merge

docs/requirements.txt

@@ -19,6 +19,7 @@ loguru
 matplotlib
 nara_wpe
 onnxruntime==1.10.0
+opencc
 pandas
 paddlenlp
 paddlespeech_feat
@@ -36,6 +37,7 @@ soundfile~=0.10
 textgrid
 timer
 tqdm
+transformers==3.4.0
 typeguard
 visualdl
 webrtcvad
@@ -50,5 +52,3 @@ keyboard
 uvicorn
 pattern_singleton
 braceexpand
-opencc
-transformers==3.4.0

examples/aishell3/tts3/run.sh

@@ -54,7 +54,7 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     
 fi
 
-# inference with onnxruntime, use fastspeech2 + hifigan by default
+# inference with onnxruntime, use fastspeech2 + pwgan by default
 if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
     ./local/ort_predict.sh ${train_output_path}
 fi

examples/ljspeech/tts3/run.sh

@@ -55,7 +55,7 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx hifigan_ljspeech
 fi
 
-# inference with onnxruntime, use fastspeech2 + hifigan by default
+# inference with onnxruntime, use fastspeech2 + pwgan by default
 if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
     ./local/ort_predict.sh ${train_output_path}
 fi

examples/vctk/tts3/run.sh

@@ -54,7 +54,7 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     
 fi
 
-# inference with onnxruntime, use fastspeech2 + hifigan by default
+# inference with onnxruntime, use fastspeech2 + pwgan by default
 if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
     ./local/ort_predict.sh ${train_output_path}
 fi

examples/wenetspeech/asr1/conf/conformer.yaml

@@ -37,6 +37,7 @@ model_conf:
     ctc_weight: 0.3
     lsm_weight: 0.1     # label smoothing option
     length_normalized_loss: false
+    init_type: 'kaiming_uniform' # !Warning: need to convergence
 
 # https://yaml.org/type/float.html
 ###########################################

examples/wenetspeech/asr1/local/export.sh

@@ -0,0 +1,28 @@
+#!/bin/bash
+
+if [ $# != 3 ];then
+    echo "usage: $0 config_path ckpt_prefix jit_model_path"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+config_path=$1
+ckpt_path_prefix=$2
+jit_model_export_path=$3
+
+python3 -u ${BIN_DIR}/export.py \
+--ngpu ${ngpu} \
+--config ${config_path} \
+--checkpoint_path ${ckpt_path_prefix} \
+--export_path ${jit_model_export_path}
+
+
+if [ $? -ne 0 ]; then
+    echo "Failed in export!"
+    exit 1
+fi
+
+
+exit 0

paddlespeech/cli/tts/infer.py

@@ -29,10 +29,21 @@ from yacs.config import CfgNode
 from ..executor import BaseExecutor
 from ..log import logger
 from ..utils import stats_wrapper
+from paddlespeech.resource import CommonTaskResource
+from paddlespeech.t2s.exps.syn_utils import get_am_inference
 from paddlespeech.t2s.exps.syn_utils import get_frontend
-from paddlespeech.t2s.modules.normalizer import ZScore
+from paddlespeech.t2s.exps.syn_utils import get_sess
+from paddlespeech.t2s.exps.syn_utils import get_voc_inference
+from paddlespeech.t2s.exps.syn_utils import run_frontend
+from paddlespeech.t2s.utils import str2bool
 
 __all__ = ['TTSExecutor']
+ONNX_SUPPORT_SET = {
+    'speedyspeech_csmsc', 'fastspeech2_csmsc', 'fastspeech2_ljspeech',
+    'fastspeech2_aishell3', 'fastspeech2_vctk', 'pwgan_csmsc', 'pwgan_ljspeech',
+    'pwgan_aishell3', 'pwgan_vctk', 'mb_melgan_csmsc', 'hifigan_csmsc',
+    'hifigan_ljspeech', 'hifigan_aishell3', 'hifigan_vctk'
+}
 
 
 class TTSExecutor(BaseExecutor):
@@ -142,6 +153,8 @@ class TTSExecutor(BaseExecutor):
             default=paddle.get_device(),
             help='Choose device to execute model inference.')
 
+        self.parser.add_argument('--cpu_threads', type=int, default=2)
+
         self.parser.add_argument(
             '--output', type=str, default='output.wav', help='output file name')
         self.parser.add_argument(
@@ -154,6 +167,16 @@ class TTSExecutor(BaseExecutor):
             '--verbose',
             action='store_true',
             help='Increase logger verbosity of current task.')
+        self.parser.add_argument(
+            "--use_onnx",
+            type=str2bool,
+            default=False,
+            help="whether to usen onnxruntime inference.")
+        self.parser.add_argument(
+            '--fs',
+            type=int,
+            default=24000,
+            help='sample rate for onnx models when use specified model files.')
 
     def _init_from_path(
             self,
@@ -164,7 +187,7 @@ class TTSExecutor(BaseExecutor):
             phones_dict: Optional[os.PathLike]=None,
             tones_dict: Optional[os.PathLike]=None,
             speaker_dict: Optional[os.PathLike]=None,
-            voc: str='pwgan_csmsc',
+            voc: str='hifigan_csmsc',
             voc_config: Optional[os.PathLike]=None,
             voc_ckpt: Optional[os.PathLike]=None,
             voc_stat: Optional[os.PathLike]=None,
@@ -208,7 +231,7 @@ class TTSExecutor(BaseExecutor):
             self.am_ckpt = os.path.abspath(am_ckpt)
             self.am_stat = os.path.abspath(am_stat)
             self.phones_dict = os.path.abspath(phones_dict)
-            self.am_res_path = os.path.dirname(os.path.abspath(self.am_config))
+            self.am_res_path = os.path.dirname(self.am_config)
 
         # for speedyspeech
         self.tones_dict = None
@@ -288,58 +311,110 @@ class TTSExecutor(BaseExecutor):
             lang=lang, phones_dict=self.phones_dict, tones_dict=self.tones_dict)
 
         # acoustic model
-        odim = self.am_config.n_mels
+        self.am_inference = get_am_inference(
-        # model: {model_name}_{dataset}
+            am=am,
-        am_name = am[:am.rindex('_')]
+            am_config=self.am_config,
+            am_ckpt=self.am_ckpt,
+            am_stat=self.am_stat,
+            phones_dict=self.phones_dict,
+            tones_dict=self.tones_dict,
+            speaker_dict=self.speaker_dict)
 
