We lifted 6 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| yl4579/pl-bert | pwc_unofficial | 3 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| build_dataloader | Ran | yl4579/pl-bert/simple_loader.py code served (permissive licence) · get_code("164d730b65626a89") |
| length_to_mask | Ran | yl4579/pl-bert/utils.py code served (permissive licence) · get_code("c992f7522596af60") |
| remove_accents | Ran | yl4579/pl-bert/text_normalize.py code served (permissive licence) · get_code("09fd0528a6789379") |
| normalize_split | Not yet run | yl4579/pl-bert/text_normalize.py code served (permissive licence) · get_code("24ba5ac0d31cfc90") |
| scan_checkpoint | Not yet run | yl4579/pl-bert/utils.py code served (permissive licence) · get_code("a78ec371614680b0") |
| split_given_size | Not yet run | yl4579/pl-bert/text_normalize.py code served (permissive licence) · get_code("4eb9eab560b3a8e9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Large-scale pre-trained language models have been shown to be helpful in improving the naturalness of text-to-speech (TTS) models by enabling them to produce more naturalistic prosodic patterns. However, these models are usually word-level or sup-phoneme-level and jointly trained with phonemes, making them inefficient for the downstream TTS task where only phonemes are needed. In this work, we propose a phoneme-level BERT (PL-BERT) with a pretext task of predicting the corresponding graphemes along with the regular masked phoneme predictions. Subjective evaluations show that our phoneme-level BERT encoder has significantly improved the mean opinion scores (MOS) of rated naturalness of synthesized speech compared with the state-of-the-art (SOTA) StyleTTS baseline on out-of-distribution (OOD) texts.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2301.08810")
get_code_for_paper("2301.08810")
have("2301.08810")
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