xiongxinlei
3 years ago
commit
4473405f82
@ -1,27 +1,107 @@
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# 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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#################################################################################
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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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##################################################################
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# CONFIG FILE #
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##################################################################
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# add engine backend type (Options: asr, tts) and config file here.
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# Adding a speech task to engine_backend means starting the service.
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engine_backend:
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asr: 'conf/asr/asr.yaml'
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tts: 'conf/tts/tts.yaml'
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# The engine_type of speech task needs to keep the same type as the config file of speech task.
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# E.g: The engine_type of asr is 'python', the engine_backend of asr is 'XX/asr.yaml'
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# E.g: The engine_type of asr is 'inference', the engine_backend of asr is 'XX/asr_pd.yaml'
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#
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# add engine type (Options: python, inference)
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engine_type:
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asr: 'python'
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tts: 'python'
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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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engine_list: ['asr_python', 'tts_python']
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#################################################################################
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# ENGINE CONFIG #
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#################################################################################
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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: 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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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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# This is the parameter configuration file for ASR server.
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# These are the static models that support paddle inference.
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##################################################################
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# ACOUSTIC MODEL SETTING #
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# am choices=['deepspeech2offline_aishell'] TODO
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##################################################################
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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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##################################################################
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# OTHERS #
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##################################################################
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# This is the parameter configuration file for TTS server.
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##################################################################
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# ACOUSTIC MODEL SETTING #
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# am choices=['speedyspeech_csmsc', 'fastspeech2_csmsc',
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# 'fastspeech2_ljspeech', 'fastspeech2_aishell3',
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# 'fastspeech2_vctk']
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##################################################################
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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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##################################################################
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# VOCODER SETTING #
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# voc choices=['pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3',
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# 'pwgan_vctk', 'mb_melgan_csmsc']
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##################################################################
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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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##################################################################
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# OTHERS #
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##################################################################
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lang: 'zh'
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device: # set 'gpu:id' or 'cpu'
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@ -1,42 +0,0 @@
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# This is the parameter configuration file for TTS server.
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# These are the static models that support paddle inference.
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##################################################################
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# ACOUSTIC MODEL SETTING #
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# am choices=['speedyspeech_csmsc', 'fastspeech2_csmsc']
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##################################################################
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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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##################################################################
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# VOCODER SETTING #
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# voc choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc']
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##################################################################
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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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##################################################################
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# OTHERS #
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##################################################################
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lang: 'zh'
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#!/bin/bash
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paddlespeech_server start --config_file ./conf/application.yaml
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paddlespeech_server start --config_file ./conf/application.yaml
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@ -0,0 +1,142 @@
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# HiFiGAN with AISHELL-3
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This example contains code used to train a [HiFiGAN](https://arxiv.org/abs/2010.05646) model with [AISHELL-3](http://www.aishelltech.com/aishell_3).
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AISHELL-3 is a large-scale and high-fidelity multi-speaker Mandarin speech corpus that could be used to train multi-speaker Text-to-Speech (TTS) systems.
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## Dataset
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### Download and Extract
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Download AISHELL-3.
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```bash
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wget https://www.openslr.org/resources/93/data_aishell3.tgz
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```
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Extract AISHELL-3.
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```bash
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mkdir data_aishell3
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tar zxvf data_aishell3.tgz -C data_aishell3
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```
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### Get MFA Result and Extract
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We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
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You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) (use MFA1.x now) of our repo.
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## Get Started
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Assume the path to the dataset is `~/datasets/data_aishell3`.
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Assume the path to the MFA result of AISHELL-3 is `./aishell3_alignment_tone`.
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Run the command below to
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1. **source path**.
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2. preprocess the dataset.
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3. train the model.
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4. synthesize wavs.
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- synthesize waveform from `metadata.jsonl`.
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```bash
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./run.sh
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```
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You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
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```bash
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./run.sh --stage 0 --stop-stage 0
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```
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### Data Preprocessing
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```bash
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./local/preprocess.sh ${conf_path}
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```
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When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
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```text
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dump
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├── dev
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│ ├── norm
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│ └── raw
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├── test
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│ ├── norm
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│ └── raw
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└── train
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├── norm
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├── raw
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└── feats_stats.npy
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```
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The dataset is split into 3 parts, namely `train`, `dev`, and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in `dump/train/feats_stats.npy`.
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Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance.
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### Model Training
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
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```
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`./local/train.sh` calls `${BIN_DIR}/train.py`.
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Here's the complete help message.
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```text
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usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
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[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
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[--ngpu NGPU] [--batch-size BATCH_SIZE] [--max-iter MAX_ITER]
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[--run-benchmark RUN_BENCHMARK]
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[--profiler_options PROFILER_OPTIONS]
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Train a ParallelWaveGAN model.
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optional arguments:
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-h, --help show this help message and exit
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--config CONFIG config file to overwrite default config.
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--train-metadata TRAIN_METADATA
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training data.
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--dev-metadata DEV_METADATA
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dev data.
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--output-dir OUTPUT_DIR
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output dir.
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--ngpu NGPU if ngpu == 0, use cpu.
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benchmark:
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arguments related to benchmark.
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--batch-size BATCH_SIZE
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batch size.
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--max-iter MAX_ITER train max steps.
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--run-benchmark RUN_BENCHMARK
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runing benchmark or not, if True, use the --batch-size
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and --max-iter.
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--profiler_options PROFILER_OPTIONS
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The option of profiler, which should be in format
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"key1=value1;key2=value2;key3=value3".
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```
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1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
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2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
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3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are saved in `checkpoints/` inside this directory.
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4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
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### Synthesizing
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`./local/synthesize.sh` calls `${BIN_DIR}/../synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
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```
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```text
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usage: synthesize.py [-h] [--generator-type GENERATOR_TYPE] [--config CONFIG]
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[--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA]
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[--output-dir OUTPUT_DIR] [--ngpu NGPU]
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Synthesize with GANVocoder.
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optional arguments:
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-h, --help show this help message and exit
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--generator-type GENERATOR_TYPE
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type of GANVocoder, should in {pwgan, mb_melgan,
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style_melgan, } now
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--config CONFIG GANVocoder config file.
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--checkpoint CHECKPOINT
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snapshot to load.
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--test-metadata TEST_METADATA
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dev data.
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--output-dir OUTPUT_DIR
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output dir.
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--ngpu NGPU if ngpu == 0, use cpu.
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```
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1. `--config` config file. You should use the same config with which the model is trained.
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2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `checkpoints` inside the training output directory.
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3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
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4. `--output-dir` is the directory to save the synthesized audio files.
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5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
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## Pretrained Models
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## Acknowledgement
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We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.
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@ -0,0 +1,168 @@
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# This is the configuration file for AISHELL-3 dataset.
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# This configuration is based on HiFiGAN V1, which is
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# an official configuration. But I found that the optimizer
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# setting does not work well with my implementation.
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# So I changed optimizer settings as follows:
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# - AdamW -> Adam
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# - betas: [0.8, 0.99] -> betas: [0.5, 0.9]
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# - Scheduler: ExponentialLR -> MultiStepLR
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# To match the shift size difference, the upsample scales
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# is also modified from the original 256 shift setting.
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###########################################################
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# FEATURE EXTRACTION SETTING #
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###########################################################
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fs: 24000 # Sampling rate.
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n_fft: 2048 # FFT size (samples).
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n_shift: 300 # Hop size (samples). 12.5ms
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win_length: 1200 # Window length (samples). 50ms
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# If set to null, it will be the same as fft_size.
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window: "hann" # Window function.
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n_mels: 80 # Number of mel basis.
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fmin: 80 # Minimum freq in mel basis calculation. (Hz)
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fmax: 7600 # Maximum frequency in mel basis calculation. (Hz)
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###########################################################
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# GENERATOR NETWORK ARCHITECTURE SETTING #
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###########################################################
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generator_params:
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in_channels: 80 # Number of input channels.
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out_channels: 1 # Number of output channels.
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channels: 512 # Number of initial channels.
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kernel_size: 7 # Kernel size of initial and final conv layers.
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upsample_scales: [5, 5, 4, 3] # Upsampling scales.
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upsample_kernel_sizes: [10, 10, 8, 6] # Kernel size for upsampling layers.
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resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks.
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resblock_dilations: # Dilations for residual blocks.
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- [1, 3, 5]
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- [1, 3, 5]
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- [1, 3, 5]
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use_additional_convs: True # Whether to use additional conv layer in residual blocks.
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bias: True # Whether to use bias parameter in conv.
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nonlinear_activation: "leakyrelu" # Nonlinear activation type.
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nonlinear_activation_params: # Nonlinear activation paramters.
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negative_slope: 0.1
|
||||
use_weight_norm: True # Whether to apply weight normalization.
|
||||
|
||||
|
||||
###########################################################
|
||||
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
discriminator_params:
|
||||
scales: 3 # Number of multi-scale discriminator.
|
||||
scale_downsample_pooling: "AvgPool1D" # Pooling operation for scale discriminator.
|
||||
scale_downsample_pooling_params:
|
||||
kernel_size: 4 # Pooling kernel size.
|
||||
stride: 2 # Pooling stride.
|
||||
padding: 2 # Padding size.
|
||||
scale_discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_sizes: [15, 41, 5, 3] # List of kernel sizes.
|
||||
channels: 128 # Initial number of channels.
|
||||
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
|
||||
max_groups: 16 # Maximum number of groups in downsampling conv layers.
|
||||
bias: True
|
||||
downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales.
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation.
|
||||
nonlinear_activation_params:
|
||||
negative_slope: 0.1
|
||||
follow_official_norm: True # Whether to follow the official norm setting.
|
||||
periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator.
|
||||
period_discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_sizes: [5, 3] # List of kernel sizes.
|
||||
channels: 32 # Initial number of channels.
|
||||
downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales.
|
||||
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
|
||||
bias: True # Whether to use bias parameter in conv layer."
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation.
|
||||
nonlinear_activation_params: # Nonlinear activation paramters.
|
||||
negative_slope: 0.1
|
||||
use_weight_norm: True # Whether to apply weight normalization.
|
||||
use_spectral_norm: False # Whether to apply spectral normalization.
|
||||
|
||||
|
||||
###########################################################
|
||||
# STFT LOSS SETTING #
|
||||
###########################################################
|
||||
use_stft_loss: False # Whether to use multi-resolution STFT loss.
|
||||
use_mel_loss: True # Whether to use Mel-spectrogram loss.
|
||||
mel_loss_params:
|
||||
fs: 24000
|
||||
fft_size: 2048
|
||||
hop_size: 300
|
||||
win_length: 1200
|
||||
window: "hann"
|
||||
num_mels: 80
|
||||
fmin: 0
|
||||
fmax: 12000
|
||||
log_base: null
|
||||
generator_adv_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
discriminator_adv_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
use_feat_match_loss: True
|
||||
feat_match_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
average_by_layers: False # Whether to average loss by #layers in each discriminator.
|
||||
include_final_outputs: False # Whether to include final outputs in feat match loss calculation.
|
||||
|
||||
###########################################################
|
||||
# ADVERSARIAL LOSS SETTING #
|
||||
###########################################################
|
||||
lambda_aux: 45.0 # Loss balancing coefficient for STFT loss.
|
||||
lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss.
|
||||
lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss..
|
||||
|
||||
###########################################################
|
||||
# DATA LOADER SETTING #
|
||||
###########################################################
|
||||
batch_size: 16 # Batch size.
|
||||
batch_max_steps: 8400 # Length of each audio in batch. Make sure dividable by hop_size.
|
||||
num_workers: 2 # Number of workers in DataLoader.
|
||||
|
||||
###########################################################
|
||||
# OPTIMIZER & SCHEDULER SETTING #
|
||||
###########################################################
|
||||
generator_optimizer_params:
|
||||
beta1: 0.5
|
||||
beta2: 0.9
|
||||
weight_decay: 0.0 # Generator's weight decay coefficient.
|
||||
generator_scheduler_params:
|
||||
learning_rate: 2.0e-4 # Generator's learning rate.
|
||||
gamma: 0.5 # Generator's scheduler gamma.
|
||||
milestones: # At each milestone, lr will be multiplied by gamma.
|
||||
- 200000
|
||||
- 400000
|
||||
- 600000
|
||||
- 800000
|
||||
generator_grad_norm: -1 # Generator's gradient norm.
|
||||
discriminator_optimizer_params:
|
||||
beta1: 0.5
|
||||
beta2: 0.9
|
||||
weight_decay: 0.0 # Discriminator's weight decay coefficient.
|
||||
discriminator_scheduler_params:
|
||||
learning_rate: 2.0e-4 # Discriminator's learning rate.
|
||||
gamma: 0.5 # Discriminator's scheduler gamma.
|
||||
milestones: # At each milestone, lr will be multiplied by gamma.
|
||||
- 200000
|
||||
- 400000
|
||||
- 600000
|
||||
- 800000
|
||||
discriminator_grad_norm: -1 # Discriminator's gradient norm.
|
||||
|
||||
###########################################################
|
||||
# INTERVAL SETTING #
|
||||
###########################################################
|
||||
generator_train_start_steps: 1 # Number of steps to start to train discriminator.
|
||||
discriminator_train_start_steps: 0 # Number of steps to start to train discriminator.
|
||||
train_max_steps: 2500000 # Number of training steps.
|
||||
save_interval_steps: 5000 # Interval steps to save checkpoint.
|
||||
eval_interval_steps: 1000 # Interval steps to evaluate the network.
|
||||
|
||||
###########################################################
|
||||
# OTHER SETTING #
|
||||
###########################################################
|
||||
num_snapshots: 10 # max number of snapshots to keep while training
|
||||
seed: 42 # random seed for paddle, random, and np.random
|
||||
@ -0,0 +1,55 @@
|
||||
#!/bin/bash
|
||||
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
config_path=$1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# get durations from MFA's result
|
||||
echo "Generate durations.txt from MFA results ..."
|
||||
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
|
||||
--inputdir=./aishell3_alignment_tone \
|
||||
--output=durations.txt \
|
||||
--config=${config_path}
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# extract features
|
||||
echo "Extract features ..."
|
||||
python3 ${BIN_DIR}/../preprocess.py \
|
||||
--rootdir=~/datasets/data_aishell3/ \
|
||||
--dataset=aishell3 \
|
||||
--dumpdir=dump \
|
||||
--dur-file=durations.txt \
|
||||
--config=${config_path} \
|
||||
--cut-sil=True \
|
||||
--num-cpu=20
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# get features' stats(mean and std)
|
||||
echo "Get features' stats ..."
|
||||
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--field-name="feats"
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# normalize, dev and test should use train's stats
|
||||
echo "Normalize ..."
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--dumpdir=dump/train/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/dev/raw/metadata.jsonl \
|
||||
--dumpdir=dump/dev/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/test/raw/metadata.jsonl \
|
||||
--dumpdir=dump/test/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
fi
|
||||
@ -0,0 +1,14 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
ckpt_name=$3
|
||||
|
||||
FLAGS_allocator_strategy=naive_best_fit \
|
||||
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
|
||||
python3 ${BIN_DIR}/../synthesize.py \
|
||||
--config=${config_path} \
|
||||
--checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
|
||||
--test-metadata=dump/test/norm/metadata.jsonl \
|
||||
--output-dir=${train_output_path}/test \
|
||||
--generator-type=hifigan
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
|
||||
FLAGS_cudnn_exhaustive_search=true \
|
||||
FLAGS_conv_workspace_size_limit=4000 \
|
||||
python ${BIN_DIR}/train.py \
|
||||
--train-metadata=dump/train/norm/metadata.jsonl \
|
||||
--dev-metadata=dump/dev/norm/metadata.jsonl \
|
||||
--config=${config_path} \
|
||||
--output-dir=${train_output_path} \
|
||||
--ngpu=1
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
|
||||
export PYTHONDONTWRITEBYTECODE=1
|
||||
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
||||
export PYTHONIOENCODING=UTF-8
|
||||
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
|
||||
|
||||
MODEL=hifigan
|
||||
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/gan_vocoder/${MODEL}
|
||||
@ -0,0 +1,32 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
source path.sh
|
||||
|
||||
gpus=0
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
conf_path=conf/default.yaml
|
||||
train_output_path=exp/default
|
||||
ckpt_name=snapshot_iter_5000.pdz
|
||||
|
||||
# with the following command, you can choose the stage range you want to run
|
||||
# such as `./run.sh --stage 0 --stop-stage 0`
|
||||
# this can not be mixed use with `$1`, `$2` ...
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# prepare data
|
||||
./local/preprocess.sh ${conf_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# synthesize
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
|
||||
fi
|
||||
@ -0,0 +1,139 @@
|
||||
# HiFiGAN with VCTK
|
||||
This example contains code used to train a [HiFiGAN](https://arxiv.org/abs/2010.05646) model with [VCTK](https://datashare.ed.ac.uk/handle/10283/3443).
|
||||
|
||||
## Dataset
|
||||
### Download and Extract
|
||||
Download VCTK-0.92 from the [official website](https://datashare.ed.ac.uk/handle/10283/3443) and extract it to `~/datasets`. Then the dataset is in directory `~/datasets/VCTK-Corpus-0.92`.
|
||||
|
||||
### Get MFA Result and Extract
|
||||
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut the silence in the edge of audio.
|
||||
You can download from here [vctk_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/VCTK-Corpus-0.92/vctk_alignment.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) of our repo.
|
||||
ps: we remove three speakers in VCTK-0.92 (see [reorganize_vctk.py](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/other/mfa/local/reorganize_vctk.py)):
|
||||
1. `p315`, because of no text for it.
|
||||
2. `p280` and `p362`, because no *_mic2.flac (which is better than *_mic1.flac) for them.
|
||||
|
||||
## Get Started
|
||||
Assume the path to the dataset is `~/datasets/VCTK-Corpus-0.92`.
|
||||
Assume the path to the MFA result of VCTK is `./vctk_alignment`.
|
||||
Run the command below to
|
||||
1. **source path**.
|
||||
2. preprocess the dataset.
|
||||
3. train the model.
|
||||
4. synthesize wavs.
|
||||
- synthesize waveform from `metadata.jsonl`.
|
||||
```bash
|
||||
./run.sh
|
||||
```
|
||||
You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, running the following command will only preprocess the dataset.
|
||||
```bash
|
||||
./run.sh --stage 0 --stop-stage 0
|
||||
```
|
||||
### Data Preprocessing
|
||||
```bash
|
||||
./local/preprocess.sh ${conf_path}
|
||||
```
|
||||
When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
|
||||
|
||||
```text
|
||||
dump
|
||||
├── dev
|
||||
│ ├── norm
|
||||
│ └── raw
|
||||
├── test
|
||||
│ ├── norm
|
||||
│ └── raw
|
||||
└── train
|
||||
├── norm
|
||||
├── raw
|
||||
└── feats_stats.npy
|
||||
```
|
||||
|
||||
The dataset is split into 3 parts, namely `train`, `dev`, and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in `dump/train/feats_stats.npy`.
|
||||
|
||||
Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance.
|
||||
|
||||
### Model Training
|
||||
```bash
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
|
||||
```
|
||||
`./local/train.sh` calls `${BIN_DIR}/train.py`.
|
||||
Here's the complete help message.
|
||||
|
||||
```text
|
||||
usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
|
||||
[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
|
||||
[--ngpu NGPU] [--batch-size BATCH_SIZE] [--max-iter MAX_ITER]
|
||||
[--run-benchmark RUN_BENCHMARK]
|
||||
[--profiler_options PROFILER_OPTIONS]
|
||||
|
||||
Train a ParallelWaveGAN model.
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
--config CONFIG config file to overwrite default config.
|
||||
--train-metadata TRAIN_METADATA
|
||||
training data.
|
||||
--dev-metadata DEV_METADATA
|
||||
dev data.
|
||||
--output-dir OUTPUT_DIR
|
||||
output dir.
|
||||
--ngpu NGPU if ngpu == 0, use cpu.
|
||||
|
||||
benchmark:
|
||||
arguments related to benchmark.
|
||||
|
||||
--batch-size BATCH_SIZE
|
||||
batch size.
|
||||
--max-iter MAX_ITER train max steps.
|
||||
--run-benchmark RUN_BENCHMARK
|
||||
runing benchmark or not, if True, use the --batch-size
|
||||
and --max-iter.
|
||||
--profiler_options PROFILER_OPTIONS
|
||||
The option of profiler, which should be in format
|
||||
"key1=value1;key2=value2;key3=value3".
|
||||
```
|
||||
|
||||
1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
|
||||
2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
|
||||
3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are saved in `checkpoints/` inside this directory.
|
||||
4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
|
||||
|
||||
### Synthesizing
|
||||
`./local/synthesize.sh` calls `${BIN_DIR}/../synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
|
||||
```bash
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
|
||||
```
|
||||
```text
|
||||
usage: synthesize.py [-h] [--generator-type GENERATOR_TYPE] [--config CONFIG]
|
||||
[--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA]
|
||||
[--output-dir OUTPUT_DIR] [--ngpu NGPU]
|
||||
|
||||
Synthesize with GANVocoder.
