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import unittest |
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import torch |
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from tests.speech import TestFairseqSpeech |
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from fairseq.data.data_utils import post_process |
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from fairseq import utils |
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from omegaconf import open_dict |
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S3_BASE_URL = "https://dl.fbaipublicfiles.com/fairseq" |
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@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") |
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class TestWav2Vec2(TestFairseqSpeech): |
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def setUp(self): |
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self._set_up( |
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"librispeech_w2v2", |
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"conformer/wav2vec2/librispeech", |
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[ |
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"test_librispeech-other.ltr", |
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"test_librispeech-other.tsv", |
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"test_librispeech-other_small.ltr_100", |
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"test_librispeech-other_small.tsv", |
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"test-other.zip", |
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"dict.ltr.txt", |
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"dict.ltr_100.txt", |
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], |
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) |
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self.unzip_files( |
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"test-other.zip", |
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) |
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def test_transformer_w2v2(self): |
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self.base_test( |
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ckpt_name="transformer_oss_small_100h.pt", |
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reference_score=38, |
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score_delta=1, |
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dataset="test_librispeech-other", |
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max_tokens=1000000, |
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max_positions=(700000, 1000), |
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arg_overrides={ |
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"task": "audio_finetuning", |
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"labels": "ltr", |
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"nbest": 1, |
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"tpu": False, |
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}, |
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strict=False, |
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) |
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def test_conformer_w2v2(self): |
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self.base_test( |
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ckpt_name="conformer_LS_PT_LS_FT_rope.pt", |
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reference_score=4.5, |
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score_delta=1, |
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dataset="test_librispeech-other_small", |
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max_tokens=1000000, |
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max_positions=(700000, 1000), |
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arg_overrides={ |
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"task": "audio_finetuning", |
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"labels": "ltr_100", |
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"nbest": 1, |
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"tpu": False, |
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}, |
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strict=True, |
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) |
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def build_generator(self, task, models, cfg): |
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try: |
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from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder |
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except Exception: |
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raise Exception("Cannot run this test without flashlight dependency") |
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with open_dict(cfg): |
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cfg.nbest = 1 |
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return W2lViterbiDecoder(cfg, task.target_dictionary) |
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def postprocess_tokens(self, task, target, hypo_tokens): |
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tgt_tokens = utils.strip_pad(target, task.target_dictionary.pad()).int().cpu() |
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tgt_str = task.target_dictionary.string(tgt_tokens) |
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tgt_str = post_process(tgt_str, "letter") |
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hypo_pieces = task.target_dictionary.string(hypo_tokens) |
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hypo_str = post_process(hypo_pieces, "letter") |
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return tgt_str, hypo_str |
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if __name__ == "__main__": |
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unittest.main() |
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