-        am_class = self.task_resource.get_model_class(am_name)
+        # vocoder
-        am_inference_class = self.task_resource.get_model_class(am_name +
+        self.voc_inference = get_voc_inference(
-                                                                '_inference')
+            voc=voc,
+            voc_config=self.voc_config,
+            voc_ckpt=self.voc_ckpt,
+            voc_stat=self.voc_stat)
 
-        if am_name == 'fastspeech2':
+    def _init_from_path_onnx(self,
-            am = am_class(
+                             am: str='fastspeech2_csmsc',
-                idim=vocab_size,
+                             am_ckpt: Optional[os.PathLike]=None,
-                odim=odim,
+                             phones_dict: Optional[os.PathLike]=None,
-                spk_num=spk_num,
+                             tones_dict: Optional[os.PathLike]=None,
-                **self.am_config["model"])
+                             speaker_dict: Optional[os.PathLike]=None,
-        elif am_name == 'speedyspeech':
+                             voc: str='hifigan_csmsc',
-            am = am_class(
+                             voc_ckpt: Optional[os.PathLike]=None,
-                vocab_size=vocab_size,
+                             lang: str='zh',
-                tone_size=tone_size,
+                             device: str='cpu',
-                **self.am_config["model"])
+                             cpu_threads: int=2,
-        elif am_name == 'tacotron2':
+                             fs: int=24000):
-            am = am_class(idim=vocab_size, odim=odim, **self.am_config["model"])
+        if hasattr(self, 'am_sess') and hasattr(self, 'voc_sess'):
-
+            logger.debug('Models had been initialized.')
-        am.set_state_dict(paddle.load(self.am_ckpt)["main_params"])
+            return
-        am.eval()
-        am_mu, am_std = np.load(self.am_stat)
-        am_mu = paddle.to_tensor(am_mu)
-        am_std = paddle.to_tensor(am_std)
-        am_normalizer = ZScore(am_mu, am_std)
-        self.am_inference = am_inference_class(am_normalizer, am)
-        self.am_inference.eval()
 
-        # vocoder
+        # am
-        # model: {model_name}_{dataset}
+        if am_ckpt is None or phones_dict is None:
-        voc_name = voc[:voc.rindex('_')]
+            use_pretrained_am = True
-        voc_class = self.task_resource.get_model_class(voc_name)
+        else:
-        voc_inference_class = self.task_resource.get_model_class(voc_name +
+            use_pretrained_am = False
-                                                                 '_inference')
+
-        if voc_name != 'wavernn':
+        am_tag = am + '_onnx' + '-' + lang
-            voc = voc_class(**self.voc_config["generator_params"])
+        self.task_resource.set_task_model(
-            voc.set_state_dict(paddle.load(self.voc_ckpt)["generator_params"])
+            model_tag=am_tag,
-            voc.remove_weight_norm()
+            model_type=0,  # am
-            voc.eval()
+            skip_download=not use_pretrained_am,
+            version=None,  # default version
+        )
+        if use_pretrained_am:
+            self.am_res_path = self.task_resource.res_dir
+            self.am_ckpt = os.path.join(self.am_res_path,
+                                        self.task_resource.res_dict['ckpt'])
+            # must have phones_dict in acoustic
+            self.phones_dict = os.path.join(
+                self.am_res_path, self.task_resource.res_dict['phones_dict'])
+            self.am_fs = self.task_resource.res_dict['sample_rate']
+            logger.debug(self.am_res_path)
+            logger.debug(self.am_ckpt)
+        else:
+            self.am_ckpt = os.path.abspath(am_ckpt)
+            self.phones_dict = os.path.abspath(phones_dict)
+            self.am_res_path = os.path.dirname(self.am_ckpt)
+            self.am_fs = fs
+
+        # for speedyspeech
+        self.tones_dict = None
+        if 'tones_dict' in self.task_resource.res_dict:
+            self.tones_dict = os.path.join(
+                self.am_res_path, self.task_resource.res_dict['tones_dict'])
+            if tones_dict:
+                self.tones_dict = tones_dict
+
+        # voc
+        if voc_ckpt is None:
+            use_pretrained_voc = True
+        else:
+            use_pretrained_voc = False
+        voc_lang = lang
+        # we must use ljspeech's voc for mix am now!
+        if lang == 'mix':
+            voc_lang = 'en'
+        voc_tag = voc + '_onnx' + '-' + voc_lang
+        self.task_resource.set_task_model(
+            model_tag=voc_tag,
+            model_type=1,  # vocoder
+            skip_download=not use_pretrained_voc,
+            version=None,  # default version
+        )
+        if use_pretrained_voc:
+            self.voc_res_path = self.task_resource.voc_res_dir
+            self.voc_ckpt = os.path.join(
+                self.voc_res_path, self.task_resource.voc_res_dict['ckpt'])
+            logger.debug(self.voc_res_path)
+            logger.debug(self.voc_ckpt)
         else:
-            voc = voc_class(**self.voc_config["model"])
+            self.voc_ckpt = os.path.abspath(voc_ckpt)
-            voc.set_state_dict(paddle.load(self.voc_ckpt)["main_params"])
+            self.voc_res_path = os.path.dirname(os.path.abspath(self.voc_ckpt))
-            voc.eval()
+
-        voc_mu, voc_std = np.load(self.voc_stat)
+        # frontend
-        voc_mu = paddle.to_tensor(voc_mu)
+        self.frontend = get_frontend(
-        voc_std = paddle.to_tensor(voc_std)
+            lang=lang, phones_dict=self.phones_dict, tones_dict=self.tones_dict)
-        voc_normalizer = ZScore(voc_mu, voc_std)
+        self.am_sess = get_sess(
-        self.voc_inference = voc_inference_class(voc_normalizer, voc)
+            model_path=self.am_ckpt, device=device, cpu_threads=cpu_threads)
-        self.voc_inference.eval()
+
+        # vocoder
+        self.voc_sess = get_sess(
+            model_path=self.voc_ckpt, device=device, cpu_threads=cpu_threads)
 
