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[TTS]rm paddlelite in setup.py (#2713)

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* rm paddlelite in setup.py

* fix setup.py
pull/2723/head
TianYuan 2 years ago committed by GitHub
parent 062a00c21c
commit 62357d876c
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 119 additions and 113 deletions
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6
paddlespeech/t2s/exps/lite_predict.py
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@ -17,10 +17,10 @@ from pathlib import Path
import soundfile as sf
from timer import timer
from paddlespeech.t2s.exps.lite_syn_utils import get_lite_am_output
from paddlespeech.t2s.exps.lite_syn_utils import get_lite_predictor
from paddlespeech.t2s.exps.lite_syn_utils import get_lite_voc_output
from paddlespeech.t2s.exps.syn_utils import get_frontend
from paddlespeech.t2s.exps.syn_utils import get_lite_am_output
from paddlespeech.t2s.exps.syn_utils import get_lite_predictor
from paddlespeech.t2s.exps.syn_utils import get_lite_voc_output
from paddlespeech.t2s.exps.syn_utils import get_sentences

8
paddlespeech/t2s/exps/lite_predict_streaming.py
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@ -18,13 +18,13 @@ import numpy as np
import soundfile as sf
from timer import timer
from paddlespeech.t2s.exps.lite_syn_utils import get_lite_am_sublayer_output
from paddlespeech.t2s.exps.lite_syn_utils import get_lite_predictor
from paddlespeech.t2s.exps.lite_syn_utils import get_lite_streaming_am_output
from paddlespeech.t2s.exps.lite_syn_utils import get_lite_voc_output
from paddlespeech.t2s.exps.syn_utils import denorm
from paddlespeech.t2s.exps.syn_utils import get_chunks
from paddlespeech.t2s.exps.syn_utils import get_frontend
from paddlespeech.t2s.exps.syn_utils import get_lite_am_sublayer_output
from paddlespeech.t2s.exps.syn_utils import get_lite_predictor
from paddlespeech.t2s.exps.syn_utils import get_lite_streaming_am_output
from paddlespeech.t2s.exps.syn_utils import get_lite_voc_output
from paddlespeech.t2s.exps.syn_utils import get_sentences
from paddlespeech.t2s.exps.syn_utils import run_frontend
from paddlespeech.t2s.utils import str2bool

111
paddlespeech/t2s/exps/lite_syn_utils.py
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@ -0,0 +1,111 @@
import os
from pathlib import Path
from typing import Optional
import numpy as np
from paddlelite.lite import create_paddle_predictor
from paddlelite.lite import MobileConfig
from .syn_utils import run_frontend
# Paddle-Lite
def get_lite_predictor(model_dir: Optional[os.PathLike]=None,
model_file: Optional[os.PathLike]=None,
cpu_threads: int=1):
config = MobileConfig()
config.set_model_from_file(str(Path(model_dir) / model_file))
predictor = create_paddle_predictor(config)
return predictor
def get_lite_am_output(
input: str,
am_predictor,
am: str,
frontend: object,
lang: str='zh',
merge_sentences: bool=True,
speaker_dict: Optional[os.PathLike]=None,
spk_id: int=0, ):
am_name = am[:am.rindex('_')]
am_dataset = am[am.rindex('_') + 1:]
get_spk_id = False
get_tone_ids = False
if am_name == 'speedyspeech':
get_tone_ids = True
if am_dataset in {"aishell3", "vctk", "mix"} and speaker_dict:
get_spk_id = True
spk_id = np.array([spk_id])
frontend_dict = run_frontend(
frontend=frontend,
text=input,
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids,
lang=lang)
if get_tone_ids:
tone_ids = frontend_dict['tone_ids']
tones = tone_ids[0].numpy()
tones_handle = am_predictor.get_input(1)
tones_handle.from_numpy(tones)
if get_spk_id:
spk_id_handle = am_predictor.get_input(1)
spk_id_handle.from_numpy(spk_id)
phone_ids = frontend_dict['phone_ids']
phones = phone_ids[0].numpy()
phones_handle = am_predictor.get_input(0)
phones_handle.from_numpy(phones)
am_predictor.run()
am_output_handle = am_predictor.get_output(0)
am_output_data = am_output_handle.numpy()
return am_output_data
def get_lite_voc_output(voc_predictor, input):
mel_handle = voc_predictor.get_input(0)
mel_handle.from_numpy(input)
voc_predictor.run()
voc_output_handle = voc_predictor.get_output(0)
wav = voc_output_handle.numpy()
return wav
def get_lite_am_sublayer_output(am_sublayer_predictor, input):
input_handle = am_sublayer_predictor.get_input(0)
input_handle.from_numpy(input)
am_sublayer_predictor.run()
am_sublayer_handle = am_sublayer_predictor.get_output(0)
am_sublayer_output = am_sublayer_handle.numpy()
return am_sublayer_output
def get_lite_streaming_am_output(input: str,
am_encoder_infer_predictor,
am_decoder_predictor,
am_postnet_predictor,
frontend,
lang: str='zh',
merge_sentences: bool=True):
get_tone_ids = False
frontend_dict = run_frontend(
frontend=frontend,
text=input,
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids,
lang=lang)
phone_ids = frontend_dict['phone_ids']
phones = phone_ids[0].numpy()
am_encoder_infer_output = get_lite_am_sublayer_output(
am_encoder_infer_predictor, input=phones)
am_decoder_output = get_lite_am_sublayer_output(
am_decoder_predictor, input=am_encoder_infer_output)
am_postnet_output = get_lite_am_sublayer_output(
am_postnet_predictor, input=np.transpose(am_decoder_output, (0, 2, 1)))
am_output_data = am_decoder_output + np.transpose(am_postnet_output,
(0, 2, 1))
normalized_mel = am_output_data[0]
return normalized_mel

