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PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
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replace logger.info with logger.debug in cli, change default log level to INFO

Browse Source
pull/2111/head
TianYuan 3 years ago
parent cf846f9ebc
commit bc93bffbb4
15 changed files with 98 additions and 95 deletions
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40
paddlespeech/cli/asr/infer.py
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@ -133,11 +133,11 @@ class ASRExecutor(BaseExecutor):
""" """
Init model and other resources from a specific path. Init model and other resources from a specific path.
""" """
logger.info("start to init the model") logger.debug("start to init the model")
# default max_len: unit:second # default max_len: unit:second
self.max_len = 50 self.max_len = 50
if hasattr(self, 'model'): if hasattr(self, 'model'):
logger.info('Model had been initialized.') logger.debug('Model had been initialized.')
return return
if cfg_path is None or ckpt_path is None: if cfg_path is None or ckpt_path is None:
@ -151,15 +151,15 @@ class ASRExecutor(BaseExecutor):
self.ckpt_path = os.path.join( self.ckpt_path = os.path.join(
self.res_path, self.res_path,
self.task_resource.res_dict['ckpt_path'] + ".pdparams") self.task_resource.res_dict['ckpt_path'] + ".pdparams")
logger.info(self.res_path) logger.debug(self.res_path)
else: else:
self.cfg_path = os.path.abspath(cfg_path) self.cfg_path = os.path.abspath(cfg_path)
self.ckpt_path = os.path.abspath(ckpt_path + ".pdparams") self.ckpt_path = os.path.abspath(ckpt_path + ".pdparams")
self.res_path = os.path.dirname( self.res_path = os.path.dirname(
os.path.dirname(os.path.abspath(self.cfg_path))) os.path.dirname(os.path.abspath(self.cfg_path)))
logger.info(self.cfg_path) logger.debug(self.cfg_path)
logger.info(self.ckpt_path) logger.debug(self.ckpt_path)
#Init body. #Init body.
self.config = CfgNode(new_allowed=True) self.config = CfgNode(new_allowed=True)
@ -216,7 +216,7 @@ class ASRExecutor(BaseExecutor):
max_len = self.config.encoder_conf.max_len max_len = self.config.encoder_conf.max_len
self.max_len = frame_shift_ms * max_len * subsample_rate self.max_len = frame_shift_ms * max_len * subsample_rate
logger.info( logger.debug(
f"The asr server limit max duration len: {self.max_len}") f"The asr server limit max duration len: {self.max_len}")
def preprocess(self, model_type: str, input: Union[str, os.PathLike]): def preprocess(self, model_type: str, input: Union[str, os.PathLike]):
@ -227,15 +227,15 @@ class ASRExecutor(BaseExecutor):
audio_file = input audio_file = input
if isinstance(audio_file, (str, os.PathLike)): if isinstance(audio_file, (str, os.PathLike)):
logger.info("Preprocess audio_file:" + audio_file) logger.debug("Preprocess audio_file:" + audio_file)
# Get the object for feature extraction # Get the object for feature extraction
if "deepspeech2" in model_type or "conformer" in model_type or "transformer" in model_type: if "deepspeech2" in model_type or "conformer" in model_type or "transformer" in model_type:
logger.info("get the preprocess conf") logger.debug("get the preprocess conf")
preprocess_conf = self.config.preprocess_config preprocess_conf = self.config.preprocess_config
preprocess_args = {"train": False} preprocess_args = {"train": False}
preprocessing = Transformation(preprocess_conf) preprocessing = Transformation(preprocess_conf)
logger.info("read the audio file") logger.debug("read the audio file")
audio, audio_sample_rate = soundfile.read( audio, audio_sample_rate = soundfile.read(
audio_file, dtype="int16", always_2d=True) audio_file, dtype="int16", always_2d=True)
if self.change_format: if self.change_format:
@ -255,7 +255,7 @@ class ASRExecutor(BaseExecutor):
else: else:
audio = audio[:, 0] audio = audio[:, 0]
logger.info(f"audio shape: {audio.shape}") logger.debug(f"audio shape: {audio.shape}")
# fbank # fbank
audio = preprocessing(audio, **preprocess_args) audio = preprocessing(audio, **preprocess_args)
@ -264,19 +264,19 @@ class ASRExecutor(BaseExecutor):
self._inputs["audio"] = audio self._inputs["audio"] = audio
self._inputs["audio_len"] = audio_len self._inputs["audio_len"] = audio_len
logger.info(f"audio feat shape: {audio.shape}") logger.debug(f"audio feat shape: {audio.shape}")
else: else:
raise Exception("wrong type") raise Exception("wrong type")
logger.info("audio feat process success") logger.debug("audio feat process success")
@paddle.no_grad() @paddle.no_grad()
def infer(self, model_type: str): def infer(self, model_type: str):
""" """
Model inference and result stored in self.output. Model inference and result stored in self.output.
""" """
logger.info("start to infer the model to get the output") logger.debug("start to infer the model to get the output")
cfg = self.config.decode cfg = self.config.decode
audio = self._inputs["audio"] audio = self._inputs["audio"]
audio_len = self._inputs["audio_len"] audio_len = self._inputs["audio_len"]
@ -293,7 +293,7 @@ class ASRExecutor(BaseExecutor):
self._outputs["result"] = result_transcripts[0] self._outputs["result"] = result_transcripts[0]
elif "conformer" in model_type or "transformer" in model_type: elif "conformer" in model_type or "transformer" in model_type:
logger.info( logger.debug(
f"we will use the transformer like model : {model_type}") f"we will use the transformer like model : {model_type}")
try: try:
result_transcripts = self.model.decode( result_transcripts = self.model.decode(
@ -352,7 +352,7 @@ class ASRExecutor(BaseExecutor):
logger.error("Please input the right audio file path") logger.error("Please input the right audio file path")
return False return False
logger.info("checking the audio file format......") logger.debug("checking the audio file format......")
try: try:
audio, audio_sample_rate = soundfile.read( audio, audio_sample_rate = soundfile.read(
audio_file, dtype="int16", always_2d=True) audio_file, dtype="int16", always_2d=True)
@ -374,7 +374,7 @@ class ASRExecutor(BaseExecutor):
sox input_audio.xx --rate 8k --bits 16 --channels 1 output_audio.wav \n \ sox input_audio.xx --rate 8k --bits 16 --channels 1 output_audio.wav \n \
") ")
return False return False
logger.info("The sample rate is %d" % audio_sample_rate) logger.debug("The sample rate is %d" % audio_sample_rate)
if audio_sample_rate != self.sample_rate: if audio_sample_rate != self.sample_rate:
logger.warning("The sample rate of the input file is not {}.\n \ logger.warning("The sample rate of the input file is not {}.\n \
The program will resample the wav file to {}.\n \ The program will resample the wav file to {}.\n \
@ -383,28 +383,28 @@ class ASRExecutor(BaseExecutor):
".format(self.sample_rate, self.sample_rate)) ".format(self.sample_rate, self.sample_rate))
if force_yes is False: if force_yes is False:
while (True): while (True):
logger.info( logger.debug(
"Whether to change the sample rate and the channel. Y: change the sample. N: exit the prgream." "Whether to change the sample rate and the channel. Y: change the sample. N: exit the prgream."
) )
content = input("Input(Y/N):") content = input("Input(Y/N):")
if content.strip() == "Y" or content.strip( if content.strip() == "Y" or content.strip(
) == "y" or content.strip() == "yes" or content.strip( ) == "y" or content.strip() == "yes" or content.strip(
) == "Yes": ) == "Yes":
logger.info( logger.debug(
"change the sampele rate, channel to 16k and 1 channel" "change the sampele rate, channel to 16k and 1 channel"
) )
break break
elif content.strip() == "N" or content.strip( elif content.strip() == "N" or content.strip(
) == "n" or content.strip() == "no" or content.strip( ) == "n" or content.strip() == "no" or content.strip(
) == "No": ) == "No":
logger.info("Exit the program") logger.debug("Exit the program")
return False return False
else: else:
logger.warning("Not regular input, please input again") logger.warning("Not regular input, please input again")
self.change_format = True self.change_format = True
else: else:
logger.info("The audio file format is right") logger.debug("The audio file format is right")
self.change_format = False self.change_format = False
return True return True