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
--generator-type GENERATOR_TYPE
|
||||
type of GANVocoder, should in {pwgan, mb_melgan,
|
||||
style_melgan, } now
|
||||
--config CONFIG GANVocoder config file.
|
||||
--checkpoint CHECKPOINT
|
||||
snapshot to load.
|
||||
--test-metadata TEST_METADATA
|
||||
dev data.
|
||||
--output-dir OUTPUT_DIR
|
||||
output dir.
|
||||
--ngpu NGPU if ngpu == 0, use cpu.
|
||||
```
|
||||
|
||||
|
||||
1. `--config` config file. You should use the same config with which the model is trained.
|
||||
2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `checkpoints` inside the training output directory.
|
||||
3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
|
||||
4. `--output-dir` is the directory to save the synthesized audio files.
|
||||
5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
|
||||
|
||||
## Pretrained Model
|
||||
|
||||
|
||||
## Acknowledgement
|
||||
We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.
|
||||
@ -0,0 +1,168 @@
|
||||
# This is the configuration file for VCTK dataset.
|
||||
# This configuration is based on HiFiGAN V1, which is
|
||||
# an official configuration. But I found that the optimizer
|
||||
# setting does not work well with my implementation.
|
||||
# So I changed optimizer settings as follows:
|
||||
# - AdamW -> Adam
|
||||
# - betas: [0.8, 0.99] -> betas: [0.5, 0.9]
|
||||
# - Scheduler: ExponentialLR -> MultiStepLR
|
||||
# To match the shift size difference, the upsample scales
|
||||
# is also modified from the original 256 shift setting.
|
||||
###########################################################
|
||||
# FEATURE EXTRACTION SETTING #
|
||||
###########################################################
|
||||
fs: 24000 # Sampling rate.
|
||||
n_fft: 2048 # FFT size (samples).
|
||||
n_shift: 300 # Hop size (samples). 12.5ms
|
||||
win_length: 1200 # Window length (samples). 50ms
|
||||
# If set to null, it will be the same as fft_size.
|
||||
window: "hann" # Window function.
|
||||
n_mels: 80 # Number of mel basis.
|
||||
fmin: 80 # Minimum freq in mel basis calculation. (Hz)
|
||||
fmax: 7600 # Maximum frequency in mel basis calculation. (Hz)
|
||||
|
||||
###########################################################
|
||||
# GENERATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
generator_params:
|
||||
in_channels: 80 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
channels: 512 # Number of initial channels.
|
||||
kernel_size: 7 # Kernel size of initial and final conv layers.
|
||||
upsample_scales: [5, 5, 4, 3] # Upsampling scales.
|
||||
upsample_kernel_sizes: [10, 10, 8, 6] # Kernel size for upsampling layers.
|
||||
resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks.
|
||||
resblock_dilations: # Dilations for residual blocks.
|
||||
- [1, 3, 5]
|
||||
- [1, 3, 5]
|
||||
- [1, 3, 5]
|
||||
use_additional_convs: True # Whether to use additional conv layer in residual blocks.
|
||||
bias: True # Whether to use bias parameter in conv.
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation type.
|
||||
nonlinear_activation_params: # Nonlinear activation paramters.
|
||||
negative_slope: 0.1
|
||||
use_weight_norm: True # Whether to apply weight normalization.
|
||||
|
||||
|
||||
###########################################################
|
||||
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
discriminator_params:
|
||||
scales: 3 # Number of multi-scale discriminator.
|
||||
scale_downsample_pooling: "AvgPool1D" # Pooling operation for scale discriminator.
|
||||
scale_downsample_pooling_params:
|
||||
kernel_size: 4 # Pooling kernel size.
|
||||
stride: 2 # Pooling stride.
|
||||
padding: 2 # Padding size.
|
||||
scale_discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_sizes: [15, 41, 5, 3] # List of kernel sizes.
|
||||
channels: 128 # Initial number of channels.
|
||||
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
|
||||
max_groups: 16 # Maximum number of groups in downsampling conv layers.
|
||||
bias: True
|
||||
downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales.
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation.
|
||||
nonlinear_activation_params:
|
||||
negative_slope: 0.1
|
||||
follow_official_norm: True # Whether to follow the official norm setting.
|
||||
periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator.
|
||||
period_discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_sizes: [5, 3] # List of kernel sizes.
|
||||
channels: 32 # Initial number of channels.
|
||||
downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales.
|
||||
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
|
||||
bias: True # Whether to use bias parameter in conv layer."
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation.
|
||||
nonlinear_activation_params: # Nonlinear activation paramters.
|
||||
negative_slope: 0.1
|
||||
use_weight_norm: True # Whether to apply weight normalization.
|
||||
use_spectral_norm: False # Whether to apply spectral normalization.
|
||||
|
||||
|
||||
###########################################################
|
||||
# STFT LOSS SETTING #
|
||||
###########################################################
|
||||
use_stft_loss: False # Whether to use multi-resolution STFT loss.
|
||||
use_mel_loss: True # Whether to use Mel-spectrogram loss.
|
||||
mel_loss_params:
|
||||
fs: 24000
|
||||
fft_size: 2048
|
||||
hop_size: 300
|
||||
win_length: 1200
|
||||
window: "hann"
|
||||
num_mels: 80
|
||||
fmin: 0
|
||||
fmax: 12000
|
||||
log_base: null
|
||||
generator_adv_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
discriminator_adv_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
use_feat_match_loss: True
|
||||
feat_match_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
average_by_layers: False # Whether to average loss by #layers in each discriminator.
|
||||
include_final_outputs: False # Whether to include final outputs in feat match loss calculation.
|
||||
|
||||
###########################################################
|
||||
# ADVERSARIAL LOSS SETTING #
|
||||
###########################################################
|
||||
lambda_aux: 45.0 # Loss balancing coefficient for STFT loss.
|
||||
lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss.
|
||||
lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss..
|
||||
|
||||
###########################################################
|
||||
# DATA LOADER SETTING #
|
||||
###########################################################
|
||||
batch_size: 16 # Batch size.
|
||||
batch_max_steps: 8400 # Length of each audio in batch. Make sure dividable by hop_size.
|
||||
num_workers: 2 # Number of workers in DataLoader.
|
||||
|
||||
###########################################################
|
||||
# OPTIMIZER & SCHEDULER SETTING #
|
||||
###########################################################
|
||||
generator_optimizer_params:
|
||||
beta1: 0.5
|
||||
beta2: 0.9
|
||||
weight_decay: 0.0 # Generator's weight decay coefficient.
|
||||
generator_scheduler_params:
|
||||
learning_rate: 2.0e-4 # Generator's learning rate.
|
||||
gamma: 0.5 # Generator's scheduler gamma.
|
||||
milestones: # At each milestone, lr will be multiplied by gamma.
|
||||
- 200000
|
||||
- 400000
|
||||
- 600000
|
||||
- 800000
|
||||
generator_grad_norm: -1 # Generator's gradient norm.
|
||||
discriminator_optimizer_params:
|
||||
beta1: 0.5
|
||||
beta2: 0.9
|
||||
weight_decay: 0.0 # Discriminator's weight decay coefficient.
|
||||
discriminator_scheduler_params:
|
||||
learning_rate: 2.0e-4 # Discriminator's learning rate.
|
||||
gamma: 0.5 # Discriminator's scheduler gamma.
|
||||
milestones: # At each milestone, lr will be multiplied by gamma.
|
||||
- 200000
|
||||
- 400000
|
||||
- 600000
|
||||
- 800000
|
||||
discriminator_grad_norm: -1 # Discriminator's gradient norm.
|
||||
|
||||
###########################################################
|
||||
# INTERVAL SETTING #
|
||||
###########################################################
|
||||
generator_train_start_steps: 1 # Number of steps to start to train discriminator.
|
||||
discriminator_train_start_steps: 0 # Number of steps to start to train discriminator.
|
||||
train_max_steps: 2500000 # Number of training steps.
|
||||
save_interval_steps: 5000 # Interval steps to save checkpoint.
|
||||
eval_interval_steps: 1000 # Interval steps to evaluate the network.
|
||||
|
||||
###########################################################
|
||||
# OTHER SETTING #
|
||||
###########################################################
|
||||
num_snapshots: 10 # max number of snapshots to keep while training
|
||||
seed: 42 # random seed for paddle, random, and np.random
|
||||
@ -0,0 +1,55 @@
|
||||
#!/bin/bash
|
||||
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
config_path=$1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# get durations from MFA's result
|
||||
echo "Generate durations.txt from MFA results ..."
|
||||
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
|
||||
--inputdir=./vctk_alignment \
|
||||
--output=durations.txt \
|
||||
--config=${config_path}
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# extract features
|
||||
echo "Extract features ..."
|
||||
python3 ${BIN_DIR}/../preprocess.py \
|
||||
--rootdir=~/datasets/VCTK-Corpus-0.92/ \
|
||||
--dataset=vctk \
|
||||
--dumpdir=dump \
|
||||
--dur-file=durations.txt \
|
||||
--config=${config_path} \
|
||||
--cut-sil=True \
|
||||
--num-cpu=20
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# get features' stats(mean and std)
|
||||
echo "Get features' stats ..."
|
||||
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--field-name="feats"
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# normalize, dev and test should use train's stats
|
||||
echo "Normalize ..."
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--dumpdir=dump/train/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/dev/raw/metadata.jsonl \
|
||||
--dumpdir=dump/dev/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/test/raw/metadata.jsonl \
|
||||
--dumpdir=dump/test/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
fi
|
||||
@ -0,0 +1,14 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
ckpt_name=$3
|
||||
|
||||
FLAGS_allocator_strategy=naive_best_fit \
|
||||
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
|
||||
python3 ${BIN_DIR}/../synthesize.py \
|
||||
--config=${config_path} \
|
||||
--checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
|
||||
--test-metadata=dump/test/norm/metadata.jsonl \
|
||||
--output-dir=${train_output_path}/test \
|
||||
--generator-type=hifigan
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
|
||||
FLAGS_cudnn_exhaustive_search=true \
|
||||
FLAGS_conv_workspace_size_limit=4000 \
|
||||
python ${BIN_DIR}/train.py \
|
||||
--train-metadata=dump/train/norm/metadata.jsonl \
|
||||
--dev-metadata=dump/dev/norm/metadata.jsonl \
|
||||
--config=${config_path} \
|
||||
--output-dir=${train_output_path} \
|
||||
--ngpu=1
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
|
||||
export PYTHONDONTWRITEBYTECODE=1
|
||||
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
||||
export PYTHONIOENCODING=UTF-8
|
||||
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
|
||||
|
||||
MODEL=hifigan
|
||||
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/gan_vocoder/${MODEL}
|
||||
@ -0,0 +1,32 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
source path.sh
|
||||
|
||||
gpus=0
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
conf_path=conf/default.yaml
|
||||
train_output_path=exp/default
|
||||
ckpt_name=snapshot_iter_5000.pdz
|
||||
|
||||
# with the following command, you can choose the stage range you want to run
|
||||
# such as `./run.sh --stage 0 --stop-stage 0`
|
||||
# this can not be mixed use with `$1`, `$2` ...
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# prepare data
|
||||
./local/preprocess.sh ${conf_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# synthesize
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
|
||||
fi
|
||||
@ -0,0 +1,25 @@
|
||||
stage=-1
|
||||
stop_stage=100
|
||||
TARGET_DIR=${MAIN_ROOT}/dataset
|
||||
|
||||
. utils/parse_options.sh || exit -1;
|
||||
|
||||
src=$1
|
||||
mkdir -p data/{dev,test}
|
||||
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
|
||||
# download data, generate manifests
|
||||
# create data/{dev,test} directory to store the manifest files
|
||||
python3 ${TARGET_DIR}/voxceleb/voxceleb1.py \
|
||||
--manifest_prefix="data/manifest" \
|
||||
--target_dir="${src}"
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Prepare Voxceleb failed. Terminated."
|
||||
exit 1
|
||||
fi
|
||||
mv data/manifest.dev data/dev
|
||||
mv data/voxceleb1.dev.meta data/dev
|
||||
|
||||
mv data/manifest.test data/test
|
||||
mv data/voxceleb1.test.meta data/test
|
||||
fi
|
||||
@ -0,0 +1 @@
|
||||
../../../utils/
|
||||
@ -1 +1,5 @@
|
||||
# Changelog
|
||||
|
||||
Date: 2022-2-25, Author: Hui Zhang.
|
||||
- Refactor architecture.
|
||||
- dtw distance and mcd style dtw
|
||||
|
||||
@ -1,170 +0,0 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
from numpy import ndarray as array
|
||||
|
||||
from ..backends import depth_convert
|
||||
from ..utils import ParameterError
|
||||
|
||||
__all__ = [
|
||||
'depth_augment',
|
||||
'spect_augment',
|
||||
'random_crop1d',
|
||||
'random_crop2d',
|
||||
'adaptive_spect_augment',
|
||||
]
|
||||
|
||||
|
||||
def randint(high: int) -> int:
|
||||
"""Generate one random integer in range [0 high)
|
||||
|
||||
This is a helper function for random data augmentaiton
|
||||
"""
|
||||
return int(np.random.randint(0, high=high))
|
||||
|
||||
|
||||
def rand() -> float:
|
||||
"""Generate one floating-point number in range [0 1)
|
||||
|
||||
This is a helper function for random data augmentaiton
|
||||
"""
|
||||
return float(np.random.rand(1))
|
||||
|
||||
|
||||
def depth_augment(y: array,
|
||||
choices: List=['int8', 'int16'],
|
||||
probs: List[float]=[0.5, 0.5]) -> array:
|
||||
""" Audio depth augmentation
|
||||
|
||||
Do audio depth augmentation to simulate the distortion brought by quantization.
|
||||
"""
|
||||
assert len(probs) == len(
|
||||
choices
|
||||
), 'number of choices {} must be equal to size of probs {}'.format(
|
||||
len(choices), len(probs))
|
||||
depth = np.random.choice(choices, p=probs)
|
||||
src_depth = y.dtype
|
||||
y1 = depth_convert(y, depth)
|
||||
y2 = depth_convert(y1, src_depth)
|
||||
|
||||
return y2
|
||||
|
||||
|
||||
def adaptive_spect_augment(spect: array, tempo_axis: int=0,
|
||||
level: float=0.1) -> array:
|
||||
"""Do adpative spectrogram augmentation
|
||||
|
||||
The level of the augmentation is gowern by the paramter level,
|
||||
ranging from 0 to 1, with 0 represents no augmentation。
|
||||
|
||||
"""
|
||||
assert spect.ndim == 2., 'only supports 2d tensor or numpy array'
|
||||
if tempo_axis == 0:
|
||||
nt, nf = spect.shape
|
||||
else:
|
||||
nf, nt = spect.shape
|
||||
|
||||
time_mask_width = int(nt * level * 0.5)
|
||||
freq_mask_width = int(nf * level * 0.5)
|
||||
|
||||
num_time_mask = int(10 * level)
|
||||
num_freq_mask = int(10 * level)
|
||||
|
||||
if tempo_axis == 0:
|
||||
for _ in range(num_time_mask):
|
||||
start = randint(nt - time_mask_width)
|
||||
spect[start:start + time_mask_width, :] = 0
|
||||
for _ in range(num_freq_mask):
|
||||
start = randint(nf - freq_mask_width)
|
||||
spect[:, start:start + freq_mask_width] = 0
|
||||
else:
|
||||
for _ in range(num_time_mask):
|
||||
start = randint(nt - time_mask_width)
|
||||
spect[:, start:start + time_mask_width] = 0
|
||||
for _ in range(num_freq_mask):
|
||||
start = randint(nf - freq_mask_width)
|
||||
spect[start:start + freq_mask_width, :] = 0
|
||||
|
||||
return spect
|
||||
|
||||
|
||||
def spect_augment(spect: array,
|
||||
tempo_axis: int=0,
|
||||
max_time_mask: int=3,
|
||||
max_freq_mask: int=3,
|
||||
max_time_mask_width: int=30,
|
||||
max_freq_mask_width: int=20) -> array:
|
||||
"""Do spectrogram augmentation in both time and freq axis
|
||||
|
||||
Reference:
|
||||
|
||||
"""
|
||||
assert spect.ndim == 2., 'only supports 2d tensor or numpy array'
|
||||
if tempo_axis == 0:
|
||||
nt, nf = spect.shape
|
||||
else:
|
||||
nf, nt = spect.shape
|
||||
|
||||
num_time_mask = randint(max_time_mask)
|
||||
num_freq_mask = randint(max_freq_mask)
|
||||
|
||||
time_mask_width = randint(max_time_mask_width)
|
||||
freq_mask_width = randint(max_freq_mask_width)
|
||||
|
||||
if tempo_axis == 0:
|
||||
for _ in range(num_time_mask):
|
||||
start = randint(nt - time_mask_width)
|
||||
spect[start:start + time_mask_width, :] = 0
|
||||
for _ in range(num_freq_mask):
|
||||
start = randint(nf - freq_mask_width)
|
||||
spect[:, start:start + freq_mask_width] = 0
|
||||
else:
|
||||
for _ in range(num_time_mask):
|
||||
start = randint(nt - time_mask_width)
|
||||
spect[:, start:start + time_mask_width] = 0
|
||||
for _ in range(num_freq_mask):
|
||||
start = randint(nf - freq_mask_width)
|
||||
spect[start:start + freq_mask_width, :] = 0
|
||||
|
||||
return spect
|
||||
|
||||
|
||||
def random_crop1d(y: array, crop_len: int) -> array:
|
||||
""" Do random cropping on 1d input signal
|
||||
|
||||
The input is a 1d signal, typically a sound waveform
|
||||
"""
|
||||
if y.ndim != 1:
|
||||
'only accept 1d tensor or numpy array'
|
||||
n = len(y)
|
||||
idx = randint(n - crop_len)
|
||||
return y[idx:idx + crop_len]
|
||||
|
||||
|
||||
def random_crop2d(s: array, crop_len: int, tempo_axis: int=0) -> array:
|
||||
""" Do random cropping for 2D array, typically a spectrogram.
|
||||
|
||||
The cropping is done in temporal direction on the time-freq input signal.
|
||||
"""
|
||||
if tempo_axis >= s.ndim:
|
||||
raise ParameterError('axis out of range')
|
||||
|
||||
n = s.shape[tempo_axis]
|
||||
idx = randint(high=n - crop_len)
|
||||
sli = [slice(None) for i in range(s.ndim)]
|
||||
sli[tempo_axis] = slice(idx, idx + crop_len)
|
||||
out = s[tuple(sli)]
|
||||
return out
|
||||
@ -1,461 +0,0 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import math
|
||||
from functools import partial
|
||||
from typing import Optional
|
||||
from typing import Union
|
||||
|
||||
import paddle
|
||||
import paddle.nn as nn
|
||||
|
||||
from .window import get_window
|
||||
|
||||
__all__ = [
|
||||
'Spectrogram',
|
||||
'MelSpectrogram',
|
||||
'LogMelSpectrogram',
|
||||
]
|
||||
|
||||
|
||||
def hz_to_mel(freq: Union[paddle.Tensor, float],
|
||||
htk: bool=False) -> Union[paddle.Tensor, float]:
|
||||
"""Convert Hz to Mels.
|
||||
Parameters:
|
||||
freq: the input tensor of arbitrary shape, or a single floating point number.
|
||||
htk: use HTK formula to do the conversion.
|
||||
The default value is False.
|
||||
Returns:
|
||||
The frequencies represented in Mel-scale.
|
||||
"""
|
||||
|
||||
if htk:
|
||||
if isinstance(freq, paddle.Tensor):
|
||||
return 2595.0 * paddle.log10(1.0 + freq / 700.0)
|
||||
else:
|
||||
return 2595.0 * math.log10(1.0 + freq / 700.0)
|
||||
|
||||
# Fill in the linear part
|
||||
f_min = 0.0
|
||||
f_sp = 200.0 / 3
|
||||
|
||||
mels = (freq - f_min) / f_sp
|
||||
|
||||
# Fill in the log-scale part
|
||||
|
||||
min_log_hz = 1000.0 # beginning of log region (Hz)
|
||||
min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
|
||||
logstep = math.log(6.4) / 27.0 # step size for log region
|
||||
|
||||
if isinstance(freq, paddle.Tensor):
|
||||
target = min_log_mel + paddle.log(
|
||||
freq / min_log_hz + 1e-10) / logstep # prevent nan with 1e-10
|
||||
mask = (freq > min_log_hz).astype(freq.dtype)
|
||||
mels = target * mask + mels * (
|
||||
1 - mask) # will replace by masked_fill OP in future
|
||||
else:
|
||||
if freq >= min_log_hz:
|
||||
mels = min_log_mel + math.log(freq / min_log_hz + 1e-10) / logstep
|
||||
|
||||
return mels
|
||||
|
||||
|
||||
def mel_to_hz(mel: Union[float, paddle.Tensor],
|
||||
htk: bool=False) -> Union[float, paddle.Tensor]:
|
||||
"""Convert mel bin numbers to frequencies.
|
||||
Parameters:
|
||||
mel: the mel frequency represented as a tensor of arbitrary shape, or a floating point number.
|
||||
htk: use HTK formula to do the conversion.
|
||||
Returns:
|
||||
The frequencies represented in hz.
