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speech integration tests (batch 1)
Summary: Adding the first batch of speech integration tests (based on test set scores on pre-trained checkpoints) for - S2T transformer - TTS transformer Reviewed By: yuntang Differential Revision: D33050653 fbshipit-source-id: fb5bb9f46e8e17cb705971ca1990c8e1cb99d5f9
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tests/speech/__init__.py
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tests/speech/__init__.py
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import os
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from pathlib import Path
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import unittest
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import torch
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S3_BASE_URL = "https://dl.fbaipublicfiles.com/fairseq"
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class TestFairseqSpeech(unittest.TestCase):
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@classmethod
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def download(cls, base_url: str, out_root: Path, filename: str):
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url = f"{base_url}/{filename}"
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path = out_root / filename
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if not path.exists():
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torch.hub.download_url_to_file(url, path.as_posix(), progress=True)
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return path
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def set_up_librispeech(self):
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self.use_cuda = torch.cuda.is_available()
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self.root = Path.home() / ".cache" / "fairseq" / "librispeech"
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self.root.mkdir(exist_ok=True, parents=True)
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os.chdir(self.root)
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self.data_filenames = [
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"cfg_librispeech.yaml",
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"spm_librispeech_unigram10000.model",
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"spm_librispeech_unigram10000.txt",
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"librispeech_test-other.tsv",
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"librispeech_test-other.zip"
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]
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self.base_url = f"{S3_BASE_URL}/s2t/librispeech"
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for filename in self.data_filenames:
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self.download(self.base_url, self.root, filename)
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def set_up_ljspeech(self):
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self.use_cuda = torch.cuda.is_available()
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self.root = Path.home() / ".cache" / "fairseq" / "ljspeech"
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self.root.mkdir(exist_ok=True, parents=True)
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os.chdir(self.root)
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self.data_filenames = [
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"cfg_ljspeech_g2p.yaml",
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"ljspeech_g2p_gcmvn_stats.npz",
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"ljspeech_g2p.txt",
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"ljspeech_test.tsv",
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"ljspeech_test.zip"
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]
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self.base_url = f"{S3_BASE_URL}/s2/ljspeech"
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for filename in self.data_filenames:
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self.download(self.base_url, self.root, filename)
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tests/speech/test_s2t_transformer.py
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tests/speech/test_s2t_transformer.py
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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from argparse import Namespace
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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.checkpoint_utils import load_model_ensemble_and_task
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from fairseq import utils
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from fairseq.scoring.wer import WerScorer
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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 TestS2TTransformer(TestFairseqSpeech):
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def setUp(self):
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self.set_up_librispeech()
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@torch.no_grad()
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def test_librispeech_s2t_transformer_s_checkpoint(self):
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checkpoint_filename = "librispeech_transformer_s.pt"
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path = self.download(self.base_url, self.root, checkpoint_filename)
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models, cfg, task = load_model_ensemble_and_task(
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[path.as_posix()], arg_overrides={
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"data": self.root.as_posix(),
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"config_yaml": "cfg_librispeech.yaml"
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}
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)
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if self.use_cuda:
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for model in models:
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model.cuda()
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generator = task.build_generator(models, cfg)
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test_split = "librispeech_test-other"
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task.load_dataset(test_split)
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batch_iterator = task.get_batch_iterator(
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dataset=task.dataset(test_split),
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max_tokens=65_536, max_positions=(4_096, 1_024), num_workers=1
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).next_epoch_itr(shuffle=False)
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scorer_args = {"wer_tokenizer": "none", "wer_lowercase": False,
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"wer_remove_punct": False, "wer_char_level": False}
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scorer = WerScorer(Namespace(**scorer_args))
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progress = tqdm(enumerate(batch_iterator), total=len(batch_iterator))
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for batch_idx, sample in progress:
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sample = utils.move_to_cuda(sample) if self.use_cuda else sample
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hypo = task.inference_step(generator, models, sample)
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for i, sample_id in enumerate(sample["id"].tolist()):
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tgt_tokens = utils.strip_pad(sample["target"][i, :], task.tgt_dict.pad()).int().cpu()
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tgt_str = task.tgt_dict.string(tgt_tokens, "sentencepiece")
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hypo_str = task.tgt_dict.string(hypo[i][0]["tokens"].int().cpu(), "sentencepiece")
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if batch_idx == 0 and i < 3:
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print(f"T-{sample_id} {tgt_str}")
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print(f"H-{sample_id} {hypo_str}")
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scorer.add_string(tgt_str, hypo_str)
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reference_wer = 9.0
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print(scorer.result_string() + f" (reference: {reference_wer})")
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self.assertAlmostEqual(scorer.score(), reference_wer, delta=0.3)
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if __name__ == "__main__":
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unittest.main()
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67
tests/speech/test_tts_transformer.py
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tests/speech/test_tts_transformer.py
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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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.checkpoint_utils import load_model_ensemble_and_task
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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 TestTTSTransformer(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_tts_transformer_checkpoint(self):
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checkpoint_filename = "ljspeech_transformer_g2p.pt"
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path = self.download(self.base_url, self.root, checkpoint_filename)
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models, cfg, task = load_model_ensemble_and_task(
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[path.as_posix()], arg_overrides={
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"data": self.root.as_posix(),
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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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if self.use_cuda:
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for model in models:
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model.cuda()
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test_split = "ljspeech_test"
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task.load_dataset(test_split)
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batch_iterator = task.get_batch_iterator(
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dataset=task.dataset(test_split),
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max_tokens=65_536, max_positions=768, num_workers=1
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).next_epoch_itr(shuffle=False)
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progress = tqdm(batch_iterator, total=len(batch_iterator))
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generator = task.build_generator(models, cfg)
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mcd, n_samples = 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 = 3.3
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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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