add integration test for fastspeech2

Summary: Adding integration test (based on test set scores on pre-trained checkpoints) for fastspeech2

Reviewed By: yuntang

Differential Revision: D33143301

fbshipit-source-id: dca0841b43dd1cb2933ce5c652ed3cdff0fc4a52

tests/speech/test_fastspeech2.py

@@ -0,0 +1,67 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import unittest
+
+import torch
+from tqdm import tqdm
+
+from fairseq.checkpoint_utils import load_model_ensemble_and_task
+from fairseq import utils
+from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion
+from tests.speech import TestFairseqSpeech
+
+
+@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
+class TestFastSpeech2(TestFairseqSpeech):
+    def setUp(self):
+        self.set_up_ljspeech()
+
+    @torch.no_grad()
+    def test_ljspeech_fastspeech2_checkpoint(self):
+        checkpoint_filename = "ljspeech_fastspeech2_g2p.pt"
+        path = self.download(self.base_url, self.root, checkpoint_filename)
+
+        models, cfg, task = load_model_ensemble_and_task(
+            [path.as_posix()], arg_overrides={
+                "data": self.root.as_posix(),
+                "config_yaml": "cfg_ljspeech_g2p.yaml",
+                "vocoder": "griffin_lim",
+                "fp16": False
+            }
+        )
+        if self.use_cuda:
+            for model in models:
+                model.cuda()
+
+        test_split = "ljspeech_test"
+        task.load_dataset(test_split)
+        batch_iterator = task.get_batch_iterator(
+            dataset=task.dataset(test_split),
+            max_tokens=65_536, max_positions=4_096, num_workers=1
+        ).next_epoch_itr(shuffle=False)
+        progress = tqdm(batch_iterator, total=len(batch_iterator))
+        generator = task.build_generator(models, cfg)
+
+        mcd, n_samples = 0., 0
+        for sample in progress:
+            sample = utils.move_to_cuda(sample) if self.use_cuda else sample
+            hypos = generator.generate(models[0], sample, has_targ=True)
+            rets = batch_mel_cepstral_distortion(
+                [hypo["targ_waveform"] for hypo in hypos],
+                [hypo["waveform"] for hypo in hypos],
+                sr=task.sr
+            )
+            mcd += sum(d.item() for d, _ in rets)
+            n_samples += len(sample["id"].tolist())
+
+        mcd = round(mcd / n_samples, 1)
+        reference_mcd = 3.2
+        print(f"MCD: {mcd} (reference: {reference_mcd})")
+        self.assertAlmostEqual(mcd, reference_mcd, delta=0.1)
+
+
+if __name__ == "__main__":
+    unittest.main()