|
||||
"""
|
||||
if htk:
|
||||
return 700.0 * (10.0**(mel / 2595.0) - 1.0)
|
||||
|
||||
f_min = 0.0
|
||||
f_sp = 200.0 / 3
|
||||
freqs = f_min + f_sp * mel
|
||||
# And now the nonlinear scale
|
||||
min_log_hz = 1000.0 # beginning of log region (Hz)
|
||||
min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
|
||||
logstep = math.log(6.4) / 27.0 # step size for log region
|
||||
if isinstance(mel, paddle.Tensor):
|
||||
target = min_log_hz * paddle.exp(logstep * (mel - min_log_mel))
|
||||
mask = (mel > min_log_mel).astype(mel.dtype)
|
||||
freqs = target * mask + freqs * (
|
||||
1 - mask) # will replace by masked_fill OP in future
|
||||
else:
|
||||
if mel >= min_log_mel:
|
||||
freqs = min_log_hz * math.exp(logstep * (mel - min_log_mel))
|
||||
|
||||
return freqs
|
||||
|
||||
|
||||
def mel_frequencies(n_mels: int=64,
|
||||
f_min: float=0.0,
|
||||
f_max: float=11025.0,
|
||||
htk: bool=False,
|
||||
dtype: str=paddle.float32):
|
||||
"""Compute mel frequencies.
|
||||
Parameters:
|
||||
n_mels(int): number of Mel bins.
|
||||
f_min(float): the lower cut-off frequency, below which the filter response is zero.
|
||||
f_max(float): the upper cut-off frequency, above which the filter response is zero.
|
||||
htk(bool): whether to use htk formula.
|
||||
dtype(str): the datatype of the return frequencies.
|
||||
Returns:
|
||||
The frequencies represented in Mel-scale
|
||||
"""
|
||||
# 'Center freqs' of mel bands - uniformly spaced between limits
|
||||
min_mel = hz_to_mel(f_min, htk=htk)
|
||||
max_mel = hz_to_mel(f_max, htk=htk)
|
||||
mels = paddle.linspace(min_mel, max_mel, n_mels, dtype=dtype)
|
||||
freqs = mel_to_hz(mels, htk=htk)
|
||||
return freqs
|
||||
|
||||
|
||||
def fft_frequencies(sr: int, n_fft: int, dtype: str=paddle.float32):
|
||||
"""Compute fourier frequencies.
|
||||
Parameters:
|
||||
sr(int): the audio sample rate.
|
||||
n_fft(float): the number of fft bins.
|
||||
dtype(str): the datatype of the return frequencies.
|
||||
Returns:
|
||||
The frequencies represented in hz.
|
||||
"""
|
||||
return paddle.linspace(0, float(sr) / 2, int(1 + n_fft // 2), dtype=dtype)
|
||||
|
||||
|
||||
def compute_fbank_matrix(sr: int,
|
||||
n_fft: int,
|
||||
n_mels: int=64,
|
||||
f_min: float=0.0,
|
||||
f_max: Optional[float]=None,
|
||||
htk: bool=False,
|
||||
norm: Union[str, float]='slaney',
|
||||
dtype: str=paddle.float32):
|
||||
"""Compute fbank matrix.
|
||||
Parameters:
|
||||
sr(int): the audio sample rate.
|
||||
n_fft(int): the number of fft bins.
|
||||
n_mels(int): the number of Mel bins.
|
||||
f_min(float): the lower cut-off frequency, below which the filter response is zero.
|
||||
f_max(float): the upper cut-off frequency, above which the filter response is zero.
|
||||
htk: whether to use htk formula.
|
||||
return_complex(bool): whether to return complex matrix. If True, the matrix will
|
||||
be complex type. Otherwise, the real and image part will be stored in the last
|
||||
axis of returned tensor.
|
||||
dtype(str): the datatype of the returned fbank matrix.
|
||||
Returns:
|
||||
The fbank matrix of shape (n_mels, int(1+n_fft//2)).
|
||||
Shape:
|
||||
output: (n_mels, int(1+n_fft//2))
|
||||
"""
|
||||
|
||||
if f_max is None:
|
||||
f_max = float(sr) / 2
|
||||
|
||||
# Initialize the weights
|
||||
weights = paddle.zeros((n_mels, int(1 + n_fft // 2)), dtype=dtype)
|
||||
|
||||
# Center freqs of each FFT bin
|
||||
fftfreqs = fft_frequencies(sr=sr, n_fft=n_fft, dtype=dtype)
|
||||
|
||||
# 'Center freqs' of mel bands - uniformly spaced between limits
|
||||
mel_f = mel_frequencies(
|
||||
n_mels + 2, f_min=f_min, f_max=f_max, htk=htk, dtype=dtype)
|
||||
|
||||
fdiff = mel_f[1:] - mel_f[:-1] #np.diff(mel_f)
|
||||
ramps = mel_f.unsqueeze(1) - fftfreqs.unsqueeze(0)
|
||||
#ramps = np.subtract.outer(mel_f, fftfreqs)
|
||||
|
||||
for i in range(n_mels):
|
||||
# lower and upper slopes for all bins
|
||||
lower = -ramps[i] / fdiff[i]
|
||||
upper = ramps[i + 2] / fdiff[i + 1]
|
||||
|
||||
# .. then intersect them with each other and zero
|
||||
weights[i] = paddle.maximum(
|
||||
paddle.zeros_like(lower), paddle.minimum(lower, upper))
|
||||
|
||||
# Slaney-style mel is scaled to be approx constant energy per channel
|
||||
if norm == 'slaney':
|
||||
enorm = 2.0 / (mel_f[2:n_mels + 2] - mel_f[:n_mels])
|
||||
weights *= enorm.unsqueeze(1)
|
||||
elif isinstance(norm, int) or isinstance(norm, float):
|
||||
weights = paddle.nn.functional.normalize(weights, p=norm, axis=-1)
|
||||
|
||||
return weights
|
||||
|
||||
|
||||
def power_to_db(magnitude: paddle.Tensor,
|
||||
ref_value: float=1.0,
|
||||
amin: float=1e-10,
|
||||
top_db: Optional[float]=None) -> paddle.Tensor:
|
||||
"""Convert a power spectrogram (amplitude squared) to decibel (dB) units.
|
||||
The function computes the scaling ``10 * log10(x / ref)`` in a numerically
|
||||
stable way.
|
||||
Parameters:
|
||||
magnitude(Tensor): the input magnitude tensor of any shape.
|
||||
ref_value(float): the reference value. If smaller than 1.0, the db level
|
||||
of the signal will be pulled up accordingly. Otherwise, the db level
|
||||
is pushed down.
|
||||
amin(float): the minimum value of input magnitude, below which the input
|
||||
magnitude is clipped(to amin).
|
||||
top_db(float): the maximum db value of resulting spectrum, above which the
|
||||
spectrum is clipped(to top_db).
|
||||
Returns:
|
||||
The spectrogram in log-scale.
|
||||
shape:
|
||||
input: any shape
|
||||
output: same as input
|
||||
"""
|
||||
if amin <= 0:
|
||||
raise Exception("amin must be strictly positive")
|
||||
|
||||
if ref_value <= 0:
|
||||
raise Exception("ref_value must be strictly positive")
|
||||
|
||||
ones = paddle.ones_like(magnitude)
|
||||
log_spec = 10.0 * paddle.log10(paddle.maximum(ones * amin, magnitude))
|
||||
log_spec -= 10.0 * math.log10(max(ref_value, amin))
|
||||
|
||||
if top_db is not None:
|
||||
if top_db < 0:
|
||||
raise Exception("top_db must be non-negative")
|
||||
log_spec = paddle.maximum(log_spec, ones * (log_spec.max() - top_db))
|
||||
|
||||
return log_spec
|
||||
|
||||
|
||||
class Spectrogram(nn.Layer):
|
||||
def __init__(self,
|
||||
n_fft: int=512,
|
||||
hop_length: Optional[int]=None,
|
||||
win_length: Optional[int]=None,
|
||||
window: str='hann',
|
||||
center: bool=True,
|
||||
pad_mode: str='reflect',
|
||||
dtype: str=paddle.float32):
|
||||
"""Compute spectrogram of a given signal, typically an audio waveform.
|
||||
The spectorgram is defined as the complex norm of the short-time
|
||||
Fourier transformation.
|
||||
Parameters:
|
||||
n_fft(int): the number of frequency components of the discrete Fourier transform.
|
||||
The default value is 2048,
|
||||
hop_length(int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
|
||||
The default value is None.
|
||||
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
|
||||
The default value is None.
|
||||
window(str): the name of the window function applied to the single before the Fourier transform.
|
||||
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
|
||||
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
|
||||
The default value is 'hann'
|
||||
center(bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
|
||||
If False, frame t begins at x[t * hop_length]
|
||||
The default value is True
|
||||
pad_mode(str): the mode to pad the signal if necessary. The supported modes are 'reflect'
|
||||
and 'constant'. The default value is 'reflect'.
|
||||
dtype(str): the data type of input and window.
|
||||
Notes:
|
||||
The Spectrogram transform relies on STFT transform to compute the spectrogram.
|
||||
By default, the weights are not learnable. To fine-tune the Fourier coefficients,
|
||||
set stop_gradient=False before training.
|
||||
For more information, see STFT().
|
||||
"""
|
||||
super(Spectrogram, self).__init__()
|
||||
|
||||
if win_length is None:
|
||||
win_length = n_fft
|
||||
|
||||
fft_window = get_window(window, win_length, fftbins=True, dtype=dtype)
|
||||
self._stft = partial(
|
||||
paddle.signal.stft,
|
||||
n_fft=n_fft,
|
||||
hop_length=hop_length,
|
||||
win_length=win_length,
|
||||
window=fft_window,
|
||||
center=center,
|
||||
pad_mode=pad_mode)
|
||||
|
||||
def forward(self, x):
|
||||
stft = self._stft(x)
|
||||
spectrogram = paddle.square(paddle.abs(stft))
|
||||
return spectrogram
|
||||
|
||||
|
||||
class MelSpectrogram(nn.Layer):
|
||||
def __init__(self,
|
||||
sr: int=22050,
|
||||
n_fft: int=512,
|
||||
hop_length: Optional[int]=None,
|
||||
win_length: Optional[int]=None,
|
||||
window: str='hann',
|
||||
center: bool=True,
|
||||
pad_mode: str='reflect',
|
||||
n_mels: int=64,
|
||||
f_min: float=50.0,
|
||||
f_max: Optional[float]=None,
|
||||
htk: bool=False,
|
||||
norm: Union[str, float]='slaney',
|
||||
dtype: str=paddle.float32):
|
||||
"""Compute the melspectrogram of a given signal, typically an audio waveform.
|
||||
The melspectrogram is also known as filterbank or fbank feature in audio community.
|
||||
It is computed by multiplying spectrogram with Mel filter bank matrix.
|
||||
Parameters:
|
||||
sr(int): the audio sample rate.
|
||||
The default value is 22050.
|
||||
n_fft(int): the number of frequency components of the discrete Fourier transform.
|
||||
The default value is 2048,
|
||||
hop_length(int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
|
||||
The default value is None.
|
||||
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
|
||||
The default value is None.
|
||||
window(str): the name of the window function applied to the single before the Fourier transform.
|
||||
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
|
||||
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
|
||||
The default value is 'hann'
|
||||
center(bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
|
||||
If False, frame t begins at x[t * hop_length]
|
||||
The default value is True
|
||||
pad_mode(str): the mode to pad the signal if necessary. The supported modes are 'reflect'
|
||||
and 'constant'.
|
||||
The default value is 'reflect'.
|
||||
n_mels(int): the mel bins.
|
||||
f_min(float): the lower cut-off frequency, below which the filter response is zero.
|
||||
f_max(float): the upper cut-off frequency, above which the filter response is zeros.
|
||||
htk(bool): whether to use HTK formula in computing fbank matrix.
|
||||
norm(str|float): the normalization type in computing fbank matrix. Slaney-style is used by default.
|
||||
You can specify norm=1.0/2.0 to use customized p-norm normalization.
|
||||
dtype(str): the datatype of fbank matrix used in the transform. Use float64 to increase numerical
|
||||
accuracy. Note that the final transform will be conducted in float32 regardless of dtype of fbank matrix.
|
||||
"""
|
||||
super(MelSpectrogram, self).__init__()
|
||||
|
||||
self._spectrogram = Spectrogram(
|
||||
n_fft=n_fft,
|
||||
hop_length=hop_length,
|
||||
win_length=win_length,
|
||||
window=window,
|
||||
center=center,
|
||||
pad_mode=pad_mode,
|
||||
dtype=dtype)
|
||||
self.n_mels = n_mels
|
||||
self.f_min = f_min
|
||||
self.f_max = f_max
|
||||
self.htk = htk
|
||||
self.norm = norm
|
||||
if f_max is None:
|
||||
f_max = sr // 2
|
||||
self.fbank_matrix = compute_fbank_matrix(
|
||||
sr=sr,
|
||||
n_fft=n_fft,
|
||||
n_mels=n_mels,
|
||||
f_min=f_min,
|
||||
f_max=f_max,
|
||||
htk=htk,
|
||||
norm=norm,
|
||||
dtype=dtype) # float64 for better numerical results
|
||||
self.register_buffer('fbank_matrix', self.fbank_matrix)
|
||||
|
||||
def forward(self, x):
|
||||
spect_feature = self._spectrogram(x)
|
||||
mel_feature = paddle.matmul(self.fbank_matrix, spect_feature)
|
||||
return mel_feature
|
||||
|
||||
|
||||
class LogMelSpectrogram(nn.Layer):
|
||||
def __init__(self,
|
||||
sr: int=22050,
|
||||
n_fft: int=512,
|
||||
hop_length: Optional[int]=None,
|
||||
win_length: Optional[int]=None,
|
||||
window: str='hann',
|
||||
center: bool=True,
|
||||
pad_mode: str='reflect',
|
||||
n_mels: int=64,
|
||||
f_min: float=50.0,
|
||||
f_max: Optional[float]=None,
|
||||
htk: bool=False,
|
||||
norm: Union[str, float]='slaney',
|
||||
ref_value: float=1.0,
|
||||
amin: float=1e-10,
|
||||
top_db: Optional[float]=None,
|
||||
dtype: str=paddle.float32):
|
||||
"""Compute log-mel-spectrogram(also known as LogFBank) feature of a given signal,
|
||||
typically an audio waveform.
|
||||
Parameters:
|
||||
sr(int): the audio sample rate.
|
||||
The default value is 22050.
|
||||
n_fft(int): the number of frequency components of the discrete Fourier transform.
|
||||
The default value is 2048,
|
||||
hop_length(int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
|
||||
The default value is None.
|
||||
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
|
||||
The default value is None.
|
||||
window(str): the name of the window function applied to the single before the Fourier transform.
|
||||
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
|
||||
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
|
||||
The default value is 'hann'
|
||||
center(bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
|
||||
If False, frame t begins at x[t * hop_length]
|
||||
The default value is True
|
||||
pad_mode(str): the mode to pad the signal if necessary. The supported modes are 'reflect'
|
||||
and 'constant'.
|
||||
The default value is 'reflect'.
|
||||
n_mels(int): the mel bins.
|
||||
f_min(float): the lower cut-off frequency, below which the filter response is zero.
|
||||
f_max(float): the upper cut-off frequency, above which the filter response is zeros.
|
||||
ref_value(float): the reference value. If smaller than 1.0, the db level
|
||||
htk(bool): whether to use HTK formula in computing fbank matrix.
|
||||
norm(str|float): the normalization type in computing fbank matrix. Slaney-style is used by default.
|
||||
You can specify norm=1.0/2.0 to use customized p-norm normalization.
|
||||
dtype(str): the datatype of fbank matrix used in the transform. Use float64 to increase numerical
|
||||
accuracy. Note that the final transform will be conducted in float32 regardless of dtype of fbank matrix.
|
||||
amin(float): the minimum value of input magnitude, below which the input of the signal will be pulled up accordingly.
|
||||
Otherwise, the db level is pushed down.
|
||||
magnitude is clipped(to amin). For numerical stability, set amin to a larger value,
|
||||
e.g., 1e-3.
|
||||
top_db(float): the maximum db value of resulting spectrum, above which the
|
||||
spectrum is clipped(to top_db).
