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DeepSpeech

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未验证 提交 9c4763ec 编写于 作者: 小湉湉's avatar 小湉湉 提交者： GitHub
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Merge pull request #2113 from yt605155624/rm_server_log 

[server]log redundancy in server
上级 e4a8e153 4b1f82d3
隐藏空白更改
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Showing with 226 addition and 223 deletion
paddlespeech/cli/tts/infer.py
浏览文件 @ 9c4763ec
......@@ -382,7 +382,7 @@ class TTSExecutor(BaseExecutor):
text, merge_sentences=merge_sentences)
phone_ids = input_ids["phone_ids"]
else:
print("lang should in {'zh', 'en'}!")
logger.error("lang should in {'zh', 'en'}!")
self.frontend_time = time.time() - frontend_st
self.am_time = 0
......
paddlespeech/server/bin/paddlespeech_client.py
浏览文件 @ 9c4763ec
......@@ -123,7 +123,6 @@ class TTSClientExecutor(BaseExecutor):
time_end = time.time()
time_consume = time_end - time_start
response_dict = res.json()
logger.info(response_dict["message"])
logger.info("Save synthesized audio successfully on %s." % (output))
logger.info("Audio duration: %f s." %
(response_dict['result']['duration']))
......@@ -702,7 +701,6 @@ class VectorClientExecutor(BaseExecutor):
test_audio=args.test,
task=task)
time_end = time.time()
logger.info(f"The vector: {res}")
logger.info("Response time %f s." % (time_end - time_start))
return True
except Exception as e:
......
paddlespeech/server/engine/acs/python/acs_engine.py
浏览文件 @ 9c4763ec
......@@ -30,7 +30,7 @@ class ACSEngine(BaseEngine):
"""The ACSEngine Engine
"""
super(ACSEngine, self).__init__()
logger.info("Create the ACSEngine Instance")
logger.debug("Create the ACSEngine Instance")
self.word_list = []
def init(self, config: dict):
......@@ -42,7 +42,7 @@ class ACSEngine(BaseEngine):
Returns:
bool: The engine instance flag
"""
logger.info("Init the acs engine")
logger.debug("Init the acs engine")
try:
self.config = config
self.device = self.config.get("device", paddle.get_device())
......@@ -50,7 +50,7 @@ class ACSEngine(BaseEngine):
# websocket default ping timeout is 20 seconds
self.ping_timeout = self.config.get("ping_timeout", 20)
paddle.set_device(self.device)
logger.info(f"ACS Engine set the device: {self.device}")
logger.debug(f"ACS Engine set the device: {self.device}")
except BaseException as e:
logger.error(
......@@ -66,7 +66,9 @@ class ACSEngine(BaseEngine):
self.url = "ws://" + self.config.asr_server_ip + ":" + str(
self.config.asr_server_port) + "/paddlespeech/asr/streaming"
logger.info("Init the acs engine successfully")
logger.info("Initialize acs server engine successfully on device: %s." %
(self.device))
return True
def read_search_words(self):
......@@ -95,12 +97,12 @@ class ACSEngine(BaseEngine):
Returns:
_type_: _description_
"""
logger.info("send a message to the server")
logger.debug("send a message to the server")
if self.url is None:
logger.error("No asr server, please input valid ip and port")
return ""
ws = websocket.WebSocket()
logger.info(f"set the ping timeout: {self.ping_timeout} seconds")
logger.debug(f"set the ping timeout: {self.ping_timeout} seconds")
ws.connect(self.url, ping_timeout=self.ping_timeout)
audio_info = json.dumps(
{
......@@ -123,7 +125,7 @@ class ACSEngine(BaseEngine):
logger.info(f"audio result: {msg}")
# 3. send chunk audio data to engine
logger.info("send the end signal")
logger.debug("send the end signal")
audio_info = json.dumps(
{
"name": "test.wav",
......@@ -197,7 +199,7 @@ class ACSEngine(BaseEngine):
start = max(time_stamp[m.start(0)]['bg'] - offset, 0)
end = min(time_stamp[m.end(0) - 1]['ed'] + offset, max_ed)
logger.info(f'start: {start}, end: {end}')
logger.debug(f'start: {start}, end: {end}')
acs_result.append({'w': w, 'bg': start, 'ed': end})
return acs_result, asr_result
......@@ -212,7 +214,7 @@ class ACSEngine(BaseEngine):
Returns:
acs_result, asr_result: the acs result and the asr result
"""
logger.info("start to process the audio content search")
logger.debug("start to process the audio content search")
msg = self.get_asr_content(io.BytesIO(audio_data))
acs_result, asr_result = self.get_macthed_word(msg)
......
paddlespeech/server/engine/asr/online/onnx/asr_engine.py
浏览文件 @ 9c4763ec
......@@ -44,7 +44,7 @@ class PaddleASRConnectionHanddler:
asr_engine (ASREngine): the global asr engine
"""
super().__init__()
logger.info(
logger.debug(
"create an paddle asr connection handler to process the websocket connection"
)
self.config = asr_engine.config # server config
......@@ -152,12 +152,12 @@ class PaddleASRConnectionHanddler:
self.output_reset()
def extract_feat(self, samples: ByteString):
logger.info("Online ASR extract the feat")
logger.debug("Online ASR extract the feat")
samples = np.frombuffer(samples, dtype=np.int16)
assert samples.ndim == 1
self.num_samples += samples.shape[0]
logger.info(
logger.debug(
f"This package receive {samples.shape[0]} pcm data. Global samples:{self.num_samples}"
)
......@@ -168,7 +168,7 @@ class PaddleASRConnectionHanddler:
else:
assert self.remained_wav.ndim == 1 # (T,)
self.remained_wav = np.concatenate([self.remained_wav, samples])
logger.info(
logger.debug(
f"The concatenation of remain and now audio samples length is: {self.remained_wav.shape}"
)
......@@ -202,14 +202,14 @@ class PaddleASRConnectionHanddler:
# update remained wav
self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
logger.info(
logger.debug(
f"process the audio feature success, the cached feat shape: {self.cached_feat.shape}"
)
logger.info(
logger.debug(
f"After extract feat, the cached remain the audio samples: {self.remained_wav.shape}"
)
logger.info(f"global samples: {self.num_samples}")
logger.info(f"global frames: {self.num_frames}")
logger.debug(f"global samples: {self.num_samples}")
logger.debug(f"global frames: {self.num_frames}")
def decode(self, is_finished=False):
"""advance decoding
......@@ -237,7 +237,7 @@ class PaddleASRConnectionHanddler:
return
num_frames = self.cached_feat.shape[1]
logger.info(
logger.debug(
f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
)
......@@ -355,7 +355,7 @@ class ASRServerExecutor(ASRExecutor):
lm_url = self.task_resource.res_dict['lm_url']
lm_md5 = self.task_resource.res_dict['lm_md5']
logger.info(f"Start to load language model {lm_url}")
logger.debug(f"Start to load language model {lm_url}")
self.download_lm(
lm_url,
os.path.dirname(self.config.decode.lang_model_path), lm_md5)
......@@ -367,7 +367,7 @@ class ASRServerExecutor(ASRExecutor):
if "deepspeech2" in self.model_type:
# AM predictor
logger.info("ASR engine start to init the am predictor")
logger.debug("ASR engine start to init the am predictor")
self.am_predictor = onnx_infer.get_sess(
model_path=self.am_model, sess_conf=self.am_predictor_conf)
else:
......@@ -400,7 +400,7 @@ class ASRServerExecutor(ASRExecutor):
self.num_decoding_left_chunks = num_decoding_left_chunks
# conf for paddleinference predictor or onnx
self.am_predictor_conf = am_predictor_conf
logger.info(f"model_type: {self.model_type}")
logger.debug(f"model_type: {self.model_type}")
sample_rate_str = '16k' if sample_rate == 16000 else '8k'
tag = model_type + '-' + lang + '-' + sample_rate_str
......@@ -422,12 +422,11 @@ class ASRServerExecutor(ASRExecutor):
# self.res_path, self.task_resource.res_dict[
# 'params']) if am_params is None else os.path.abspath(am_params)
logger.info("Load the pretrained model:")
logger.info(f" tag = {tag}")
logger.info(f" res_path: {self.res_path}")
logger.info(f" cfg path: {self.cfg_path}")
logger.info(f" am_model path: {self.am_model}")
# logger.info(f" am_params path: {self.am_params}")
logger.debug("Load the pretrained model:")
logger.debug(f" tag = {tag}")
logger.debug(f" res_path: {self.res_path}")
logger.debug(f" cfg path: {self.cfg_path}")
logger.debug(f" am_model path: {self.am_model}")
#Init body.
