outputs for TDT, small fixes. * Separate CTC and TDT generate outputs. * Work with auto device, better init, * Test timestamps and expose token duration. * Add reproducer link. * fix: align TDT training and decoding with NeMo implementation - Use -100 label padding for training (HF convention) - Fix timestamp recording in inner blank-seeking loop - Add max_symbols_per_step guard matching NeMo - Clean up decoding loop - Add TDT training example to docs - Use setUpClass for TDT integration tests * revert: restore lasr generated files to original state * warn: torchaudio rnnt_loss does not train duration head * Relax timestamp test, and test nits. * feat: TDT training * chore: for cuda detection and run without patching * Equivalent timestamp processing as Nemo, and various nits/cleanup. * Simplify durations config. * Update training examples. * chore: enable parralelism * chore: performance optimization * fix: formatting * Doc and testing nits * Use active mask from current step, and nits. * Better pre-allocate. * TDT has separate pad token and blank token. * Regenerate lasr. * Style checks and nits * Nits, put back ctc loss test * More standard model output. * Style * Remove compute_loss flag and allow monkey patching to tdt loss * Update src/transformers/models/parakeet/modular_parakeet.py Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com> * Address various comments. * More compatible with Transformers forward/generate approach * compile option for generation and decoder cache * Cleaner, better conventions. * Update with main. * doc nits * Imitate whisper for encoder outputs as input * Address tests and nits. * Inherit from GenerateMixIn for get_compiled_call * Comment nit * forward cleanup * generate cleanup + separate generation file * generate: add _supported_generation_modes * automatic init of the loss * modular cleanups * use is_encoder_decoder * timestamp processing fully from tokens + durations * convertion script update * test update * make * test update * test update * ensure correct loss computation * kernel loss * test loss integration * push to hub pr * integration tests to rely fully on transcripts * udpate fixtures * we don't need to monkey patch with numba anymore! * fix pipeline usage * nit * fix usage * Pass through tests and examples: improve kernel fallback, update with nvidia checkpoint, style checks. * Update checkpoint * Add TDT to mapping after merge. * Fix lasr generate test. * Output attention mask if labels provided for computing loss. * Apply suggestion from @ArthurZucker Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Improve ParakeetTDTDecoderCache definition and usage. * Remove tuple parsing. * processor refactor * Update conversion. * Remove kernel to address in separate PR, modular nit. * Lasr modular * Add attention mask docstring. --------- Co-authored-by: Hainan Xu <hainanx@nvidia.com> Co-authored-by: Eric B <ebezzam@gmail.com> Co-authored-by: Eric Bezzam <4757445+ebezzam@users.noreply.github.com> Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Tarek Ziade <tarek@ziade.org>

@ArthurZucker

* parakeet tdt intergration * Add TDT decoder support for Parakeet ASR models Implement Token-and-Duration Transducer (TDT) decoding for Parakeet models, extending the existing CTC-only support. This adds ParakeetForTDT with greedy TDT decoding in generate(), per-token timestamp generation, and full integration with AutoModelForTDT, processors, and ASR pipeline. * Add expected outputs for TDT, small fixes. * Separate CTC and TDT generate outputs. * Work with auto device, better init, * Test timestamps and expose token duration. * Add reproducer link. * fix: align TDT training and decoding with NeMo implementation - Use -100 label padding for training (HF convention) - Fix timestamp recording in inner blank-seeking loop - Add max_symbols_per_step guard matching NeMo - Clean up decoding loop - Add TDT training example to docs - Use setUpClass for TDT integration tests * revert: restore lasr generated files to original state * warn: torchaudio rnnt_loss does not train duration head * Relax timestamp test, and test nits. * feat: TDT training * chore: for cuda detection and run without patching * Equivalent timestamp processing as Nemo, and various nits/cleanup. * Simplify durations config. * Update training examples. * chore: enable parralelism * chore: performance optimization * fix: formatting * Doc and testing nits * Use active mask from current step, and nits. * Better pre-allocate. * TDT has separate pad token and blank token. * Regenerate lasr. * Style checks and nits * Nits, put back ctc loss test * More standard model output. * Style * Remove compute_loss flag and allow monkey patching to tdt loss * Update src/transformers/models/parakeet/modular_parakeet.py Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com> * Address various comments. * More compatible with Transformers forward/generate approach * compile option for generation and decoder cache * Cleaner, better conventions. * Update with main. * doc nits * Imitate whisper for encoder outputs as input * Address tests and nits. * Inherit from GenerateMixIn for get_compiled_call * Comment nit * forward cleanup * generate cleanup + separate generation file * generate: add _supported_generation_modes * automatic init of the loss * modular cleanups * use is_encoder_decoder * timestamp processing fully from tokens + durations * convertion script update * test update * make * test update * test update * ensure correct loss computation * kernel loss * test loss integration * push to hub pr * integration tests to rely fully on transcripts * udpate fixtures * we don't need to monkey patch with numba anymore! * fix pipeline usage * nit * fix usage * Pass through tests and examples: improve kernel fallback, update with nvidia checkpoint, style checks. * Update checkpoint * Add TDT to mapping after merge. * Fix lasr generate test. * Output attention mask if labels provided for computing loss. * Apply suggestion from @ArthurZucker Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Improve ParakeetTDTDecoderCache definition and usage. * Remove tuple parsing. * processor refactor * Update conversion. * Remove kernel to address in separate PR, modular nit. * Lasr modular * Add attention mask docstring. --------- Co-authored-by: Hainan Xu <hainanx@nvidia.com> Co-authored-by: Eric B <ebezzam@gmail.com> Co-authored-by: Eric Bezzam <4757445+ebezzam@users.noreply.github.com> Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Tarek Ziade <tarek@ziade.org>

