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113 lines
3.8 KiB
113 lines
3.8 KiB
import os
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from pathlib import Path
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from typing import Optional
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import numpy as np
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from paddlelite.lite import create_paddle_predictor
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from paddlelite.lite import MobileConfig
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from .syn_utils import run_frontend
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# Paddle-Lite
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def get_lite_predictor(model_dir: Optional[os.PathLike]=None,
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model_file: Optional[os.PathLike]=None,
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cpu_threads: int=1):
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config = MobileConfig()
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config.set_model_from_file(str(Path(model_dir) / model_file))
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predictor = create_paddle_predictor(config)
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return predictor
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def get_lite_am_output(input: str,
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am_predictor,
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am: str,
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frontend: object,
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lang: str='zh',
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merge_sentences: bool=True,
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speaker_dict: Optional[os.PathLike]=None,
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spk_id: int=0,
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add_blank: bool=False):
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am_name = am[:am.rindex('_')]
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am_dataset = am[am.rindex('_') + 1:]
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get_spk_id = False
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get_tone_ids = False
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if am_name == 'speedyspeech':
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get_tone_ids = True
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if am_dataset in {"aishell3", "vctk", "mix"} and speaker_dict:
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get_spk_id = True
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spk_id = np.array([spk_id])
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frontend_dict = run_frontend(
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frontend=frontend,
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text=input,
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merge_sentences=merge_sentences,
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get_tone_ids=get_tone_ids,
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lang=lang,
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add_blank=add_blank, )
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if get_tone_ids:
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tone_ids = frontend_dict['tone_ids']
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tones = tone_ids[0].numpy()
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tones_handle = am_predictor.get_input(1)
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tones_handle.from_numpy(tones)
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if get_spk_id:
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spk_id_handle = am_predictor.get_input(1)
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spk_id_handle.from_numpy(spk_id)
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phone_ids = frontend_dict['phone_ids']
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phones = phone_ids[0].numpy()
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phones_handle = am_predictor.get_input(0)
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phones_handle.from_numpy(phones)
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am_predictor.run()
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am_output_handle = am_predictor.get_output(0)
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am_output_data = am_output_handle.numpy()
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return am_output_data
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def get_lite_voc_output(voc_predictor, input):
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mel_handle = voc_predictor.get_input(0)
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mel_handle.from_numpy(input)
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voc_predictor.run()
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voc_output_handle = voc_predictor.get_output(0)
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wav = voc_output_handle.numpy()
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return wav
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def get_lite_am_sublayer_output(am_sublayer_predictor, input):
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input_handle = am_sublayer_predictor.get_input(0)
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input_handle.from_numpy(input)
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am_sublayer_predictor.run()
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am_sublayer_handle = am_sublayer_predictor.get_output(0)
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am_sublayer_output = am_sublayer_handle.numpy()
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return am_sublayer_output
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def get_lite_streaming_am_output(input: str,
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am_encoder_infer_predictor,
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am_decoder_predictor,
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am_postnet_predictor,
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frontend,
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lang: str='zh',
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merge_sentences: bool=True):
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get_tone_ids = False
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frontend_dict = run_frontend(
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frontend=frontend,
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text=input,
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merge_sentences=merge_sentences,
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get_tone_ids=get_tone_ids,
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lang=lang)
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phone_ids = frontend_dict['phone_ids']
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phones = phone_ids[0].numpy()
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am_encoder_infer_output = get_lite_am_sublayer_output(
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am_encoder_infer_predictor, input=phones)
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am_decoder_output = get_lite_am_sublayer_output(
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am_decoder_predictor, input=am_encoder_infer_output)
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am_postnet_output = get_lite_am_sublayer_output(
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am_postnet_predictor, input=np.transpose(am_decoder_output, (0, 2, 1)))
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am_output_data = am_decoder_output + np.transpose(am_postnet_output,
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(0, 2, 1))
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normalized_mel = am_output_data[0]
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return normalized_mel
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