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Fix fastpitch test nightly #6730

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merged 4 commits into from
May 26, 2023
Merged

Fix fastpitch test nightly #6730

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67b0fb7
fix test fastpitch nightly
hsiehjackson May 25, 2023
7b6f740
Reformat
hsiehjackson May 25, 2023
87f9982
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] May 25, 2023
91b88e2
Fix if elif condition
hsiehjackson May 26, 2023
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24 changes: 23 additions & 1 deletion tests/collections/tts/models/test_fastpitch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,10 @@
This file implemented unit tests for loading all pretrained FastPitch NGC checkpoints and generating Mel-spectrograms.
The test duration breakdowns are shown below. In general, each test for a single model is ~25 seconds on an NVIDIA RTX A6000.
"""
import random

import pytest
import torch

from nemo.collections.tts.models import FastPitchModel

Expand All @@ -38,4 +41,23 @@ def test_inference(pretrained_model, language_specific_text_example):
model, language_id = pretrained_model
text = language_specific_text_example[language_id]
parsed_text = model.parse(text)
_ = model.generate_spectrogram(tokens=parsed_text)

# Multi-Speaker
speaker_id = None
reference_spec = None
reference_spec_lens = None

if hasattr(model.fastpitch, 'speaker_emb'):
speaker_id = 0
Comment on lines +46 to +51
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Nitpick: could create a helper function to handle the branching logic easier, like:

def _get_inputs(model):
    if hasattr(model.fastpitch, 'speaker_emb'):
        return 0, None, None
    if not hasattr(model.fastpitch, 'speaker_encoder'):
        None, None, None
    ...

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agreed. but it seems this add-ons are only applied to fastpitch models, not all. It looks good to just keep as-is.


if hasattr(model.fastpitch, 'speaker_encoder'):
if hasattr(model.fastpitch.speaker_encoder, 'lookup_module'):
speaker_id = 0
if hasattr(model.fastpitch.speaker_encoder, 'gst_module'):
bs, lens, t_spec = parsed_text.shape[0], random.randint(50, 100), model.cfg.n_mel_channels
reference_spec = torch.rand(bs, lens, t_spec)
reference_spec_lens = torch.tensor([lens]).long().expand(bs)

_ = model.generate_spectrogram(
tokens=parsed_text, speaker=speaker_id, reference_spec=reference_spec, reference_spec_lens=reference_spec_lens
)