|
||||
"""
|
||||
super(LogMelSpectrogram, self).__init__()
|
||||
|
||||
self._melspectrogram = MelSpectrogram(
|
||||
sr=sr,
|
||||
n_fft=n_fft,
|
||||
hop_length=hop_length,
|
||||
win_length=win_length,
|
||||
window=window,
|
||||
center=center,
|
||||
pad_mode=pad_mode,
|
||||
n_mels=n_mels,
|
||||
f_min=f_min,
|
||||
f_max=f_max,
|
||||
htk=htk,
|
||||
norm=norm,
|
||||
dtype=dtype)
|
||||
|
||||
self.ref_value = ref_value
|
||||
self.amin = amin
|
||||
self.top_db = top_db
|
||||
|
||||
def forward(self, x):
|
||||
# import ipdb; ipdb.set_trace()
|
||||
mel_feature = self._melspectrogram(x)
|
||||
log_mel_feature = power_to_db(
|
||||
mel_feature,
|
||||
ref_value=self.ref_value,
|
||||
amin=self.amin,
|
||||
top_db=self.top_db)
|
||||
return log_mel_feature
|
||||
@ -0,0 +1,22 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from . import compliance
|
||||
from . import datasets
|
||||
from . import features
|
||||
from . import functional
|
||||
from . import io
|
||||
from . import metric
|
||||
from . import sox_effects
|
||||
from .backends import load
|
||||
from .backends import save
|
||||
@ -0,0 +1,19 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from .soundfile_backend import depth_convert
|
||||
from .soundfile_backend import load
|
||||
from .soundfile_backend import normalize
|
||||
from .soundfile_backend import resample
|
||||
from .soundfile_backend import save
|
||||
from .soundfile_backend import to_mono
|
||||
@ -0,0 +1,13 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
@ -0,0 +1,638 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# Modified from torchaudio(https://github.com/pytorch/audio)
|
||||
import math
|
||||
from typing import Tuple
|
||||
|
||||
import paddle
|
||||
from paddle import Tensor
|
||||
|
||||
from ..functional import create_dct
|
||||
from ..functional.window import get_window
|
||||
|
||||
__all__ = [
|
||||
'spectrogram',
|
||||
'fbank',
|
||||
'mfcc',
|
||||
]
|
||||
|
||||
# window types
|
||||
HANNING = 'hann'
|
||||
HAMMING = 'hamming'
|
||||
POVEY = 'povey'
|
||||
RECTANGULAR = 'rect'
|
||||
BLACKMAN = 'blackman'
|
||||
|
||||
|
||||
def _get_epsilon(dtype):
|
||||
return paddle.to_tensor(1e-07, dtype=dtype)
|
||||
|
||||
|
||||
def _next_power_of_2(x: int) -> int:
|
||||
return 1 if x == 0 else 2**(x - 1).bit_length()
|
||||
|
||||
|
||||
def _get_strided(waveform: Tensor,
|
||||
window_size: int,
|
||||
window_shift: int,
|
||||
snip_edges: bool) -> Tensor:
|
||||
assert waveform.dim() == 1
|
||||
num_samples = waveform.shape[0]
|
||||
|
||||
if snip_edges:
|
||||
if num_samples < window_size:
|
||||
return paddle.empty((0, 0), dtype=waveform.dtype)
|
||||
else:
|
||||
m = 1 + (num_samples - window_size) // window_shift
|
||||
else:
|
||||
reversed_waveform = paddle.flip(waveform, [0])
|
||||
m = (num_samples + (window_shift // 2)) // window_shift
|
||||
pad = window_size // 2 - window_shift // 2
|
||||
pad_right = reversed_waveform
|
||||
if pad > 0:
|
||||
pad_left = reversed_waveform[-pad:]
|
||||
waveform = paddle.concat((pad_left, waveform, pad_right), axis=0)
|
||||
else:
|
||||
waveform = paddle.concat((waveform[-pad:], pad_right), axis=0)
|
||||
|
||||
return paddle.signal.frame(waveform, window_size, window_shift)[:, :m].T
|
||||
|
||||
|
||||
def _feature_window_function(
|
||||
window_type: str,
|
||||
window_size: int,
|
||||
blackman_coeff: float,
|
||||
dtype: int, ) -> Tensor:
|
||||
if window_type == HANNING:
|
||||
return get_window('hann', window_size, fftbins=False, dtype=dtype)
|
||||
elif window_type == HAMMING:
|
||||
return get_window('hamming', window_size, fftbins=False, dtype=dtype)
|
||||
elif window_type == POVEY:
|
||||
return get_window(
|
||||
'hann', window_size, fftbins=False, dtype=dtype).pow(0.85)
|
||||
elif window_type == RECTANGULAR:
|
||||
return paddle.ones([window_size], dtype=dtype)
|
||||
elif window_type == BLACKMAN:
|
||||
a = 2 * math.pi / (window_size - 1)
|
||||
window_function = paddle.arange(window_size, dtype=dtype)
|
||||
return (blackman_coeff - 0.5 * paddle.cos(a * window_function) +
|
||||
(0.5 - blackman_coeff) * paddle.cos(2 * a * window_function)
|
||||
).astype(dtype)
|
||||
else:
|
||||
raise Exception('Invalid window type ' + window_type)
|
||||
|
||||
|
||||
def _get_log_energy(strided_input: Tensor, epsilon: Tensor,
|
||||
energy_floor: float) -> Tensor:
|
||||
log_energy = paddle.maximum(strided_input.pow(2).sum(1), epsilon).log()
|
||||
if energy_floor == 0.0:
|
||||
return log_energy
|
||||
return paddle.maximum(
|
||||
log_energy,
|
||||
paddle.to_tensor(math.log(energy_floor), dtype=strided_input.dtype))
|
||||
|
||||
|
||||
def _get_waveform_and_window_properties(
|
||||
waveform: Tensor,
|
||||
channel: int,
|
||||
sr: int,
|
||||
frame_shift: float,
|
||||
frame_length: float,
|
||||
round_to_power_of_two: bool,
|
||||
preemphasis_coefficient: float) -> Tuple[Tensor, int, int, int]:
|
||||
channel = max(channel, 0)
|
||||
assert channel < waveform.shape[0], (
|
||||
'Invalid channel {} for size {}'.format(channel, waveform.shape[0]))
|
||||
waveform = waveform[channel, :] # size (n)
|
||||
window_shift = int(
|
||||
sr * frame_shift *
|
||||
0.001) # pass frame_shift and frame_length in milliseconds
|
||||
window_size = int(sr * frame_length * 0.001)
|
||||
padded_window_size = _next_power_of_2(
|
||||
window_size) if round_to_power_of_two else window_size
|
||||
|
||||
assert 2 <= window_size <= len(waveform), (
|
||||
'choose a window size {} that is [2, {}]'.format(window_size,
|
||||
len(waveform)))
|
||||
assert 0 < window_shift, '`window_shift` must be greater than 0'
|
||||
assert padded_window_size % 2 == 0, 'the padded `window_size` must be divisible by two.' \
|
||||
' use `round_to_power_of_two` or change `frame_length`'
|
||||
assert 0. <= preemphasis_coefficient <= 1.0, '`preemphasis_coefficient` must be between [0,1]'
|
||||
assert sr > 0, '`sr` must be greater than zero'
|
||||
return waveform, window_shift, window_size, padded_window_size
|
||||
|
||||
|
||||
def _get_window(waveform: Tensor,
|
||||
padded_window_size: int,
|
||||
window_size: int,
|
||||
window_shift: int,
|
||||
window_type: str,
|
||||
blackman_coeff: float,
|
||||
snip_edges: bool,
|
||||
raw_energy: bool,
|
||||
energy_floor: float,
|
||||
dither: float,
|
||||
remove_dc_offset: bool,
|
||||
preemphasis_coefficient: float) -> Tuple[Tensor, Tensor]:
|
||||
dtype = waveform.dtype
|
||||
epsilon = _get_epsilon(dtype)
|
||||
|
||||
# (m, window_size)
|
||||
strided_input = _get_strided(waveform, window_size, window_shift,
|
||||
snip_edges)
|
||||
|
||||
if dither != 0.0:
|
||||
x = paddle.maximum(epsilon,
|
||||
paddle.rand(strided_input.shape, dtype=dtype))
|
||||
rand_gauss = paddle.sqrt(-2 * x.log()) * paddle.cos(2 * math.pi * x)
|
||||
strided_input = strided_input + rand_gauss * dither
|
||||
|
||||
if remove_dc_offset:
|
||||
row_means = paddle.mean(strided_input, axis=1).unsqueeze(1) # (m, 1)
|
||||
strided_input = strided_input - row_means
|
||||
|
||||
if raw_energy:
|
||||
signal_log_energy = _get_log_energy(strided_input, epsilon,
|
||||
energy_floor) # (m)
|
||||
|
||||
if preemphasis_coefficient != 0.0:
|
||||
offset_strided_input = paddle.nn.functional.pad(
|
||||
strided_input.unsqueeze(0), (1, 0),
|
||||
data_format='NCL',
|
||||
mode='replicate').squeeze(0) # (m, window_size + 1)
|
||||
strided_input = strided_input - preemphasis_coefficient * offset_strided_input[:, :
|
||||
-1]
|
||||
|
||||
window_function = _feature_window_function(
|
||||
window_type, window_size, blackman_coeff,
|
||||
dtype).unsqueeze(0) # (1, window_size)
|
||||
strided_input = strided_input * window_function # (m, window_size)
|
||||
|
||||
# (m, padded_window_size)
|
||||
if padded_window_size != window_size:
|
||||
padding_right = padded_window_size - window_size
|
||||
strided_input = paddle.nn.functional.pad(
|
||||
strided_input.unsqueeze(0), (0, padding_right),
|
||||
data_format='NCL',
|
||||
mode='constant',
|
||||
value=0).squeeze(0)
|
||||
|
||||
if not raw_energy:
|
||||
signal_log_energy = _get_log_energy(strided_input, epsilon,
|
||||
energy_floor) # size (m)
|
||||
|
||||
return strided_input, signal_log_energy
|
||||
|
||||
|
||||
def _subtract_column_mean(tensor: Tensor, subtract_mean: bool) -> Tensor:
|
||||
if subtract_mean:
|
||||
col_means = paddle.mean(tensor, axis=0).unsqueeze(0)
|
||||
tensor = tensor - col_means
|
||||
return tensor
|
||||
|
||||
|
||||
def spectrogram(waveform: Tensor,
|
||||
blackman_coeff: float=0.42,
|
||||
channel: int=-1,
|
||||
dither: float=0.0,
|
||||
energy_floor: float=1.0,
|
||||
frame_length: float=25.0,
|
||||
frame_shift: float=10.0,
|
||||
preemphasis_coefficient: float=0.97,
|
||||
raw_energy: bool=True,
|
||||
remove_dc_offset: bool=True,
|
||||
round_to_power_of_two: bool=True,
|
||||
sr: int=16000,
|
||||
snip_edges: bool=True,
|
||||
subtract_mean: bool=False,
|
||||
window_type: str=POVEY) -> Tensor:
|
||||
"""Compute and return a spectrogram from a waveform. The output is identical to Kaldi's.
|
||||
|
||||
Args:
|
||||
waveform (Tensor): A waveform tensor with shape [C, T].
|
||||
blackman_coeff (float, optional): Coefficient for Blackman window.. Defaults to 0.42.
|
||||
channel (int, optional): Select the channel of waveform. Defaults to -1.
|
||||
dither (float, optional): Dithering constant . Defaults to 0.0.
|
||||
energy_floor (float, optional): Floor on energy of the output Spectrogram. Defaults to 1.0.
|
||||
frame_length (float, optional): Frame length in milliseconds. Defaults to 25.0.
|
||||
frame_shift (float, optional): Shift between adjacent frames in milliseconds. Defaults to 10.0.
|
||||
preemphasis_coefficient (float, optional): Preemphasis coefficient for input waveform. Defaults to 0.97.
|
||||
raw_energy (bool, optional): Whether to compute before preemphasis and windowing. Defaults to True.
|
||||
remove_dc_offset (bool, optional): Whether to subtract mean from waveform on frames. Defaults to True.
|
||||
round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
|
||||
to FFT. Defaults to True.
|
||||
sr (int, optional): Sample rate of input waveform. Defaults to 16000.
|
||||
snip_edges (bool, optional): Drop samples in the end of waveform that cann't fit a singal frame when it
|
||||
is set True. Otherwise performs reflect padding to the end of waveform. Defaults to True.
|
||||
subtract_mean (bool, optional): Whether to subtract mean of feature files. Defaults to False.
|
||||
window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
|
||||
|
||||
Returns:
|
||||
Tensor: A spectrogram tensor with shape (m, padded_window_size // 2 + 1) where m is the number of frames
|
||||
depends on frame_length and frame_shift.
|
||||
"""
|
||||
dtype = waveform.dtype
|
||||
epsilon = _get_epsilon(dtype)
|
||||
|
||||
waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties(
|
||||
waveform, channel, sr, frame_shift, frame_length, round_to_power_of_two,
|
||||
preemphasis_coefficient)
|
||||
|
||||
strided_input, signal_log_energy = _get_window(
|
||||
waveform, padded_window_size, window_size, window_shift, window_type,
|
||||
blackman_coeff, snip_edges, raw_energy, energy_floor, dither,
|
||||
remove_dc_offset, preemphasis_coefficient)
|
||||
|
||||
# (m, padded_window_size // 2 + 1, 2)
|
||||
fft = paddle.fft.rfft(strided_input)
|
||||
|
||||
power_spectrum = paddle.maximum(
|
||||
fft.abs().pow(2.), epsilon).log() # (m, padded_window_size // 2 + 1)
|
||||
power_spectrum[:, 0] = signal_log_energy
|
||||
|
||||
power_spectrum = _subtract_column_mean(power_spectrum, subtract_mean)
|
||||
return power_spectrum
|
||||
|
||||
|
||||
def _inverse_mel_scale_scalar(mel_freq: float) -> float:
|
||||
return 700.0 * (math.exp(mel_freq / 1127.0) - 1.0)
|
||||
|
||||
|
||||
def _inverse_mel_scale(mel_freq: Tensor) -> Tensor:
|
||||
return 700.0 * ((mel_freq / 1127.0).exp() - 1.0)
|
||||
|
||||
|
||||
def _mel_scale_scalar(freq: float) -> float:
|
||||
return 1127.0 * math.log(1.0 + freq / 700.0)
|
||||
|
||||
|
||||
def _mel_scale(freq: Tensor) -> Tensor:
|
||||
return 1127.0 * (1.0 + freq / 700.0).log()
|
||||
|
||||
|
||||
def _vtln_warp_freq(vtln_low_cutoff: float,
|
||||
vtln_high_cutoff: float,
|
||||
low_freq: float,
|
||||
high_freq: float,
|
||||
vtln_warp_factor: float,
|
||||
freq: Tensor) -> Tensor:
|
||||
assert vtln_low_cutoff > low_freq, 'be sure to set the vtln_low option higher than low_freq'
|
||||
assert vtln_high_cutoff < high_freq, 'be sure to set the vtln_high option lower than high_freq [or negative]'
|
||||
l = vtln_low_cutoff * max(1.0, vtln_warp_factor)
|
||||
h = vtln_high_cutoff * min(1.0, vtln_warp_factor)
|
||||
scale = 1.0 / vtln_warp_factor
|
||||
Fl = scale * l
|
||||
Fh = scale * h
|
||||
assert l > low_freq and h < high_freq
|
||||
scale_left = (Fl - low_freq) / (l - low_freq)
|
||||
scale_right = (high_freq - Fh) / (high_freq - h)
|
||||
res = paddle.empty_like(freq)
|
||||
|
||||
outside_low_high_freq = paddle.less_than(freq, paddle.to_tensor(low_freq)) \
|
||||
| paddle.greater_than(freq, paddle.to_tensor(high_freq))
|
||||
before_l = paddle.less_than(freq, paddle.to_tensor(l))
|
||||
before_h = paddle.less_than(freq, paddle.to_tensor(h))
|
||||
after_h = paddle.greater_equal(freq, paddle.to_tensor(h))
|
||||
|
||||
res[after_h] = high_freq + scale_right * (freq[after_h] - high_freq)
|
||||
res[before_h] = scale * freq[before_h]
|
||||
res[before_l] = low_freq + scale_left * (freq[before_l] - low_freq)
|
||||
res[outside_low_high_freq] = freq[outside_low_high_freq]
|
||||
|
||||
return res
|
||||
|
||||
|
||||
def _vtln_warp_mel_freq(vtln_low_cutoff: float,
|
||||
vtln_high_cutoff: float,
|
||||
low_freq,
|
||||
high_freq: float,
|
||||
vtln_warp_factor: float,
|
||||
mel_freq: Tensor) -> Tensor:
|
||||
return _mel_scale(
|
||||
_vtln_warp_freq(vtln_low_cutoff, vtln_high_cutoff, low_freq, high_freq,
|
||||
vtln_warp_factor, _inverse_mel_scale(mel_freq)))
|
||||
|
||||
|
||||
def _get_mel_banks(num_bins: int,
|
||||
window_length_padded: int,
|
||||
sample_freq: float,
|
||||
low_freq: float,
|
||||
high_freq: float,
|
||||
vtln_low: float,
|
||||
vtln_high: float,
|
||||
vtln_warp_factor: float) -> Tuple[Tensor, Tensor]:
|
||||
assert num_bins > 3, 'Must have at least 3 mel bins'
|
||||
assert window_length_padded % 2 == 0
|
||||
num_fft_bins = window_length_padded / 2
|
||||
nyquist = 0.5 * sample_freq
|
||||
|
||||
if high_freq <= 0.0:
|
||||
high_freq += nyquist
|
||||
|
||||
assert (0.0 <= low_freq < nyquist) and (0.0 < high_freq <= nyquist) and (low_freq < high_freq), \
|
||||
('Bad values in options: low-freq {} and high-freq {} vs. nyquist {}'.format(low_freq, high_freq, nyquist))
|
||||
|
||||
fft_bin_width = sample_freq / window_length_padded
|
||||
mel_low_freq = _mel_scale_scalar(low_freq)
|
||||
mel_high_freq = _mel_scale_scalar(high_freq)
|
||||
|
||||
mel_freq_delta = (mel_high_freq - mel_low_freq) / (num_bins + 1)
|
||||
|
||||
if vtln_high < 0.0:
|
||||
vtln_high += nyquist
|
||||
|
||||
assert vtln_warp_factor == 1.0 or ((low_freq < vtln_low < high_freq) and
|
||||
(0.0 < vtln_high < high_freq) and (vtln_low < vtln_high)), \
|
||||
('Bad values in options: vtln-low {} and vtln-high {}, versus '
|
||||
'low-freq {} and high-freq {}'.format(vtln_low, vtln_high, low_freq, high_freq))
|
||||
|
||||
bin = paddle.arange(num_bins).unsqueeze(1)
|
||||
left_mel = mel_low_freq + bin * mel_freq_delta # (num_bins, 1)
|
||||
center_mel = mel_low_freq + (bin + 1.0) * mel_freq_delta # (num_bins, 1)
|
||||
right_mel = mel_low_freq + (bin + 2.0) * mel_freq_delta # (num_bins, 1)
|
||||
|
||||
if vtln_warp_factor != 1.0:
|
||||
left_mel = _vtln_warp_mel_freq(vtln_low, vtln_high, low_freq, high_freq,
|
||||
vtln_warp_factor, left_mel)
|
||||
center_mel = _vtln_warp_mel_freq(vtln_low, vtln_high, low_freq,
|
||||
high_freq, vtln_warp_factor,
|
||||
center_mel)
|
||||
right_mel = _vtln_warp_mel_freq(vtln_low, vtln_high, low_freq,
|
||||
high_freq, vtln_warp_factor, right_mel)
|
||||
|
||||
center_freqs = _inverse_mel_scale(center_mel) # (num_bins)
|
||||
# (1, num_fft_bins)
|
||||
mel = _mel_scale(fft_bin_width * paddle.arange(num_fft_bins)).unsqueeze(0)
|
||||
|
||||
# (num_bins, num_fft_bins)
|
||||
up_slope = (mel - left_mel) / (center_mel - left_mel)
|
||||
down_slope = (right_mel - mel) / (right_mel - center_mel)
|
||||
|
||||
if vtln_warp_factor == 1.0:
|
||||
bins = paddle.maximum(
|
||||
paddle.zeros([1]), paddle.minimum(up_slope, down_slope))
|
||||
else:
|
||||
bins = paddle.zeros_like(up_slope)
|
||||
up_idx = paddle.greater_than(mel, left_mel) & paddle.less_than(
|
||||
mel, center_mel)
|
||||
down_idx = paddle.greater_than(mel, center_mel) & paddle.less_than(
|
||||
mel, right_mel)
|
||||
bins[up_idx] = up_slope[up_idx]
|
||||
bins[down_idx] = down_slope[down_idx]
|
||||
|
||||
return bins, center_freqs
|
||||
|
||||
|
||||
def fbank(waveform: Tensor,
|
||||
blackman_coeff: float=0.42,
|
||||
channel: int=-1,
|
||||
dither: float=0.0,
|
||||
energy_floor: float=1.0,
|
||||
frame_length: float=25.0,
|
||||
frame_shift: float=10.0,
|
||||
high_freq: float=0.0,
|
||||
htk_compat: bool=False,
|
||||
low_freq: float=20.0,
|
||||
n_mels: int=23,
|
||||
preemphasis_coefficient: float=0.97,
|
||||
raw_energy: bool=True,
|
||||
remove_dc_offset: bool=True,
|
||||
round_to_power_of_two: bool=True,
|
||||
sr: int=16000,
|
||||
snip_edges: bool=True,
|
||||
subtract_mean: bool=False,
|
||||
use_energy: bool=False,
|
||||
use_log_fbank: bool=True,
|
||||
use_power: bool=True,
|
||||
vtln_high: float=-500.0,
|
||||
vtln_low: float=100.0,
|
||||
vtln_warp: float=1.0,
|
||||
window_type: str=POVEY) -> Tensor:
|
||||
"""Compute and return filter banks from a waveform. The output is identical to Kaldi's.
|
||||
|
||||
Args:
|
||||
waveform (Tensor): A waveform tensor with shape [C, T].
|
||||
blackman_coeff (float, optional): Coefficient for Blackman window.. Defaults to 0.42.
|
||||
channel (int, optional): Select the channel of waveform. Defaults to -1.
|
||||
dither (float, optional): Dithering constant . Defaults to 0.0.
|
||||
energy_floor (float, optional): Floor on energy of the output Spectrogram. Defaults to 1.0.
|
||||
frame_length (float, optional): Frame length in milliseconds. Defaults to 25.0.
|
||||
frame_shift (float, optional): Shift between adjacent frames in milliseconds. Defaults to 10.0.
|
||||
high_freq (float, optional): The upper cut-off frequency. Defaults to 0.0.
|
||||
htk_compat (bool, optional): Put energy to the last when it is set True. Defaults to False.
|
||||
low_freq (float, optional): The lower cut-off frequency. Defaults to 20.0.
|
||||
n_mels (int, optional): Number of output mel bins. Defaults to 23.
|
||||
preemphasis_coefficient (float, optional): Preemphasis coefficient for input waveform. Defaults to 0.97.
|
||||
raw_energy (bool, optional): Whether to compute before preemphasis and windowing. Defaults to True.
|
||||
remove_dc_offset (bool, optional): Whether to subtract mean from waveform on frames. Defaults to True.
|
||||
round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
|
||||
to FFT. Defaults to True.
|
||||
sr (int, optional): Sample rate of input waveform. Defaults to 16000.
|
||||
snip_edges (bool, optional): Drop samples in the end of waveform that cann't fit a singal frame when it
|
||||
is set True. Otherwise performs reflect padding to the end of waveform. Defaults to True.
|
||||
subtract_mean (bool, optional): Whether to subtract mean of feature files. Defaults to False.
|
||||
use_energy (bool, optional): Add an dimension with energy of spectrogram to the output. Defaults to False.
|
||||
use_log_fbank (bool, optional): Return log fbank when it is set True. Defaults to True.
|
||||
use_power (bool, optional): Whether to use power instead of magnitude. Defaults to True.
|
||||
vtln_high (float, optional): High inflection point in piecewise linear VTLN warping function. Defaults to -500.0.
|
||||
vtln_low (float, optional): Low inflection point in piecewise linear VTLN warping function. Defaults to 100.0.
|
||||
vtln_warp (float, optional): Vtln warp factor. Defaults to 1.0.
|
||||
window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
|
||||
|
||||
Returns:
|
||||
Tensor: A filter banks tensor with shape (m, n_mels).
|
||||
"""
|
||||
dtype = waveform.dtype
|
||||
|
||||
waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties(
|
||||
waveform, channel, sr, frame_shift, frame_length, round_to_power_of_two,
|
||||
preemphasis_coefficient)
|
||||
|
||||
strided_input, signal_log_energy = _get_window(
|
||||
waveform, padded_window_size, window_size, window_shift, window_type,
|
||||
blackman_coeff, snip_edges, raw_energy, energy_floor, dither,
|
||||
remove_dc_offset, preemphasis_coefficient)
|
||||
|
||||
# (m, padded_window_size // 2 + 1)
|
||||
spectrum = paddle.fft.rfft(strided_input).abs()
|
||||
if use_power:
|
||||
spectrum = spectrum.pow(2.)