self.config = CfgNode(new_allowed=True)
......@@ -436,7 +435,7 @@ class ASRServerExecutor(ASRExecutor):
if self.config.spm_model_prefix:
self.config.spm_model_prefix = os.path.join(
self.res_path, self.config.spm_model_prefix)
logger.info(f"spm model path: {self.config.spm_model_prefix}")
logger.debug(f"spm model path: {self.config.spm_model_prefix}")
self.vocab = self.config.vocab_filepath
......@@ -450,7 +449,7 @@ class ASRServerExecutor(ASRExecutor):
# AM predictor
self.init_model()
logger.info(f"create the {model_type} model success")
logger.debug(f"create the {model_type} model success")
return True
......@@ -501,7 +500,7 @@ class ASREngine(BaseEngine):
"If all GPU or XPU is used, you can set the server to 'cpu'")
sys.exit(-1)
logger.info(f"paddlespeech_server set the device: {self.device}")
logger.debug(f"paddlespeech_server set the device: {self.device}")
if not self.init_model():
logger.error(
......@@ -509,7 +508,8 @@ class ASREngine(BaseEngine):
)
return False
logger.info("Initialize ASR server engine successfully.")
logger.info("Initialize ASR server engine successfully on device: %s." %
(self.device))
return True
def new_handler(self):
......
paddlespeech/server/engine/asr/online/paddleinference/asr_engine.py
浏览文件 @ 9c4763ec
......@@ -44,7 +44,7 @@ class PaddleASRConnectionHanddler:
asr_engine (ASREngine): the global asr engine
"""
super().__init__()
logger.info(
logger.debug(
"create an paddle asr connection handler to process the websocket connection"
)
self.config = asr_engine.config # server config
......@@ -157,7 +157,7 @@ class PaddleASRConnectionHanddler:
assert samples.ndim == 1
self.num_samples += samples.shape[0]
logger.info(
logger.debug(
f"This package receive {samples.shape[0]} pcm data. Global samples:{self.num_samples}"
)
......@@ -168,7 +168,7 @@ class PaddleASRConnectionHanddler:
else:
assert self.remained_wav.ndim == 1 # (T,)
self.remained_wav = np.concatenate([self.remained_wav, samples])
logger.info(
logger.debug(
f"The concatenation of remain and now audio samples length is: {self.remained_wav.shape}"
)
......@@ -202,14 +202,14 @@ class PaddleASRConnectionHanddler:
# update remained wav
self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
logger.info(
logger.debug(
f"process the audio feature success, the cached feat shape: {self.cached_feat.shape}"
)
logger.info(
logger.debug(
f"After extract feat, the cached remain the audio samples: {self.remained_wav.shape}"
)
logger.info(f"global samples: {self.num_samples}")
logger.info(f"global frames: {self.num_frames}")
logger.debug(f"global samples: {self.num_samples}")
logger.debug(f"global frames: {self.num_frames}")
def decode(self, is_finished=False):
"""advance decoding
......@@ -237,13 +237,13 @@ class PaddleASRConnectionHanddler:
return
num_frames = self.cached_feat.shape[1]
logger.info(
logger.debug(
f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
)
# the cached feat must be larger decoding_window
if num_frames < decoding_window and not is_finished:
logger.info(
logger.debug(
f"frame feat num is less than {decoding_window}, please input more pcm data"
)
return None, None
......@@ -294,7 +294,7 @@ class PaddleASRConnectionHanddler:
Returns:
logprob: poster probability.
"""
logger.info("start to decoce one chunk for deepspeech2")
logger.debug("start to decoce one chunk for deepspeech2")
input_names = self.am_predictor.get_input_names()
audio_handle = self.am_predictor.get_input_handle(input_names[0])
audio_len_handle = self.am_predictor.get_input_handle(input_names[1])
......@@ -369,7 +369,7 @@ class ASRServerExecutor(ASRExecutor):
lm_url = self.task_resource.res_dict['lm_url']
lm_md5 = self.task_resource.res_dict['lm_md5']
logger.info(f"Start to load language model {lm_url}")
logger.debug(f"Start to load language model {lm_url}")
self.download_lm(
lm_url,
os.path.dirname(self.config.decode.lang_model_path), lm_md5)
......@@ -381,7 +381,7 @@ class ASRServerExecutor(ASRExecutor):
if "deepspeech2" in self.model_type:
# AM predictor
logger.info("ASR engine start to init the am predictor")
logger.debug("ASR engine start to init the am predictor")
self.am_predictor = init_predictor(
model_file=self.am_model,
params_file=self.am_params,
......@@ -415,7 +415,7 @@ class ASRServerExecutor(ASRExecutor):
self.num_decoding_left_chunks = num_decoding_left_chunks
# conf for paddleinference predictor or onnx
self.am_predictor_conf = am_predictor_conf
logger.info(f"model_type: {self.model_type}")
logger.debug(f"model_type: {self.model_type}")
sample_rate_str = '16k' if sample_rate == 16000 else '8k'
tag = model_type + '-' + lang + '-' + sample_rate_str
......@@ -437,12 +437,12 @@ class ASRServerExecutor(ASRExecutor):
self.res_path = os.path.dirname(
os.path.dirname(os.path.abspath(self.cfg_path)))
logger.info("Load the pretrained model:")
logger.info(f" tag = {tag}")
logger.info(f" res_path: {self.res_path}")
logger.info(f" cfg path: {self.cfg_path}")
logger.info(f" am_model path: {self.am_model}")
logger.info(f" am_params path: {self.am_params}")
logger.debug("Load the pretrained model:")
logger.debug(f" tag = {tag}")
logger.debug(f" res_path: {self.res_path}")
logger.debug(f" cfg path: {self.cfg_path}")
logger.debug(f" am_model path: {self.am_model}")
logger.debug(f" am_params path: {self.am_params}")
#Init body.