6
paddlespeech/cli/cls/infer.py
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@ -92,7 +92,7 @@ class CLSExecutor(BaseExecutor):
Init model and other resources from a specific path. Init model and other resources from a specific path.
""" """
if hasattr(self, 'model'): if hasattr(self, 'model'):
logger.info('Model had been initialized.') logger.debug('Model had been initialized.')
return return
if label_file is None or ckpt_path is None: if label_file is None or ckpt_path is None:
@ -135,14 +135,14 @@ class CLSExecutor(BaseExecutor):
Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet). Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
""" """
feat_conf = self._conf['feature'] feat_conf = self._conf['feature']
logger.info(feat_conf) logger.debug(feat_conf)
waveform, _ = load( waveform, _ = load(
file=audio_file, file=audio_file,
sr=feat_conf['sample_rate'], sr=feat_conf['sample_rate'],
mono=True, mono=True,
dtype='float32') dtype='float32')
if isinstance(audio_file, (str, os.PathLike)): if isinstance(audio_file, (str, os.PathLike)):
logger.info("Preprocessing audio_file:" + audio_file) logger.debug("Preprocessing audio_file:" + audio_file)
# Feature extraction # Feature extraction
feature_extractor = LogMelSpectrogram( feature_extractor = LogMelSpectrogram(