|
||||
|
||||
# (n_mels, padded_window_size // 2)
|
||||
mel_energies, _ = _get_mel_banks(n_mels, padded_window_size, sr, low_freq,
|
||||
high_freq, vtln_low, vtln_high, vtln_warp)
|
||||
mel_energies = mel_energies.astype(dtype)
|
||||
|
||||
# (n_mels, padded_window_size // 2 + 1)
|
||||
mel_energies = paddle.nn.functional.pad(
|
||||
mel_energies.unsqueeze(0), (0, 1),
|
||||
data_format='NCL',
|
||||
mode='constant',
|
||||
value=0).squeeze(0)
|
||||
|
||||
# (m, n_mels)
|
||||
mel_energies = paddle.mm(spectrum, mel_energies.T)
|
||||
if use_log_fbank:
|
||||
mel_energies = paddle.maximum(mel_energies, _get_epsilon(dtype)).log()
|
||||
|
||||
if use_energy:
|
||||
signal_log_energy = signal_log_energy.unsqueeze(1)
|
||||
if htk_compat:
|
||||
mel_energies = paddle.concat(
|
||||
(mel_energies, signal_log_energy), axis=1)
|
||||
else:
|
||||
mel_energies = paddle.concat(
|
||||
(signal_log_energy, mel_energies), axis=1)
|
||||
|
||||
# (m, n_mels + 1)
|
||||
mel_energies = _subtract_column_mean(mel_energies, subtract_mean)
|
||||
return mel_energies
|
||||
|
||||
|
||||
def _get_dct_matrix(n_mfcc: int, n_mels: int) -> Tensor:
|
||||
dct_matrix = create_dct(n_mels, n_mels, 'ortho')
|
||||
dct_matrix[:, 0] = math.sqrt(1 / float(n_mels))
|
||||
dct_matrix = dct_matrix[:, :n_mfcc] # (n_mels, n_mfcc)
|
||||
return dct_matrix
|
||||
|
||||
|
||||
def _get_lifter_coeffs(n_mfcc: int, cepstral_lifter: float) -> Tensor:
|
||||
i = paddle.arange(n_mfcc)
|
||||
return 1.0 + 0.5 * cepstral_lifter * paddle.sin(math.pi * i /
|
||||
cepstral_lifter)
|
||||
|
||||
|
||||
def mfcc(waveform: Tensor,
|
||||
blackman_coeff: float=0.42,
|
||||
cepstral_lifter: float=22.0,
|
||||
channel: int=-1,
|
||||
dither: float=0.0,
|
||||
energy_floor: float=1.0,
|
||||
frame_length: float=25.0,
|
||||
frame_shift: float=10.0,
|
||||
high_freq: float=0.0,
|
||||
htk_compat: bool=False,
|
||||
low_freq: float=20.0,
|
||||
n_mfcc: int=13,
|
||||
n_mels: int=23,
|
||||
preemphasis_coefficient: float=0.97,
|
||||
raw_energy: bool=True,
|
||||
remove_dc_offset: bool=True,
|
||||
round_to_power_of_two: bool=True,
|
||||
sr: int=16000,
|
||||
snip_edges: bool=True,
|
||||
subtract_mean: bool=False,
|
||||
use_energy: bool=False,
|
||||
vtln_high: float=-500.0,
|
||||
vtln_low: float=100.0,
|
||||
vtln_warp: float=1.0,
|
||||
window_type: str=POVEY) -> Tensor:
|
||||
"""Compute and return mel frequency cepstral coefficients from a waveform. The output is
|
||||
identical to Kaldi's.
|
||||
|
||||
Args:
|
||||
waveform (Tensor): A waveform tensor with shape [C, T].
|
||||
blackman_coeff (float, optional): Coefficient for Blackman window.. Defaults to 0.42.
|
||||
cepstral_lifter (float, optional): Scaling of output mfccs. Defaults to 22.0.
|
||||
channel (int, optional): Select the channel of waveform. Defaults to -1.
|
||||
dither (float, optional): Dithering constant . Defaults to 0.0.
|
||||
energy_floor (float, optional): Floor on energy of the output Spectrogram. Defaults to 1.0.
|
||||
frame_length (float, optional): Frame length in milliseconds. Defaults to 25.0.
|
||||
frame_shift (float, optional): Shift between adjacent frames in milliseconds. Defaults to 10.0.
|
||||
high_freq (float, optional): The upper cut-off frequency. Defaults to 0.0.
|
||||
htk_compat (bool, optional): Put energy to the last when it is set True. Defaults to False.
|
||||
low_freq (float, optional): The lower cut-off frequency. Defaults to 20.0.
|
||||
n_mfcc (int, optional): Number of cepstra in MFCC. Defaults to 13.
|
||||
n_mels (int, optional): Number of output mel bins. Defaults to 23.
|
||||
preemphasis_coefficient (float, optional): Preemphasis coefficient for input waveform. Defaults to 0.97.
|
||||
raw_energy (bool, optional): Whether to compute before preemphasis and windowing. Defaults to True.
|
||||
remove_dc_offset (bool, optional): Whether to subtract mean from waveform on frames. Defaults to True.
|
||||
round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
|
||||
to FFT. Defaults to True.
|
||||
sr (int, optional): Sample rate of input waveform. Defaults to 16000.
|
||||
snip_edges (bool, optional): Drop samples in the end of waveform that cann't fit a singal frame when it
|
||||
is set True. Otherwise performs reflect padding to the end of waveform. Defaults to True.
|
||||
subtract_mean (bool, optional): Whether to subtract mean of feature files. Defaults to False.
|
||||
use_energy (bool, optional): Add an dimension with energy of spectrogram to the output. Defaults to False.
|
||||
vtln_high (float, optional): High inflection point in piecewise linear VTLN warping function. Defaults to -500.0.
|
||||
vtln_low (float, optional): Low inflection point in piecewise linear VTLN warping function. Defaults to 100.0.
|
||||
vtln_warp (float, optional): Vtln warp factor. Defaults to 1.0.
|
||||
window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
|
||||
|
||||
Returns:
|
||||
Tensor: A mel frequency cepstral coefficients tensor with shape (m, n_mfcc).
|
||||
"""
|
||||
assert n_mfcc <= n_mels, 'n_mfcc cannot be larger than n_mels: %d vs %d' % (
|
||||
n_mfcc, n_mels)
|
||||
|
||||
dtype = waveform.dtype
|
||||
|
||||
# (m, n_mels + use_energy)
|
||||
feature = fbank(
|
||||
waveform=waveform,
|
||||
blackman_coeff=blackman_coeff,
|
||||
channel=channel,
|
||||
dither=dither,
|
||||
energy_floor=energy_floor,
|
||||
frame_length=frame_length,
|
||||
frame_shift=frame_shift,
|
||||
high_freq=high_freq,
|
||||
htk_compat=htk_compat,
|
||||
low_freq=low_freq,
|
||||
n_mels=n_mels,
|
||||
preemphasis_coefficient=preemphasis_coefficient,
|
||||
raw_energy=raw_energy,
|
||||
remove_dc_offset=remove_dc_offset,
|
||||
round_to_power_of_two=round_to_power_of_two,
|
||||
sr=sr,
|
||||
snip_edges=snip_edges,
|
||||
subtract_mean=False,
|
||||
use_energy=use_energy,
|
||||
use_log_fbank=True,
|
||||
use_power=True,
|
||||
vtln_high=vtln_high,
|
||||
vtln_low=vtln_low,
|
||||
vtln_warp=vtln_warp,
|
||||
window_type=window_type)
|
||||
|
||||
if use_energy:
|
||||
# (m)
|
||||
signal_log_energy = feature[:, n_mels if htk_compat else 0]
|
||||
mel_offset = int(not htk_compat)
|
||||
feature = feature[:, mel_offset:(n_mels + mel_offset)]
|
||||
|
||||
# (n_mels, n_mfcc)
|
||||
dct_matrix = _get_dct_matrix(n_mfcc, n_mels).astype(dtype=dtype)
|
||||
|
||||
# (m, n_mfcc)
|
||||
feature = feature.matmul(dct_matrix)
|
||||
|
||||
if cepstral_lifter != 0.0:
|
||||
# (1, n_mfcc)
|
||||
lifter_coeffs = _get_lifter_coeffs(n_mfcc, cepstral_lifter).unsqueeze(0)
|
||||
feature *= lifter_coeffs.astype(dtype=dtype)
|
||||
|
||||
if use_energy:
|
||||
feature[:, 0] = signal_log_energy
|
||||
|
||||
if htk_compat:
|
||||
energy = feature[:, 0].unsqueeze(1) # (m, 1)
|
||||
feature = feature[:, 1:] # (m, n_mfcc - 1)
|
||||
if not use_energy:
|
||||
energy *= math.sqrt(2)
|
||||
|
||||
feature = paddle.concat((feature, energy), axis=1)
|
||||
|
||||
feature = _subtract_column_mean(feature, subtract_mean)
|
||||
return feature
|
||||
@ -0,0 +1,344 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from functools import partial
|
||||
from typing import Optional
|
||||
from typing import Union
|
||||
|
||||
import paddle
|
||||
import paddle.nn as nn
|
||||
|
||||
from ..functional import compute_fbank_matrix
|
||||
from ..functional import create_dct
|
||||
from ..functional import power_to_db
|
||||
from ..functional.window import get_window
|
||||
|
||||
__all__ = [
|
||||
'Spectrogram',
|
||||
'MelSpectrogram',
|
||||
'LogMelSpectrogram',
|
||||
'MFCC',
|
||||
]
|
||||
|
||||
|
||||
class Spectrogram(nn.Layer):
|
||||
def __init__(self,
|
||||
n_fft: int=512,
|
||||
hop_length: Optional[int]=None,
|
||||
win_length: Optional[int]=None,
|
||||
window: str='hann',
|
||||
center: bool=True,
|
||||
pad_mode: str='reflect',
|
||||
dtype: str=paddle.float32):
|
||||
"""Compute spectrogram of a given signal, typically an audio waveform.
|
||||
The spectorgram is defined as the complex norm of the short-time
|
||||
Fourier transformation.
|
||||
Parameters:
|
||||
n_fft (int): the number of frequency components of the discrete Fourier transform.
|
||||
The default value is 2048,
|
||||
hop_length (int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
|
||||
The default value is None.
|
||||
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
|
||||
The default value is None.
|
||||
window (str): the name of the window function applied to the single before the Fourier transform.
|
||||
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
|
||||
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
|
||||
The default value is 'hann'
|
||||
center (bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
|
||||
If False, frame t begins at x[t * hop_length]
|
||||
The default value is True
|
||||
pad_mode (str): the mode to pad the signal if necessary. The supported modes are 'reflect'
|
||||
and 'constant'. The default value is 'reflect'.
|
||||
dtype (str): the data type of input and window.
|
||||
Notes:
|
||||
The Spectrogram transform relies on STFT transform to compute the spectrogram.
|
||||
By default, the weights are not learnable. To fine-tune the Fourier coefficients,
|
||||
set stop_gradient=False before training.
|
||||
For more information, see STFT().
|
||||
"""
|
||||
super(Spectrogram, self).__init__()
|
||||
|
||||
if win_length is None:
|
||||
win_length = n_fft
|
||||
|
||||
self.fft_window = get_window(
|
||||
window, win_length, fftbins=True, dtype=dtype)
|
||||
self._stft = partial(
|
||||
paddle.signal.stft,
|
||||
n_fft=n_fft,
|
||||
hop_length=hop_length,
|
||||
win_length=win_length,
|
||||
window=self.fft_window,
|
||||
center=center,
|
||||
pad_mode=pad_mode)
|
||||
self.register_buffer('fft_window', self.fft_window)
|
||||
|
||||
def forward(self, x):
|
||||
stft = self._stft(x)
|
||||
spectrogram = paddle.square(paddle.abs(stft))
|
||||
return spectrogram
|
||||
|
||||
|
||||
class MelSpectrogram(nn.Layer):
|
||||
def __init__(self,
|
||||
sr: int=22050,
|
||||
n_fft: int=512,
|
||||
hop_length: Optional[int]=None,
|
||||
win_length: Optional[int]=None,
|
||||
window: str='hann',
|
||||
center: bool=True,
|
||||
pad_mode: str='reflect',
|
||||
n_mels: int=64,
|
||||
f_min: float=50.0,
|
||||
f_max: Optional[float]=None,
|
||||
htk: bool=False,
|
||||
norm: Union[str, float]='slaney',
|
||||
dtype: str=paddle.float32):
|
||||
"""Compute the melspectrogram of a given signal, typically an audio waveform.
|
||||
The melspectrogram is also known as filterbank or fbank feature in audio community.
|
||||
It is computed by multiplying spectrogram with Mel filter bank matrix.
|
||||
Parameters:
|
||||
sr(int): the audio sample rate.
|
||||
The default value is 22050.
|
||||
n_fft(int): the number of frequency components of the discrete Fourier transform.
|
||||
The default value is 2048,
|
||||
hop_length(int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
|
||||
The default value is None.
|
||||
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
|
||||
The default value is None.
|
||||
window(str): the name of the window function applied to the single before the Fourier transform.
|
||||
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
|
||||
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
|
||||
The default value is 'hann'
|
||||
center(bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
|
||||
If False, frame t begins at x[t * hop_length]
|
||||
The default value is True
|
||||
pad_mode(str): the mode to pad the signal if necessary. The supported modes are 'reflect'
|
||||
and 'constant'.
|
||||
The default value is 'reflect'.
|
||||
n_mels(int): the mel bins.
|
||||
f_min(float): the lower cut-off frequency, below which the filter response is zero.
|
||||
f_max(float): the upper cut-off frequency, above which the filter response is zeros.
|
||||
htk(bool): whether to use HTK formula in computing fbank matrix.
|
||||
norm(str|float): the normalization type in computing fbank matrix. Slaney-style is used by default.
|
||||
You can specify norm=1.0/2.0 to use customized p-norm normalization.
|
||||
dtype(str): the datatype of fbank matrix used in the transform. Use float64 to increase numerical
|
||||
accuracy. Note that the final transform will be conducted in float32 regardless of dtype of fbank matrix.
|
||||
"""
|
||||
super(MelSpectrogram, self).__init__()
|
||||
|
||||
self._spectrogram = Spectrogram(
|
||||
n_fft=n_fft,
|
||||
hop_length=hop_length,
|
||||
win_length=win_length,
|
||||
window=window,
|
||||
center=center,
|
||||
pad_mode=pad_mode,
|
||||
dtype=dtype)
|
||||
self.n_mels = n_mels
|
||||
self.f_min = f_min
|
||||
self.f_max = f_max
|
||||
self.htk = htk
|
||||
self.norm = norm
|
||||
if f_max is None:
|
||||
f_max = sr // 2
|
||||
self.fbank_matrix = compute_fbank_matrix(
|
||||
sr=sr,
|
||||
n_fft=n_fft,
|
||||
n_mels=n_mels,
|
||||
f_min=f_min,
|
||||
f_max=f_max,
|
||||
htk=htk,
|
||||
norm=norm,
|
||||
dtype=dtype) # float64 for better numerical results
|
||||
self.register_buffer('fbank_matrix', self.fbank_matrix)
|
||||
|
||||
def forward(self, x):
|
||||
spect_feature = self._spectrogram(x)
|
||||
mel_feature = paddle.matmul(self.fbank_matrix, spect_feature)
|
||||
return mel_feature
|
||||
|
||||
|
||||
class LogMelSpectrogram(nn.Layer):
|
||||
def __init__(self,
|
||||
sr: int=22050,
|
||||
n_fft: int=512,
|
||||
hop_length: Optional[int]=None,
|
||||
win_length: Optional[int]=None,
|
||||
window: str='hann',
|
||||
center: bool=True,
|
||||
pad_mode: str='reflect',
|
||||
n_mels: int=64,
|
||||
f_min: float=50.0,
|
||||
f_max: Optional[float]=None,
|
||||
htk: bool=False,
|
||||
norm: Union[str, float]='slaney',
|
||||
ref_value: float=1.0,
|
||||
amin: float=1e-10,
|
||||
top_db: Optional[float]=None,
|
||||
dtype: str=paddle.float32):
|
||||
"""Compute log-mel-spectrogram(also known as LogFBank) feature of a given signal,
|
||||
typically an audio waveform.
|
||||
Parameters:
|
||||
sr (int): the audio sample rate.
|
||||
The default value is 22050.
|
||||
n_fft (int): the number of frequency components of the discrete Fourier transform.
|
||||
The default value is 2048,
|
||||
hop_length (int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
|
||||
The default value is None.
|
||||
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
|
||||
The default value is None.
|
||||
window (str): the name of the window function applied to the single before the Fourier transform.
|
||||
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
|
||||
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
|
||||
The default value is 'hann'
|
||||
center (bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
|
||||
If False, frame t begins at x[t * hop_length]
|
||||
The default value is True
|
||||
pad_mode (str): the mode to pad the signal if necessary. The supported modes are 'reflect'
|
||||
and 'constant'.
|
||||
The default value is 'reflect'.
|
||||
n_mels (int): the mel bins.
|
||||
f_min (float): the lower cut-off frequency, below which the filter response is zero.
|
||||
f_max (float): the upper cut-off frequency, above which the filter response is zeros.
|
||||
htk (bool): whether to use HTK formula in computing fbank matrix.
|
||||
norm (str|float): the normalization type in computing fbank matrix. Slaney-style is used by default.
|
||||
You can specify norm=1.0/2.0 to use customized p-norm normalization.
|
||||
ref_value (float): the reference value. If smaller than 1.0, the db level
|
||||
amin (float): the minimum value of input magnitude, below which the input of the signal will be pulled up accordingly.
|
||||
Otherwise, the db level is pushed down.
|
||||
magnitude is clipped(to amin). For numerical stability, set amin to a larger value,
|
||||
e.g., 1e-3.
|
||||
top_db (float): the maximum db value of resulting spectrum, above which the
|
||||
spectrum is clipped(to top_db).
|
||||
dtype (str): the datatype of fbank matrix used in the transform. Use float64 to increase numerical
|
||||
accuracy. Note that the final transform will be conducted in float32 regardless of dtype of fbank matrix.
|
||||
"""
|
||||
super(LogMelSpectrogram, self).__init__()
|
||||
|
||||
self._melspectrogram = MelSpectrogram(
|
||||
sr=sr,
|
||||
n_fft=n_fft,
|
||||
hop_length=hop_length,
|
||||
win_length=win_length,
|
||||
window=window,
|
||||
center=center,
|
||||
pad_mode=pad_mode,
|
||||
n_mels=n_mels,
|
||||
f_min=f_min,
|
||||
f_max=f_max,
|
||||
htk=htk,
|
||||
norm=norm,
|
||||
dtype=dtype)
|
||||
|
||||
self.ref_value = ref_value
|
||||
self.amin = amin
|
||||
self.top_db = top_db
|
||||
|
||||
def forward(self, x):
|
||||
# import ipdb; ipdb.set_trace()
|
||||
mel_feature = self._melspectrogram(x)
|
||||
log_mel_feature = power_to_db(
|
||||
mel_feature,
|
||||
ref_value=self.ref_value,
|
||||
amin=self.amin,
|
||||
top_db=self.top_db)
|
||||
return log_mel_feature
|
||||
|
||||
|
||||
class MFCC(nn.Layer):
|
||||
def __init__(self,
|
||||
sr: int=22050,
|
||||
n_mfcc: int=40,
|
||||
n_fft: int=512,
|
||||
hop_length: Optional[int]=None,
|
||||
win_length: Optional[int]=None,
|
||||
window: str='hann',
|
||||
center: bool=True,
|
||||
pad_mode: str='reflect',
|
||||
n_mels: int=64,
|
||||
f_min: float=50.0,
|
||||
f_max: Optional[float]=None,
|
||||
htk: bool=False,
|
||||
norm: Union[str, float]='slaney',
|
||||
ref_value: float=1.0,
|
||||
amin: float=1e-10,
|
||||
top_db: Optional[float]=None,
|
||||
dtype: str=paddle.float32):
|
||||
"""Compute mel frequency cepstral coefficients(MFCCs) feature of given waveforms.
|
||||
|
||||
Parameters:
|
||||
sr(int): the audio sample rate.
|
||||
The default value is 22050.
|
||||
n_mfcc (int, optional): Number of cepstra in MFCC. Defaults to 40.
|
||||
n_fft (int): the number of frequency components of the discrete Fourier transform.
|
||||
The default value is 2048,
|
||||
hop_length (int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
|
||||
The default value is None.
|
||||
win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
|
||||
The default value is None.
|
||||
window (str): the name of the window function applied to the single before the Fourier transform.
|
||||
The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
|
||||
'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
|
||||
The default value is 'hann'
|
||||
center (bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
|
||||
If False, frame t begins at x[t * hop_length]
|
||||
The default value is True
|
||||
pad_mode (str): the mode to pad the signal if necessary. The supported modes are 'reflect'
|
||||
and 'constant'.
|
||||
The default value is 'reflect'.
|
||||
n_mels (int): the mel bins.
|
||||
f_min (float): the lower cut-off frequency, below which the filter response is zero.
|
||||
f_max (float): the upper cut-off frequency, above which the filter response is zeros.
|
||||
htk (bool): whether to use HTK formula in computing fbank matrix.
|
||||
norm (str|float): the normalization type in computing fbank matrix. Slaney-style is used by default.
|
||||
You can specify norm=1.0/2.0 to use customized p-norm normalization.
|
||||
ref_value (float): the reference value. If smaller than 1.0, the db level
|
||||
amin (float): the minimum value of input magnitude, below which the input of the signal will be pulled up accordingly.
|
||||
Otherwise, the db level is pushed down.
|
||||
magnitude is clipped(to amin). For numerical stability, set amin to a larger value,
|
||||
e.g., 1e-3.
|
||||
top_db (float): the maximum db value of resulting spectrum, above which the
|
||||
spectrum is clipped(to top_db).
|
||||
dtype (str): the datatype of fbank matrix used in the transform. Use float64 to increase numerical
|
||||
accuracy. Note that the final transform will be conducted in float32 regardless of dtype of fbank matrix.
