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

* rm paddlelite in setup.py

* fix setup.py

paddlespeech/t2s/exps/lite_predict.py

@@ -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
 
 

paddlespeech/t2s/exps/lite_predict_streaming.py

@@ -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

paddlespeech/t2s/exps/lite_syn_utils.py

@@ -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

paddlespeech/t2s/exps/syn_utils.py

@@ -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

setup.py

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