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@ -49,7 +49,7 @@ class PaddleASRConnectionHanddler:
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asr_engine (ASREngine): the global asr engine
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"""
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super().__init__()
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logger.info(
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logger.debug(
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"create an paddle asr connection handler to process the websocket connection"
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)
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self.config = asr_engine.config # server config
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@ -107,7 +107,7 @@ class PaddleASRConnectionHanddler:
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# acoustic model
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self.model = self.asr_engine.executor.model
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self.continuous_decoding = self.config.continuous_decoding
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logger.info(f"continue decoding: {self.continuous_decoding}")
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logger.debug(f"continue decoding: {self.continuous_decoding}")
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# ctc decoding config
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self.ctc_decode_config = self.asr_engine.executor.config.decode
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@ -207,7 +207,7 @@ class PaddleASRConnectionHanddler:
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assert samples.ndim == 1
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self.num_samples += samples.shape[0]
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logger.info(
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logger.debug(
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f"This package receive {samples.shape[0]} pcm data. Global samples:{self.num_samples}"
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)
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@ -218,7 +218,7 @@ class PaddleASRConnectionHanddler:
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else:
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assert self.remained_wav.ndim == 1 # (T,)
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self.remained_wav = np.concatenate([self.remained_wav, samples])
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logger.info(
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logger.debug(
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f"The concatenation of remain and now audio samples length is: {self.remained_wav.shape}"
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)
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@ -252,14 +252,14 @@ class PaddleASRConnectionHanddler:
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# update remained wav
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self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
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logger.info(
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logger.debug(
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f"process the audio feature success, the cached feat shape: {self.cached_feat.shape}"
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)
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logger.info(
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logger.debug(
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f"After extract feat, the cached remain the audio samples: {self.remained_wav.shape}"
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)
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logger.info(f"global samples: {self.num_samples}")
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logger.info(f"global frames: {self.num_frames}")
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logger.debug(f"global samples: {self.num_samples}")
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logger.debug(f"global frames: {self.num_frames}")
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def decode(self, is_finished=False):
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"""advance decoding
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@ -283,24 +283,24 @@ class PaddleASRConnectionHanddler:
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stride = subsampling * decoding_chunk_size
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if self.cached_feat is None:
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logger.info("no audio feat, please input more pcm data")
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logger.debug("no audio feat, please input more pcm data")
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return
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num_frames = self.cached_feat.shape[1]
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logger.info(
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logger.debug(
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f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
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)
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# the cached feat must be larger decoding_window
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if num_frames < decoding_window and not is_finished:
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logger.info(
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logger.debug(
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f"frame feat num is less than {decoding_window}, please input more pcm data"
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)
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return None, None
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# if is_finished=True, we need at least context frames
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if num_frames < context:
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logger.info(
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logger.debug(
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"flast {num_frames} is less than context {context} frames, and we cannot do model forward"
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)
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return None, None
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@ -354,7 +354,7 @@ class PaddleASRConnectionHanddler:
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Returns:
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logprob: poster probability.