16
paddlespeech/cli/download.py
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@ -61,7 +61,7 @@ def _get_unique_endpoints(trainer_endpoints):
continue continue
ips.add(ip) ips.add(ip)
unique_endpoints.add(endpoint) unique_endpoints.add(endpoint)
logger.info("unique_endpoints {}".format(unique_endpoints)) logger.debug("unique_endpoints {}".format(unique_endpoints))
return unique_endpoints return unique_endpoints
@ -96,7 +96,7 @@ def get_path_from_url(url,
# data, and the same ip will only download data once. # data, and the same ip will only download data once.
unique_endpoints = _get_unique_endpoints(ParallelEnv().trainer_endpoints[:]) unique_endpoints = _get_unique_endpoints(ParallelEnv().trainer_endpoints[:])
if osp.exists(fullpath) and check_exist and _md5check(fullpath, md5sum): if osp.exists(fullpath) and check_exist and _md5check(fullpath, md5sum):
logger.info("Found {}".format(fullpath)) logger.debug("Found {}".format(fullpath))
else: else:
if ParallelEnv().current_endpoint in unique_endpoints: if ParallelEnv().current_endpoint in unique_endpoints:
fullpath = _download(url, root_dir, md5sum, method=method) fullpath = _download(url, root_dir, md5sum, method=method)
@ -118,7 +118,7 @@ def _get_download(url, fullname):
try: try:
req = requests.get(url, stream=True) req = requests.get(url, stream=True)
except Exception as e: # requests.exceptions.ConnectionError except Exception as e: # requests.exceptions.ConnectionError
logger.info("Downloading {} from {} failed with exception {}".format( logger.debug("Downloading {} from {} failed with exception {}".format(
fname, url, str(e))) fname, url, str(e)))
return False return False
@ -190,7 +190,7 @@ def _download(url, path, md5sum=None, method='get'):
fullname = osp.join(path, fname) fullname = osp.join(path, fname)
retry_cnt = 0 retry_cnt = 0
logger.info("Downloading {} from {}".format(fname, url)) logger.debug("Downloading {} from {}".format(fname, url))
while not (osp.exists(fullname) and _md5check(fullname, md5sum)): while not (osp.exists(fullname) and _md5check(fullname, md5sum)):
if retry_cnt < DOWNLOAD_RETRY_LIMIT: if retry_cnt < DOWNLOAD_RETRY_LIMIT:
retry_cnt += 1 retry_cnt += 1
@ -209,7 +209,7 @@ def _md5check(fullname, md5sum=None):
if md5sum is None: if md5sum is None:
return True return True
logger.info("File {} md5 checking...".format(fullname)) logger.debug("File {} md5 checking...".format(fullname))
md5 = hashlib.md5() md5 = hashlib.md5()
with open(fullname, 'rb') as f: with open(fullname, 'rb') as f:
for chunk in iter(lambda: f.read(4096), b""): for chunk in iter(lambda: f.read(4096), b""):
@ -217,8 +217,8 @@ def _md5check(fullname, md5sum=None):
calc_md5sum = md5.hexdigest() calc_md5sum = md5.hexdigest()
if calc_md5sum != md5sum: if calc_md5sum != md5sum:
logger.info("File {} md5 check failed, {}(calc) != " logger.debug("File {} md5 check failed, {}(calc) != "
"{}(base)".format(fullname, calc_md5sum, md5sum)) "{}(base)".format(fullname, calc_md5sum, md5sum))
return False return False
return True return True
@ -227,7 +227,7 @@ def _decompress(fname):
""" """
Decompress for zip and tar file Decompress for zip and tar file
""" """
logger.info("Decompressing {}...".format(fname)) logger.debug("Decompressing {}...".format(fname))
# For protecting decompressing interupted, # For protecting decompressing interupted,
# decompress to fpath_tmp directory firstly, if decompress # decompress to fpath_tmp directory firstly, if decompress

4
paddlespeech/cli/kws/infer.py
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@ -88,7 +88,7 @@ class KWSExecutor(BaseExecutor):
Init model and other resources from a specific path. Init model and other resources from a specific path.
""" """
if hasattr(self, 'model'): if hasattr(self, 'model'):
logger.info('Model had been initialized.') logger.debug('Model had been initialized.')
return return
if ckpt_path is None: if ckpt_path is None:
@ -141,7 +141,7 @@ class KWSExecutor(BaseExecutor):
assert os.path.isfile(audio_file) assert os.path.isfile(audio_file)
waveform, _ = load(audio_file) waveform, _ = load(audio_file)
if isinstance(audio_file, (str, os.PathLike)): if isinstance(audio_file, (str, os.PathLike)):
logger.info("Preprocessing audio_file:" + audio_file) logger.debug("Preprocessing audio_file:" + audio_file)
# Feature extraction # Feature extraction
waveform = paddle.to_tensor(waveform).unsqueeze(0) waveform = paddle.to_tensor(waveform).unsqueeze(0)