     def preprocess(self, input: Any, *args, **kwargs):
         """
@@ -362,40 +437,28 @@ class TTSExecutor(BaseExecutor):
         """
         am_name = am[:am.rindex('_')]
         am_dataset = am[am.rindex('_') + 1:]
-        get_tone_ids = False
         merge_sentences = False
-        frontend_st = time.time()
+        get_tone_ids = False
         if am_name == 'speedyspeech':
             get_tone_ids = True
-        if lang == 'zh':
+        frontend_st = time.time()
-            input_ids = self.frontend.get_input_ids(
+        frontend_dict = run_frontend(
-                text,
+            frontend=self.frontend,
+            text=text,
             merge_sentences=merge_sentences,
-                get_tone_ids=get_tone_ids)
+            get_tone_ids=get_tone_ids,
-            phone_ids = input_ids["phone_ids"]
+            lang=lang)
-            if get_tone_ids:
-                tone_ids = input_ids["tone_ids"]
-        elif lang == 'en':
-            input_ids = self.frontend.get_input_ids(
-                text, merge_sentences=merge_sentences)
-            phone_ids = input_ids["phone_ids"]
-        elif lang == 'mix':
-            input_ids = self.frontend.get_input_ids(
-                text, merge_sentences=merge_sentences)
-            phone_ids = input_ids["phone_ids"]
-        else:
-            logger.error("lang should in {'zh', 'en', 'mix'}!")
         self.frontend_time = time.time() - frontend_st
-
         self.am_time = 0
         self.voc_time = 0
         flags = 0
+        phone_ids = frontend_dict['phone_ids']
         for i in range(len(phone_ids)):
             am_st = time.time()
             part_phone_ids = phone_ids[i]
             # am
             if am_name == 'speedyspeech':
-                part_tone_ids = tone_ids[i]
+                part_tone_ids = frontend_dict['tone_ids'][i]
                 mel = self.am_inference(part_phone_ids, part_tone_ids)
             # fastspeech2
             else:
@@ -417,6 +480,62 @@ class TTSExecutor(BaseExecutor):
             self.voc_time += (time.time() - voc_st)
         self._outputs['wav'] = wav_all
 
+    def infer_onnx(self,
+                   text: str,
+                   lang: str='zh',
+                   am: str='fastspeech2_csmsc',
+                   spk_id: int=0):
+        am_name = am[:am.rindex('_')]
+        am_dataset = am[am.rindex('_') + 1:]
+        merge_sentences = False
+        get_tone_ids = False
+        if am_name == 'speedyspeech':
+            get_tone_ids = True
+        am_input_feed = {}
+        frontend_st = time.time()
+        frontend_dict = run_frontend(
+            frontend=self.frontend,
+            text=text,
+            merge_sentences=merge_sentences,
+            get_tone_ids=get_tone_ids,
+            lang=lang,
+            to_tensor=False)
+        self.frontend_time = time.time() - frontend_st
+        phone_ids = frontend_dict['phone_ids']
+        self.am_time = 0
+        self.voc_time = 0
+        flags = 0
+        for i in range(len(phone_ids)):
+            am_st = time.time()
+            part_phone_ids = phone_ids[i]
+            if am_name == 'fastspeech2':
+                am_input_feed.update({'text': part_phone_ids})
+                if am_dataset in {"aishell3", "vctk"}:
+                    # NOTE: 'spk_id' should be List[int] rather than int here!!
+                    am_input_feed.update({'spk_id': [spk_id]})
+            elif am_name == 'speedyspeech':
+                part_tone_ids = frontend_dict['tone_ids'][i]
+                am_input_feed.update({
+                    'phones': part_phone_ids,
+                    'tones': part_tone_ids
+                })
+            mel = self.am_sess.run(output_names=None, input_feed=am_input_feed)
+            mel = mel[0]
+            self.am_time += (time.time() - am_st)
+            # voc
+            voc_st = time.time()
+            wav = self.voc_sess.run(
+                output_names=None, input_feed={'logmel': mel})
+            wav = wav[0]
+            if flags == 0:
+                wav_all = wav
+                flags = 1
+            else:
+                wav_all = np.concatenate([wav_all, wav])
+            self.voc_time += (time.time() - voc_st)
+
+        self._outputs['wav'] = wav_all
+
     def postprocess(self, output: str='output.wav') -> Union[str, os.PathLike]:
         """
         Output postprocess and return results.
@@ -430,6 +549,20 @@ class TTSExecutor(BaseExecutor):
             output, self._outputs['wav'].numpy(), samplerate=self.am_config.fs)
         return output
 
+    def postprocess_onnx(self,
+                         output: str='output.wav') -> Union[str, os.PathLike]:
+        """
+        Output postprocess and return results.
+        This method get model output from self._outputs and convert it into human-readable results.
+
+        Returns:
+            Union[str, os.PathLike]: Human-readable results such as texts and audio files.
+        """
+        output = os.path.abspath(os.path.expanduser(output))
+        sf.write(output, self._outputs['wav'], samplerate=self.am_fs)
+        return output
+
+    # 命令行的入口是这里
     def execute(self, argv: List[str]) -> bool:
         """
         Command line entry.
@@ -451,6 +584,9 @@ class TTSExecutor(BaseExecutor):
         lang = args.lang
         device = args.device
         spk_id = args.spk_id
+        use_onnx = args.use_onnx
+        cpu_threads = args.cpu_threads
+        fs = args.fs
 
         if not args.verbose:
             self.disable_task_loggers()
@@ -487,7 +623,10 @@ class TTSExecutor(BaseExecutor):
                     # other
                     lang=lang,
                     device=device,
-                    output=output)
+                    output=output,
+                    use_onnx=use_onnx,
+                    cpu_threads=cpu_threads,
+                    fs=fs)
                 task_results[id_] = res
             except Exception as e:
                 has_exceptions = True
@@ -501,6 +640,7 @@ class TTSExecutor(BaseExecutor):
         else:
             return True
 
+    # pyton api 的入口是这里
     @stats_wrapper
     def __call__(self,
                  text: str,
@@ -512,16 +652,20 @@ class TTSExecutor(BaseExecutor):
                  phones_dict: Optional[os.PathLike]=None,
                  tones_dict: Optional[os.PathLike]=None,
                  speaker_dict: Optional[os.PathLike]=None,
-                 voc: str='pwgan_csmsc',
+                 voc: str='hifigan_csmsc',
                  voc_config: Optional[os.PathLike]=None,
                  voc_ckpt: Optional[os.PathLike]=None,
                  voc_stat: Optional[os.PathLike]=None,
                  lang: str='zh',
                  device: str=paddle.get_device(),
-                 output: str='output.wav'):
+                 output: str='output.wav',
+                 use_onnx: bool=False,
+                 cpu_threads: int=2,
+                 fs: int=24000):
         """
         Python API to call an executor.
         """
+        if not use_onnx:
             paddle.set_device(device)
             self._init_from_path(
                 am=am,
@@ -538,7 +682,29 @@ class TTSExecutor(BaseExecutor):
                 lang=lang)
 