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"""
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logger.info("start to decoce one chunk for deepspeech2")
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logger.debug("start to decoce one chunk for deepspeech2")
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input_names = self.am_predictor.get_input_names()
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audio_handle = self.am_predictor.get_input_handle(input_names[0])
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audio_len_handle = self.am_predictor.get_input_handle(input_names[1])
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@ -391,7 +391,7 @@ class PaddleASRConnectionHanddler:
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self.decoder.next(output_chunk_probs, output_chunk_lens)
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trans_best, trans_beam = self.decoder.decode()
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logger.info(f"decode one best result for deepspeech2: {trans_best[0]}")
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logger.debug(f"decode one best result for deepspeech2: {trans_best[0]}")
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return trans_best[0]
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@paddle.no_grad()
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@ -402,7 +402,7 @@ class PaddleASRConnectionHanddler:
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# reset endpiont state
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self.endpoint_state = False
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logger.info(
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logger.debug(
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"Conformer/Transformer: start to decode with advanced_decoding method"
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)
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cfg = self.ctc_decode_config
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@ -427,25 +427,25 @@ class PaddleASRConnectionHanddler:
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stride = subsampling * decoding_chunk_size
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if self.cached_feat is None:
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logger.info("no audio feat, please input more pcm data")
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logger.debug("no audio feat, please input more pcm data")
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return
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# (B=1,T,D)
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num_frames = self.cached_feat.shape[1]
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logger.info(
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logger.debug(
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f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
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)
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# the cached feat must be larger decoding_window
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if num_frames < decoding_window and not is_finished:
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logger.info(
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logger.debug(
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f"frame feat num is less than {decoding_window}, please input more pcm data"
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)
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return None, None
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# if is_finished=True, we need at least context frames
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if num_frames < context:
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logger.info(
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logger.debug(
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"flast {num_frames} is less than context {context} frames, and we cannot do model forward"
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)
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return None, None
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@ -489,7 +489,7 @@ class PaddleASRConnectionHanddler:
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self.encoder_out = ys
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else:
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self.encoder_out = paddle.concat([self.encoder_out, ys], axis=1)
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logger.info(
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logger.debug(
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f"This connection handler encoder out shape: {self.encoder_out.shape}"
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)
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@ -513,7 +513,8 @@ class PaddleASRConnectionHanddler:
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if self.endpointer.endpoint_detected(ctc_probs.numpy(),
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decoding_something):
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self.endpoint_state = True
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logger.info(f"Endpoint is detected at {self.num_frames} frame.")
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logger.debug(
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f"Endpoint is detected at {self.num_frames} frame.")
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# advance cache of feat
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assert self.cached_feat.shape[0] == 1 #(B=1,T,D)
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@ -526,7 +527,7 @@ class PaddleASRConnectionHanddler:
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def update_result(self):
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"""Conformer/Transformer hyps to result.
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"""
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logger.info("update the final result")
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logger.debug("update the final result")
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hyps = self.hyps
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# output results and tokenids
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@ -560,16 +561,16 @@ class PaddleASRConnectionHanddler:
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only for conformer and transformer model.
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"""
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if "deepspeech2" in self.model_type:
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logger.info("deepspeech2 not support rescoring decoding.")
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logger.debug("deepspeech2 not support rescoring decoding.")
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return
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if "attention_rescoring" != self.ctc_decode_config.decoding_method:
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logger.info(
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logger.debug(
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f"decoding method not match: {self.ctc_decode_config.decoding_method}, need attention_rescoring"
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)
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return
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logger.info("rescoring the final result")
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logger.debug("rescoring the final result")
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# last decoding for last audio
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self.searcher.finalize_search()
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@ -685,7 +686,6 @@ class PaddleASRConnectionHanddler:
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"bg": global_offset_in_sec + start,
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"ed": global_offset_in_sec + end
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})
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# logger.info(f"{word_time_stamp[-1]}")
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self.word_time_stamp = word_time_stamp
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logger.info(f"word time stamp: {self.word_time_stamp}")
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@ -707,13 +707,13 @@ class ASRServerExecutor(ASRExecutor):
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lm_url = self.task_resource.res_dict['lm_url']