2
paddlespeech/cli/log.py
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@ -49,7 +49,7 @@ class Logger(object):
self.handler.setFormatter(self.format) self.handler.setFormatter(self.format)
self.logger.addHandler(self.handler) self.logger.addHandler(self.handler)
self.logger.setLevel(logging.DEBUG) self.logger.setLevel(logging.INFO)
self.logger.propagate = False self.logger.propagate = False
def __call__(self, log_level: str, msg: str): def __call__(self, log_level: str, msg: str):

10
paddlespeech/cli/st/infer.py
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@ -110,7 +110,7 @@ class STExecutor(BaseExecutor):
""" """
decompressed_path = download_and_decompress(self.kaldi_bins, MODEL_HOME) decompressed_path = download_and_decompress(self.kaldi_bins, MODEL_HOME)
decompressed_path = os.path.abspath(decompressed_path) decompressed_path = os.path.abspath(decompressed_path)
logger.info("Kaldi_bins stored in: {}".format(decompressed_path)) logger.debug("Kaldi_bins stored in: {}".format(decompressed_path))
if "LD_LIBRARY_PATH" in os.environ: if "LD_LIBRARY_PATH" in os.environ:
os.environ["LD_LIBRARY_PATH"] += f":{decompressed_path}" os.environ["LD_LIBRARY_PATH"] += f":{decompressed_path}"
else: else:
@ -128,7 +128,7 @@ class STExecutor(BaseExecutor):
Init model and other resources from a specific path. Init model and other resources from a specific path.
""" """
if hasattr(self, 'model'): if hasattr(self, 'model'):
logger.info('Model had been initialized.') logger.debug('Model had been initialized.')
return return
if cfg_path is None or ckpt_path is None: if cfg_path is None or ckpt_path is None:
@ -140,8 +140,8 @@ class STExecutor(BaseExecutor):
self.ckpt_path = os.path.join( self.ckpt_path = os.path.join(
self.task_resource.res_dir, self.task_resource.res_dir,
self.task_resource.res_dict['ckpt_path']) self.task_resource.res_dict['ckpt_path'])
logger.info(self.cfg_path) logger.debug(self.cfg_path)
logger.info(self.ckpt_path) logger.debug(self.ckpt_path)
res_path = self.task_resource.res_dir res_path = self.task_resource.res_dir
else: else:
self.cfg_path = os.path.abspath(cfg_path) self.cfg_path = os.path.abspath(cfg_path)
@ -192,7 +192,7 @@ class STExecutor(BaseExecutor):
Input content can be a file(wav). Input content can be a file(wav).
""" """
audio_file = os.path.abspath(wav_file) audio_file = os.path.abspath(wav_file)
logger.info("Preprocess audio_file:" + audio_file) logger.debug("Preprocess audio_file:" + audio_file)
if "fat_st" in model_type: if "fat_st" in model_type:
cmvn = self.config.cmvn_path cmvn = self.config.cmvn_path

2
paddlespeech/cli/text/infer.py
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@ -98,7 +98,7 @@ class TextExecutor(BaseExecutor):
Init model and other resources from a specific path. Init model and other resources from a specific path.
""" """
if hasattr(self, 'model'): if hasattr(self, 'model'):
logger.info('Model had been initialized.') logger.debug('Model had been initialized.')
return return
self.task = task self.task = task

14
paddlespeech/cli/tts/infer.py
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@ -173,7 +173,7 @@ class TTSExecutor(BaseExecutor):
Init model and other resources from a specific path. Init model and other resources from a specific path.
""" """
if hasattr(self, 'am_inference') and hasattr(self, 'voc_inference'): if hasattr(self, 'am_inference') and hasattr(self, 'voc_inference'):
logger.info('Models had been initialized.') logger.debug('Models had been initialized.')
return return
# am # am
@ -200,9 +200,9 @@ class TTSExecutor(BaseExecutor):
# must have phones_dict in acoustic # must have phones_dict in acoustic
self.phones_dict = os.path.join( self.phones_dict = os.path.join(
self.am_res_path, self.task_resource.res_dict['phones_dict']) self.am_res_path, self.task_resource.res_dict['phones_dict'])
logger.info(self.am_res_path) logger.debug(self.am_res_path)
logger.info(self.am_config) logger.debug(self.am_config)
logger.info(self.am_ckpt) logger.debug(self.am_ckpt)
else: else:
self.am_config = os.path.abspath(am_config) self.am_config = os.path.abspath(am_config)
self.am_ckpt = os.path.abspath(am_ckpt) self.am_ckpt = os.path.abspath(am_ckpt)
@ -248,9 +248,9 @@ class TTSExecutor(BaseExecutor):
self.voc_stat = os.path.join( self.voc_stat = os.path.join(
self.voc_res_path, self.voc_res_path,
self.task_resource.voc_res_dict['speech_stats']) self.task_resource.voc_res_dict['speech_stats'])
logger.info(self.voc_res_path) logger.debug(self.voc_res_path)
logger.info(self.voc_config) logger.debug(self.voc_config)
logger.info(self.voc_ckpt) logger.debug(self.voc_ckpt)
else: else:
self.voc_config = os.path.abspath(voc_config) self.voc_config = os.path.abspath(voc_config)
self.voc_ckpt = os.path.abspath(voc_ckpt) self.voc_ckpt = os.path.abspath(voc_ckpt)