             self.infer(text=text, lang=lang, am=am, spk_id=spk_id)
-
             res = self.postprocess(output=output)
-
+            return res
+        else:
+            # use onnx
+            # we use `cpu` for onnxruntime by default
+            # please see description in https://github.com/PaddlePaddle/PaddleSpeech/pull/2220
+            self.task_resource = CommonTaskResource(
+                task='tts', model_format='onnx')
+            assert (
+                am in ONNX_SUPPORT_SET and voc in ONNX_SUPPORT_SET
+            ), f'the am and voc you choose, they should be in {ONNX_SUPPORT_SET}'
+            self._init_from_path_onnx(
+                am=am,
+                am_ckpt=am_ckpt,
+                phones_dict=phones_dict,
+                tones_dict=tones_dict,
+                speaker_dict=speaker_dict,
+                voc=voc,
+                voc_ckpt=voc_ckpt,
+                lang=lang,
+                device=device,
+                cpu_threads=cpu_threads,
+                fs=fs)
+            self.infer_onnx(text=text, lang=lang, am=am, spk_id=spk_id)
+            res = self.postprocess_onnx(output=output)
             return res

paddlespeech/resource/pretrained_models.py

@@ -1095,7 +1095,8 @@ tts_onnx_pretrained_models = {
             'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_onnx_0.2.0.zip',
             'md5':
             '3e9c45af9ef70675fc1968ed5074fc88',
-            'ckpt': ['speedyspeech_csmsc.onnx'],
+            'ckpt':
+            'speedyspeech_csmsc.onnx',
             'phones_dict':
             'phone_id_map.txt',
             'tones_dict':
@@ -1111,7 +1112,8 @@ tts_onnx_pretrained_models = {
             'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_csmsc_onnx_0.2.0.zip',
             'md5':
             'fd3ad38d83273ad51f0ea4f4abf3ab4e',
-            'ckpt': ['fastspeech2_csmsc.onnx'],
+            'ckpt':
+            'fastspeech2_csmsc.onnx',
             'phones_dict':
             'phone_id_map.txt',
             'sample_rate':
@@ -1124,7 +1126,8 @@ tts_onnx_pretrained_models = {
             'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_ljspeech_onnx_1.1.0.zip',
             'md5':
             '00754307636a48c972a5f3e65cda3d18',
-            'ckpt': ['fastspeech2_ljspeech.onnx'],
+            'ckpt':
+            'fastspeech2_ljspeech.onnx',
             'phones_dict':
             'phone_id_map.txt',
             'sample_rate':
@@ -1137,7 +1140,8 @@ tts_onnx_pretrained_models = {
             'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_aishell3_onnx_1.1.0.zip',
             'md5':
             'a1d6ee21de897ce394f5469e2bb4df0d',
-            'ckpt': ['fastspeech2_aishell3.onnx'],
+            'ckpt':
+            'fastspeech2_aishell3.onnx',
             'phones_dict':
             'phone_id_map.txt',
             'speaker_dict':
@@ -1149,10 +1153,11 @@ tts_onnx_pretrained_models = {
     "fastspeech2_vctk_onnx-en": {
         '1.0': {
             'url':
-            'hhttps://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_vctk_onnx_1.1.0.zip',
+            'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_vctk_onnx_1.1.0.zip',
             'md5':
             'd9c3a9b02204a2070504dd99f5f959bf',
-            'ckpt': ['fastspeech2_vctk.onnx'],
+            'ckpt':
+            'fastspeech2_vctk.onnx',
             'phones_dict':
             'phone_id_map.txt',
             'speaker_dict':

paddlespeech/s2t/io/dataloader.py

@@ -389,6 +389,7 @@ class DataLoaderFactory():
                 config['mini_batch_size'] = args.ngpu
                 config['subsampling_factor'] = 1
                 config['num_encs'] = 1
+                config['shortest_first'] = False
             elif mode == 'valid':
                 config['manifest'] = config.dev_manifest
                 config['train_mode'] = False

paddlespeech/server/utils/audio_handler.py

@@ -160,7 +160,7 @@ class ASRWsAudioHandler:
                 separators=(',', ': '))
             await ws.send(audio_info)
             msg = await ws.recv()
-            logger.debug("client receive msg={}".format(msg))
+            logger.info("client receive msg={}".format(msg))
 
             # 3. send chunk audio data to engine
             for chunk_data in self.read_wave(wavfile_path):
@@ -170,7 +170,7 @@ class ASRWsAudioHandler:
 
                 if self.punc_server and len(msg["result"]) > 0:
                     msg["result"] = self.punc_server.run(msg["result"])
-                logger.debug("client receive msg={}".format(msg))
+                logger.info("client receive msg={}".format(msg))
 
             # 4. we must send finished signal to the server
             audio_info = json.dumps(
@@ -317,7 +317,7 @@ class TTSWsHandler:
             start_request = json.dumps({"task": "tts", "signal": "start"})
             await ws.send(start_request)
             msg = await ws.recv()
-            logger.debug(f"client receive msg={msg}")
+            logger.info(f"client receive msg={msg}")
             msg = json.loads(msg)
             session = msg["session"]
 

paddlespeech/t2s/exps/inference.py

@@ -86,11 +86,6 @@ def parse_args():
         "--inference_dir", type=str, help="dir to save inference models")
     parser.add_argument("--output_dir", type=str, help="output dir")
     # inference
-    parser.add_argument(
-        "--use_trt",
-        type=str2bool,
-        default=False,
-        help="Whether to use inference engin TensorRT.", )
     parser.add_argument(
         "--int8",
         type=str2bool,
@@ -187,7 +182,7 @@ def main():
         speed = wav.size / t.elapse
         rtf = fs / speed
 
-        sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=24000)
+        sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=fs)
         print(
             f"{utt_id}, mel: {am_output_data.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
         )

paddlespeech/t2s/exps/inference_streaming.py

@@ -27,6 +27,7 @@ from paddlespeech.t2s.exps.syn_utils import get_predictor
 from paddlespeech.t2s.exps.syn_utils import get_sentences
 from paddlespeech.t2s.exps.syn_utils import get_streaming_am_output
 from paddlespeech.t2s.exps.syn_utils import get_voc_output
+from paddlespeech.t2s.exps.syn_utils import run_frontend
 from paddlespeech.t2s.utils import str2bool
 