self.config = CfgNode(new_allowed=True)
......@@ -451,7 +451,7 @@ class ASRServerExecutor(ASRExecutor):
if self.config.spm_model_prefix:
self.config.spm_model_prefix = os.path.join(
self.res_path, self.config.spm_model_prefix)
logger.info(f"spm model path: {self.config.spm_model_prefix}")
logger.debug(f"spm model path: {self.config.spm_model_prefix}")
self.vocab = self.config.vocab_filepath
......@@ -465,7 +465,7 @@ class ASRServerExecutor(ASRExecutor):
# AM predictor
self.init_model()
logger.info(f"create the {model_type} model success")
logger.debug(f"create the {model_type} model success")
return True
......@@ -516,7 +516,7 @@ class ASREngine(BaseEngine):
"If all GPU or XPU is used, you can set the server to 'cpu'")
sys.exit(-1)
logger.info(f"paddlespeech_server set the device: {self.device}")
logger.debug(f"paddlespeech_server set the device: {self.device}")
if not self.init_model():
logger.error(
......@@ -524,7 +524,9 @@ class ASREngine(BaseEngine):
)
return False
logger.info("Initialize ASR server engine successfully.")
logger.info("Initialize ASR server engine successfully on device: %s." %
(self.device))
return True
def new_handler(self):
......
paddlespeech/server/engine/asr/online/python/asr_engine.py
浏览文件 @ 9c4763ec
......@@ -49,7 +49,7 @@ class PaddleASRConnectionHanddler:
asr_engine (ASREngine): the global asr engine
"""
super().__init__()
logger.info(
logger.debug(
"create an paddle asr connection handler to process the websocket connection"
)
self.config = asr_engine.config # server config
......@@ -107,7 +107,7 @@ class PaddleASRConnectionHanddler:
# acoustic model
self.model = self.asr_engine.executor.model
self.continuous_decoding = self.config.continuous_decoding
logger.info(f"continue decoding: {self.continuous_decoding}")
logger.debug(f"continue decoding: {self.continuous_decoding}")
# ctc decoding config
self.ctc_decode_config = self.asr_engine.executor.config.decode
......@@ -207,7 +207,7 @@ class PaddleASRConnectionHanddler:
assert samples.ndim == 1
self.num_samples += samples.shape[0]
logger.info(
logger.debug(
f"This package receive {samples.shape[0]} pcm data. Global samples:{self.num_samples}"
)
......@@ -218,7 +218,7 @@ class PaddleASRConnectionHanddler:
else:
assert self.remained_wav.ndim == 1 # (T,)
self.remained_wav = np.concatenate([self.remained_wav, samples])
logger.info(
logger.debug(
f"The concatenation of remain and now audio samples length is: {self.remained_wav.shape}"
)
......@@ -252,14 +252,14 @@ class PaddleASRConnectionHanddler:
# update remained wav
self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
logger.info(
logger.debug(
f"process the audio feature success, the cached feat shape: {self.cached_feat.shape}"
)
logger.info(
logger.debug(
f"After extract feat, the cached remain the audio samples: {self.remained_wav.shape}"
)
logger.info(f"global samples: {self.num_samples}")
logger.info(f"global frames: {self.num_frames}")
logger.debug(f"global samples: {self.num_samples}")
logger.debug(f"global frames: {self.num_frames}")
def decode(self, is_finished=False):
"""advance decoding
......@@ -283,24 +283,24 @@ class PaddleASRConnectionHanddler:
stride = subsampling * decoding_chunk_size
if self.cached_feat is None:
logger.info("no audio feat, please input more pcm data")
logger.debug("no audio feat, please input more pcm data")
return
num_frames = self.cached_feat.shape[1]
logger.info(
logger.debug(
f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
)
# the cached feat must be larger decoding_window
if num_frames < decoding_window and not is_finished:
logger.info(
logger.debug(
f"frame feat num is less than {decoding_window}, please input more pcm data"
)
return None, None
# if is_finished=True, we need at least context frames
if num_frames < context:
logger.info(
logger.debug(
"flast {num_frames} is less than context {context} frames, and we cannot do model forward"
)
return None, None
......@@ -354,7 +354,7 @@ class PaddleASRConnectionHanddler:
Returns:
logprob: poster probability.
"""
logger.info("start to decoce one chunk for deepspeech2")
logger.debug("start to decoce one chunk for deepspeech2")
input_names = self.am_predictor.get_input_names()
audio_handle = self.am_predictor.get_input_handle(input_names[0])
audio_len_handle = self.am_predictor.get_input_handle(input_names[1])
......@@ -391,7 +391,7 @@ class PaddleASRConnectionHanddler:
self.decoder.next(output_chunk_probs, output_chunk_lens)
trans_best, trans_beam = self.decoder.decode()
logger.info(f"decode one best result for deepspeech2: {trans_best[0]}")
logger.debug(f"decode one best result for deepspeech2: {trans_best[0]}")
return trans_best[0]
@paddle.no_grad()
......@@ -402,7 +402,7 @@ class PaddleASRConnectionHanddler:
# reset endpiont state
self.endpoint_state = False
logger.info(
logger.debug(
"Conformer/Transformer: start to decode with advanced_decoding method"
)
cfg = self.ctc_decode_config
......@@ -427,25 +427,25 @@ class PaddleASRConnectionHanddler:
stride = subsampling * decoding_chunk_size
if self.cached_feat is None:
logger.info("no audio feat, please input more pcm data")
logger.debug("no audio feat, please input more pcm data")
return
# (B=1,T,D)
num_frames = self.cached_feat.shape[1]
logger.info(
logger.debug(
f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
)
# the cached feat must be larger decoding_window
if num_frames < decoding_window and not is_finished:
logger.info(
logger.debug(
f"frame feat num is less than {decoding_window}, please input more pcm data"
)
return None, None
# if is_finished=True, we need at least context frames
if num_frames < context:
logger.info(
logger.debug(
"flast {num_frames} is less than context {context} frames, and we cannot do model forward"
)
return None, None
......@@ -489,7 +489,7 @@ class PaddleASRConnectionHanddler:
self.encoder_out = ys
else:
self.encoder_out = paddle.concat([self.encoder_out, ys], axis=1)
logger.info(
logger.debug(
f"This connection handler encoder out shape: {self.encoder_out.shape}"
)
......@@ -513,7 +513,8 @@ class PaddleASRConnectionHanddler:
if self.endpointer.endpoint_detected(ctc_probs.numpy(),
decoding_something):
self.endpoint_state = True
logger.info(f"Endpoint is detected at {self.num_frames} frame.")
logger.debug(
f"Endpoint is detected at {self.num_frames} frame.")
# advance cache of feat
assert self.cached_feat.shape[0] == 1 #(B=1,T,D)
......@@ -526,7 +527,7 @@ class PaddleASRConnectionHanddler:
def update_result(self):
"""Conformer/Transformer hyps to result.
"""
logger.info("update the final result")
logger.debug("update the final result")
hyps = self.hyps
# output results and tokenids
......@@ -560,16 +561,16 @@ class PaddleASRConnectionHanddler:
only for conformer and transformer model.