61
paddlespeech/cli/vector/infer.py
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@ -117,7 +117,7 @@ class VectorExecutor(BaseExecutor):
# stage 2: read the input data and store them as a list # stage 2: read the input data and store them as a list
task_source = self.get_input_source(parser_args.input) task_source = self.get_input_source(parser_args.input)
logger.info(f"task source: {task_source}") logger.debug(f"task source: {task_source}")
# stage 3: process the audio one by one # stage 3: process the audio one by one
# we do action according the task type # we do action according the task type
@ -127,13 +127,13 @@ class VectorExecutor(BaseExecutor):
try: try:
# extract the speaker audio embedding # extract the speaker audio embedding
if parser_args.task == "spk": if parser_args.task == "spk":
logger.info("do vector spk task") logger.debug("do vector spk task")
res = self(input_, model, sample_rate, config, ckpt_path, res = self(input_, model, sample_rate, config, ckpt_path,
device) device)
task_result[id_] = res task_result[id_] = res
elif parser_args.task == "score": elif parser_args.task == "score":
logger.info("do vector score task") logger.debug("do vector score task")
logger.info(f"input content {input_}") logger.debug(f"input content {input_}")
if len(input_.split()) != 2: if len(input_.split()) != 2:
logger.error( logger.error(
f"vector score task input {input_} wav num is not two," f"vector score task input {input_} wav num is not two,"
@ -142,7 +142,7 @@ class VectorExecutor(BaseExecutor):
# get the enroll and test embedding # get the enroll and test embedding
enroll_audio, test_audio = input_.split() enroll_audio, test_audio = input_.split()
logger.info( logger.debug(
f"score task, enroll audio: {enroll_audio}, test audio: {test_audio}" f"score task, enroll audio: {enroll_audio}, test audio: {test_audio}"
) )
enroll_embedding = self(enroll_audio, model, sample_rate, enroll_embedding = self(enroll_audio, model, sample_rate,
@ -158,8 +158,8 @@ class VectorExecutor(BaseExecutor):
has_exceptions = True has_exceptions = True
task_result[id_] = f'{e.__class__.__name__}: {e}' task_result[id_] = f'{e.__class__.__name__}: {e}'
logger.info("task result as follows: ") logger.debug("task result as follows: ")
logger.info(f"{task_result}") logger.debug(f"{task_result}")
# stage 4: process the all the task results # stage 4: process the all the task results
self.process_task_results(parser_args.input, task_result, self.process_task_results(parser_args.input, task_result,
@ -207,7 +207,7 @@ class VectorExecutor(BaseExecutor):
""" """
if not hasattr(self, "score_func"): if not hasattr(self, "score_func"):
self.score_func = paddle.nn.CosineSimilarity(axis=0) self.score_func = paddle.nn.CosineSimilarity(axis=0)
logger.info("create the cosine score function ") logger.debug("create the cosine score function ")
score = self.score_func( score = self.score_func(
paddle.to_tensor(enroll_embedding), paddle.to_tensor(enroll_embedding),
@ -244,7 +244,7 @@ class VectorExecutor(BaseExecutor):
sys.exit(-1) sys.exit(-1)
# stage 1: set the paddle runtime host device # stage 1: set the paddle runtime host device
logger.info(f"device type: {device}") logger.debug(f"device type: {device}")
paddle.device.set_device(device) paddle.device.set_device(device)
# stage 2: read the specific pretrained model # stage 2: read the specific pretrained model
@ -283,7 +283,7 @@ class VectorExecutor(BaseExecutor):
# stage 0: avoid to init the mode again # stage 0: avoid to init the mode again
self.task = task self.task = task
if hasattr(self, "model"): if hasattr(self, "model"):
logger.info("Model has been initialized") logger.debug("Model has been initialized")
return return
# stage 1: get the model and config path # stage 1: get the model and config path
@ -294,7 +294,7 @@ class VectorExecutor(BaseExecutor):
sample_rate_str = "16k" if sample_rate == 16000 else "8k" sample_rate_str = "16k" if sample_rate == 16000 else "8k"
tag = model_type + "-" + sample_rate_str tag = model_type + "-" + sample_rate_str
self.task_resource.set_task_model(tag, version=None) self.task_resource.set_task_model(tag, version=None)
logger.info(f"load the pretrained model: {tag}") logger.debug(f"load the pretrained model: {tag}")
# get the model from the pretrained list # get the model from the pretrained list
# we download the pretrained model and store it in the res_path # we download the pretrained model and store it in the res_path
self.res_path = self.task_resource.res_dir self.res_path = self.task_resource.res_dir
@ -312,19 +312,19 @@ class VectorExecutor(BaseExecutor):
self.res_path = os.path.dirname( self.res_path = os.path.dirname(
os.path.dirname(os.path.abspath(self.cfg_path))) os.path.dirname(os.path.abspath(self.cfg_path)))
logger.info(f"start to read the ckpt from {self.ckpt_path}") logger.debug(f"start to read the ckpt from {self.ckpt_path}")
logger.info(f"read the config from {self.cfg_path}") logger.debug(f"read the config from {self.cfg_path}")
logger.info(f"get the res path {self.res_path}") logger.debug(f"get the res path {self.res_path}")
# stage 2: read and config and init the model body # stage 2: read and config and init the model body
self.config = CfgNode(new_allowed=True) self.config = CfgNode(new_allowed=True)
self.config.merge_from_file(self.cfg_path) self.config.merge_from_file(self.cfg_path)
# stage 3: get the model name to instance the model network with dynamic_import # stage 3: get the model name to instance the model network with dynamic_import
logger.info("start to dynamic import the model class") logger.debug("start to dynamic import the model class")