 
@@ -175,14 +176,13 @@ def main():
     for utt_id, sentence in sentences:
         with timer() as t:
             # frontend
-            if args.lang == 'zh':
+            frontend_dict = run_frontend(
-                input_ids = frontend.get_input_ids(
+                frontend=frontend,
-                    sentence,
+                text=sentence,
                 merge_sentences=merge_sentences,
-                    get_tone_ids=get_tone_ids)
+                get_tone_ids=get_tone_ids,
-                phone_ids = input_ids["phone_ids"]
+                lang=args.lang)
-            else:
+            phone_ids = frontend_dict['phone_ids']
-                print("lang should be 'zh' here!")
             phones = phone_ids[0].numpy()
             # acoustic model
             orig_hs = get_am_sublayer_output(

paddlespeech/t2s/exps/ort_predict.py

@@ -41,17 +41,17 @@ def ort_predict(args):
 
     # am
     am_sess = get_sess(
-        model_dir=args.inference_dir,
+        model_path=str(Path(args.inference_dir) / (args.am + '.onnx')),
-        model_file=args.am + ".onnx",
         device=args.device,
-        cpu_threads=args.cpu_threads)
+        cpu_threads=args.cpu_threads,
+        use_trt=args.use_trt)
 
     # vocoder
     voc_sess = get_sess(
-        model_dir=args.inference_dir,
+        model_path=str(Path(args.inference_dir) / (args.voc + '.onnx')),
-        model_file=args.voc + ".onnx",
         device=args.device,
-        cpu_threads=args.cpu_threads)
+        cpu_threads=args.cpu_threads,
+        use_trt=args.use_trt)
 
     # am warmup
     for T in [27, 38, 54]:

paddlespeech/t2s/exps/ort_predict_e2e.py

@@ -22,6 +22,7 @@ from timer import timer
 from paddlespeech.t2s.exps.syn_utils import get_frontend
 from paddlespeech.t2s.exps.syn_utils import get_sentences
 from paddlespeech.t2s.exps.syn_utils import get_sess
+from paddlespeech.t2s.exps.syn_utils import run_frontend
 from paddlespeech.t2s.utils import str2bool
 
 
@@ -42,17 +43,17 @@ def ort_predict(args):
     fs = 24000 if am_dataset != 'ljspeech' else 22050
 
     am_sess = get_sess(
-        model_dir=args.inference_dir,
+        model_path=str(Path(args.inference_dir) / (args.am + '.onnx')),
-        model_file=args.am + ".onnx",
         device=args.device,
-        cpu_threads=args.cpu_threads)
+        cpu_threads=args.cpu_threads,
+        use_trt=args.use_trt)
 
     # vocoder
     voc_sess = get_sess(
-        model_dir=args.inference_dir,
+        model_path=str(Path(args.inference_dir) / (args.voc + '.onnx')),
-        model_file=args.voc + ".onnx",
         device=args.device,
-        cpu_threads=args.cpu_threads)
+        cpu_threads=args.cpu_threads,
+        use_trt=args.use_trt)
 
     merge_sentences = True
 
@@ -78,7 +79,6 @@ def ort_predict(args):
             am_input_feed.update({'text': phone_ids})
             if am_dataset in {"aishell3", "vctk"}:
                 am_input_feed.update({'spk_id': spk_id})
-
         elif am_name == 'speedyspeech':
             phone_ids = np.random.randint(1, 92, size=(T, ))
             tone_ids = np.random.randint(1, 5, size=(T, ))
@@ -93,50 +93,51 @@ def ort_predict(args):
 
     N = 0
     T = 0
-    merge_sentences = True
+    merge_sentences = False
     get_tone_ids = False
-    am_input_feed = {}
     if am_name == 'speedyspeech':
         get_tone_ids = True
+    am_input_feed = {}
     for utt_id, sentence in sentences:
         with timer() as t:
-            if args.lang == 'zh':
+            frontend_dict = run_frontend(
-                input_ids = frontend.get_input_ids(
+                frontend=frontend,
-                    sentence,
+                text=sentence,
                 merge_sentences=merge_sentences,
-                    get_tone_ids=get_tone_ids)
+                get_tone_ids=get_tone_ids,
-                phone_ids = input_ids["phone_ids"]
+                lang=args.lang)
-                if get_tone_ids:
+            phone_ids = frontend_dict['phone_ids']
-                    tone_ids = input_ids["tone_ids"]
+            flags = 0
-            elif args.lang == 'en':
+            for i in range(len(phone_ids)):
-                input_ids = frontend.get_input_ids(
+                part_phone_ids = phone_ids[i].numpy()
-                    sentence, merge_sentences=merge_sentences)
-                phone_ids = input_ids["phone_ids"]
-            else:
-                print("lang should in {'zh', 'en'}!")
-            # merge_sentences=True here, so we only use the first item of phone_ids
-            phone_ids = phone_ids[0].numpy()
                 if am_name == 'fastspeech2':
-                am_input_feed.update({'text': phone_ids})
+                    am_input_feed.update({'text': part_phone_ids})
                     if am_dataset in {"aishell3", "vctk"}:
                         am_input_feed.update({'spk_id': spk_id})
                 elif am_name == 'speedyspeech':
-                tone_ids = tone_ids[0].numpy()
+                    part_tone_ids = frontend_dict['tone_ids'][i].numpy()
-                am_input_feed.update({'phones': phone_ids, 'tones': tone_ids})
+                    am_input_feed.update({
+                        'phones': part_phone_ids,
+                        'tones': part_tone_ids
+                    })
                 mel = am_sess.run(output_names=None, input_feed=am_input_feed)
                 mel = mel[0]
-            wav = voc_sess.run(output_names=None, input_feed={'logmel': mel})
+                wav = voc_sess.run(
-
+                    output_names=None, input_feed={'logmel': mel})
-            N += len(wav[0])
+                wav = wav[0]
+                if flags == 0:
+                    wav_all = wav
+                    flags = 1
+                else:
+                    wav_all = np.concatenate([wav_all, wav])
+        wav = wav_all
+        N += len(wav)
         T += t.elapse
-            speed = len(wav[0]) / t.elapse
+        speed = len(wav) / t.elapse
         rtf = fs / speed
-        sf.write(
+        sf.write(str(output_dir / (utt_id + ".wav")), wav, samplerate=fs)
-            str(output_dir / (utt_id + ".wav")),
-            np.array(wav)[0],
-            samplerate=fs)
         print(
-            f"{utt_id}, mel: {mel.shape}, wave: {len(wav[0])}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
+            f"{utt_id}, mel: {mel.shape}, wave: {len(wav)}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
         )
     print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }")
 

paddlespeech/t2s/exps/ort_predict_streaming.py

@@ -24,6 +24,7 @@ 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_sentences
 from paddlespeech.t2s.exps.syn_utils import get_sess
+from paddlespeech.t2s.exps.syn_utils import run_frontend
 from paddlespeech.t2s.utils import str2bool
 