"""
if "deepspeech2" in self.model_type:
logger.info("deepspeech2 not support rescoring decoding.")
logger.debug("deepspeech2 not support rescoring decoding.")
return
if "attention_rescoring" != self.ctc_decode_config.decoding_method:
logger.info(
logger.debug(
f"decoding method not match: {self.ctc_decode_config.decoding_method}, need attention_rescoring"
)
return
logger.info("rescoring the final result")
logger.debug("rescoring the final result")
# last decoding for last audio
self.searcher.finalize_search()
......@@ -685,7 +686,6 @@ class PaddleASRConnectionHanddler:
"bg": global_offset_in_sec + start,
"ed": global_offset_in_sec + end
})
# logger.info(f"{word_time_stamp[-1]}")
self.word_time_stamp = word_time_stamp
logger.info(f"word time stamp: {self.word_time_stamp}")
......@@ -707,13 +707,13 @@ class ASRServerExecutor(ASRExecutor):
lm_url = self.task_resource.res_dict['lm_url']
lm_md5 = self.task_resource.res_dict['lm_md5']
logger.info(f"Start to load language model {lm_url}")
logger.debug(f"Start to load language model {lm_url}")
self.download_lm(
lm_url,
os.path.dirname(self.config.decode.lang_model_path), lm_md5)
elif "conformer" in self.model_type or "transformer" in self.model_type:
with UpdateConfig(self.config):
logger.info("start to create the stream conformer asr engine")
logger.debug("start to create the stream conformer asr engine")
# update the decoding method
if self.decode_method:
self.config.decode.decoding_method = self.decode_method
......@@ -726,7 +726,7 @@ class ASRServerExecutor(ASRExecutor):
if self.config.decode.decoding_method not in [
"ctc_prefix_beam_search", "attention_rescoring"
]:
logger.info(
logger.debug(
"we set the decoding_method to attention_rescoring")
self.config.decode.decoding_method = "attention_rescoring"
......@@ -739,7 +739,7 @@ class ASRServerExecutor(ASRExecutor):
def init_model(self) -> None:
if "deepspeech2" in self.model_type:
# AM predictor
logger.info("ASR engine start to init the am predictor")
logger.debug("ASR engine start to init the am predictor")
self.am_predictor = init_predictor(
model_file=self.am_model,
params_file=self.am_params,
......@@ -748,7 +748,7 @@ class ASRServerExecutor(ASRExecutor):
# load model
# model_type: {model_name}_{dataset}
model_name = self.model_type[:self.model_type.rindex('_')]
logger.info(f"model name: {model_name}")
logger.debug(f"model name: {model_name}")
model_class = self.task_resource.get_model_class(model_name)
model = model_class.from_config(self.config)
self.model = model
......@@ -782,7 +782,7 @@ class ASRServerExecutor(ASRExecutor):
self.num_decoding_left_chunks = num_decoding_left_chunks
# conf for paddleinference predictor or onnx
self.am_predictor_conf = am_predictor_conf
logger.info(f"model_type: {self.model_type}")
logger.debug(f"model_type: {self.model_type}")
sample_rate_str = '16k' if sample_rate == 16000 else '8k'
tag = model_type + '-' + lang + '-' + sample_rate_str
......@@ -804,12 +804,12 @@ class ASRServerExecutor(ASRExecutor):
self.res_path = os.path.dirname(
os.path.dirname(os.path.abspath(self.cfg_path)))
logger.info("Load the pretrained model:")
logger.info(f" tag = {tag}")
logger.info(f" res_path: {self.res_path}")
logger.info(f" cfg path: {self.cfg_path}")
logger.info(f" am_model path: {self.am_model}")
logger.info(f" am_params path: {self.am_params}")
logger.debug("Load the pretrained model:")
logger.debug(f" tag = {tag}")
logger.debug(f" res_path: {self.res_path}")
logger.debug(f" cfg path: {self.cfg_path}")
logger.debug(f" am_model path: {self.am_model}")
logger.debug(f" am_params path: {self.am_params}")
#Init body.
self.config = CfgNode(new_allowed=True)
......@@ -818,7 +818,7 @@ class ASRServerExecutor(ASRExecutor):
if self.config.spm_model_prefix:
self.config.spm_model_prefix = os.path.join(
self.res_path, self.config.spm_model_prefix)
logger.info(f"spm model path: {self.config.spm_model_prefix}")
logger.debug(f"spm model path: {self.config.spm_model_prefix}")
self.vocab = self.config.vocab_filepath
......@@ -832,7 +832,7 @@ class ASRServerExecutor(ASRExecutor):
# AM predictor
self.init_model()
logger.info(f"create the {model_type} model success")
logger.debug(f"create the {model_type} model success")
return True
......@@ -883,7 +883,7 @@ class ASREngine(BaseEngine):
"If all GPU or XPU is used, you can set the server to 'cpu'")
sys.exit(-1)
logger.info(f"paddlespeech_server set the device: {self.device}")
logger.debug(f"paddlespeech_server set the device: {self.device}")
if not self.init_model():
logger.error(
......@@ -891,7 +891,9 @@ class ASREngine(BaseEngine):
)
return False
logger.info("Initialize ASR server engine successfully.")
logger.info("Initialize ASR server engine successfully on device: %s." %
(self.device))
return True
def new_handler(self):
......
paddlespeech/server/engine/asr/paddleinference/asr_engine.py
浏览文件 @ 9c4763ec
......@@ -65,10 +65,10 @@ class ASRServerExecutor(ASRExecutor):
self.task_resource.res_dict['model'])
self.am_params = os.path.join(self.res_path,
self.task_resource.res_dict['params'])
logger.info(self.res_path)
logger.info(self.cfg_path)
logger.info(self.am_model)
logger.info(self.am_params)
logger.debug(self.res_path)
logger.debug(self.cfg_path)
logger.debug(self.am_model)
logger.debug(self.am_params)
else:
self.cfg_path = os.path.abspath(cfg_path)
self.am_model = os.path.abspath(am_model)
......@@ -236,16 +236,16 @@ class PaddleASRConnectionHandler(ASRServerExecutor):
if self._check(
io.BytesIO(audio_data), self.asr_engine.config.sample_rate,
self.asr_engine.config.force_yes):
logger.info("start running asr engine")
logger.debug("start running asr engine")
self.preprocess(self.asr_engine.config.model_type,
io.BytesIO(audio_data))
st = time.time()
self.infer(self.asr_engine.config.model_type)
infer_time = time.time() - st
self.output = self.postprocess() # Retrieve result of asr.
logger.info("end inferring asr engine")
logger.debug("end inferring asr engine")
else:
logger.info("file check failed!")
logger.error("file check failed!")
self.output = None
logger.info("inference time: {}".format(infer_time))
......
paddlespeech/server/engine/asr/python/asr_engine.py
浏览文件 @ 9c4763ec
......@@ -104,7 +104,7 @@ class PaddleASRConnectionHandler(ASRServerExecutor):
if self._check(
io.BytesIO(audio_data), self.asr_engine.config.sample_rate,
self.asr_engine.config.force_yes):
logger.info("start run asr engine")
logger.debug("start run asr engine")
self.preprocess(self.asr_engine.config.model,
io.BytesIO(audio_data))
st = time.time()
......@@ -112,7 +112,7 @@ class PaddleASRConnectionHandler(ASRServerExecutor):
infer_time = time.time() - st
self.output = self.postprocess() # Retrieve result of asr.
else:
logger.info("file check failed!")
logger.error("file check failed!")
self.output = None
logger.info("inference time: {}".format(infer_time))
......
paddlespeech/server/engine/cls/paddleinference/cls_engine.py
浏览文件 @ 9c4763ec
......@@ -67,22 +67,22 @@ class CLSServerExecutor(CLSExecutor):
self.params_path = os.path.abspath(params_path)
self.label_file = os.path.abspath(label_file)
logger.info(self.cfg_path)
logger.info(self.model_path)
logger.info(self.params_path)
logger.info(self.label_file)
logger.debug(self.cfg_path)
logger.debug(self.model_path)
logger.debug(self.params_path)
logger.debug(self.label_file)
# config
with open(self.cfg_path, 'r') as f:
self._conf = yaml.safe_load(f)
logger.info("Read cfg file successfully.")
logger.debug("Read cfg file successfully.")
# labels
self._label_list = []
with open(self.label_file, 'r') as f:
for line in f:
self._label_list.append(line.strip())
logger.info("Read label file successfully.")
logger.debug("Read label file successfully.")