model_name = model_type[:model_type.rindex('_')] model_name = model_type[:model_type.rindex('_')]
model_class = self.task_resource.get_model_class(model_name) model_class = self.task_resource.get_model_class(model_name)
logger.info(f"model name {model_name}") logger.debug(f"model name {model_name}")
model_conf = self.config.model model_conf = self.config.model
backbone = model_class(**model_conf) backbone = model_class(**model_conf)
model = SpeakerIdetification( model = SpeakerIdetification(
@ -333,11 +333,11 @@ class VectorExecutor(BaseExecutor):
self.model.eval() self.model.eval()
# stage 4: load the model parameters # stage 4: load the model parameters
logger.info("start to set the model parameters to model") logger.debug("start to set the model parameters to model")
model_dict = paddle.load(self.ckpt_path) model_dict = paddle.load(self.ckpt_path)
self.model.set_state_dict(model_dict) self.model.set_state_dict(model_dict)
logger.info("create the model instance success") logger.debug("create the model instance success")
@paddle.no_grad() @paddle.no_grad()
def infer(self, model_type: str): def infer(self, model_type: str):
@ -349,14 +349,14 @@ class VectorExecutor(BaseExecutor):
# stage 0: get the feat and length from _inputs # stage 0: get the feat and length from _inputs
feats = self._inputs["feats"] feats = self._inputs["feats"]
lengths = self._inputs["lengths"] lengths = self._inputs["lengths"]
logger.info("start to do backbone network model forward") logger.debug("start to do backbone network model forward")
logger.info( logger.debug(
f"feats shape:{feats.shape}, lengths shape: {lengths.shape}") f"feats shape:{feats.shape}, lengths shape: {lengths.shape}")
# stage 1: get the audio embedding # stage 1: get the audio embedding
# embedding from (1, emb_size, 1) -> (emb_size) # embedding from (1, emb_size, 1) -> (emb_size)
embedding = self.model.backbone(feats, lengths).squeeze().numpy() embedding = self.model.backbone(feats, lengths).squeeze().numpy()
logger.info(f"embedding size: {embedding.shape}") logger.debug(f"embedding size: {embedding.shape}")
# stage 2: put the embedding and dim info to _outputs property # stage 2: put the embedding and dim info to _outputs property
# the embedding type is numpy.array # the embedding type is numpy.array
@ -380,12 +380,13 @@ class VectorExecutor(BaseExecutor):
""" """
audio_file = input_file audio_file = input_file
if isinstance(audio_file, (str, os.PathLike)): if isinstance(audio_file, (str, os.PathLike)):
logger.info(f"Preprocess audio file: {audio_file}") logger.debug(f"Preprocess audio file: {audio_file}")
# stage 1: load the audio sample points # stage 1: load the audio sample points
# Note: this process must match the training process # Note: this process must match the training process
waveform, sr = load_audio(audio_file) waveform, sr = load_audio(audio_file)
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 # stage 2: get the audio feat
# Note: Now we only support fbank feature # Note: Now we only support fbank feature
@ -396,9 +397,9 @@ class VectorExecutor(BaseExecutor):
n_mels=self.config.n_mels, n_mels=self.config.n_mels,
window_size=self.config.window_size, window_size=self.config.window_size,
hop_length=self.config.hop_size) hop_length=self.config.hop_size)
logger.info(f"extract the audio feat, shape is: {feat.shape}") logger.debug(f"extract the audio feat, shape is: {feat.shape}")
except Exception as e: except Exception as e:
logger.info(f"feat occurs exception {e}") logger.debug(f"feat occurs exception {e}")
sys.exit(-1) sys.exit(-1)
feat = paddle.to_tensor(feat).unsqueeze(0) feat = paddle.to_tensor(feat).unsqueeze(0)
@ -411,11 +412,11 @@ class VectorExecutor(BaseExecutor):
# stage 4: store the feat and length in the _inputs, # stage 4: store the feat and length in the _inputs,
# which will be used in other function # which will be used in other function
logger.info(f"feats shape: {feat.shape}") logger.debug(f"feats shape: {feat.shape}")
self._inputs["feats"] = feat self._inputs["feats"] = feat
self._inputs["lengths"] = lengths self._inputs["lengths"] = lengths
logger.info("audio extract the feat success") logger.debug("audio extract the feat success")
def _check(self, audio_file: str, sample_rate: int): def _check(self, audio_file: str, sample_rate: int):
"""Check if the model sample match the audio sample rate """Check if the model sample match the audio sample rate
@ -441,7 +442,7 @@ class VectorExecutor(BaseExecutor):
logger.error("Please input the right audio file path") logger.error("Please input the right audio file path")
return False return False
logger.info("checking the aduio file format......") logger.debug("checking the aduio file format......")
try: try:
audio, audio_sample_rate = soundfile.read( audio, audio_sample_rate = soundfile.read(
audio_file, dtype="float32", always_2d=True) audio_file, dtype="float32", always_2d=True)
@ -458,7 +459,7 @@ class VectorExecutor(BaseExecutor):
") ")
return False return False
logger.info(f"The sample rate is {audio_sample_rate}") logger.debug(f"The sample rate is {audio_sample_rate}")
if audio_sample_rate != self.sample_rate: if audio_sample_rate != self.sample_rate:
logger.error("The sample rate of the input file is not {}.\n \ logger.error("The sample rate of the input file is not {}.\n \
@ -468,6 +469,6 @@ class VectorExecutor(BaseExecutor):
".format(self.sample_rate, self.sample_rate)) ".format(self.sample_rate, self.sample_rate))
sys.exit(-1) sys.exit(-1)
else: else:
logger.info("The audio file format is right") logger.debug("The audio file format is right")
return True return True

