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[TTS] Add callback for saving audio during FastPitch training #6665

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merged 2 commits into from
May 18, 2023

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What does this PR do ?

Add an optional and configurable callback for FastPitch training which allows providing a vocoder checkpoint and saving any combination of model output (spectrogram, alignment, audio, audio with gt duration, prosody features) to (local_disk, tensorboard, wandb).

The callback expects the user to provide a separate manifest with all of the examples to generate the audio for. A user can create this by manually selecting 5-10 rows from their validation manifest.

The callback interface should be generic enough to use it for logging any training recipe, such as vocoder training (which will be added in a future PR).

Collection: [TTS]

Changelog

  • Add generic LoggingCallback interface for logging model predictions
  • Add FastPitchArtifactGenerator to log model outputs for FastPitch
  • Modify new fastpitch example config to show to configure the logger
  • Set FastPitch min_token_duration to 1 to avoid various edge cases during inference where output audio is empty array (all duration predictions are 0). This is also more accurate as the aligner requires the alignment to assign at least 1 audio frame to each input token. I could make this is a config (defaulting to 0) if anyone thinks the change is risky.

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PR Type:

  • New Feature
  • Bugfix
  • Documentation

@rlangman rlangman force-pushed the tts_callback branch 2 times, most recently from f19569b to 6cb8745 Compare May 17, 2023 22:15
Signed-off-by: Ryan <rlangman@nvidia.com>
nemo/collections/tts/modules/fastpitch.py Outdated Show resolved Hide resolved
nemo/collections/tts/parts/utils/callbacks.py Show resolved Hide resolved
Comment on lines 38 to 59
if type == "hifigan":
from nemo.collections.tts.models import HifiGanModel

vocoder = HifiGanModel.load_from_checkpoint(checkpoint_path).eval()
elif type == "univnet":
from nemo.collections.tts.models import UnivNetModel

vocoder = UnivNetModel.load_from_checkpoint(checkpoint_path).eval()
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Should we allow users to pick the NGC checkpoints as a default?

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I can see how easy/difficult it is to add logic to accept either checkpoint, or string name of pretrained model.

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Not a huge deal if not since they can always download it manually. Could be convenient, though.

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Added

Signed-off-by: Ryan <rlangman@nvidia.com>
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LGTM!

@rlangman rlangman merged commit 4c6e2bc into main May 18, 2023
@rlangman rlangman deleted the tts_callback branch May 18, 2023 16:39
KunalDhawan added a commit that referenced this pull request May 18, 2023
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* Add support for RNNT/hybrid models to partial transcribe (#6609)

* Add support for RNNT/hybrid models to partial transcribe

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* eval_beamsearch_ngram.py with hybrid ctc (#6656)

* separate_punctuation = false

* ctc decoding strategy = model.decoding

* transcribe(files, logprobs=True) returns logprobs



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* minor fix for missing chat attr (#6671)

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* [TTS] Add callback for saving audio during FastPitch training (#6665)

* [TTS] Add callback for saving audio during FastPitch training

Signed-off-by: Ryan <rlangman@nvidia.com>

* [TTS] Allow NGC model name for vocoder

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hsiehjackson pushed a commit to hsiehjackson/NeMo that referenced this pull request Jun 2, 2023
…#6665)

* [TTS] Add callback for saving audio during FastPitch training

Signed-off-by: Ryan <rlangman@nvidia.com>

* [TTS] Allow NGC model name for vocoder

Signed-off-by: Ryan <rlangman@nvidia.com>

---------

Signed-off-by: Ryan <rlangman@nvidia.com>
Signed-off-by: hsiehjackson <c2hsieh@ucsd.edu>
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