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import unittest |
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from collections import namedtuple |
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from pathlib import Path |
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import torch |
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from tqdm import tqdm |
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import fairseq |
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from fairseq import utils |
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from fairseq.checkpoint_utils import load_model_ensemble_and_task |
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from fairseq.scoring.bleu import SacrebleuScorer |
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from fairseq.tasks import import_tasks |
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from tests.speech import S3_BASE_URL, TestFairseqSpeech |
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@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") |
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class TestLibrispeechDualInputWavTransformer(TestFairseqSpeech): |
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def setUp(self): |
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dataset_id = "librispeech_wvtrasnformer" |
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base_url = "https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/librispeech/finetuned" |
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data_filenames = [ |
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"checkpoint_ave_10.pt", |
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"spm.model", |
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"src_dict.txt", |
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"tgt_dict.txt", |
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"config.yaml", |
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] |
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self._set_up( |
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dataset_id, |
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"s2t", |
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[ |
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"librispeech_flac_test-other.tsv", |
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"librispeech_flac_test-other.zip", |
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], |
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) |
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for filename in data_filenames: |
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self.download(base_url, self.root, filename) |
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def import_user_module(self): |
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user_dir = ( |
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Path(fairseq.__file__).parent.parent / "examples/speech_text_joint_to_text" |
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) |
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Arg = namedtuple("Arg", ["user_dir"]) |
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arg = Arg(user_dir.__str__()) |
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utils.import_user_module(arg) |
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@torch.no_grad() |
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def test_librispeech_dualinput_wav_transformer_checkpoint(self): |
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self.import_user_module() |
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checkpoint_filename = "checkpoint_ave_10.pt" |
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arg_overrides = { |
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"config_yaml": "config.yaml", |
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"load_pretrained_speech_text_encoder": "", |
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"load_pretrained_speech_text_decoder": "", |
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"beam": 10, |
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"nbest": 1, |
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"lenpen": 1.0, |
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"load_speech_only": True, |
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} |
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self.base_test( |
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checkpoint_filename, |
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4.6, |
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dataset="librispeech_flac_test-other", |
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max_tokens=800000, |
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max_positions=(800000, 1024), |
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arg_overrides=arg_overrides, |
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) |
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if __name__ == "__main__": |
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unittest.main() |
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