# Create predictor
self.predictor_conf = predictor_conf
......@@ -90,7 +90,7 @@ class CLSServerExecutor(CLSExecutor):
model_file=self.model_path,
params_file=self.params_path,
predictor_conf=self.predictor_conf)
logger.info("Create predictor successfully.")
logger.debug("Create predictor successfully.")
@paddle.no_grad()
def infer(self):
......@@ -148,7 +148,8 @@ class CLSEngine(BaseEngine):
logger.error(e)
return False
logger.info("Initialize CLS server engine successfully.")
logger.info("Initialize CLS server engine successfully on device: %s." %
(self.device))
return True
......@@ -160,7 +161,7 @@ class PaddleCLSConnectionHandler(CLSServerExecutor):
cls_engine (CLSEngine): The CLS engine
"""
super().__init__()
logger.info(
logger.debug(
"Create PaddleCLSConnectionHandler to process the cls request")
self._inputs = OrderedDict()
......@@ -183,7 +184,7 @@ class PaddleCLSConnectionHandler(CLSServerExecutor):
self.infer()
infer_time = time.time() - st
logger.info("inference time: {}".format(infer_time))
logger.debug("inference time: {}".format(infer_time))
logger.info("cls engine type: inference")
def postprocess(self, topk: int):
......
paddlespeech/server/engine/cls/python/cls_engine.py
浏览文件 @ 9c4763ec
......@@ -88,7 +88,7 @@ class PaddleCLSConnectionHandler(CLSServerExecutor):
cls_engine (CLSEngine): The CLS engine
"""
super().__init__()
logger.info(
logger.debug(
"Create PaddleCLSConnectionHandler to process the cls request")
self._inputs = OrderedDict()
......@@ -110,7 +110,7 @@ class PaddleCLSConnectionHandler(CLSServerExecutor):
self.infer()
infer_time = time.time() - st
logger.info("inference time: {}".format(infer_time))
logger.debug("inference time: {}".format(infer_time))
logger.info("cls engine type: python")
def postprocess(self, topk: int):
......
paddlespeech/server/engine/engine_warmup.py
浏览文件 @ 9c4763ec
......@@ -45,7 +45,7 @@ def warm_up(engine_and_type: str, warm_up_time: int=3) -> bool:
logger.error("Please check tte engine type.")
try:
logger.info("Start to warm up tts engine.")
logger.debug("Start to warm up tts engine.")
for i in range(warm_up_time):
connection_handler = PaddleTTSConnectionHandler(tts_engine)
if flag_online:
......@@ -53,7 +53,7 @@ def warm_up(engine_and_type: str, warm_up_time: int=3) -> bool:
text=sentence,
lang=tts_engine.lang,
am=tts_engine.config.am):
logger.info(
logger.debug(
f"The first response time of the {i} warm up: {connection_handler.first_response_time} s"
)
break
......@@ -62,7 +62,7 @@ def warm_up(engine_and_type: str, warm_up_time: int=3) -> bool:
st = time.time()
connection_handler.infer(text=sentence)
et = time.time()
logger.info(
logger.debug(
f"The response time of the {i} warm up: {et - st} s")
except Exception as e:
logger.error("Failed to warm up on tts engine.")
......
paddlespeech/server/engine/text/python/text_engine.py
浏览文件 @ 9c4763ec
......@@ -28,7 +28,7 @@ class PaddleTextConnectionHandler:
text_engine (TextEngine): The Text engine
"""
super().__init__()
logger.info(
logger.debug(
"Create PaddleTextConnectionHandler to process the text request")
self.text_engine = text_engine
self.task = self.text_engine.executor.task
......@@ -130,7 +130,7 @@ class TextEngine(BaseEngine):
"""The Text Engine
"""
super(TextEngine, self).__init__()
logger.info("Create the TextEngine Instance")
logger.debug("Create the TextEngine Instance")
def init(self, config: dict):
"""Init the Text Engine
......@@ -141,7 +141,7 @@ class TextEngine(BaseEngine):
Returns:
bool: The engine instance flag
"""
logger.info("Init the text engine")
logger.debug("Init the text engine")
try:
self.config = config
if self.config.device:
......@@ -150,7 +150,7 @@ class TextEngine(BaseEngine):
self.device = paddle.get_device()
paddle.set_device(self.device)
logger.info(f"Text Engine set the device: {self.device}")
logger.debug(f"Text Engine set the device: {self.device}")
except BaseException as e:
logger.error(
"Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
......@@ -168,5 +168,6 @@ class TextEngine(BaseEngine):
ckpt_path=config.ckpt_path,
vocab_file=config.vocab_file)
logger.info("Init the text engine successfully")
logger.info("Initialize Text server engine successfully on device: %s."
% (self.device))
return True
paddlespeech/server/engine/tts/online/onnx/tts_engine.py
浏览文件 @ 9c4763ec
......@@ -62,7 +62,7 @@ class TTSServerExecutor(TTSExecutor):
(hasattr(self, 'am_encoder_infer_sess') and
hasattr(self, 'am_decoder_sess') and hasattr(
self, 'am_postnet_sess'))) and hasattr(self, 'voc_inference'):
logger.info('Models had been initialized.')
logger.debug('Models had been initialized.')
return
# am
am_tag = am + '-' + lang
......@@ -85,8 +85,7 @@ class TTSServerExecutor(TTSExecutor):
else:
self.am_ckpt = os.path.abspath(am_ckpt[0])
self.phones_dict = os.path.abspath(phones_dict)
self.am_res_path = os.path.dirname(
os.path.abspath(am_ckpt))
self.am_res_path = os.path.dirname(os.path.abspath(am_ckpt))
# create am sess
self.am_sess = get_sess(self.am_ckpt, am_sess_conf)
......@@ -119,8 +118,7 @@ class TTSServerExecutor(TTSExecutor):
self.am_postnet = os.path.abspath(am_ckpt[2])
self.phones_dict = os.path.abspath(phones_dict)
self.am_stat = os.path.abspath(am_stat)
self.am_res_path = os.path.dirname(
os.path.abspath(am_ckpt[0]))
self.am_res_path = os.path.dirname(os.path.abspath(am_ckpt[0]))
# create am sess
self.am_encoder_infer_sess = get_sess(self.am_encoder_infer,
......@@ -130,13 +128,13 @@ class TTSServerExecutor(TTSExecutor):
self.am_mu, self.am_std = np.load(self.am_stat)
logger.info(f"self.phones_dict: {self.phones_dict}")
logger.info(f"am model dir: {self.am_res_path}")
logger.info("Create am sess successfully.")
logger.debug(f"self.phones_dict: {self.phones_dict}")
logger.debug(f"am model dir: {self.am_res_path}")
logger.debug("Create am sess successfully.")
