- 16 Feb, 2023 1 commit
-
-
Zhaoheng Ni authored
Summary: In https://github.com/pytorch/audio/issues/2873, layer normalization is applied to waveforms for SSL models trained on large scale datasets. The word error rate is significantly reduced after the change. The PR updates the results for the affected models. Without the change in https://github.com/pytorch/audio/issues/2873, here is the WER result table: | Model | dev-clean | dev-other | test-clean | test-other | |:------------------------------------------------------------------------------------------------|-----------:|-----------:|-----------:|-----------:| | [WAV2VEC2_ASR_LARGE_LV60K_10M](https://pytorch.org/audio/main/generated/torchaudio.pipelines.WAV2VEC2_ASR_LARGE_LV60K_10M.html#torchaudio.pipelines.WAV2VEC2_ASR_LARGE_LV60K_10M) | 10.59| 15.62| 9.58| 16.33| | [WAV2VEC2_ASR_LARGE_LV60K_100H](https://pytorch.org/audio/main/generated/torchaudio.pipelines.WAV2VEC2_ASR_LARGE_LV60K_100H.html#torchaudio.pipelines.WAV2VEC2_ASR_LARGE_LV60K_100H) | 2.80| 6.01| 2.82| 6.34| | [WAV2VEC2_ASR_LARGE_LV60K_960H](https://pytorch.org/audio/main/generated/torchaudio.pipelines.WAV2VEC2_ASR_LARGE_LV60K_960H.html#torchaudio.pipelines.WAV2VEC2_ASR_LARGE_LV60K_960H) | 2.36| 4.43| 2.41| 4.96| | [HUBERT_ASR_LARGE](https://pytorch.org/audio/main/generated/torchaudio.pipelines.HUBERT_ASR_LARGE.html#torchaudio.pipelines.HUBERT_ASR_LARGE) | 1.85| 3.46| 2.09| 3.89| | [HUBERT_ASR_XLARGE](https://pytorch.org/audio/main/generated/torchaudio.pipelines.HUBERT_ASR_XLARGE.html#torchaudio.pipelines.HUBERT_ASR_XLARGE) | 2.21| 3.40| 2.26| 4.05| After applying layer normalization, here is the updated result: | Model | dev-clean | dev-other | test-clean | test-other | |:------------------------------------------------------------------------------------------------|-----------:|-----------:|-----------:|-----------:| | [WAV2VEC2_ASR_LARGE_LV60K_10M](https://pytorch.org/audio/main/generated/torchaudio.pipelines.WAV2VEC2_ASR_LARGE_LV60K_10M.html#torchaudio.pipelines.WAV2VEC2_ASR_LARGE_LV60K_10M) | 6.77| 10.03| 6.87| 10.51| | [WAV2VEC2_ASR_LARGE_LV60K_100H](https://pytorch.org/audio/main/generated/torchaudio.pipelines.WAV2VEC2_ASR_LARGE_LV60K_100H.html#torchaudio.pipelines.WAV2VEC2_ASR_LARGE_LV60K_100H) | 2.19| 4.55| 2.32| 4.64| | [WAV2VEC2_ASR_LARGE_LV60K_960H](https://pytorch.org/audio/main/generated/torchaudio.pipelines.WAV2VEC2_ASR_LARGE_LV60K_960H.html#torchaudio.pipelines.WAV2VEC2_ASR_LARGE_LV60K_960H) | 1.78| 3.51| 2.03| 3.68| | [HUBERT_ASR_LARGE](https://pytorch.org/audio/main/generated/torchaudio.pipelines.HUBERT_ASR_LARGE.html#torchaudio.pipelines.HUBERT_ASR_LARGE) | 1.77| 3.32| 2.03| 3.68| | [HUBERT_ASR_XLARGE](https://pytorch.org/audio/main/generated/torchaudio.pipelines.HUBERT_ASR_XLARGE.html#torchaudio.pipelines.HUBERT_ASR_XLARGE) | 1.73| 2.72| 1.90| 3.16| Pull Request resolved: https://github.com/pytorch/audio/pull/3070 Reviewed By: mthrok Differential Revision: D43365313 Pulled By: nateanl fbshipit-source-id: 34a60ad2e5eb1299da64ef88ff0208ec8ec76e91
-
- 18 Jan, 2022 1 commit
-
-
Caroline Chen authored
Summary: additionally add decoding results for wav2vec2 large and also on the test-clean dataset Pull Request resolved: https://github.com/pytorch/audio/pull/2161 Reviewed By: mthrok Differential Revision: D33644670 Pulled By: carolineechen fbshipit-source-id: a219a15af46f82a6bd90169bb3001dbad8f0a96e
-
- 05 Jan, 2022 1 commit
-
-
Caroline Chen authored
Summary: add script for running CTC beam search decoder on librispeech dataset with torchaudio pretrained wav2vec2 models Pull Request resolved: https://github.com/pytorch/audio/pull/2130 Reviewed By: mthrok Differential Revision: D33419436 Pulled By: carolineechen fbshipit-source-id: 0a0d00f4c17ecdbb497c9eda78673aa939d73c57
-