@ArthurZucker

* parakeet tdt intergration * Add TDT decoder support for Parakeet ASR models Implement Token-and-Duration Transducer (TDT) decoding for Parakeet models, extending the existing CTC-only support. This adds ParakeetForTDT with greedy TDT decoding in generate(), per-token timestamp generation, and full integration with AutoModelForTDT, processors, and ASR pipeline. * Add expected outputs for TDT, small fixes. * Separate CTC and TDT generate outputs. * Work with auto device, better init, * Test timestamps and expose token duration. * Add reproducer link. * fix: align TDT training and decoding with NeMo implementation - Use -100 label padding for training (HF convention) - Fix timestamp recording in inner blank-seeking loop - Add max_symbols_per_step guard matching NeMo - Clean up decoding loop - Add TDT training example to docs - Use setUpClass for TDT integration tests * revert: restore lasr generated files to original state * warn: torchaudio rnnt_loss does not train duration head * Relax timestamp test, and test nits. * feat: TDT training * chore: for cuda detection and run without patching * Equivalent timestamp processing as Nemo, and various nits/cleanup. * Simplify durations config. * Update training examples. * chore: enable parralelism * chore: performance optimization * fix: formatting * Doc and testing nits * Use active mask from current step, and nits. * Better pre-allocate. * TDT has separate pad token and blank token. * Regenerate lasr. * Style checks and nits * Nits, put back ctc loss test * More standard model output. * Style * Remove compute_loss flag and allow monkey patching to tdt loss * Update src/transformers/models/parakeet/modular_parakeet.py Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com> * Address various comments. * More compatible with Transformers forward/generate approach * compile option for generation and decoder cache * Cleaner, better conventions. * Update with main. * doc nits * Imitate whisper for encoder outputs as input * Address tests and nits. * Inherit from GenerateMixIn for get_compiled_call * Comment nit * forward cleanup * generate cleanup + separate generation file * generate: add _supported_generation_modes * automatic init of the loss * modular cleanups * use is_encoder_decoder * timestamp processing fully from tokens + durations * convertion script update * test update * make * test update * test update * ensure correct loss computation * kernel loss * test loss integration * push to hub pr * integration tests to rely fully on transcripts * udpate fixtures * we don't need to monkey patch with numba anymore! * fix pipeline usage * nit * fix usage * Pass through tests and examples: improve kernel fallback, update with nvidia checkpoint, style checks. * Update checkpoint * Add TDT to mapping after merge. * Fix lasr generate test. * Output attention mask if labels provided for computing loss. * Apply suggestion from @ArthurZucker Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Improve ParakeetTDTDecoderCache definition and usage. * Remove tuple parsing. * processor refactor * Update conversion. * Remove kernel to address in separate PR, modular nit. * Lasr modular * Add attention mask docstring. --------- Co-authored-by: Hainan Xu <hainanx@nvidia.com> Co-authored-by: Eric B <ebezzam@gmail.com> Co-authored-by: Eric Bezzam <4757445+ebezzam@users.noreply.github.com> Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Tarek Ziade <tarek@ziade.org>