# voc model info
voc_tag = voc + '-' + lang
if voc_ckpt is None:
self.task_resource.set_task_model(
model_tag=voc_tag,
......@@ -149,16 +147,16 @@ class TTSServerExecutor(TTSExecutor):
else:
self.voc_ckpt = os.path.abspath(voc_ckpt)
self.voc_res_path = os.path.dirname(os.path.abspath(self.voc_ckpt))
logger.info(self.voc_res_path)
logger.debug(self.voc_res_path)
# create voc sess
self.voc_sess = get_sess(self.voc_ckpt, voc_sess_conf)
logger.info("Create voc sess successfully.")
logger.debug("Create voc sess successfully.")
with open(self.phones_dict, "r") as f:
phn_id = [line.strip().split() for line in f.readlines()]
self.vocab_size = len(phn_id)
logger.info(f"vocab_size: {self.vocab_size}")
logger.debug(f"vocab_size: {self.vocab_size}")
# frontend
self.tones_dict = None
......@@ -169,7 +167,7 @@ class TTSServerExecutor(TTSExecutor):
elif lang == 'en':
self.frontend = English(phone_vocab_path=self.phones_dict)
logger.info("frontend done!")
logger.debug("frontend done!")
class TTSEngine(BaseEngine):
......@@ -267,7 +265,7 @@ class PaddleTTSConnectionHandler:
tts_engine (TTSEngine): The TTS engine
"""
super().__init__()
logger.info(
logger.debug(
"Create PaddleTTSConnectionHandler to process the tts request")
self.tts_engine = tts_engine
......
paddlespeech/server/engine/tts/online/python/tts_engine.py
浏览文件 @ 9c4763ec
......@@ -102,7 +102,7 @@ class TTSServerExecutor(TTSExecutor):
Init model and other resources from a specific path.
"""
if hasattr(self, 'am_inference') and hasattr(self, 'voc_inference'):
logger.info('Models had been initialized.')
logger.debug('Models had been initialized.')
return
# am model info
if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None:
......@@ -128,17 +128,15 @@ class TTSServerExecutor(TTSExecutor):
# must have phones_dict in acoustic
self.phones_dict = os.path.join(
self.am_res_path, self.task_resource.res_dict['phones_dict'])
print("self.phones_dict:", self.phones_dict)
logger.info(self.am_res_path)
logger.info(self.am_config)
logger.info(self.am_ckpt)
logger.debug(self.am_res_path)
logger.debug(self.am_config)
logger.debug(self.am_ckpt)
else:
self.am_config = os.path.abspath(am_config)
self.am_ckpt = os.path.abspath(am_ckpt)
self.am_stat = os.path.abspath(am_stat)
self.phones_dict = os.path.abspath(phones_dict)
self.am_res_path = os.path.dirname(os.path.abspath(self.am_config))
print("self.phones_dict:", self.phones_dict)
self.tones_dict = None
self.speaker_dict = None
......@@ -165,9 +163,9 @@ class TTSServerExecutor(TTSExecutor):
self.voc_stat = os.path.join(
self.voc_res_path,
self.task_resource.voc_res_dict['speech_stats'])
logger.info(self.voc_res_path)
logger.info(self.voc_config)
logger.info(self.voc_ckpt)
logger.debug(self.voc_res_path)
logger.debug(self.voc_config)
logger.debug(self.voc_ckpt)
else:
self.voc_config = os.path.abspath(voc_config)
self.voc_ckpt = os.path.abspath(voc_ckpt)
......@@ -184,7 +182,6 @@ class TTSServerExecutor(TTSExecutor):
with open(self.phones_dict, "r") as f:
phn_id = [line.strip().split() for line in f.readlines()]
self.vocab_size = len(phn_id)
print("vocab_size:", self.vocab_size)
# frontend
if lang == 'zh':
......@@ -194,7 +191,6 @@ class TTSServerExecutor(TTSExecutor):
elif lang == 'en':
self.frontend = English(phone_vocab_path=self.phones_dict)
print("frontend done!")
# am infer info
self.am_name = am[:am.rindex('_')]
......@@ -209,7 +205,6 @@ class TTSServerExecutor(TTSExecutor):
self.am_name + '_inference')
self.am_inference = am_inference_class(am_normalizer, am)
self.am_inference.eval()
print("acoustic model done!")
# voc infer info
self.voc_name = voc[:voc.rindex('_')]
......@@ -220,7 +215,6 @@ class TTSServerExecutor(TTSExecutor):
'_inference')
self.voc_inference = voc_inference_class(voc_normalizer, voc)
self.voc_inference.eval()
print("voc done!")
class TTSEngine(BaseEngine):
......@@ -309,7 +303,7 @@ class PaddleTTSConnectionHandler:
tts_engine (TTSEngine): The TTS engine
"""
super().__init__()
logger.info(
logger.debug(
"Create PaddleTTSConnectionHandler to process the tts request")
self.tts_engine = tts_engine
......@@ -369,7 +363,7 @@ class PaddleTTSConnectionHandler:
text, merge_sentences=merge_sentences)
phone_ids = input_ids["phone_ids"]
else:
print("lang should in {'zh', 'en'}!")
logger.error("lang should in {'zh', 'en'}!")
frontend_et = time.time()
self.frontend_time = frontend_et - frontend_st
......
paddlespeech/server/engine/tts/paddleinference/tts_engine.py
浏览文件 @ 9c4763ec
......@@ -65,7 +65,7 @@ class TTSServerExecutor(TTSExecutor):
Init model and other resources from a specific path.
"""
if hasattr(self, 'am_predictor') and hasattr(self, 'voc_predictor'):
logger.info('Models had been initialized.')
logger.debug('Models had been initialized.')
return
# am
if am_model is None or am_params is None or phones_dict is None:
......@@ -91,16 +91,16 @@ class TTSServerExecutor(TTSExecutor):
self.am_res_path, self.task_resource.res_dict['phones_dict'])
self.am_sample_rate = self.task_resource.res_dict['sample_rate']
logger.info(self.am_res_path)
logger.info(self.am_model)
logger.info(self.am_params)
logger.debug(self.am_res_path)
logger.debug(self.am_model)
logger.debug(self.am_params)
else:
self.am_model = os.path.abspath(am_model)
self.am_params = os.path.abspath(am_params)
self.phones_dict = os.path.abspath(phones_dict)
self.am_sample_rate = am_sample_rate
self.am_res_path = os.path.dirname(os.path.abspath(self.am_model))
logger.info("self.phones_dict: {}".format(self.phones_dict))
logger.debug("self.phones_dict: {}".format(self.phones_dict))
# for speedyspeech
self.tones_dict = None
......@@ -139,9 +139,9 @@ class TTSServerExecutor(TTSExecutor):
self.voc_res_path, self.task_resource.voc_res_dict['params'])
self.voc_sample_rate = self.task_resource.voc_res_dict[
'sample_rate']
logger.info(self.voc_res_path)
logger.info(self.voc_model)
logger.info(self.voc_params)
logger.debug(self.voc_res_path)
logger.debug(self.voc_model)
logger.debug(self.voc_params)
else:
self.voc_model = os.path.abspath(voc_model)
self.voc_params = os.path.abspath(voc_params)
......@@ -156,21 +156,21 @@ class TTSServerExecutor(TTSExecutor):
with open(self.phones_dict, "r") as f:
phn_id = [line.strip().split() for line in f.readlines()]
vocab_size = len(phn_id)
logger.info("vocab_size: {}".format(vocab_size))
logger.debug("vocab_size: {}".format(vocab_size))
tone_size = None
if self.tones_dict:
with open(self.tones_dict, "r") as f:
tone_id = [line.strip().split() for line in f.readlines()]
tone_size = len(tone_id)
logger.info("tone_size: {}".format(tone_size))
logger.debug("tone_size: {}".format(tone_size))
spk_num = None
if self.speaker_dict:
with open(self.speaker_dict, 'rt') as f:
spk_id = [line.strip().split() for line in f.readlines()]
spk_num = len(spk_id)
logger.info("spk_num: {}".format(spk_num))
logger.debug("spk_num: {}".format(spk_num))
# frontend
if lang == 'zh':
......@@ -180,7 +180,7 @@ class TTSServerExecutor(TTSExecutor):
elif lang == 'en':
self.frontend = English(phone_vocab_path=self.phones_dict)
logger.info("frontend done!")
logger.debug("frontend done!")
