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import unittest |
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import torch |
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from tqdm import tqdm |
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from fairseq import utils |
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from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion |
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from tests.speech import TestFairseqSpeech |
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@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") |
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class TestFastSpeech2(TestFairseqSpeech): |
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def setUp(self): |
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self.set_up_ljspeech() |
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@torch.no_grad() |
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def test_ljspeech_fastspeech2_checkpoint(self): |
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models, cfg, task, generator = self.download_and_load_checkpoint( |
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"ljspeech_fastspeech2_g2p.pt", |
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arg_overrides={ |
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"config_yaml": "cfg_ljspeech_g2p.yaml", |
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"vocoder": "griffin_lim", |
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"fp16": False, |
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}, |
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) |
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batch_iterator = self.get_batch_iterator(task, "ljspeech_test", 65_536, 4_096) |
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progress = tqdm(batch_iterator, total=len(batch_iterator)) |
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mcd, n_samples = 0.0, 0 |
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for sample in progress: |
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sample = utils.move_to_cuda(sample) if self.use_cuda else sample |
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hypos = generator.generate(models[0], sample, has_targ=True) |
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rets = batch_mel_cepstral_distortion( |
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[hypo["targ_waveform"] for hypo in hypos], |
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[hypo["waveform"] for hypo in hypos], |
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sr=task.sr, |
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) |
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mcd += sum(d.item() for d, _ in rets) |
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n_samples += len(sample["id"].tolist()) |
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mcd = round(mcd / n_samples, 1) |
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reference_mcd = 3.2 |
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print(f"MCD: {mcd} (reference: {reference_mcd})") |
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self.assertAlmostEqual(mcd, reference_mcd, delta=0.1) |
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if __name__ == "__main__": |
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unittest.main() |
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