6
paddlespeech/s2t/frontend/augmentor/spec_augment.py
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@ -16,7 +16,7 @@ import random
import numpy as np import numpy as np
from PIL import Image from PIL import Image
from PIL.Image import BICUBIC from PIL.Image import Resampling
from paddlespeech.s2t.frontend.augmentor.base import AugmentorBase from paddlespeech.s2t.frontend.augmentor.base import AugmentorBase
from paddlespeech.s2t.utils.log import Log from paddlespeech.s2t.utils.log import Log
@ -164,9 +164,9 @@ class SpecAugmentor(AugmentorBase):
window) + 1 # 1 ... t - 1 window) + 1 # 1 ... t - 1
left = Image.fromarray(x[:center]).resize((x.shape[1], warped), left = Image.fromarray(x[:center]).resize((x.shape[1], warped),
BICUBIC) Resampling.BICUBIC)
right = Image.fromarray(x[center:]).resize((x.shape[1], t - warped), right = Image.fromarray(x[center:]).resize((x.shape[1], t - warped),
BICUBIC) Resampling.BICUBIC)
if self.inplace: if self.inplace:
x[:warped] = left x[:warped] = left
x[warped:] = right x[warped:] = right

12
paddlespeech/s2t/frontend/featurizer/text_featurizer.py
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@ -226,10 +226,10 @@ class TextFeaturizer():
sos_id = vocab_list.index(SOS) if SOS in vocab_list else -1 sos_id = vocab_list.index(SOS) if SOS in vocab_list else -1
space_id = vocab_list.index(SPACE) if SPACE in vocab_list else -1 space_id = vocab_list.index(SPACE) if SPACE in vocab_list else -1
logger.info(f"BLANK id: {blank_id}") logger.debug(f"BLANK id: {blank_id}")
logger.info(f"UNK id: {unk_id}") logger.debug(f"UNK id: {unk_id}")
logger.info(f"EOS id: {eos_id}") logger.debug(f"EOS id: {eos_id}")
logger.info(f"SOS id: {sos_id}") logger.debug(f"SOS id: {sos_id}")
logger.info(f"SPACE id: {space_id}") logger.debug(f"SPACE id: {space_id}")
logger.info(f"MASKCTC id: {maskctc_id}") logger.debug(f"MASKCTC id: {maskctc_id}")
return token2id, id2token, vocab_list, unk_id, eos_id, blank_id return token2id, id2token, vocab_list, unk_id, eos_id, blank_id