|
||||
"""
|
||||
super(MFCC, self).__init__()
|
||||
assert n_mfcc <= n_mels, 'n_mfcc cannot be larger than n_mels: %d vs %d' % (
|
||||
n_mfcc, n_mels)
|
||||
self._log_melspectrogram = LogMelSpectrogram(
|
||||
sr=sr,
|
||||
n_fft=n_fft,
|
||||
hop_length=hop_length,
|
||||
win_length=win_length,
|
||||
window=window,
|
||||
center=center,
|
||||
pad_mode=pad_mode,
|
||||
n_mels=n_mels,
|
||||
f_min=f_min,
|
||||
f_max=f_max,
|
||||
htk=htk,
|
||||
norm=norm,
|
||||
ref_value=ref_value,
|
||||
amin=amin,
|
||||
top_db=top_db,
|
||||
dtype=dtype)
|
||||
self.dct_matrix = create_dct(n_mfcc=n_mfcc, n_mels=n_mels, dtype=dtype)
|
||||
self.register_buffer('dct_matrix', self.dct_matrix)
|
||||
|
||||
def forward(self, x):
|
||||
log_mel_feature = self._log_melspectrogram(x)
|
||||
mfcc = paddle.matmul(
|
||||
log_mel_feature.transpose((0, 2, 1)), self.dct_matrix).transpose(
|
||||
(0, 2, 1)) # (B, n_mels, L)
|
||||
return mfcc
|
||||
@ -0,0 +1,20 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from .functional import compute_fbank_matrix
|
||||
from .functional import create_dct
|
||||
from .functional import fft_frequencies
|
||||
from .functional import hz_to_mel
|
||||
from .functional import mel_frequencies
|
||||
from .functional import mel_to_hz
|
||||
from .functional import power_to_db
|
||||
@ -0,0 +1,265 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# Modified from librosa(https://github.com/librosa/librosa)
|
||||
import math
|
||||
from typing import Optional
|
||||
from typing import Union
|
||||
|
||||
import paddle
|
||||
|
||||
__all__ = [
|
||||
'hz_to_mel',
|
||||
'mel_to_hz',
|
||||
'mel_frequencies',
|
||||
'fft_frequencies',
|
||||
'compute_fbank_matrix',
|
||||
'power_to_db',
|
||||
'create_dct',
|
||||
]
|
||||
|
||||
|
||||
def hz_to_mel(freq: Union[paddle.Tensor, float],
|
||||
htk: bool=False) -> Union[paddle.Tensor, float]:
|
||||
"""Convert Hz to Mels.
|
||||
Parameters:
|
||||
freq: the input tensor of arbitrary shape, or a single floating point number.
|
||||
htk: use HTK formula to do the conversion.
|
||||
The default value is False.
|
||||
Returns:
|
||||
The frequencies represented in Mel-scale.
|
||||
"""
|
||||
|
||||
if htk:
|
||||
if isinstance(freq, paddle.Tensor):
|
||||
return 2595.0 * paddle.log10(1.0 + freq / 700.0)
|
||||
else:
|
||||
return 2595.0 * math.log10(1.0 + freq / 700.0)
|
||||
|
||||
# Fill in the linear part
|
||||
f_min = 0.0
|
||||
f_sp = 200.0 / 3
|
||||
|
||||
mels = (freq - f_min) / f_sp
|
||||
|
||||
# Fill in the log-scale part
|
||||
|
||||
min_log_hz = 1000.0 # beginning of log region (Hz)
|
||||
min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
|
||||
logstep = math.log(6.4) / 27.0 # step size for log region
|
||||
|
||||
if isinstance(freq, paddle.Tensor):
|
||||
target = min_log_mel + paddle.log(
|
||||
freq / min_log_hz + 1e-10) / logstep # prevent nan with 1e-10
|
||||
mask = (freq > min_log_hz).astype(freq.dtype)
|
||||
mels = target * mask + mels * (
|
||||
1 - mask) # will replace by masked_fill OP in future
|
||||
else:
|
||||
if freq >= min_log_hz:
|
||||
mels = min_log_mel + math.log(freq / min_log_hz + 1e-10) / logstep
|
||||
|
||||
return mels
|
||||
|
||||
|
||||
def mel_to_hz(mel: Union[float, paddle.Tensor],
|
||||
htk: bool=False) -> Union[float, paddle.Tensor]:
|
||||
"""Convert mel bin numbers to frequencies.
|
||||
Parameters:
|
||||
mel: the mel frequency represented as a tensor of arbitrary shape, or a floating point number.
|
||||
htk: use HTK formula to do the conversion.
|
||||
Returns:
|
||||
The frequencies represented in hz.
|
||||
"""
|
||||
if htk:
|
||||
return 700.0 * (10.0**(mel / 2595.0) - 1.0)
|
||||
|
||||
f_min = 0.0
|
||||
f_sp = 200.0 / 3
|
||||
freqs = f_min + f_sp * mel
|
||||
# And now the nonlinear scale
|
||||
min_log_hz = 1000.0 # beginning of log region (Hz)
|
||||
min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
|
||||
logstep = math.log(6.4) / 27.0 # step size for log region
|
||||
if isinstance(mel, paddle.Tensor):
|
||||
target = min_log_hz * paddle.exp(logstep * (mel - min_log_mel))
|
||||
mask = (mel > min_log_mel).astype(mel.dtype)
|
||||
freqs = target * mask + freqs * (
|
||||
1 - mask) # will replace by masked_fill OP in future
|
||||
else:
|
||||
if mel >= min_log_mel:
|
||||
freqs = min_log_hz * math.exp(logstep * (mel - min_log_mel))
|
||||
|
||||
return freqs
|
||||
|
||||
|
||||
def mel_frequencies(n_mels: int=64,
|
||||
f_min: float=0.0,
|
||||
f_max: float=11025.0,
|
||||
htk: bool=False,
|
||||
dtype: str=paddle.float32):
|
||||
"""Compute mel frequencies.
|
||||
Parameters:
|
||||
n_mels(int): number of Mel bins.
|
||||
f_min(float): the lower cut-off frequency, below which the filter response is zero.
|
||||
f_max(float): the upper cut-off frequency, above which the filter response is zero.
|
||||
htk(bool): whether to use htk formula.
|
||||
dtype(str): the datatype of the return frequencies.
|
||||
Returns:
|
||||
The frequencies represented in Mel-scale
|
||||
"""
|
||||
# 'Center freqs' of mel bands - uniformly spaced between limits
|
||||
min_mel = hz_to_mel(f_min, htk=htk)
|
||||
max_mel = hz_to_mel(f_max, htk=htk)
|
||||
mels = paddle.linspace(min_mel, max_mel, n_mels, dtype=dtype)
|
||||
freqs = mel_to_hz(mels, htk=htk)
|
||||
return freqs
|
||||
|
||||
|
||||
def fft_frequencies(sr: int, n_fft: int, dtype: str=paddle.float32):
|
||||
"""Compute fourier frequencies.
|
||||
Parameters:
|
||||
sr(int): the audio sample rate.
|
||||
n_fft(float): the number of fft bins.
|
||||
dtype(str): the datatype of the return frequencies.
|
||||
Returns:
|
||||
The frequencies represented in hz.
|
||||
"""
|
||||
return paddle.linspace(0, float(sr) / 2, int(1 + n_fft // 2), dtype=dtype)
|
||||
|
||||
|
||||
def compute_fbank_matrix(sr: int,
|
||||
n_fft: int,
|
||||
n_mels: int=64,
|
||||
f_min: float=0.0,
|
||||
f_max: Optional[float]=None,
|
||||
htk: bool=False,
|
||||
norm: Union[str, float]='slaney',
|
||||
dtype: str=paddle.float32):
|
||||
"""Compute fbank matrix.
|
||||
Parameters:
|
||||
sr(int): the audio sample rate.
|
||||
n_fft(int): the number of fft bins.
|
||||
n_mels(int): the number of Mel bins.
|
||||
f_min(float): the lower cut-off frequency, below which the filter response is zero.
|
||||
f_max(float): the upper cut-off frequency, above which the filter response is zero.
|
||||
htk: whether to use htk formula.
|
||||
return_complex(bool): whether to return complex matrix. If True, the matrix will
|
||||
be complex type. Otherwise, the real and image part will be stored in the last
|
||||
axis of returned tensor.
|
||||
dtype(str): the datatype of the returned fbank matrix.
|
||||
Returns:
|
||||
The fbank matrix of shape (n_mels, int(1+n_fft//2)).
|
||||
Shape:
|
||||
output: (n_mels, int(1+n_fft//2))
|
||||
"""
|
||||
|
||||
if f_max is None:
|
||||
f_max = float(sr) / 2
|
||||
|
||||
# Initialize the weights
|
||||
weights = paddle.zeros((n_mels, int(1 + n_fft // 2)), dtype=dtype)
|
||||
|
||||
# Center freqs of each FFT bin
|
||||
fftfreqs = fft_frequencies(sr=sr, n_fft=n_fft, dtype=dtype)
|
||||
|
||||
# 'Center freqs' of mel bands - uniformly spaced between limits
|
||||
mel_f = mel_frequencies(
|
||||
n_mels + 2, f_min=f_min, f_max=f_max, htk=htk, dtype=dtype)
|
||||
|
||||
fdiff = mel_f[1:] - mel_f[:-1] #np.diff(mel_f)
|
||||
ramps = mel_f.unsqueeze(1) - fftfreqs.unsqueeze(0)
|
||||
#ramps = np.subtract.outer(mel_f, fftfreqs)
|
||||
|
||||
for i in range(n_mels):
|
||||
# lower and upper slopes for all bins
|
||||
lower = -ramps[i] / fdiff[i]
|
||||
upper = ramps[i + 2] / fdiff[i + 1]
|
||||
|
||||
# .. then intersect them with each other and zero
|
||||
weights[i] = paddle.maximum(
|
||||
paddle.zeros_like(lower), paddle.minimum(lower, upper))
|
||||
|
||||
# Slaney-style mel is scaled to be approx constant energy per channel
|
||||
if norm == 'slaney':
|
||||
enorm = 2.0 / (mel_f[2:n_mels + 2] - mel_f[:n_mels])
|
||||
weights *= enorm.unsqueeze(1)
|
||||
elif isinstance(norm, int) or isinstance(norm, float):
|
||||
weights = paddle.nn.functional.normalize(weights, p=norm, axis=-1)
|
||||
|
||||
return weights
|
||||
|
||||
|
||||
def power_to_db(magnitude: paddle.Tensor,
|
||||
ref_value: float=1.0,
|
||||
amin: float=1e-10,
|
||||
top_db: Optional[float]=None) -> paddle.Tensor:
|
||||
"""Convert a power spectrogram (amplitude squared) to decibel (dB) units.
|
||||
The function computes the scaling ``10 * log10(x / ref)`` in a numerically
|
||||
stable way.
|
||||
Parameters:
|
||||
magnitude(Tensor): the input magnitude tensor of any shape.
|
||||
ref_value(float): the reference value. If smaller than 1.0, the db level
|
||||
of the signal will be pulled up accordingly. Otherwise, the db level
|
||||
is pushed down.
|
||||
amin(float): the minimum value of input magnitude, below which the input
|
||||
magnitude is clipped(to amin).
|
||||
top_db(float): the maximum db value of resulting spectrum, above which the
|
||||
spectrum is clipped(to top_db).
|
||||
Returns:
|
||||
The spectrogram in log-scale.
|
||||
shape:
|
||||
input: any shape
|
||||
output: same as input
|
||||
"""
|
||||
if amin <= 0:
|
||||
raise Exception("amin must be strictly positive")
|
||||
|
||||
if ref_value <= 0:
|
||||
raise Exception("ref_value must be strictly positive")
|
||||
|
||||
ones = paddle.ones_like(magnitude)
|
||||
log_spec = 10.0 * paddle.log10(paddle.maximum(ones * amin, magnitude))
|
||||
log_spec -= 10.0 * math.log10(max(ref_value, amin))
|
||||
|
||||
if top_db is not None:
|
||||
if top_db < 0:
|
||||
raise Exception("top_db must be non-negative")
|
||||
log_spec = paddle.maximum(log_spec, ones * (log_spec.max() - top_db))
|
||||
|
||||
return log_spec
|
||||
|
||||
|
||||
def create_dct(n_mfcc: int,
|
||||
n_mels: int,
|
||||
norm: Optional[str]='ortho',
|
||||
dtype: Optional[str]=paddle.float32) -> paddle.Tensor:
|
||||
"""Create a discrete cosine transform(DCT) matrix.
|
||||
|
||||
Parameters:
|
||||
n_mfcc (int): Number of mel frequency cepstral coefficients.
|
||||
n_mels (int): Number of mel filterbanks.
|
||||
norm (str, optional): Normalizaiton type. Defaults to 'ortho'.
|
||||
Returns:
|
||||
Tensor: The DCT matrix with shape (n_mels, n_mfcc).
|
||||
"""
|
||||
n = paddle.arange(n_mels, dtype=dtype)
|
||||
k = paddle.arange(n_mfcc, dtype=dtype).unsqueeze(1)
|
||||
dct = paddle.cos(math.pi / float(n_mels) * (n + 0.5) *
|
||||
k) # size (n_mfcc, n_mels)
|
||||
if norm is None:
|
||||
dct *= 2.0
|
||||
else:
|
||||
assert norm == "ortho"
|
||||
dct[0] *= 1.0 / math.sqrt(2.0)
|
||||
dct *= math.sqrt(2.0 / float(n_mels))
|
||||
return dct.T
|
||||
@ -0,0 +1,42 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import numpy as np
|
||||
from dtaidistance import dtw_ndim
|
||||
|
||||
__all__ = [
|
||||
'dtw_distance',
|
||||
]
|
||||
|
||||
|
||||
def dtw_distance(xs: np.ndarray, ys: np.ndarray) -> float:
|
||||
"""dtw distance
|
||||
|
||||
Dynamic Time Warping.
|
||||
This function keeps a compact matrix, not the full warping paths matrix.
|
||||
Uses dynamic programming to compute:
|
||||
|
||||
wps[i, j] = (s1[i]-s2[j])**2 + min(
|
||||
wps[i-1, j ] + penalty, // vertical / insertion / expansion
|
||||
wps[i , j-1] + penalty, // horizontal / deletion / compression
|
||||
wps[i-1, j-1]) // diagonal / match
|
||||
dtw = sqrt(wps[-1, -1])
|
||||
|
||||
Args:
|
||||
xs (np.ndarray): ref sequence, [T,D]
|
||||
ys (np.ndarray): hyp sequence, [T,D]
|
||||
|
||||
Returns:
|
||||
float: dtw distance
|
||||
"""
|
||||
return dtw_ndim.distance(xs, ys)
|
||||
@ -0,0 +1,48 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import mcd.metrics_fast as mt
|
||||
import numpy as np
|
||||
from mcd import dtw
|
||||
|
||||
__all__ = [
|
||||
'mcd_distance',
|
||||
]
|
||||
|
||||
|
||||
def mcd_distance(xs: np.ndarray, ys: np.ndarray, cost_fn=mt.logSpecDbDist):
|
||||
"""Mel cepstral distortion (MCD), dtw distance.
|
||||
|
||||
Dynamic Time Warping.
|
||||
Uses dynamic programming to compute:
|
||||
wps[i, j] = cost_fn(xs[i], ys[j]) + min(
|
||||
wps[i-1, j ], // vertical / insertion / expansion
|
||||
wps[i , j-1], // horizontal / deletion / compression
|
||||
wps[i-1, j-1]) // diagonal / match
|
||||
dtw = sqrt(wps[-1, -1])
|
||||
|
||||
Cost Function:
|
||||
logSpecDbConst = 10.0 / math.log(10.0) * math.sqrt(2.0)
|
||||
def logSpecDbDist(x, y):
|
||||
diff = x - y
|
||||
return logSpecDbConst * math.sqrt(np.inner(diff, diff))
|
||||
|
||||
Args:
|
||||
xs (np.ndarray): ref sequence, [T,D]
|
||||
ys (np.ndarray): hyp sequence, [T,D]
|
||||
|
||||
Returns:
|
||||
float: dtw distance
|
||||
"""
|
||||
min_cost, path = dtw.dtw(xs, ys, cost_fn)
|
||||
return min_cost
|
||||
@ -0,0 +1,13 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
@ -0,0 +1,25 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License"
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from .download import decompress
|
||||
from .download import download_and_decompress
|
||||
from .download import load_state_dict_from_url
|
||||
from .env import DATA_HOME
|
||||
from .env import MODEL_HOME
|
||||
from .env import PPAUDIO_HOME
|
||||
from .env import USER_HOME
|
||||
from .error import ParameterError
|
||||
from .log import Logger
|
||||
from .log import logger
|
||||
from .time import seconds_to_hms
|
||||
from .time import Timer
|
||||
@ -1,27 +1,107 @@
|
||||
# This is the parameter configuration file for PaddleSpeech Serving.
|
||||
|
||||
##################################################################
|
||||
# SERVER SETTING #
|
||||
##################################################################
|
||||
host: '127.0.0.1'
|
||||
#################################################################################
|
||||
# SERVER SETTING #
|
||||
#################################################################################
|
||||
host: 127.0.0.1
|
||||
port: 8090
|
||||
|
||||
##################################################################
|
||||
# CONFIG FILE #
|
||||
##################################################################
|
||||
# add engine backend type (Options: asr, tts) and config file here.
|
||||
# Adding a speech task to engine_backend means starting the service.
|
||||
engine_backend:
|
||||
asr: 'conf/asr/asr.yaml'
|
||||
tts: 'conf/tts/tts.yaml'
|
||||
|
||||
# The engine_type of speech task needs to keep the same type as the config file of speech task.
|
||||
# E.g: The engine_type of asr is 'python', the engine_backend of asr is 'XX/asr.yaml'
|
||||
# E.g: The engine_type of asr is 'inference', the engine_backend of asr is 'XX/asr_pd.yaml'
|
||||
#
|
||||
# add engine type (Options: python, inference)
|
||||
engine_type:
|
||||
asr: 'python'
|
||||
tts: 'python'
|
||||
# The task format in the engin_list is: <speech task>_<engine type>
|
||||
# task choices = ['asr_python', 'asr_inference', 'tts_python', 'tts_inference']
|
||||
|
||||
engine_list: ['asr_python', 'tts_python']
|
||||
|
||||
|
||||
#################################################################################
|
||||
# ENGINE CONFIG #
|
||||
#################################################################################
|
||||
################### speech task: asr; engine_type: python #######################
|
||||
asr_python:
|
||||
model: 'conformer_wenetspeech'
|
||||
lang: 'zh'
|
||||
sample_rate: 16000
|
||||
cfg_path: # [optional]
|
||||
ckpt_path: # [optional]
|
||||
decode_method: 'attention_rescoring'
|
||||
force_yes: True
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
|
||||
|
||||
################### speech task: asr; engine_type: inference #######################
|
||||
asr_inference:
|
||||
# model_type choices=['deepspeech2offline_aishell']
|
||||
model_type: 'deepspeech2offline_aishell'
|
||||
am_model: # the pdmodel file of am static model [optional]
|
||||
am_params: # the pdiparams file of am static model [optional]
|
||||
lang: 'zh'
|
||||
sample_rate: 16000
|
||||
cfg_path:
|
||||
decode_method:
|
||||
force_yes: True
|
||||
|
||||
am_predictor_conf:
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
switch_ir_optim: True
|
||||
glog_info: False # True -> print glog
|
||||
summary: True # False -> do not show predictor config
|
||||
|
||||
|
||||
################### speech task: tts; engine_type: python #######################
|
||||
tts_python:
|
||||
# am (acoustic model) choices=['speedyspeech_csmsc', 'fastspeech2_csmsc',
|
||||
# 'fastspeech2_ljspeech', 'fastspeech2_aishell3',
|
||||
# 'fastspeech2_vctk']
|
||||
am: 'fastspeech2_csmsc'
|
||||
am_config:
|
||||
am_ckpt:
|
||||
am_stat:
|
||||
phones_dict:
|
||||
tones_dict:
|
||||
speaker_dict:
|
||||
spk_id: 0
|
||||
|
||||
# voc (vocoder) choices=['pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3',
|
||||
# 'pwgan_vctk', 'mb_melgan_csmsc']
|
||||
voc: 'pwgan_csmsc'
|
||||
voc_config:
|
||||
voc_ckpt:
|
||||
voc_stat:
|
||||
|
||||
# others
|
||||
lang: 'zh'
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
|
||||
|
||||
################### speech task: tts; engine_type: inference #######################
|
||||
tts_inference:
|
||||
# am (acoustic model) choices=['speedyspeech_csmsc', 'fastspeech2_csmsc']
|
||||
am: 'fastspeech2_csmsc'
|
||||
am_model: # the pdmodel file of your am static model (XX.pdmodel)
|
||||
am_params: # the pdiparams file of your am static model (XX.pdipparams)
|
||||
am_sample_rate: 24000
|
||||
phones_dict:
|
||||
tones_dict:
|
||||
speaker_dict:
|
||||
spk_id: 0
|
||||
|
||||
am_predictor_conf:
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
switch_ir_optim: True
|
||||
glog_info: False # True -> print glog
|
||||
summary: True # False -> do not show predictor config
|
||||
|
||||
# voc (vocoder) choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc']
|
||||
voc: 'pwgan_csmsc'
|
||||
voc_model: # the pdmodel file of your vocoder static model (XX.pdmodel)
|
||||
voc_params: # the pdiparams file of your vocoder static model (XX.pdipparams)
|
||||
voc_sample_rate: 24000
|
||||
|
||||
voc_predictor_conf:
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
switch_ir_optim: True
|
||||
glog_info: False # True -> print glog
|
||||
summary: True # False -> do not show predictor config
|
||||
|
||||
# others
|
||||
lang: 'zh'
|
||||
|
||||
|
||||
@ -1,8 +0,0 @@
|
||||
model: 'conformer_wenetspeech'
|
||||
lang: 'zh'
|
||||
sample_rate: 16000
|
||||
cfg_path: # [optional]
|
||||
ckpt_path: # [optional]
|
||||
decode_method: 'attention_rescoring'