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lm_md5 = self.task_resource.res_dict['lm_md5']
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logger.info(f"Start to load language model {lm_url}")
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logger.debug(f"Start to load language model {lm_url}")
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self.download_lm(
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lm_url,
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os.path.dirname(self.config.decode.lang_model_path), lm_md5)
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elif "conformer" in self.model_type or "transformer" in self.model_type:
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with UpdateConfig(self.config):
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logger.info("start to create the stream conformer asr engine")
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logger.debug("start to create the stream conformer asr engine")
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# update the decoding method
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if self.decode_method:
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self.config.decode.decoding_method = self.decode_method
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@ -726,7 +726,7 @@ class ASRServerExecutor(ASRExecutor):
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if self.config.decode.decoding_method not in [
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"ctc_prefix_beam_search", "attention_rescoring"
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]:
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logger.info(
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logger.debug(
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"we set the decoding_method to attention_rescoring")
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self.config.decode.decoding_method = "attention_rescoring"
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@ -739,7 +739,7 @@ class ASRServerExecutor(ASRExecutor):
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def init_model(self) -> None:
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if "deepspeech2" in self.model_type:
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# AM predictor
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logger.info("ASR engine start to init the am predictor")
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logger.debug("ASR engine start to init the am predictor")
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self.am_predictor = init_predictor(
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model_file=self.am_model,
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params_file=self.am_params,
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@ -748,7 +748,7 @@ class ASRServerExecutor(ASRExecutor):
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# load model
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# model_type: {model_name}_{dataset}
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model_name = self.model_type[:self.model_type.rindex('_')]
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logger.info(f"model name: {model_name}")
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logger.debug(f"model name: {model_name}")
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model_class = self.task_resource.get_model_class(model_name)
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model = model_class.from_config(self.config)
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self.model = model
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@ -782,7 +782,7 @@ class ASRServerExecutor(ASRExecutor):
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self.num_decoding_left_chunks = num_decoding_left_chunks
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# conf for paddleinference predictor or onnx
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self.am_predictor_conf = am_predictor_conf
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logger.info(f"model_type: {self.model_type}")
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logger.debug(f"model_type: {self.model_type}")
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sample_rate_str = '16k' if sample_rate == 16000 else '8k'
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tag = model_type + '-' + lang + '-' + sample_rate_str
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@ -804,12 +804,12 @@ class ASRServerExecutor(ASRExecutor):
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self.res_path = os.path.dirname(
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os.path.dirname(os.path.abspath(self.cfg_path)))
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logger.info("Load the pretrained model:")
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logger.info(f" tag = {tag}")
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logger.info(f" res_path: {self.res_path}")
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logger.info(f" cfg path: {self.cfg_path}")
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logger.info(f" am_model path: {self.am_model}")
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logger.info(f" am_params path: {self.am_params}")
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logger.debug("Load the pretrained model:")
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logger.debug(f" tag = {tag}")
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logger.debug(f" res_path: {self.res_path}")
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logger.debug(f" cfg path: {self.cfg_path}")
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logger.debug(f" am_model path: {self.am_model}")
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logger.debug(f" am_params path: {self.am_params}")
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#Init body.
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self.config = CfgNode(new_allowed=True)
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@ -818,7 +818,7 @@ class ASRServerExecutor(ASRExecutor):
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if self.config.spm_model_prefix:
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self.config.spm_model_prefix = os.path.join(
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self.res_path, self.config.spm_model_prefix)
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logger.info(f"spm model path: {self.config.spm_model_prefix}")
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logger.debug(f"spm model path: {self.config.spm_model_prefix}")
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self.vocab = self.config.vocab_filepath
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@ -832,7 +832,7 @@ class ASRServerExecutor(ASRExecutor):
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# AM predictor
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self.init_model()
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logger.info(f"create the {model_type} model success")
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logger.debug(f"create the {model_type} model success")
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return True
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@ -883,7 +883,7 @@ class ASREngine(BaseEngine):
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"If all GPU or XPU is used, you can set the server to 'cpu'")
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sys.exit(-1)
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logger.info(f"paddlespeech_server set the device: {self.device}")
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logger.debug(f"paddlespeech_server set the device: {self.device}")
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if not self.init_model():
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logger.error(
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@ -891,7 +891,9 @@ class ASREngine(BaseEngine):
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)
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return False
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logger.info("Initialize ASR server engine successfully.")
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logger.info("Initialize ASR server engine successfully on device: %s." %
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(self.device))
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return True
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def new_handler(self):
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TianYuan
3 years ago
committed by
GitHub