104
paddlespeech/t2s/exps/syn_utils.py
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@ -26,8 +26,6 @@ import paddle
from paddle import inference
from paddle import jit
from paddle.static import InputSpec
from paddlelite.lite import create_paddle_predictor
from paddlelite.lite import MobileConfig
from yacs.config import CfgNode
from paddlespeech.t2s.datasets.data_table import DataTable
@ -512,105 +510,3 @@ def get_sess(model_path: Optional[os.PathLike],
sess = ort.InferenceSession(
model_path, providers=providers, sess_options=sess_options)
return sess
# Paddle-Lite
def get_lite_predictor(model_dir: Optional[os.PathLike]=None,
model_file: Optional[os.PathLike]=None,
cpu_threads: int=1):
config = MobileConfig()
config.set_model_from_file(str(Path(model_dir) / model_file))
predictor = create_paddle_predictor(config)
return predictor
def get_lite_am_output(
input: str,
am_predictor,
am: str,
frontend: object,
lang: str='zh',
merge_sentences: bool=True,
speaker_dict: Optional[os.PathLike]=None,
spk_id: int=0, ):
am_name = am[:am.rindex('_')]
am_dataset = am[am.rindex('_') + 1:]
get_spk_id = False
get_tone_ids = False
if am_name == 'speedyspeech':
get_tone_ids = True
if am_dataset in {"aishell3", "vctk", "mix"} and speaker_dict:
get_spk_id = True
spk_id = np.array([spk_id])
frontend_dict = run_frontend(
frontend=frontend,
text=input,
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids,
lang=lang)
if get_tone_ids:
tone_ids = frontend_dict['tone_ids']
tones = tone_ids[0].numpy()
tones_handle = am_predictor.get_input(1)
tones_handle.from_numpy(tones)
if get_spk_id:
spk_id_handle = am_predictor.get_input(1)
spk_id_handle.from_numpy(spk_id)
phone_ids = frontend_dict['phone_ids']
phones = phone_ids[0].numpy()
phones_handle = am_predictor.get_input(0)
phones_handle.from_numpy(phones)
am_predictor.run()
am_output_handle = am_predictor.get_output(0)
am_output_data = am_output_handle.numpy()
return am_output_data
def get_lite_voc_output(voc_predictor, input):
mel_handle = voc_predictor.get_input(0)
mel_handle.from_numpy(input)
voc_predictor.run()
voc_output_handle = voc_predictor.get_output(0)
wav = voc_output_handle.numpy()
return wav
def get_lite_am_sublayer_output(am_sublayer_predictor, input):
input_handle = am_sublayer_predictor.get_input(0)
input_handle.from_numpy(input)
am_sublayer_predictor.run()
am_sublayer_handle = am_sublayer_predictor.get_output(0)
am_sublayer_output = am_sublayer_handle.numpy()
return am_sublayer_output
def get_lite_streaming_am_output(input: str,
am_encoder_infer_predictor,
am_decoder_predictor,
am_postnet_predictor,
frontend,
lang: str='zh',
merge_sentences: bool=True):
get_tone_ids = False
frontend_dict = run_frontend(
frontend=frontend,
text=input,
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids,
lang=lang)
phone_ids = frontend_dict['phone_ids']
phones = phone_ids[0].numpy()
am_encoder_infer_output = get_lite_am_sublayer_output(
am_encoder_infer_predictor, input=phones)
am_decoder_output = get_lite_am_sublayer_output(
am_decoder_predictor, input=am_encoder_infer_output)
am_postnet_output = get_lite_am_sublayer_output(
am_postnet_predictor, input=np.transpose(am_decoder_output, (0, 2, 1)))
am_output_data = am_decoder_output + np.transpose(am_postnet_output,
(0, 2, 1))
normalized_mel = am_output_data[0]
return normalized_mel

3
setup.py
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@ -71,11 +71,10 @@ base = [
"prettytable",
"zhon",
"colorlog",
"pathos == 0.2.8",
"pathos==0.2.8",
"braceexpand",
"pyyaml",
"pybind11",
"paddlelite",
"paddleslim==2.3.4",
]

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