paddlespeech.t2s.exps.syn_utils module

paddlespeech.t2s.exps.syn_utils.am_to_static(am_inference, am: str = 'fastspeech2_csmsc', inference_dir=typing.Union[os.PathLike, NoneType], speaker_dict: Optional[PathLike] = None)

paddlespeech.t2s.exps.syn_utils.denorm(data, mean, std)

paddlespeech.t2s.exps.syn_utils.get_am_inference(am: str = 'fastspeech2_csmsc', am_config: Optional[CfgNode] = None, am_ckpt: Optional[PathLike] = None, am_stat: Optional[PathLike] = None, phones_dict: Optional[PathLike] = None, tones_dict: Optional[PathLike] = None, speaker_dict: Optional[PathLike] = None, return_am: bool = False)

paddlespeech.t2s.exps.syn_utils.get_am_output(input: str, am_predictor: Layer, am: str, frontend: object, lang: str = 'zh', merge_sentences: bool = True, speaker_dict: Optional[PathLike] = None, spk_id: int = 0)

paddlespeech.t2s.exps.syn_utils.get_am_sublayer_output(am_sublayer_predictor, input)

paddlespeech.t2s.exps.syn_utils.get_chunks(data, block_size: int, pad_size: int)

paddlespeech.t2s.exps.syn_utils.get_frontend(lang: str = 'zh', phones_dict: Optional[PathLike] = None, tones_dict: Optional[PathLike] = None, use_rhy=False)

paddlespeech.t2s.exps.syn_utils.get_predictor(model_dir: Optional[PathLike] = None, model_file: Optional[PathLike] = None, params_file: Optional[PathLike] = None, device: str = 'cpu')

paddlespeech.t2s.exps.syn_utils.get_sentences(text_file: Optional[PathLike], lang: str = 'zh')

paddlespeech.t2s.exps.syn_utils.get_sess(model_path: Optional[PathLike], device: str = 'cpu', cpu_threads: int = 1, use_trt: bool = False)

paddlespeech.t2s.exps.syn_utils.get_streaming_am_output(input: str, am_encoder_infer_predictor, am_decoder_predictor, am_postnet_predictor, frontend, lang: str = 'zh', merge_sentences: bool = True)

paddlespeech.t2s.exps.syn_utils.get_test_dataset(test_metadata: List[Dict[str, Any]], am: str, speaker_dict: Optional[PathLike] = None, voice_cloning: bool = False)

paddlespeech.t2s.exps.syn_utils.get_voc_inference(voc: str = 'pwgan_csmsc', voc_config: Optional[PathLike] = None, voc_ckpt: Optional[PathLike] = None, voc_stat: Optional[PathLike] = None)

paddlespeech.t2s.exps.syn_utils.get_voc_output(voc_predictor, input)

paddlespeech.t2s.exps.syn_utils.norm(data, mean, std)

paddlespeech.t2s.exps.syn_utils.run_frontend(frontend: object, text: str, merge_sentences: bool = False, get_tone_ids: bool = False, lang: str = 'zh', to_tensor: bool = True)

paddlespeech.t2s.exps.syn_utils.voc_to_static(voc_inference, voc: str = 'pwgan_csmsc', inference_dir=typing.Union[os.PathLike, NoneType])