# Create am predictor
self.am_predictor_conf = am_predictor_conf
......@@ -188,7 +188,7 @@ class TTSServerExecutor(TTSExecutor):
model_file=self.am_model,
params_file=self.am_params,
predictor_conf=self.am_predictor_conf)
logger.info("Create AM predictor successfully.")
logger.debug("Create AM predictor successfully.")
# Create voc predictor
self.voc_predictor_conf = voc_predictor_conf
......@@ -196,7 +196,7 @@ class TTSServerExecutor(TTSExecutor):
model_file=self.voc_model,
params_file=self.voc_params,
predictor_conf=self.voc_predictor_conf)
logger.info("Create Vocoder predictor successfully.")
logger.debug("Create Vocoder predictor successfully.")
@paddle.no_grad()
def infer(self,
......@@ -328,7 +328,8 @@ class TTSEngine(BaseEngine):
logger.error(e)
return False
logger.info("Initialize TTS server engine successfully.")
logger.info("Initialize TTS server engine successfully on device: %s." %
(self.device))
return True
......@@ -340,7 +341,7 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
tts_engine (TTSEngine): The TTS engine
"""
super().__init__()
logger.info(
logger.debug(
"Create PaddleTTSConnectionHandler to process the tts request")
self.tts_engine = tts_engine
......@@ -378,23 +379,23 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
if target_fs == 0 or target_fs > original_fs:
target_fs = original_fs
wav_tar_fs = wav
logger.info(
logger.debug(
"The sample rate of synthesized audio is the same as model, which is {}Hz".
format(original_fs))
else:
wav_tar_fs = librosa.resample(
np.squeeze(wav), original_fs, target_fs)
logger.info(
logger.debug(
"The sample rate of model is {}Hz and the target sample rate is {}Hz. Converting the sample rate of the synthesized audio successfully.".
format(original_fs, target_fs))
# transform volume
wav_vol = wav_tar_fs * volume
logger.info("Transform the volume of the audio successfully.")
logger.debug("Transform the volume of the audio successfully.")
# transform speed
try: # windows not support soxbindings
wav_speed = change_speed(wav_vol, speed, target_fs)
logger.info("Transform the speed of the audio successfully.")
logger.debug("Transform the speed of the audio successfully.")
except ServerBaseException:
raise ServerBaseException(
ErrorCode.SERVER_INTERNAL_ERR,
......@@ -411,7 +412,7 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
wavfile.write(buf, target_fs, wav_speed)
base64_bytes = base64.b64encode(buf.read())
wav_base64 = base64_bytes.decode('utf-8')
logger.info("Audio to string successfully.")
logger.debug("Audio to string successfully.")
# save audio
if audio_path is not None:
......@@ -499,15 +500,15 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
logger.error(e)
sys.exit(-1)
logger.info("AM model: {}".format(self.config.am))
logger.info("Vocoder model: {}".format(self.config.voc))
logger.info("Language: {}".format(lang))
logger.debug("AM model: {}".format(self.config.am))
logger.debug("Vocoder model: {}".format(self.config.voc))
logger.debug("Language: {}".format(lang))
logger.info("tts engine type: python")
logger.info("audio duration: {}".format(duration))
logger.info("frontend inference time: {}".format(self.frontend_time))
logger.info("AM inference time: {}".format(self.am_time))
logger.info("Vocoder inference time: {}".format(self.voc_time))
logger.debug("frontend inference time: {}".format(self.frontend_time))
logger.debug("AM inference time: {}".format(self.am_time))
logger.debug("Vocoder inference time: {}".format(self.voc_time))
logger.info("total inference time: {}".format(infer_time))
logger.info(
"postprocess (change speed, volume, target sample rate) time: {}".
......@@ -515,6 +516,6 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
logger.info("total generate audio time: {}".format(infer_time +
postprocess_time))
logger.info("RTF: {}".format(rtf))
logger.info("device: {}".format(self.tts_engine.device))
logger.debug("device: {}".format(self.tts_engine.device))
return lang, target_sample_rate, duration, wav_base64
paddlespeech/server/engine/tts/python/tts_engine.py
浏览文件 @ 9c4763ec
......@@ -105,7 +105,7 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
tts_engine (TTSEngine): The TTS engine
"""
super().__init__()
logger.info(
logger.debug(
"Create PaddleTTSConnectionHandler to process the tts request")
self.tts_engine = tts_engine
......@@ -143,23 +143,23 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
if target_fs == 0 or target_fs > original_fs:
target_fs = original_fs
wav_tar_fs = wav
logger.info(
logger.debug(
"The sample rate of synthesized audio is the same as model, which is {}Hz".
format(original_fs))
else:
wav_tar_fs = librosa.resample(
np.squeeze(wav), original_fs, target_fs)
logger.info(
logger.debug(
"The sample rate of model is {}Hz and the target sample rate is {}Hz. Converting the sample rate of the synthesized audio successfully.".
format(original_fs, target_fs))
# transform volume
wav_vol = wav_tar_fs * volume
logger.info("Transform the volume of the audio successfully.")
logger.debug("Transform the volume of the audio successfully.")
# transform speed
try: # windows not support soxbindings
wav_speed = change_speed(wav_vol, speed, target_fs)
logger.info("Transform the speed of the audio successfully.")
logger.debug("Transform the speed of the audio successfully.")
except ServerBaseException:
raise ServerBaseException(
ErrorCode.SERVER_INTERNAL_ERR,
......@@ -176,7 +176,7 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
wavfile.write(buf, target_fs, wav_speed)
base64_bytes = base64.b64encode(buf.read())
wav_base64 = base64_bytes.decode('utf-8')
logger.info("Audio to string successfully.")
logger.debug("Audio to string successfully.")
# save audio
if audio_path is not None:
......@@ -264,15 +264,15 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
logger.error(e)
sys.exit(-1)
logger.info("AM model: {}".format(self.config.am))
logger.info("Vocoder model: {}".format(self.config.voc))
logger.info("Language: {}".format(lang))
logger.debug("AM model: {}".format(self.config.am))
logger.debug("Vocoder model: {}".format(self.config.voc))
logger.debug("Language: {}".format(lang))
logger.info("tts engine type: python")
logger.info("audio duration: {}".format(duration))
logger.info("frontend inference time: {}".format(self.frontend_time))
logger.info("AM inference time: {}".format(self.am_time))
logger.info("Vocoder inference time: {}".format(self.voc_time))
logger.debug("frontend inference time: {}".format(self.frontend_time))
logger.debug("AM inference time: {}".format(self.am_time))
logger.debug("Vocoder inference time: {}".format(self.voc_time))
logger.info("total inference time: {}".format(infer_time))
logger.info(
"postprocess (change speed, volume, target sample rate) time: {}".