@ArthurZucker

* parakeet tdt intergration * Add TDT decoder support for Parakeet ASR models Implement Token-and-Duration Transducer (TDT) decoding for Parakeet models, extending the existing CTC-only support. This adds ParakeetForTDT with greedy TDT decoding in generate(), per-token timestamp generation, and full integration with AutoModelForTDT, processors, and ASR pipeline. * Add expected outputs for TDT, small fixes. * Separate CTC and TDT generate outputs. * Work with auto device, better init, * Test timestamps and expose token duration. * Add reproducer link. * fix: align TDT training and decoding with NeMo implementation - Use -100 label padding for training (HF convention) - Fix timestamp recording in inner blank-seeking loop - Add max_symbols_per_step guard matching NeMo - Clean up decoding loop - Add TDT training example to docs - Use setUpClass for TDT integration tests * revert: restore lasr generated files to original state * warn: torchaudio rnnt_loss does not train duration head * Relax timestamp test, and test nits. * feat: TDT training * chore: for cuda detection and run without patching * Equivalent timestamp processing as Nemo, and various nits/cleanup. * Simplify durations config. * Update training examples. * chore: enable parralelism * chore: performance optimization * fix: formatting * Doc and testing nits * Use active mask from current step, and nits. * Better pre-allocate. * TDT has separate pad token and blank token. * Regenerate lasr. * Style checks and nits * Nits, put back ctc loss test * More standard model output. * Style * Remove compute_loss flag and allow monkey patching to tdt loss * Update src/transformers/models/parakeet/modular_parakeet.py Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com> * Address various comments. * More compatible with Transformers forward/generate approach * compile option for generation and decoder cache * Cleaner, better conventions. * Update with main. * doc nits * Imitate whisper for encoder outputs as input * Address tests and nits. * Inherit from GenerateMixIn for get_compiled_call * Comment nit * forward cleanup * generate cleanup + separate generation file * generate: add _supported_generation_modes * automatic init of the loss * modular cleanups * use is_encoder_decoder * timestamp processing fully from tokens + durations * convertion script update * test update * make * test update * test update * ensure correct loss computation * kernel loss * test loss integration * push to hub pr * integration tests to rely fully on transcripts * udpate fixtures * we don't need to monkey patch with numba anymore! * fix pipeline usage * nit * fix usage * Pass through tests and examples: improve kernel fallback, update with nvidia checkpoint, style checks. * Update checkpoint * Add TDT to mapping after merge. * Fix lasr generate test. * Output attention mask if labels provided for computing loss. * Apply suggestion from @ArthurZucker Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Improve ParakeetTDTDecoderCache definition and usage. * Remove tuple parsing. * processor refactor * Update conversion. * Remove kernel to address in separate PR, modular nit. * Lasr modular * Add attention mask docstring. --------- Co-authored-by: Hainan Xu <hainanx@nvidia.com> Co-authored-by: Eric B <ebezzam@gmail.com> Co-authored-by: Eric Bezzam <4757445+ebezzam@users.noreply.github.com> Co-authored-by: eustlb <94853470+eustlb@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Tarek Ziade <tarek@ziade.org>

@eustlb

## What does this PR do? Goal of this PR is to add a [kernel](https://huggingface.co/eustlb/tdt-loss/tree/v1) that has been drafted by @eustlb for the recently merged [Parakeet TDT](huggingface/transformers#44171). It's a very rough draft with a lot of Claude help as it's my first time working with kernels 😅 TODO - [ ] @eustlb: I guess you had a .cu file to create the .so file [here](https://huggingface.co/eustlb/tdt-loss/tree/v1/build/torch211-cxx11-cu128-x86_64-linux/tdt_loss)? From what I understand, we will need to add it here. - [ ] Add the kernel to https://huggingface.co/kernels-community - [ ] Update the tests - [ ] Double-check with [this checklist](https://github.com/huggingface/kernels-community/blob/main/CONTRIBUTING.md#how-to-add-a-new-kernel) Perhaps we can see how other kernels [used in Transformers](https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/hub_kernels.py) were integrated. As the kernel is for a function, maybe [rotary_pos_emb](https://github.com/huggingface/transformers/blob/0df9b7fcaab447c75543598e6d959065c2296a24/src/transformers/integrations/hub_kernels.py#L221) is a good example? [Here](https://github.com/huggingface/kernels-community/tree/main/rotary) are the files in this repo. Here is a draft PR for adding/using this kernel in Transformers: huggingface/transformers#46048 cc @vasqu --------- Co-authored-by: Deep-unlearning <steven@huggingface.co> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>

