paddlespeech.t2s.datasets.preprocess_utils module

paddlespeech.t2s.datasets.preprocess_utils.compare_duration_and_mel_length(sentences, utt, mel)

check duration error, correct sentences[utt] if possible, else pop sentences[utt] Args:

sentences (Dict): sentences[utt] = [phones_list ,durations_list] utt (str): utt_id mel (np.ndarry): features (num_frames, n_mels)

paddlespeech.t2s.datasets.preprocess_utils.get_input_token(sentence, output_path, dataset='baker')

get phone set from training data and save it Args:

sentence (Dict): sentence: {'utt': ([char], [int])} output_path (str or path):path to save phone_id_map

paddlespeech.t2s.datasets.preprocess_utils.get_phn_dur(file_name)

read MFA duration.txt Args:

file_name (str or Path): path of gen_duration_from_textgrid.py's result

Returns:

Dict: sentence: {'utt': ([char], [int])}

paddlespeech.t2s.datasets.preprocess_utils.get_phones_tones(sentence, phones_output_path, tones_output_path, dataset='baker')

get phone set and tone set from training data and save it Args:

sentence (Dict): sentence: {'utt': ([char], [int])} phones_output_path (str or path): path to save phone_id_map tones_output_path (str or path): path to save tone_id_map

paddlespeech.t2s.datasets.preprocess_utils.get_spk_id_map(speaker_set, output_path)

paddlespeech.t2s.datasets.preprocess_utils.merge_silence(sentence)

merge silences Args:

sentence (Dict): sentence: {'utt': (([char], [int]), str)}