Junkun Chen, He Bai, Renjie Zheng, Xintong Li, Mingbo Ma, Liang Huang
We lifted 7 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| PaddlePaddle/PaddleSpeech | canonical | 1 of 3 |
| richardbaihe/a3t | pwc_unofficial | 1 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| parse_infofile | Ran | richardbaihe/a3t/aggregate_output/sedit_decode.py code served (permissive licence) · get_code("eaea9b1425847241") |
| simple_repr | Ran | PaddlePaddle/PaddleSpeech/paddlespeech/audiotools/core/_julius.py code served (permissive licence) · get_code("7ac28de669f54554") |
| adadelta_eps_decay | Not yet run | richardbaihe/a3t/espnet/asr/asr_utils.py code served (permissive licence) · get_code("bb3665ddb6b8dd2d") |
| adam_lr_decay | Not yet run | richardbaihe/a3t/espnet/asr/asr_utils.py code served (permissive licence) · get_code("1fa5ca494ed9750a") |
| format_figure | Not yet run | PaddlePaddle/PaddleSpeech/paddlespeech/audiotools/core/display.py code served (permissive licence) · get_code("965fb4d02c97778c") |
| r128stats | Not yet run | PaddlePaddle/PaddleSpeech/paddlespeech/audiotools/core/ffmpeg.py code served (permissive licence) · get_code("4d1f42e7242859a7") |
| restore_snapshot | Not yet run | richardbaihe/a3t/espnet/asr/asr_utils.py code served (permissive licence) · get_code("96081a60369c59e0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recently, speech representation learning has improved many speech-related tasks such as speech recognition, speech classification, and speech-totext translation. However, all the above tasks are in the direction of speech understanding, but for the inverse direction, speech synthesis, the potential of representation learning is yet to be realized, due to the challenging nature of generating high-quality speech. To address this problem, we propose our framework, Alignment-Aware Acoustic-Text Pretraining (A 3 T), which reconstructs masked acoustic signals with text input and acoustic-text alignment during training. In this way, the pretrained model can generate high quality reconstructed spectrogram, which can be applied to the speech editing and unseen speaker TTS directly. Experiments show A 3 T outperforms SOTA models on speech editing, and improves multi-speaker speech synthesis without the external speaker verification model. 1
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2203.09690")
get_code_for_paper("2203.09690")
have("2203.09690")
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