 
@@ -45,29 +46,33 @@ def ort_predict(args):
 
     # streaming acoustic model
     am_encoder_infer_sess = get_sess(
-        model_dir=args.inference_dir,
+        model_path=str(
-        model_file=args.am + "_am_encoder_infer" + ".onnx",
+            Path(args.inference_dir) /
+            (args.am + '_am_encoder_infer' + '.onnx')),
         device=args.device,
-        cpu_threads=args.cpu_threads)
+        cpu_threads=args.cpu_threads,
+        use_trt=args.use_trt)
     am_decoder_sess = get_sess(
-        model_dir=args.inference_dir,
+        model_path=str(
-        model_file=args.am + "_am_decoder" + ".onnx",
+            Path(args.inference_dir) / (args.am + '_am_decoder' + '.onnx')),
         device=args.device,
-        cpu_threads=args.cpu_threads)
+        cpu_threads=args.cpu_threads,
+        use_trt=args.use_trt)
 
     am_postnet_sess = get_sess(
-        model_dir=args.inference_dir,
+        model_path=str(
-        model_file=args.am + "_am_postnet" + ".onnx",
+            Path(args.inference_dir) / (args.am + '_am_postnet' + '.onnx')),
         device=args.device,
-        cpu_threads=args.cpu_threads)
+        cpu_threads=args.cpu_threads,
+        use_trt=args.use_trt)
     am_mu, am_std = np.load(args.am_stat)
 
     # vocoder
     voc_sess = get_sess(
-        model_dir=args.inference_dir,
+        model_path=str(Path(args.inference_dir) / (args.voc + '.onnx')),
-        model_file=args.voc + ".onnx",
         device=args.device,
-        cpu_threads=args.cpu_threads)
+        cpu_threads=args.cpu_threads,
+        use_trt=args.use_trt)
 
     # frontend warmup
     # Loading model cost 0.5+ seconds
@@ -102,14 +107,13 @@ def ort_predict(args):
 
     for utt_id, sentence in sentences:
         with timer() as t:
-            if args.lang == 'zh':
+            frontend_dict = run_frontend(
-                input_ids = frontend.get_input_ids(
+                frontend=frontend,
-                    sentence,
+                text=sentence,
                 merge_sentences=merge_sentences,
-                    get_tone_ids=get_tone_ids)
+                get_tone_ids=get_tone_ids,
-                phone_ids = input_ids["phone_ids"]
+                lang=args.lang)
-            else:
+            phone_ids = frontend_dict['phone_ids']
-                print("lang should in be 'zh' here!")
             # merge_sentences=True here, so we only use the first item of phone_ids
             phone_ids = phone_ids[0].numpy()
             orig_hs = am_encoder_infer_sess.run(

paddlespeech/t2s/exps/syn_utils.py

@@ -33,6 +33,8 @@ from paddlespeech.t2s.frontend.mix_frontend import MixFrontend
 from paddlespeech.t2s.frontend.zh_frontend import Frontend
 from paddlespeech.t2s.modules.normalizer import ZScore
 from paddlespeech.utils.dynamic_import import dynamic_import
+# remove [W:onnxruntime: xxx] from ort
+ort.set_default_logger_severity(3)
 
 model_alias = {
     # acoustic model
@@ -161,13 +163,42 @@ def get_frontend(lang: str='zh',
     elif lang == 'mix':
         frontend = MixFrontend(
             phone_vocab_path=phones_dict, tone_vocab_path=tones_dict)
-
     else:
         print("wrong lang!")
-    print("frontend done!")
     return frontend
 
 
+def run_frontend(frontend: object,
+                 text: str,
+                 merge_sentences: bool=False,
+                 get_tone_ids: bool=False,
+                 lang: str='zh',
+                 to_tensor: bool=True):
+    outs = dict()
+    if lang == 'zh':
+        input_ids = frontend.get_input_ids(
+            text,
+            merge_sentences=merge_sentences,
+            get_tone_ids=get_tone_ids,
+            to_tensor=to_tensor)
+        phone_ids = input_ids["phone_ids"]
+        if get_tone_ids:
+            tone_ids = input_ids["tone_ids"]
+            outs.update({'tone_ids': tone_ids})
+    elif lang == 'en':
+        input_ids = frontend.get_input_ids(
+            text, merge_sentences=merge_sentences, to_tensor=to_tensor)
+        phone_ids = input_ids["phone_ids"]
+    elif lang == 'mix':
+        input_ids = frontend.get_input_ids(
+            text, merge_sentences=merge_sentences, to_tensor=to_tensor)
+        phone_ids = input_ids["phone_ids"]
+    else:
+        print("lang should in {'zh', 'en', 'mix'}!")
+    outs.update({'phone_ids': phone_ids})
+    return outs
+
+
 # dygraph
 def get_am_inference(am: str='fastspeech2_csmsc',
                      am_config: CfgNode=None,
@@ -180,30 +211,22 @@ def get_am_inference(am: str='fastspeech2_csmsc',
     with open(phones_dict, "r") as f:
         phn_id = [line.strip().split() for line in f.readlines()]
     vocab_size = len(phn_id)
-    print("vocab_size:", vocab_size)
-
     tone_size = None
     if tones_dict is not None:
         with open(tones_dict, "r") as f:
             tone_id = [line.strip().split() for line in f.readlines()]
         tone_size = len(tone_id)
-        print("tone_size:", tone_size)
-
     spk_num = None
     if speaker_dict is not None:
         with open(speaker_dict, 'rt') as f:
             spk_id = [line.strip().split() for line in f.readlines()]
         spk_num = len(spk_id)
-        print("spk_num:", spk_num)
-
     odim = am_config.n_mels
     # model: {model_name}_{dataset}
     am_name = am[:am.rindex('_')]
     am_dataset = am[am.rindex('_') + 1:]
-
     am_class = dynamic_import(am_name, model_alias)
     am_inference_class = dynamic_import(am_name + '_inference', model_alias)
-
     if am_name == 'fastspeech2':
         am = am_class(
             idim=vocab_size, odim=odim, spk_num=spk_num, **am_config["model"])
@@ -228,7 +251,6 @@ def get_am_inference(am: str='fastspeech2_csmsc',
     am_normalizer = ZScore(am_mu, am_std)
     am_inference = am_inference_class(am_normalizer, am)
     am_inference.eval()
-    print("acoustic model done!")
     if return_am:
         return am_inference, am
     else:
@@ -260,7 +282,6 @@ def get_voc_inference(
     voc_normalizer = ZScore(voc_mu, voc_std)
     voc_inference = voc_inference_class(voc_normalizer, voc)
     voc_inference.eval()
-    print("voc done!")
     return voc_inference
 