|
||||
force_yes: True
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
@ -1,26 +0,0 @@
|
||||
# This is the parameter configuration file for ASR server.
|
||||
# These are the static models that support paddle inference.
|
||||
|
||||
##################################################################
|
||||
# ACOUSTIC MODEL SETTING #
|
||||
# am choices=['deepspeech2offline_aishell'] TODO
|
||||
##################################################################
|
||||
model_type: 'deepspeech2offline_aishell'
|
||||
am_model: # the pdmodel file of am static model [optional]
|
||||
am_params: # the pdiparams file of am static model [optional]
|
||||
lang: 'zh'
|
||||
sample_rate: 16000
|
||||
cfg_path:
|
||||
decode_method:
|
||||
force_yes: True
|
||||
|
||||
am_predictor_conf:
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
switch_ir_optim: True
|
||||
glog_info: False # True -> print glog
|
||||
summary: True # False -> do not show predictor config
|
||||
|
||||
|
||||
##################################################################
|
||||
# OTHERS #
|
||||
##################################################################
|
||||
@ -1,32 +0,0 @@
|
||||
# This is the parameter configuration file for TTS server.
|
||||
|
||||
##################################################################
|
||||
# ACOUSTIC MODEL SETTING #
|
||||
# am choices=['speedyspeech_csmsc', 'fastspeech2_csmsc',
|
||||
# 'fastspeech2_ljspeech', 'fastspeech2_aishell3',
|
||||
# 'fastspeech2_vctk']
|
||||
##################################################################
|
||||
am: 'fastspeech2_csmsc'
|
||||
am_config:
|
||||
am_ckpt:
|
||||
am_stat:
|
||||
phones_dict:
|
||||
tones_dict:
|
||||
speaker_dict:
|
||||
spk_id: 0
|
||||
|
||||
##################################################################
|
||||
# VOCODER SETTING #
|
||||
# voc choices=['pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3',
|
||||
# 'pwgan_vctk', 'mb_melgan_csmsc']
|
||||
##################################################################
|
||||
voc: 'pwgan_csmsc'
|
||||
voc_config:
|
||||
voc_ckpt:
|
||||
voc_stat:
|
||||
|
||||
##################################################################
|
||||
# OTHERS #
|
||||
##################################################################
|
||||
lang: 'zh'
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
@ -1,42 +0,0 @@
|
||||
# This is the parameter configuration file for TTS server.
|
||||
# These are the static models that support paddle inference.
|
||||
|
||||
##################################################################
|
||||
# ACOUSTIC MODEL SETTING #
|
||||
# am choices=['speedyspeech_csmsc', 'fastspeech2_csmsc']
|
||||
##################################################################
|
||||
am: 'fastspeech2_csmsc'
|
||||
am_model: # the pdmodel file of your am static model (XX.pdmodel)
|
||||
am_params: # the pdiparams file of your am static model (XX.pdipparams)
|
||||
am_sample_rate: 24000
|
||||
phones_dict:
|
||||
tones_dict:
|
||||
speaker_dict:
|
||||
spk_id: 0
|
||||
|
||||
am_predictor_conf:
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
switch_ir_optim: True
|
||||
glog_info: False # True -> print glog
|
||||
summary: True # False -> do not show predictor config
|
||||
|
||||
|
||||
##################################################################
|
||||
# VOCODER SETTING #
|
||||
# voc choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc']
|
||||
##################################################################
|
||||
voc: 'pwgan_csmsc'
|
||||
voc_model: # the pdmodel file of your vocoder static model (XX.pdmodel)
|
||||
voc_params: # the pdiparams file of your vocoder static model (XX.pdipparams)
|
||||
voc_sample_rate: 24000
|
||||
|
||||
voc_predictor_conf:
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
switch_ir_optim: True
|
||||
glog_info: False # True -> print glog
|
||||
summary: True # False -> do not show predictor config
|
||||
|
||||
##################################################################
|
||||
# OTHERS #
|
||||
##################################################################
|
||||
lang: 'zh'
|
||||
@ -0,0 +1,142 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import sys
|
||||
import random
|
||||
import numpy as np
|
||||
import kaldi_python_io as k_io
|
||||
from paddle.io import Dataset
|
||||
from paddlespeech.vector.utils.data_utils import batch_pad_right
|
||||
import paddlespeech.vector.utils as utils
|
||||
from paddlespeech.vector.utils.utils import read_map_file
|
||||
from paddlespeech.vector import _logger as log
|
||||
|
||||
def ark_collate_fn(batch):
|
||||
"""
|
||||
Custom collate function] for kaldi feats dataset
|
||||
|
||||
Args:
|
||||
min_chunk_size: min chunk size of a utterance
|
||||
max_chunk_size: max chunk size of a utterance
|
||||
|
||||
Returns:
|
||||
ark_collate_fn: collate funtion for dataloader
|
||||
"""
|
||||
|
||||
data = []
|
||||
target = []
|
||||
for items in batch:
|
||||
for x, y in zip(items[0], items[1]):
|
||||
data.append(np.array(x))
|
||||
target.append(y)
|
||||
|
||||
data, lengths = batch_pad_right(data)
|
||||
return np.array(data, dtype=np.float32), \
|
||||
np.array(lengths, dtype=np.float32), \
|
||||
np.array(target, dtype=np.long).reshape((len(target), 1))
|
||||
|
||||
|
||||
class KaldiArkDataset(Dataset):
|
||||
"""
|
||||
Dataset used to load kaldi ark/scp files.
|
||||
"""
|
||||
def __init__(self, scp_file, label2utt, min_item_size=1,
|
||||
max_item_size=1, repeat=50, min_chunk_size=200,
|
||||
max_chunk_size=400, select_by_speaker=True):
|
||||
self.scp_file = scp_file
|
||||
self.scp_reader = None
|
||||
self.repeat = repeat
|
||||
self.min_item_size = min_item_size
|
||||
self.max_item_size = max_item_size
|
||||
self.min_chunk_size = min_chunk_size
|
||||
self.max_chunk_size = max_chunk_size
|
||||
self._collate_fn = ark_collate_fn
|
||||
self._is_select_by_speaker = select_by_speaker
|
||||
if utils.is_exist(self.scp_file):
|
||||
self.scp_reader = k_io.ScriptReader(self.scp_file)
|
||||
|
||||
label2utts, utt2label = read_map_file(label2utt, key_func=int)
|
||||
self.utt_info = list(label2utts.items()) if self._is_select_by_speaker else list(utt2label.items())
|
||||
|
||||
@property
|
||||
def collate_fn(self):
|
||||
"""
|
||||
Return a collate funtion.
|
||||
"""
|
||||
return self._collate_fn
|
||||
|
||||
def _random_chunk(self, length):
|
||||
chunk_size = random.randint(self.min_chunk_size, self.max_chunk_size)
|
||||
if chunk_size >= length:
|
||||
return 0, length
|
||||
start = random.randint(0, length - chunk_size)
|
||||
end = start + chunk_size
|
||||
|
||||
return start, end
|
||||
|
||||
def _select_by_speaker(self, index):
|
||||
if self.scp_reader is None or not self.utt_info:
|
||||
return []
|
||||
index = index % (len(self.utt_info))
|
||||
inputs = []
|
||||
labels = []
|
||||
item_size = random.randint(self.min_item_size, self.max_item_size)
|
||||
for loop_idx in range(item_size):
|
||||
try:
|
||||
utt_index = random.randint(0, len(self.utt_info[index][1])) \
|
||||
% len(self.utt_info[index][1])
|
||||
key = self.utt_info[index][1][utt_index]
|
||||
except:
|
||||
print(index, utt_index, len(self.utt_info[index][1]))
|
||||
sys.exit(-1)
|
||||
x = self.scp_reader[key]
|
||||
x = np.transpose(x)
|
||||
bg, end = self._random_chunk(x.shape[-1])
|
||||
inputs.append(x[:, bg: end])
|
||||
labels.append(self.utt_info[index][0])
|
||||
return inputs, labels
|
||||
|
||||
def _select_by_utt(self, index):
|
||||
if self.scp_reader is None or len(self.utt_info) == 0:
|
||||
return {}
|
||||
index = index % (len(self.utt_info))
|
||||
key = self.utt_info[index][0]
|
||||
x = self.scp_reader[key]
|
||||
x = np.transpose(x)
|
||||
bg, end = self._random_chunk(x.shape[-1])
|
||||
|
||||
y = self.utt_info[index][1]
|
||||
|
||||
return [x[:, bg: end]], [y]
|
||||
|
||||
def __getitem__(self, index):
|
||||
if self._is_select_by_speaker:
|
||||
return self._select_by_speaker(index)
|
||||
else:
|
||||
return self._select_by_utt(index)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.utt_info) * self.repeat
|
||||
|
||||
def __iter__(self):
|
||||
self._start = 0
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
if self._start < len(self):
|
||||
ret = self[self._start]
|
||||
self._start += 1
|
||||
return ret
|
||||
else:
|
||||
raise StopIteration
|
||||
@ -0,0 +1,143 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import sys
|
||||
import random
|
||||
import numpy as np
|
||||
import kaldi_python_io as k_io
|
||||
from paddle.io import Dataset
|
||||
from paddlespeech.vector.utils.data_utils import batch_pad_right
|
||||
import paddlespeech.vector.utils as utils
|
||||
from paddlespeech.vector.utils.utils import read_map_file
|
||||
|
||||
def ark_collate_fn(batch):
|
||||
"""
|
||||
Custom collate function for kaldi feats dataset
|
||||
|
||||
Args:
|
||||
min_chunk_size: min chunk size of a utterance
|
||||
max_chunk_size: max chunk size of a utterance
|
||||
|
||||
Returns:
|
||||
ark_collate_fn: collate funtion for dataloader
|
||||
"""
|
||||
|
||||
data = []
|
||||
target = []
|
||||
for items in batch:
|
||||
for x, y in zip(items[0], items[1]):
|
||||
data.append(np.array(x))
|
||||
target.append(y)
|
||||
|
||||
data, lengths = batch_pad_right(data)
|
||||
return np.array(data, dtype=np.float32), \
|
||||
np.array(lengths, dtype=np.float32), \
|
||||
np.array(target, dtype=np.long).reshape((len(target), 1))
|
||||
|
||||
|
||||
class KaldiArkDataset(Dataset):
|
||||
"""
|
||||
Dataset used to load kaldi ark/scp files.
|
||||
"""
|
||||
def __init__(self, scp_file, label2utt, min_item_size=1,
|
||||
max_item_size=1, repeat=50, min_chunk_size=200,
|
||||
max_chunk_size=400, select_by_speaker=True):
|
||||
self.scp_file = scp_file
|
||||
self.scp_reader = None
|
||||
self.repeat = repeat
|
||||
self.min_item_size = min_item_size
|
||||
self.max_item_size = max_item_size
|
||||
self.min_chunk_size = min_chunk_size
|
||||
self.max_chunk_size = max_chunk_size
|
||||
self._collate_fn = ark_collate_fn
|
||||
self._is_select_by_speaker = select_by_speaker
|
||||
if utils.is_exist(self.scp_file):
|
||||
self.scp_reader = k_io.ScriptReader(self.scp_file)
|
||||
|
||||
label2utts, utt2label = read_map_file(label2utt, key_func=int)
|
||||
self.utt_info = list(label2utts.items()) if self._is_select_by_speaker else list(utt2label.items())
|
||||
|
||||
@property
|
||||
def collate_fn(self):
|
||||
"""
|
||||
Return a collate funtion.
|
||||
"""
|
||||
return self._collate_fn
|
||||
|
||||
def _random_chunk(self, length):
|
||||
chunk_size = random.randint(self.min_chunk_size, self.max_chunk_size)
|
||||
if chunk_size >= length:
|
||||
return 0, length
|
||||
start = random.randint(0, length - chunk_size)
|
||||
end = start + chunk_size
|
||||
|
||||
return start, end
|
||||
|
||||
def _select_by_speaker(self, index):
|
||||
if self.scp_reader is None or not self.utt_info:
|
||||
return []
|
||||
index = index % (len(self.utt_info))
|
||||
inputs = []
|
||||
labels = []
|
||||
item_size = random.randint(self.min_item_size, self.max_item_size)
|
||||
for loop_idx in range(item_size):
|
||||
try:
|
||||
utt_index = random.randint(0, len(self.utt_info[index][1])) \
|
||||
% len(self.utt_info[index][1])
|
||||
key = self.utt_info[index][1][utt_index]
|
||||
except:
|
||||
print(index, utt_index, len(self.utt_info[index][1]))
|
||||
sys.exit(-1)
|
||||
x = self.scp_reader[key]
|
||||
x = np.transpose(x)
|
||||
bg, end = self._random_chunk(x.shape[-1])
|
||||
inputs.append(x[:, bg: end])
|
||||
labels.append(self.utt_info[index][0])
|
||||
return inputs, labels
|
||||
|
||||
def _select_by_utt(self, index):
|
||||
if self.scp_reader is None or len(self.utt_info) == 0:
|
||||
return {}
|
||||
index = index % (len(self.utt_info))
|
||||
key = self.utt_info[index][0]
|
||||
x = self.scp_reader[key]
|
||||
x = np.transpose(x)
|
||||
bg, end = self._random_chunk(x.shape[-1])
|
||||
|
||||
y = self.utt_info[index][1]
|
||||
|
||||
return [x[:, bg: end]], [y]
|
||||
|
||||
def __getitem__(self, index):
|
||||
if self._is_select_by_speaker:
|
||||
return self._select_by_speaker(index)
|
||||
else:
|
||||
return self._select_by_utt(index)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.utt_info) * self.repeat
|
||||
|
||||
def __iter__(self):
|
||||
self._start = 0
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
if self._start < len(self):
|
||||
ret = self[self._start]
|
||||
self._start += 1
|
||||
return ret
|
||||
else:
|
||||
raise StopIteration
|
||||
|
||||
return KaldiArkDataset
|
||||
@ -0,0 +1,91 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
Load nnet3 training egs which generated by kaldi
|
||||
"""
|
||||
|
||||
import random
|
||||
import numpy as np
|
||||
import kaldi_python_io as k_io
|
||||
from paddle.io import Dataset
|
||||
import paddlespeech.vector.utils.utils as utils
|
||||
from paddlespeech.vector import _logger as log
|
||||
class KaldiEgsDataset(Dataset):
|
||||
"""
|
||||
Dataset used to load kaldi nnet3 egs files.
|
||||
"""
|
||||
def __init__(self, egs_list_file, egs_idx, transforms=None):
|
||||
self.scp_reader = None
|
||||
self.subset_idx = egs_idx - 1
|
||||
self.transforms = transforms
|
||||
if not utils.is_exist(egs_list_file):
|
||||
return
|
||||
|
||||
self.egs_files = []
|
||||
with open(egs_list_file, 'r') as in_fh:
|
||||
for line in in_fh:
|
||||
if line.strip():
|
||||
self.egs_files.append(line.strip())
|
||||
|
||||
self.next_subset()
|
||||
|
||||
def next_subset(self, target_index=None, delta_index=None):
|
||||
"""
|
||||
Use next specific subset
|
||||
|
||||
Args:
|
||||
target_index: target egs index
|
||||
delta_index: incremental value of egs index
|
||||
"""
|
||||
if self.egs_files:
|
||||
if target_index:
|
||||
self.subset_idx = target_index
|
||||
else:
|
||||
delta_index = delta_index if delta_index else 1
|
||||
self.subset_idx += delta_index
|
||||
log.info("egs dataset subset index: %d" % (self.subset_idx))
|
||||
egs_file = self.egs_files[self.subset_idx % len(self.egs_files)]
|
||||
if utils.is_exist(egs_file):
|
||||
self.scp_reader = k_io.Nnet3EgsScriptReader(egs_file)
|
||||
else:
|
||||
log.warning("No such file or directory: %s" % (egs_file))
|
||||
|
||||
def __getitem__(self, index):
|
||||
if self.scp_reader is None:
|
||||
return {}
|
||||
index %= len(self)
|
||||
in_dict, out_dict = self.scp_reader[index]
|
||||
x = np.array(in_dict['matrix'])
|
||||
x = np.transpose(x)
|
||||
y = np.array(out_dict['matrix'][0][0][0], dtype=np.int).reshape((1,))
|
||||
if self.transforms is not None:
|
||||
idx = random.randint(0, len(self.transforms) - 1)
|
||||
x = self.transforms[idx](x)
|
||||
return x, y
|
||||
|
||||
def __len__(self):
|
||||
return len(self.scp_reader)
|
||||
|
||||
def __iter__(self):
|
||||
self._start = 0
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
if self._start < len(self):
|
||||
ret = self[self._start]
|
||||
self._start += 1
|
||||
return ret
|
||||
else:
|
||||
raise StopIteration
|
||||
@ -0,0 +1,125 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
data utilities
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import numpy
|
||||
import paddle
|
||||
|
||||
|
||||
def pad_right_to(array, target_shape, mode="constant", value=0):
|
||||
"""
|
||||
This function takes a numpy array of arbitrary shape and pads it to target
|
||||
shape by appending values on the right.
|
||||
|
||||
Args:
|
||||
array: input numpy array. Input array whose dimension we need to pad.
|
||||
target_shape : (list, tuple). Target shape we want for the target array its len must be equal to array.ndim
|
||||
mode : str. Pad mode, please refer to numpy.pad documentation.
|
||||
value : float. Pad value, please refer to numpy.pad documentation.
|
||||
|
||||
Returns:
|
||||
array: numpy.array. Padded array.
|
||||
valid_vals : list. List containing proportion for each dimension of original, non-padded values.
|
||||
"""
|
||||
assert len(target_shape) == array.ndim
|
||||
pads = [] # this contains the abs length of the padding for each dimension.
|
||||
valid_vals = [] # thic contains the relative lengths for each dimension.
|
||||
i = 0 # iterating over target_shape ndims
|
||||
while i < len(target_shape):
|
||||
assert (
|
||||
target_shape[i] >= array.shape[i]
|
||||
), "Target shape must be >= original shape for every dim"
|
||||
pads.append([0, target_shape[i] - array.shape[i]])
|
||||
valid_vals.append(array.shape[i] / target_shape[i])
|
||||
i += 1
|
||||
|
||||
array = numpy.pad(array, pads, mode=mode, constant_values=value)
|
||||
|
||||
return array, valid_vals
|
||||
|
||||
|
||||
def batch_pad_right(arrays, mode="constant", value=0):
|
||||
"""Given a list of numpy arrays it batches them together by padding to the right
|
||||
on each dimension in order to get same length for all.
|
||||
|
||||
Args:
|
||||
arrays : list. List of array we wish to pad together.
|
||||
mode : str. Padding mode see numpy.pad documentation.
|
||||
value : float. Padding value see numpy.pad documentation.
|
||||
|
||||
Returns:
|
||||
array : numpy.array. Padded array.
|
||||
valid_vals : list. List containing proportion for each dimension of original, non-padded values.
|
||||
"""
|
||||
|
||||
if not len(arrays):
|
||||
raise IndexError("arrays list must not be empty")
|
||||
|
||||
if len(arrays) == 1:
|
||||
# if there is only one array in the batch we simply unsqueeze it.
|
||||
return numpy.expand_dims(arrays[0], axis=0), numpy.array([1.0])
|
||||
|
||||
if not (
|
||||
any(
|
||||
[arrays[i].ndim == arrays[0].ndim for i in range(1, len(arrays))]
|
||||
)
|
||||
):
|
||||
raise IndexError("All arrays must have same number of dimensions")
|
||||
|
||||
# FIXME we limit the support here: we allow padding of only the last dimension
|
||||
# need to remove this when feat extraction is updated to handle multichannel.
|
||||
max_shape = []
|
||||
for dim in range(arrays[0].ndim):
|
||||
if dim != (arrays[0].ndim - 1):
|
||||
if not all(
|
||||
[x.shape[dim] == arrays[0].shape[dim] for x in arrays[1:]]
|
||||
):
|
||||
raise EnvironmentError(
|
||||
"arrays should have same dimensions except for last one"
|
||||
)
|
||||
max_shape.append(max([x.shape[dim] for x in arrays]))
|
||||
|
||||
batched = []
|
||||
valid = []
|
||||
for t in arrays:
|
||||
# for each array we apply pad_right_to
|
||||
padded, valid_percent = pad_right_to(
|
||||
t, max_shape, mode=mode, value=value
|
||||
)
|
||||
batched.append(padded)
|
||||
valid.append(valid_percent[-1])
|
||||
|
||||
batched = numpy.stack(batched)
|
||||
|
||||
return batched, numpy.array(valid)
|
||||
|
||||
|
||||
def length_to_mask(length, max_len=None, dtype=None):
|
||||
"""Creates a binary mask for each sequence.