Hainan Xu and others added 2 commits February 20, 2026 09:45

parakeet tdt intergration

fa7d6e0

lmaksym force-pushed the parakeet-tdt branch from 6c98cb8 to f2b4938 Compare February 20, 2026 08:46

lmaksym mentioned this pull request Feb 20, 2026

Add Parakeet TDT model support #43357

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3 of 5 tasks

ebezzam self-assigned this Feb 20, 2026

ebezzam added New model Audio labels Feb 20, 2026

ebezzam added 5 commits February 25, 2026 17:23

Add expected outputs for TDT, small fixes.

fa36657

Separate CTC and TDT generate outputs.

05e2e34

Work with auto device, better init,

bb5ff33

Test timestamps and expose token duration.

9ec79b0

Add reproducer link.

33f128e

ebezzam reviewed Feb 26, 2026

View reviewed changes

Comment thread src/transformers/models/parakeet/modular_parakeet.py Resolved

Comment thread src/transformers/models/parakeet/modular_parakeet.py Resolved

lmaksym force-pushed the parakeet-tdt branch from 7f70c24 to 760b4b6 Compare February 27, 2026 15:32

revert: restore lasr generated files to original state

b33002f

hainan-xv suggested changes Feb 27, 2026

View reviewed changes

Comment thread src/transformers/models/parakeet/modeling_parakeet.py Resolved

lmaksym and others added 2 commits February 27, 2026 17:27

warn: torchaudio rnnt_loss does not train duration head

48b39dd

Relax timestamp test, and test nits.

e9f23ab

ebezzam reviewed Mar 2, 2026

View reviewed changes

Comment thread tests/models/parakeet/test_modeling_parakeet.py Resolved

lmaksym and others added 6 commits March 3, 2026 15:19

feat: TDT training

e2b97aa

chore: for cuda detection and run without patching

6b9fc73

Equivalent timestamp processing as Nemo, and various nits/cleanup.

6c879bc

Merge branch 'parakeet-tdt' of github.com:lmaksym/transformers into p…

149e17f

…arakeet-tdt

Simplify durations config.

36bfa63

Update training examples.

2df0ccc

ebezzam reviewed Mar 3, 2026

View reviewed changes

Comment thread src/transformers/models/parakeet/modular_parakeet.py Resolved

chore: enable parralelism

388c6d3

chore: performance optimization

08b2b55

ebezzam and others added 3 commits May 19, 2026 04:55

Remove kernel to address in separate PR, modular nit.

88810b9

Merge branch 'main' into parakeet-tdt

00111b3

Lasr modular

c2e2287

ebezzam and others added 2 commits May 19, 2026 06:46

Add attention mask docstring.

a969bc7

Merge branch 'main' into parakeet-tdt

bd2ae42

ebezzam enabled auto-merge May 19, 2026 04:54

ebezzam added this pull request to the merge queue May 19, 2026

github-merge-queue Bot removed this pull request from the merge queue due to failed status checks May 19, 2026

tarekziade added this pull request to the merge queue May 19, 2026

github-merge-queue Bot removed this pull request from the merge queue due to failed status checks May 19, 2026

Merge branch 'main' into parakeet-tdt

f978098

tarekziade enabled auto-merge May 19, 2026 06:05

ebezzam disabled auto-merge May 19, 2026 06:15

ArthurZucker merged commit 38a8b55 into huggingface:main May 19, 2026
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ebezzam mentioned this pull request May 19, 2026

tdt-loss: add TDT loss kernel huggingface/kernels-community#882

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jmayank1511 mentioned this pull request May 20, 2026

Add cache-aware streaming and RNN-T head for Parakeet #46122

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BBC-Esq mentioned this pull request May 27, 2026

Faster preprocessing when using new Parakeet TDT please #46247

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feat(serve): native HTTP/WebSocket serving binary (starling-serve) sims1253/starling#9

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penguinwang96825 mentioned this pull request Sep 12, 2026

Fix Parakeet TDT conversion for nvidia/parakeet-tdt-1.1b (no <pad> in the NeMo vocab) #48733

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lmaksym commented Feb 20, 2026

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