 
@@ -342,9 +363,9 @@ def get_predictor(model_dir: Optional[os.PathLike]=None,
 
 def get_am_output(
         input: str,
-        am_predictor,
+        am_predictor: paddle.nn.Layer,
-        am,
+        am: str,
-        frontend,
+        frontend: object,
         lang: str='zh',
         merge_sentences: bool=True,
         speaker_dict: Optional[os.PathLike]=None,
@@ -352,30 +373,23 @@ def get_am_output(
     am_name = am[:am.rindex('_')]
     am_dataset = am[am.rindex('_') + 1:]
     am_input_names = am_predictor.get_input_names()
-    get_tone_ids = False
     get_spk_id = False
+    get_tone_ids = False
     if am_name == 'speedyspeech':
         get_tone_ids = True
     if am_dataset in {"aishell3", "vctk"} and speaker_dict:
         get_spk_id = True
         spk_id = np.array([spk_id])
-    if lang == 'zh':
+
-        input_ids = frontend.get_input_ids(
+    frontend_dict = run_frontend(
-            input, merge_sentences=merge_sentences, get_tone_ids=get_tone_ids)
+        frontend=frontend,
-        phone_ids = input_ids["phone_ids"]
+        text=input,
-    elif lang == 'en':
+        merge_sentences=merge_sentences,
-        input_ids = frontend.get_input_ids(
+        get_tone_ids=get_tone_ids,
-            input, merge_sentences=merge_sentences)
+        lang=lang)
-        phone_ids = input_ids["phone_ids"]
-    elif lang == 'mix':
-        input_ids = frontend.get_input_ids(
-            input, merge_sentences=merge_sentences)
-        phone_ids = input_ids["phone_ids"]
-    else:
-        print("lang should in {'zh', 'en', 'mix'}!")
 
     if get_tone_ids:
-        tone_ids = input_ids["tone_ids"]
+        tone_ids = frontend_dict['tone_ids']
         tones = tone_ids[0].numpy()
         tones_handle = am_predictor.get_input_handle(am_input_names[1])
         tones_handle.reshape(tones.shape)
@@ -384,6 +398,7 @@ def get_am_output(
         spk_id_handle = am_predictor.get_input_handle(am_input_names[1])
         spk_id_handle.reshape(spk_id.shape)
         spk_id_handle.copy_from_cpu(spk_id)
+    phone_ids = frontend_dict['phone_ids']
     phones = phone_ids[0].numpy()
     phones_handle = am_predictor.get_input_handle(am_input_names[0])
     phones_handle.reshape(phones.shape)
@@ -432,13 +447,13 @@ def get_streaming_am_output(input: str,
                             lang: str='zh',
                             merge_sentences: bool=True):
     get_tone_ids = False
-    if lang == 'zh':
+    frontend_dict = run_frontend(
-        input_ids = frontend.get_input_ids(
+        frontend=frontend,
-            input, merge_sentences=merge_sentences, get_tone_ids=get_tone_ids)
+        text=input,
-        phone_ids = input_ids["phone_ids"]
+        merge_sentences=merge_sentences,
-    else:
+        get_tone_ids=get_tone_ids,
-        print("lang should be 'zh' here!")
+        lang=lang)
-
+    phone_ids = frontend_dict['phone_ids']
     phones = phone_ids[0].numpy()
     am_encoder_infer_output = get_am_sublayer_output(
         am_encoder_infer_predictor, input=phones)
@@ -455,26 +470,25 @@ def get_streaming_am_output(input: str,
 
 
 # onnx
-def get_sess(model_dir: Optional[os.PathLike]=None,
+def get_sess(model_path: Optional[os.PathLike],
-             model_file: Optional[os.PathLike]=None,
              device: str='cpu',
              cpu_threads: int=1,
              use_trt: bool=False):
-
-    model_dir = str(Path(model_dir) / model_file)
     sess_options = ort.SessionOptions()
     sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
     sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
-
+    if 'gpu' in device.lower():
-    if device == "gpu":
+        device_id = int(device.split(':')[1]) if len(
+            device.split(':')) == 2 else 0
         # fastspeech2/mb_melgan can't use trt now!
         if use_trt:
-            providers = ['TensorrtExecutionProvider']
+            provider_name = 'TensorrtExecutionProvider'
         else:
-            providers = ['CUDAExecutionProvider']
+            provider_name = 'CUDAExecutionProvider'
-    elif device == "cpu":
+        providers = [(provider_name, {'device_id': device_id})]
+    elif device.lower() == 'cpu':
         providers = ['CPUExecutionProvider']
     sess_options.intra_op_num_threads = cpu_threads
     sess = ort.InferenceSession(
-        model_dir, providers=providers, sess_options=sess_options)
+        model_path, providers=providers, sess_options=sess_options)
     return sess

paddlespeech/t2s/exps/synthesize_e2e.py

@@ -25,6 +25,7 @@ from paddlespeech.t2s.exps.syn_utils import get_am_inference
 from paddlespeech.t2s.exps.syn_utils import get_frontend
 from paddlespeech.t2s.exps.syn_utils import get_sentences
 from paddlespeech.t2s.exps.syn_utils import get_voc_inference
+from paddlespeech.t2s.exps.syn_utils import run_frontend
 from paddlespeech.t2s.exps.syn_utils import voc_to_static
 
 
@@ -49,6 +50,7 @@ def evaluate(args):
         lang=args.lang,
         phones_dict=args.phones_dict,
         tones_dict=args.tones_dict)
+    print("frontend done!")
 
