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169 lines
5.9 KiB
169 lines
5.9 KiB
# This is the parameter configuration file for PaddleSpeech Serving.
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#################################################################################
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# SERVER SETTING #
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#################################################################################
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host: 127.0.0.1
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port: 8090
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# The task format in the engin_list is: <speech task>_<engine type>
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# task choices = ['asr_python', 'asr_inference', 'tts_python', 'tts_inference']
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# protocol = ['websocket', 'http'] (only one can be selected).
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# http only support offline engine type.
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protocol: 'http'
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engine_list: ['asr_python', 'tts_python', 'cls_python', 'text_python']
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#################################################################################
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# ENGINE CONFIG #
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#################################################################################
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################################### ASR #########################################
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################### speech task: asr; engine_type: python #######################
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asr_python:
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model: 'conformer_wenetspeech'
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lang: 'zh'
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sample_rate: 16000
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cfg_path: # [optional]
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ckpt_path: # [optional]
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decode_method: 'attention_rescoring'
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force_yes: True
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device: # set 'gpu:id' or 'cpu'
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################### speech task: asr; engine_type: inference #######################
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asr_inference:
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# model_type choices=['deepspeech2offline_aishell']
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model_type: 'deepspeech2offline_aishell'
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am_model: # the pdmodel file of am static model [optional]
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am_params: # the pdiparams file of am static model [optional]
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lang: 'zh'
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sample_rate: 16000
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cfg_path:
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decode_method:
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force_yes: True
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am_predictor_conf:
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device: # set 'gpu:id' or 'cpu'
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switch_ir_optim: True
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glog_info: False # True -> print glog
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summary: True # False -> do not show predictor config
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################### speech task: asr; engine_type: online #######################
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asr_online:
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model_type: 'deepspeech2online_aishell'
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am_model: # the pdmodel file of am static model [optional]
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am_params: # the pdiparams file of am static model [optional]
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lang: 'zh'
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sample_rate: 16000
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cfg_path:
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decode_method:
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force_yes: True
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am_predictor_conf:
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device: # set 'gpu:id' or 'cpu'
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switch_ir_optim: True
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glog_info: False # True -> print glog
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summary: True # False -> do not show predictor config
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################################### TTS #########################################
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################### speech task: tts; engine_type: python #######################
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tts_python:
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# am (acoustic model) choices=['speedyspeech_csmsc', 'fastspeech2_csmsc',
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# 'fastspeech2_ljspeech', 'fastspeech2_aishell3',
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# 'fastspeech2_vctk']
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am: 'fastspeech2_csmsc'
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am_config:
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am_ckpt:
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am_stat:
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phones_dict:
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tones_dict:
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speaker_dict:
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spk_id: 0
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# voc (vocoder) choices=['pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3',
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# 'pwgan_vctk', 'mb_melgan_csmsc']
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voc: 'pwgan_csmsc'
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voc_config:
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voc_ckpt:
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voc_stat:
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# others
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lang: 'zh'
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device: # set 'gpu:id' or 'cpu'
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################### speech task: tts; engine_type: inference #######################
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tts_inference:
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# am (acoustic model) choices=['speedyspeech_csmsc', 'fastspeech2_csmsc']
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am: 'fastspeech2_csmsc'
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am_model: # the pdmodel file of your am static model (XX.pdmodel)
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am_params: # the pdiparams file of your am static model (XX.pdipparams)
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am_sample_rate: 24000
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phones_dict:
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tones_dict:
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speaker_dict:
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spk_id: 0
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am_predictor_conf:
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device: # set 'gpu:id' or 'cpu'
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switch_ir_optim: True
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glog_info: False # True -> print glog
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summary: True # False -> do not show predictor config
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# voc (vocoder) choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc']
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voc: 'pwgan_csmsc'
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voc_model: # the pdmodel file of your vocoder static model (XX.pdmodel)
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voc_params: # the pdiparams file of your vocoder static model (XX.pdipparams)
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voc_sample_rate: 24000
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voc_predictor_conf:
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device: # set 'gpu:id' or 'cpu'
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switch_ir_optim: True
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glog_info: False # True -> print glog
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summary: True # False -> do not show predictor config
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# others
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lang: 'zh'
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################################### CLS #########################################
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################### speech task: cls; engine_type: python #######################
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cls_python:
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# model choices=['panns_cnn14', 'panns_cnn10', 'panns_cnn6']
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model: 'panns_cnn14'
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cfg_path: # [optional] Config of cls task.
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ckpt_path: # [optional] Checkpoint file of model.
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label_file: # [optional] Label file of cls task.
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device: # set 'gpu:id' or 'cpu'
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################### speech task: cls; engine_type: inference #######################
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cls_inference:
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# model_type choices=['panns_cnn14', 'panns_cnn10', 'panns_cnn6']
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model_type: 'panns_cnn14'
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cfg_path:
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model_path: # the pdmodel file of am static model [optional]
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params_path: # the pdiparams file of am static model [optional]
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label_file: # [optional] Label file of cls task.
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predictor_conf:
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device: # set 'gpu:id' or 'cpu'
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switch_ir_optim: True
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glog_info: False # True -> print glog
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summary: True # False -> do not show predictor config
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################################### Text #########################################
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################### text task: punc; engine_type: python #######################
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text_python:
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task: punc
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model_type: 'ernie_linear_p3_wudao'
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lang: 'zh'
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sample_rate: 16000
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cfg_path: # [optional]
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ckpt_path: # [optional]
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vocab_file: # [optional]
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device: # set 'gpu:id' or 'cpu' |