|
||||
"""
|
||||
assert len(length.shape) == 1
|
||||
|
||||
if max_len is None:
|
||||
max_len = paddle.cast(paddle.max(length), dtype="int64") # using arange to generate mask
|
||||
mask = paddle.arange(max_len, dtype=length.dtype).expand([paddle.shape(length)[0], max_len]) < length.unsqueeze(1)
|
||||
|
||||
if dtype is None:
|
||||
dtype = length.dtype
|
||||
|
||||
mask = paddle.cast(mask, dtype=dtype)
|
||||
return mask
|
||||
@ -0,0 +1,132 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
utilities
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import paddle
|
||||
import numpy as np
|
||||
|
||||
from paddlespeech.vector import _logger as log
|
||||
|
||||
|
||||
def exit_if_not_exist(in_path):
|
||||
"""
|
||||
Check the existence of a file or directory, if not exit, exit the program.
|
||||
|
||||
Args:
|
||||
in_path: input dicrector
|
||||
"""
|
||||
if not is_exist(in_path):
|
||||
sys.exit(-1)
|
||||
|
||||
|
||||
def is_exist(in_path):
|
||||
"""
|
||||
Check the existence of a file or directory
|
||||
|
||||
Args:
|
||||
in_path: input dicrector
|
||||
|
||||
Returns:
|
||||
True or False
|
||||
"""
|
||||
if not os.path.exists(in_path):
|
||||
log.error("No such file or directory: %s" % (in_path))
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def get_latest_file(target_dir):
|
||||
"""
|
||||
Get the latest file in target directory
|
||||
|
||||
Args:
|
||||
target_dir: target directory
|
||||
|
||||
Returns:
|
||||
latest_file: a string or None
|
||||
"""
|
||||
items = os.listdir(target_dir)
|
||||
items.sort(key=lambda fn: os.path.getmtime(os.path.join(target_dir, fn)) \
|
||||
if not os.path.isdir(os.path.join(target_dir, fn)) else 0)
|
||||
latest_file = None if not items else os.path.join(target_dir, items[-1])
|
||||
return latest_file
|
||||
|
||||
|
||||
def avg_models(models):
|
||||
"""
|
||||
merge multiple models
|
||||
"""
|
||||
checkpoint_dict = paddle.load(models[0])
|
||||
final_state_dict = checkpoint_dict
|
||||
|
||||
if len(models) > 1:
|
||||
for model in models[1:]:
|
||||
checkpoint_dict = paddle.load(model)
|
||||
for k, v in checkpoint_dict.items():
|
||||
final_state_dict[k] += v
|
||||
for k in final_state_dict.keys():
|
||||
final_state_dict[k] /= float(len(models))
|
||||
if np.any(np.isnan(final_state_dict[k])):
|
||||
print("Nan in %s" % (k))
|
||||
|
||||
return final_state_dict
|
||||
|
||||
def Q_from_tokens(token_num):
|
||||
"""
|
||||
get prior model, data from uniform, would support others(guassian) in future
|
||||
"""
|
||||
freq = [1] * token_num
|
||||
Q = paddle.to_tensor(freq, dtype = 'float64')
|
||||
return Q / Q.sum()
|
||||
|
||||
|
||||
def read_map_file(map_file, key_func=None, value_func=None, values_func=None):
|
||||
""" Read map file. First colume is key, the rest columes are values.
|
||||
|
||||
Args:
|
||||
map_file: map file
|
||||
key_func: convert function for key
|
||||
value_func: convert function for each value
|
||||
values_func: convert function for values
|
||||
|
||||
Returns:
|
||||
dict: key 2 value
|
||||
dict: value 2 key
|
||||
"""
|
||||
if not is_exist(map_file):
|
||||
sys.exit(0)
|
||||
|
||||
key2val = {}
|
||||
val2key = {}
|
||||
with open(map_file, 'r') as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
items = line.split()
|
||||
assert len(items) >= 2
|
||||
key = items[0] if not key_func else key_func(items[0])
|
||||
values = items[1:] if not value_func else [value_func(item) for item in items[1:]]
|
||||
if values_func:
|
||||
values = values_func(values)
|
||||
key2val[key] = values
|
||||
for value in values:
|
||||
val2key[value] = key
|
||||
|
||||
return key2val, val2key
|
||||
@ -0,0 +1,105 @@
|
||||
#!/usr/bin/python
|
||||
import argparse
|
||||
import os
|
||||
|
||||
import yaml
|
||||
|
||||
|
||||
def change_device(yamlfile: str, engine: str, device: str):
|
||||
"""Change the settings of the device under the voice task configuration file
|
||||
|
||||
Args:
|
||||
yaml_name (str): asr or asr_pd or tts or tts_pd
|
||||
cpu (bool): True means set device to "cpu"
|
||||
model_type (dict): change model type
|
||||
"""
|
||||
tmp_yamlfile = yamlfile.split(".yaml")[0] + "_tmp.yaml"
|
||||
os.system("cp %s %s" % (yamlfile, tmp_yamlfile))
|
||||
|
||||
if device == 'cpu':
|
||||
set_device = 'cpu'
|
||||
elif device == 'gpu':
|
||||
set_device = 'gpu:0'
|
||||
else:
|
||||
print("Please set correct device: cpu or gpu.")
|
||||
|
||||
with open(tmp_yamlfile) as f, open(yamlfile, "w+", encoding="utf-8") as fw:
|
||||
y = yaml.safe_load(f)
|
||||
if engine == 'asr_python' or engine == 'tts_python':
|
||||
y[engine]['device'] = set_device
|
||||
elif engine == 'asr_inference':
|
||||
y[engine]['am_predictor_conf']['device'] = set_device
|
||||
elif engine == 'tts_inference':
|
||||
y[engine]['am_predictor_conf']['device'] = set_device
|
||||
y[engine]['voc_predictor_conf']['device'] = set_device
|
||||
else:
|
||||
print(
|
||||
"Please set correct engine: asr_python, tts_python, asr_inference, tts_inference."
|
||||
)
|
||||
|
||||
print(yaml.dump(y, default_flow_style=False, sort_keys=False))
|
||||
yaml.dump(y, fw, allow_unicode=True)
|
||||
os.system("rm %s" % (tmp_yamlfile))
|
||||
print("Change %s successfully." % (yamlfile))
|
||||
|
||||
|
||||
def change_engine_type(yamlfile: str, engine_type):
|
||||
"""Change the engine type and corresponding configuration file of the speech task in application.yaml
|
||||
|
||||
Args:
|
||||
task (str): asr or tts
|
||||
"""
|
||||
tmp_yamlfile = yamlfile.split(".yaml")[0] + "_tmp.yaml"
|
||||
os.system("cp %s %s" % (yamlfile, tmp_yamlfile))
|
||||
speech_task = engine_type.split("_")[0]
|
||||
|
||||
with open(tmp_yamlfile) as f, open(yamlfile, "w+", encoding="utf-8") as fw:
|
||||
y = yaml.safe_load(f)
|
||||
engine_list = y['engine_list']
|
||||
for engine in engine_list:
|
||||
if speech_task in engine:
|
||||
engine_list.remove(engine)
|
||||
engine_list.append(engine_type)
|
||||
y['engine_list'] = engine_list
|
||||
print(yaml.dump(y, default_flow_style=False, sort_keys=False))
|
||||
yaml.dump(y, fw, allow_unicode=True)
|
||||
os.system("rm %s" % (tmp_yamlfile))
|
||||
print("Change %s successfully." % (yamlfile))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
'--config_file',
|
||||
type=str,
|
||||
default='./conf/application.yaml',
|
||||
help='server yaml file.')
|
||||
parser.add_argument(
|
||||
'--change_task',
|
||||
type=str,
|
||||
default=None,
|
||||
help='Change task',
|
||||
choices=[
|
||||
'enginetype-asr_python',
|
||||
'enginetype-asr_inference',
|
||||
'enginetype-tts_python',
|
||||
'enginetype-tts_inference',
|
||||
'device-asr_python-cpu',
|
||||
'device-asr_python-gpu',
|
||||
'device-asr_inference-cpu',
|
||||
'device-asr_inference-gpu',
|
||||
'device-tts_python-cpu',
|
||||
'device-tts_python-gpu',
|
||||
'device-tts_inference-cpu',
|
||||
'device-tts_inference-gpu',
|
||||
],
|
||||
required=True)
|
||||
args = parser.parse_args()
|
||||
|
||||
types = args.change_task.split("-")
|
||||
if types[0] == "enginetype":
|
||||
change_engine_type(args.config_file, types[1])
|
||||
elif types[0] == "device":
|
||||
change_device(args.config_file, types[1], types[2])
|
||||
else:
|
||||
print("Error change task, please check change_task.")
|
||||
@ -0,0 +1,107 @@
|
||||
# This is the parameter configuration file for PaddleSpeech Serving.
|
||||
|
||||
#################################################################################
|
||||
# SERVER SETTING #
|
||||
#################################################################################
|
||||
host: 127.0.0.1
|
||||
port: 8090
|
||||
|
||||
# The task format in the engin_list is: <speech task>_<engine type>
|
||||
# task choices = ['asr_python', 'asr_inference', 'tts_python', 'tts_inference']
|
||||
|
||||
engine_list: ['asr_python', 'tts_python']
|
||||
|
||||
|
||||
#################################################################################
|
||||
# ENGINE CONFIG #
|
||||
#################################################################################
|
||||
################### speech task: asr; engine_type: python #######################
|
||||
asr_python:
|
||||
model: 'conformer_wenetspeech'
|
||||
lang: 'zh'
|
||||
sample_rate: 16000
|
||||
cfg_path: # [optional]
|
||||
ckpt_path: # [optional]
|
||||
decode_method: 'attention_rescoring'
|
||||
force_yes: True
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
|
||||
|
||||
################### speech task: asr; engine_type: inference #######################
|
||||
asr_inference:
|
||||
# model_type choices=['deepspeech2offline_aishell']
|
||||
model_type: 'deepspeech2offline_aishell'
|
||||
am_model: # the pdmodel file of am static model [optional]
|
||||
am_params: # the pdiparams file of am static model [optional]
|
||||
lang: 'zh'
|
||||
sample_rate: 16000
|
||||
cfg_path:
|
||||
decode_method:
|
||||
force_yes: True
|
||||
|
||||
am_predictor_conf:
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
switch_ir_optim: True
|
||||
glog_info: False # True -> print glog
|
||||
summary: True # False -> do not show predictor config
|
||||
|
||||
|
||||
################### speech task: tts; engine_type: python #######################
|
||||
tts_python:
|
||||
# am (acoustic model) choices=['speedyspeech_csmsc', 'fastspeech2_csmsc',
|
||||
# 'fastspeech2_ljspeech', 'fastspeech2_aishell3',
|
||||
# 'fastspeech2_vctk']
|
||||
am: 'fastspeech2_csmsc'
|
||||
am_config:
|
||||
am_ckpt:
|
||||
am_stat:
|
||||
phones_dict:
|
||||
tones_dict:
|
||||
speaker_dict:
|
||||
spk_id: 0
|
||||
|
||||
# voc (vocoder) choices=['pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3',
|
||||
# 'pwgan_vctk', 'mb_melgan_csmsc']
|
||||
voc: 'pwgan_csmsc'
|
||||
voc_config:
|
||||
voc_ckpt:
|
||||
voc_stat:
|
||||
|
||||
# others
|
||||
lang: 'zh'
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
|
||||
|
||||
################### speech task: tts; engine_type: inference #######################
|
||||
tts_inference:
|
||||
# am (acoustic model) choices=['speedyspeech_csmsc', 'fastspeech2_csmsc']
|
||||
am: 'fastspeech2_csmsc'
|
||||
am_model: # the pdmodel file of your am static model (XX.pdmodel)
|
||||
am_params: # the pdiparams file of your am static model (XX.pdipparams)
|
||||
am_sample_rate: 24000
|
||||
phones_dict:
|
||||
tones_dict:
|
||||
speaker_dict:
|
||||
spk_id: 0
|
||||
|
||||
am_predictor_conf:
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
switch_ir_optim: True
|
||||
glog_info: False # True -> print glog
|
||||
summary: True # False -> do not show predictor config
|
||||
|
||||
# voc (vocoder) choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc']
|
||||
voc: 'pwgan_csmsc'
|
||||
voc_model: # the pdmodel file of your vocoder static model (XX.pdmodel)
|
||||
voc_params: # the pdiparams file of your vocoder static model (XX.pdipparams)
|
||||
voc_sample_rate: 24000
|
||||
|
||||
voc_predictor_conf:
|
||||
device: # set 'gpu:id' or 'cpu'
|
||||
switch_ir_optim: True
|
||||
glog_info: False # True -> print glog
|
||||
summary: True # False -> do not show predictor config
|
||||
|
||||
# others
|
||||
lang: 'zh'
|
||||
|
||||
@ -0,0 +1,186 @@
|
||||
#!/bin/bash
|
||||
# bash test_server_client.sh
|
||||
|
||||
StartService(){
|
||||
# Start service
|
||||
paddlespeech_server start --config_file $config_file 1>>log/server.log 2>>log/server.log.wf &
|
||||
echo $! > pid
|
||||
|
||||
start_num=$(cat log/server.log.wf | grep "INFO: Uvicorn running on http://" -c)
|
||||
flag="normal"
|
||||
while [[ $start_num -lt $target_start_num && $flag == "normal" ]]
|
||||
do
|
||||
start_num=$(cat log/server.log.wf | grep "INFO: Uvicorn running on http://" -c)
|
||||
# start service failed
|
||||
if [ $(cat log/server.log.wf | grep -i "error" -c) -gt $error_time ];then
|
||||
echo "Service started failed." | tee -a ./log/test_result.log
|
||||
error_time=$(cat log/server.log.wf | grep -i "error" -c)
|
||||
flag="unnormal"
|
||||
fi
|
||||
done
|
||||
}
|
||||
|
||||
ClientTest(){
|
||||
# Client test
|
||||
# test asr client
|
||||
paddlespeech_client asr --server_ip $server_ip --port $port --input ./zh.wav
|
||||
((test_times+=1))
|
||||
paddlespeech_client asr --server_ip $server_ip --port $port --input ./zh.wav
|
||||
((test_times+=1))
|
||||
|
||||
# test tts client
|
||||
paddlespeech_client tts --server_ip $server_ip --port $port --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav
|
||||
((test_times+=1))
|
||||
paddlespeech_client tts --server_ip $server_ip --port $port --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav
|
||||
((test_times+=1))
|
||||
}
|
||||
|
||||
GetTestResult() {
|
||||
# Determine if the test was successful
|
||||
response_success_time=$(cat log/server.log | grep "200 OK" -c)
|
||||
if (( $response_success_time == $test_times )) ; then
|
||||
echo "Testing successfully. The service configuration is: asr engine type: $1; tts engine type: $1; device: $2." | tee -a ./log/test_result.log
|
||||
else
|
||||
echo "Testing failed. The service configuration is: asr engine type: $1; tts engine type: $1; device: $2." | tee -a ./log/test_result.log
|
||||
fi
|
||||
test_times=$response_success_time
|
||||
}
|
||||
|
||||
|
||||
mkdir -p log
|
||||
rm -rf log/server.log.wf
|
||||
rm -rf log/server.log
|
||||
rm -rf log/test_result.log
|
||||
|
||||
config_file=./conf/application.yaml
|
||||
server_ip=$(cat $config_file | grep "host" | awk -F " " '{print $2}')
|
||||
port=$(cat $config_file | grep "port" | awk '/port:/ {print $2}')
|
||||
|
||||
echo "Sevice ip: $server_ip" | tee ./log/test_result.log
|
||||
echo "Sevice port: $port" | tee -a ./log/test_result.log
|
||||
|
||||
# whether a process is listening on $port
|
||||
pid=`lsof -i :"$port"|grep -v "PID" | awk '{print $2}'`
|
||||
if [ "$pid" != "" ]; then
|
||||
echo "The port: $port is occupied, please change another port"
|
||||
exit
|
||||
fi
|
||||
|
||||
# download test audios for ASR client
|
||||
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
|
||||
|
||||
|
||||
target_start_num=0 # the number of start service
|
||||
test_times=0 # The number of client test
|
||||
error_time=0 # The number of error occurrences in the startup failure server.log.wf file
|
||||
|
||||
# start server: asr engine type: python; tts engine type: python; device: gpu
|
||||
echo "Start the service: asr engine type: python; tts engine type: python; device: gpu" | tee -a ./log/test_result.log
|
||||
((target_start_num+=1))
|
||||
StartService
|
||||
|
||||
if [[ $start_num -eq $target_start_num && $flag == "normal" ]]; then
|
||||
echo "Service started successfully." | tee -a ./log/test_result.log
|
||||
ClientTest
|
||||
echo "This round of testing is over." | tee -a ./log/test_result.log
|
||||
|
||||
GetTestResult python gpu
|
||||
else
|
||||
echo "Service failed to start, no client test."
|
||||
target_start_num=$start_num
|
||||
|
||||
fi
|
||||
|
||||
kill -9 `cat pid`
|
||||
rm -rf pid
|
||||
sleep 2s
|
||||
echo "**************************************************************************************" | tee -a ./log/test_result.log
|
||||
|
||||
|
||||
|
||||
# start server: asr engine type: python; tts engine type: python; device: cpu
|
||||
python change_yaml.py --change_task device-asr_python-cpu # change asr.yaml device: cpu
|
||||
python change_yaml.py --change_task device-tts_python-cpu # change tts.yaml device: cpu
|
||||
|
||||
echo "Start the service: asr engine type: python; tts engine type: python; device: cpu" | tee -a ./log/test_result.log
|
||||
((target_start_num+=1))
|
||||
StartService
|
||||
|
||||
if [[ $start_num -eq $target_start_num && $flag == "normal" ]]; then
|
||||
echo "Service started successfully." | tee -a ./log/test_result.log
|
||||
ClientTest
|
||||
echo "This round of testing is over." | tee -a ./log/test_result.log
|
||||
|
||||
GetTestResult python cpu
|
||||
else
|
||||
echo "Service failed to start, no client test."
|
||||
target_start_num=$start_num
|
||||
|
||||
fi
|
||||
|
||||
kill -9 `cat pid`
|
||||
rm -rf pid
|
||||
sleep 2s
|
||||
echo "**************************************************************************************" | tee -a ./log/test_result.log
|
||||
|
||||
|
||||
# start server: asr engine type: inference; tts engine type: inference; device: gpu
|
||||
python change_yaml.py --change_task enginetype-asr_inference # change application.yaml, asr engine_type: inference; asr engine_backend: asr_pd.yaml
|
||||
python change_yaml.py --change_task enginetype-tts_inference # change application.yaml, tts engine_type: inference; tts engine_backend: tts_pd.yaml
|
||||
|
||||
echo "Start the service: asr engine type: inference; tts engine type: inference; device: gpu" | tee -a ./log/test_result.log
|
||||
((target_start_num+=1))
|
||||
StartService
|
||||
|
||||
if [[ $start_num -eq $target_start_num && $flag == "normal" ]]; then
|
||||
echo "Service started successfully." | tee -a ./log/test_result.log
|
||||
ClientTest
|
||||
echo "This round of testing is over." | tee -a ./log/test_result.log
|
||||
|
||||
GetTestResult inference gpu
|
||||
else
|
||||
echo "Service failed to start, no client test."
|
||||
target_start_num=$start_num
|
||||
|
||||
fi
|
||||
|
||||
kill -9 `cat pid`
|
||||
rm -rf pid
|
||||
sleep 2s
|
||||
echo "**************************************************************************************" | tee -a ./log/test_result.log
|
||||
|
||||
|
||||
# start server: asr engine type: inference; tts engine type: inference; device: cpu
|
||||
python change_yaml.py --change_task device-asr_inference-cpu # change asr_pd.yaml device: cpu
|
||||
python change_yaml.py --change_task device-tts_inference-cpu # change tts_pd.yaml device: cpu
|
||||
|
||||
echo "start the service: asr engine type: inference; tts engine type: inference; device: cpu" | tee -a ./log/test_result.log
|
||||
((target_start_num+=1))
|
||||
StartService
|
||||
|
||||
if [[ $start_num -eq $target_start_num && $flag == "normal" ]]; then
|
||||
echo "Service started successfully." | tee -a ./log/test_result.log
|
||||
ClientTest
|
||||
echo "This round of testing is over." | tee -a ./log/test_result.log
|
||||
|
||||
GetTestResult inference cpu
|
||||
else
|
||||
echo "Service failed to start, no client test."
|
||||
target_start_num=$start_num
|
||||
|
||||
fi
|
||||
|
||||
kill -9 `cat pid`
|
||||
rm -rf pid
|
||||
sleep 2s
|
||||
echo "**************************************************************************************" | tee -a ./log/test_result.log
|
||||
|
||||
echo "All tests completed." | tee -a ./log/test_result.log
|
||||
|
||||
# sohw all the test results
|
||||
echo "***************** Here are all the test results ********************"
|
||||
cat ./log/test_result.log
|
||||
|
||||
# Restoring conf is the same as demos/speech_server
|
||||
rm -rf ./conf
|
||||
cp ../../../demos/speech_server/conf/ ./ -rf
|
||||
Loading…
Reference in new issue