     # acoustic model
     am_name = args.am[:args.am.rindex('_')]
@@ -62,13 +64,14 @@ def evaluate(args):
         phones_dict=args.phones_dict,
         tones_dict=args.tones_dict,
         speaker_dict=args.speaker_dict)
-
+    print("acoustic model done!")
     # vocoder
     voc_inference = get_voc_inference(
         voc=args.voc,
         voc_config=voc_config,
         voc_ckpt=args.voc_ckpt,
         voc_stat=args.voc_stat)
+    print("voc done!")
 
     # whether dygraph to static
     if args.inference_dir:
@@ -78,7 +81,6 @@ def evaluate(args):
             am=args.am,
             inference_dir=args.inference_dir,
             speaker_dict=args.speaker_dict)
-
         # vocoder
         voc_inference = voc_to_static(
             voc_inference=voc_inference,
@@ -101,24 +103,13 @@ def evaluate(args):
     T = 0
     for utt_id, sentence in sentences:
         with timer() as t:
-            if args.lang == 'zh':
+            frontend_dict = run_frontend(
-                input_ids = frontend.get_input_ids(
+                frontend=frontend,
-                    sentence,
+                text=sentence,
                 merge_sentences=merge_sentences,
-                    get_tone_ids=get_tone_ids)
+                get_tone_ids=get_tone_ids,
-                phone_ids = input_ids["phone_ids"]
+                lang=args.lang)
-                if get_tone_ids:
+            phone_ids = frontend_dict['phone_ids']
-                    tone_ids = input_ids["tone_ids"]
-            elif args.lang == 'en':
-                input_ids = frontend.get_input_ids(
-                    sentence, merge_sentences=merge_sentences)
-                phone_ids = input_ids["phone_ids"]
-            elif args.lang == 'mix':
-                input_ids = frontend.get_input_ids(
-                    sentence, merge_sentences=merge_sentences)
-                phone_ids = input_ids["phone_ids"]
-            else:
-                print("lang should in {'zh', 'en', 'mix'}!")
             with paddle.no_grad():
                 flags = 0
                 for i in range(len(phone_ids)):
@@ -132,7 +123,7 @@ def evaluate(args):
                         else:
                             mel = am_inference(part_phone_ids)
                     elif am_name == 'speedyspeech':
-                        part_tone_ids = tone_ids[i]
+                        part_tone_ids = frontend_dict['tone_ids'][i]
                         if am_dataset in {"aishell3", "vctk"}:
                             spk_id = paddle.to_tensor(args.spk_id)
                             mel = am_inference(part_phone_ids, part_tone_ids,

paddlespeech/t2s/exps/synthesize_streaming.py

@@ -30,6 +30,7 @@ from paddlespeech.t2s.exps.syn_utils import get_frontend
 from paddlespeech.t2s.exps.syn_utils import get_sentences
 from paddlespeech.t2s.exps.syn_utils import get_voc_inference
 from paddlespeech.t2s.exps.syn_utils import model_alias
+from paddlespeech.t2s.exps.syn_utils import run_frontend
 from paddlespeech.t2s.exps.syn_utils import voc_to_static
 from paddlespeech.t2s.utils import str2bool
 from paddlespeech.utils.dynamic_import import dynamic_import
@@ -138,15 +139,13 @@ def evaluate(args):
 
     for utt_id, sentence in sentences:
         with timer() as t:
-            if args.lang == 'zh':
+            frontend_dict = run_frontend(
-                input_ids = frontend.get_input_ids(
+                frontend=frontend,
-                    sentence,
+                text=sentence,
                 merge_sentences=merge_sentences,
-                    get_tone_ids=get_tone_ids)
+                get_tone_ids=get_tone_ids,
-
+                lang=args.lang)
-                phone_ids = input_ids["phone_ids"]
+            phone_ids = frontend_dict['phone_ids']
-            else:
-                print("lang should be 'zh' here!")
             # merge_sentences=True here, so we only use the first item of phone_ids
             phone_ids = phone_ids[0]
             with paddle.no_grad():

paddlespeech/t2s/frontend/g2pw/onnx_api.py

@@ -1,18 +1,11 @@
 import os
 import json
-import requests
-import zipfile
 import onnxruntime
 import numpy as np
-from io import BytesIO
-import shutil
 
-from transformers import BertTokenizer
+from opencc import OpenCC
 
-try:
+from transformers import BertTokenizer
-    from opencc import OpenCC
-except:
-    pass
 
 from paddlespeech.t2s.frontend.g2pw.dataset import prepare_data, prepare_onnx_input, get_phoneme_labels, get_char_phoneme_labels
 from paddlespeech.t2s.frontend.g2pw.utils import load_config

paddlespeech/t2s/frontend/mix_frontend.py

@@ -136,7 +136,8 @@ class MixFrontend():
                       sentence: str,
                       merge_sentences: bool=True,
                       get_tone_ids: bool=False,
-                      add_sp: bool=True) -> Dict[str, List[paddle.Tensor]]:
+                      add_sp: bool=True,
+                      to_tensor: bool=True) -> Dict[str, List[paddle.Tensor]]:
 
         sentences = self._split(sentence)
         phones_list = []
@@ -152,11 +153,12 @@ class MixFrontend():
                     input_ids = self.zh_frontend.get_input_ids(
                         content,
                         merge_sentences=True,
-                        get_tone_ids=get_tone_ids)
+                        get_tone_ids=get_tone_ids,
+                        to_tensor=to_tensor)
 
                 elif lang == "en":
                     input_ids = self.en_frontend.get_input_ids(
-                        content, merge_sentences=True)
+                        content, merge_sentences=True, to_tensor=to_tensor)
 
                 phones_seg.append(input_ids["phone_ids"][0])
                 if add_sp:

paddlespeech/t2s/frontend/phonectic.py

@@ -82,8 +82,10 @@ class English(Phonetics):
         phone_ids = [self.vocab_phones[item] for item in phonemes]
         return np.array(phone_ids, np.int64)
 
-    def get_input_ids(self, sentence: str,
+    def get_input_ids(self,
-                      merge_sentences: bool=False) -> paddle.Tensor:
+                      sentence: str,
+                      merge_sentences: bool=False,
+                      to_tensor: bool=True) -> paddle.Tensor:
         result = {}
         sentences = self.text_normalizer._split(sentence, lang="en")
         phones_list = []
@@ -112,6 +114,7 @@ class English(Phonetics):
 
         for part_phones_list in phones_list:
             phone_ids = self._p2id(part_phones_list)
+            if to_tensor:
                 phone_ids = paddle.to_tensor(phone_ids)
             temp_phone_ids.append(phone_ids)
         result["phone_ids"] = temp_phone_ids