......@@ -280,6 +280,6 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
logger.info("total generate audio time: {}".format(infer_time +
postprocess_time))
logger.info("RTF: {}".format(rtf))
logger.info("device: {}".format(self.tts_engine.device))
logger.debug("device: {}".format(self.tts_engine.device))
return lang, target_sample_rate, duration, wav_base64
paddlespeech/server/engine/vector/python/vector_engine.py
浏览文件 @ 9c4763ec
......@@ -33,7 +33,7 @@ class PaddleVectorConnectionHandler:
vector_engine (VectorEngine): The Vector engine
"""
super().__init__()
logger.info(
logger.debug(
"Create PaddleVectorConnectionHandler to process the vector request")
self.vector_engine = vector_engine
self.executor = self.vector_engine.executor
......@@ -54,7 +54,7 @@ class PaddleVectorConnectionHandler:
Returns:
str: the punctuation text
"""
logger.info(
logger.debug(
f"start to extract the do vector {self.task} from the http request")
if self.task == "spk" and task == "spk":
embedding = self.extract_audio_embedding(audio_data)
......@@ -81,17 +81,17 @@ class PaddleVectorConnectionHandler:
Returns:
float: the score between enroll and test audio
"""
logger.info("start to extract the enroll audio embedding")
logger.debug("start to extract the enroll audio embedding")
enroll_emb = self.extract_audio_embedding(enroll_audio)
logger.info("start to extract the test audio embedding")
logger.debug("start to extract the test audio embedding")
test_emb = self.extract_audio_embedding(test_audio)
logger.info(
logger.debug(
"start to get the score between the enroll and test embedding")
score = self.executor.get_embeddings_score(enroll_emb, test_emb)
logger.info(f"get the enroll vs test score: {score}")
logger.debug(f"get the enroll vs test score: {score}")
return score
@paddle.no_grad()
......@@ -106,11 +106,12 @@ class PaddleVectorConnectionHandler:
# because the soundfile will change the io.BytesIO(audio) to the end
# thus we should convert the base64 string to io.BytesIO when we need the audio data
if not self.executor._check(io.BytesIO(audio), sample_rate):
logger.info("check the audio sample rate occurs error")
logger.debug("check the audio sample rate occurs error")
return np.array([0.0])
waveform, sr = load_audio(io.BytesIO(audio))
logger.info(f"load the audio sample points, shape is: {waveform.shape}")
logger.debug(
f"load the audio sample points, shape is: {waveform.shape}")
# stage 2: get the audio feat
# Note: Now we only support fbank feature
......@@ -121,9 +122,9 @@ class PaddleVectorConnectionHandler:
n_mels=self.config.n_mels,
window_size=self.config.window_size,
hop_length=self.config.hop_size)
logger.info(f"extract the audio feats, shape is: {feats.shape}")
logger.debug(f"extract the audio feats, shape is: {feats.shape}")
except Exception as e:
logger.info(f"feats occurs exception {e}")
logger.error(f"feats occurs exception {e}")
sys.exit(-1)
feats = paddle.to_tensor(feats).unsqueeze(0)
......@@ -159,7 +160,7 @@ class VectorEngine(BaseEngine):
"""The Vector Engine
"""
super(VectorEngine, self).__init__()
logger.info("Create the VectorEngine Instance")
logger.debug("Create the VectorEngine Instance")
def init(self, config: dict):
"""Init the Vector Engine
......@@ -170,7 +171,7 @@ class VectorEngine(BaseEngine):
Returns:
bool: The engine instance flag
"""
logger.info("Init the vector engine")
logger.debug("Init the vector engine")
try:
self.config = config
if self.config.device:
......@@ -179,7 +180,7 @@ class VectorEngine(BaseEngine):
self.device = paddle.get_device()
paddle.set_device(self.device)
logger.info(f"Vector Engine set the device: {self.device}")
logger.debug(f"Vector Engine set the device: {self.device}")
except BaseException as e:
logger.error(
"Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
......@@ -196,5 +197,7 @@ class VectorEngine(BaseEngine):
ckpt_path=config.ckpt_path,
task=config.task)
logger.info("Init the Vector engine successfully")
logger.info(
"Initialize Vector server engine successfully on device: %s." %
(self.device))
return True
paddlespeech/server/utils/audio_handler.py
浏览文件 @ 9c4763ec
......@@ -138,7 +138,7 @@ class ASRWsAudioHandler:
Returns:
str: the final asr result
"""
logging.info("send a message to the server")
logging.debug("send a message to the server")
if self.url is None:
logger.error("No asr server, please input valid ip and port")
......@@ -160,7 +160,7 @@ class ASRWsAudioHandler:
separators=(',', ': '))
await ws.send(audio_info)
msg = await ws.recv()
logger.info("client receive msg={}".format(msg))
logger.debug("client receive msg={}".format(msg))
# 3. send chunk audio data to engine
for chunk_data in self.read_wave(wavfile_path):
......@@ -170,7 +170,7 @@ class ASRWsAudioHandler:
if self.punc_server and len(msg["result"]) > 0:
msg["result"] = self.punc_server.run(msg["result"])
logger.info("client receive msg={}".format(msg))
logger.debug("client receive msg={}".format(msg))
# 4. we must send finished signal to the server
audio_info = json.dumps(
......@@ -310,7 +310,7 @@ class TTSWsHandler:
start_request = json.dumps({"task": "tts", "signal": "start"})
await ws.send(start_request)
msg = await ws.recv()
logger.info(f"client receive msg={msg}")
logger.debug(f"client receive msg={msg}")
msg = json.loads(msg)
session = msg["session"]
......@@ -319,7 +319,7 @@ class TTSWsHandler:
request = json.dumps({"text": text_base64})
st = time.time()
await ws.send(request)
logging.info("send a message to the server")
logging.debug("send a message to the server")
# 4. Process the received response
message = await ws.recv()
......@@ -543,7 +543,6 @@ class VectorHttpHandler:
"sample_rate": sample_rate,
}
logger.info(self.url)
res = requests.post(url=self.url, data=json.dumps(data))
return res.json()
......
paddlespeech/server/utils/audio_process.py
浏览文件 @ 9c4763ec
......@@ -169,7 +169,7 @@ def save_audio(bytes_data, audio_path, sample_rate: int=24000) -> bool:
sample_rate=sample_rate)
os.remove("./tmp.pcm")
else:
print("Only supports saved audio format is pcm or wav")
logger.error("Only supports saved audio format is pcm or wav")
return False
return True
paddlespeech/server/utils/onnx_infer.py
浏览文件 @ 9c4763ec
......@@ -20,7 +20,7 @@ from paddlespeech.cli.log import logger
def get_sess(model_path: Optional[os.PathLike]=None, sess_conf: dict=None):
logger.info(f"ort sessconf: {sess_conf}")
logger.debug(f"ort sessconf: {sess_conf}")
sess_options = ort.SessionOptions()
sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
if sess_conf.get('graph_optimization_level', 99) == 0:
......@@ -34,7 +34,7 @@ def get_sess(model_path: Optional[os.PathLike]=None, sess_conf: dict=None):
# fastspeech2/mb_melgan can't use trt now!
if sess_conf.get("use_trt", 0):
providers = ['TensorrtExecutionProvider']
logger.info(f"ort providers: {providers}")
logger.debug(f"ort providers: {providers}")
if 'cpu_threads' in sess_conf:
sess_options.intra_op_num_threads = sess_conf.get("cpu_threads", 0)
......
paddlespeech/server/utils/util.py
浏览文件 @ 9c4763ec
......@@ -13,6 +13,8 @@
import base64
import math
from paddlespeech.cli.log import logger
def wav2base64(wav_file: str):
"""
......@@ -61,7 +63,7 @@ def get_chunks(data, block_size, pad_size, step):
elif step == "voc":
data_len = data.shape[0]
else:
print("Please set correct type to get chunks, am or voc")
logger.error("Please set correct type to get chunks, am or voc")
chunks = []
n = math.ceil(data_len / block_size)
......@@ -73,7 +75,7 @@ def get_chunks(data, block_size, pad_size, step):
elif step == "voc":
chunks.append(data[start:end, :])
else:
print("Please set correct type to get chunks, am or voc")
logger.error("Please set correct type to get chunks, am or voc")
return chunks
......
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