4
paddlespeech/s2t/models/u2/u2.py
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@ -827,7 +827,7 @@ class U2Model(U2DecodeModel):
# encoder # encoder
encoder_type = configs.get('encoder', 'transformer') encoder_type = configs.get('encoder', 'transformer')
logger.info(f"U2 Encoder type: {encoder_type}") logger.debug(f"U2 Encoder type: {encoder_type}")
if encoder_type == 'transformer': if encoder_type == 'transformer':
encoder = TransformerEncoder( encoder = TransformerEncoder(
input_dim, global_cmvn=global_cmvn, **configs['encoder_conf']) input_dim, global_cmvn=global_cmvn, **configs['encoder_conf'])
@ -894,7 +894,7 @@ class U2Model(U2DecodeModel):
if checkpoint_path: if checkpoint_path:
infos = checkpoint.Checkpoint().load_parameters( infos = checkpoint.Checkpoint().load_parameters(
model, checkpoint_path=checkpoint_path) model, checkpoint_path=checkpoint_path)
logger.info(f"checkpoint info: {infos}") logger.debug(f"checkpoint info: {infos}")
layer_tools.summary(model) layer_tools.summary(model)
return model return model

7
paddlespeech/s2t/modules/loss.py
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@ -37,9 +37,9 @@ class CTCLoss(nn.Layer):
self.loss = nn.CTCLoss(blank=blank, reduction=reduction) self.loss = nn.CTCLoss(blank=blank, reduction=reduction)
self.batch_average = batch_average self.batch_average = batch_average
logger.info( logger.debug(
f"CTCLoss Loss reduction: {reduction}, div-bs: {batch_average}") f"CTCLoss Loss reduction: {reduction}, div-bs: {batch_average}")
logger.info(f"CTCLoss Grad Norm Type: {grad_norm_type}") logger.debug(f"CTCLoss Grad Norm Type: {grad_norm_type}")
assert grad_norm_type in ('instance', 'batch', 'frame', None) assert grad_norm_type in ('instance', 'batch', 'frame', None)
self.norm_by_times = False self.norm_by_times = False
@ -70,7 +70,8 @@ class CTCLoss(nn.Layer):
param = {} param = {}
self._kwargs = {k: v for k, v in kwargs.items() if k in param} self._kwargs = {k: v for k, v in kwargs.items() if k in param}
_notin = {k: v for k, v in kwargs.items() if k not in param} _notin = {k: v for k, v in kwargs.items() if k not in param}
logger.info(f"{self.loss} kwargs:{self._kwargs}, not support: {_notin}") logger.debug(
f"{self.loss} kwargs:{self._kwargs}, not support: {_notin}")
def forward(self, logits, ys_pad, hlens, ys_lens): def forward(self, logits, ys_pad, hlens, ys_lens):
"""Compute CTC loss. """Compute CTC loss.

7
paddlespeech/s2t/transform/spec_augment.py
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@ -17,7 +17,7 @@ import random
import numpy import numpy
from PIL import Image from PIL import Image
from PIL.Image import BICUBIC from PIL.Image import Resampling
from paddlespeech.s2t.transform.functional import FuncTrans from paddlespeech.s2t.transform.functional import FuncTrans
@ -46,9 +46,10 @@ def time_warp(x, max_time_warp=80, inplace=False, mode="PIL"):
warped = random.randrange(center - window, center + warped = random.randrange(center - window, center +
window) + 1 # 1 ... t - 1 window) + 1 # 1 ... t - 1
left = Image.fromarray(x[:center]).resize((x.shape[1], warped), BICUBIC) left = Image.fromarray(x[:center]).resize((x.shape[1], warped),
Resampling.BICUBIC)
right = Image.fromarray(x[center:]).resize((x.shape[1], t - warped), right = Image.fromarray(x[center:]).resize((x.shape[1], t - warped),
BICUBIC) Resampling.BICUBIC)
if inplace: if inplace:
x[:warped] = left x[:warped] = left
x[warped:] = right x[warped:] = right

2
paddlespeech/s2t/utils/tensor_utils.py
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@ -94,7 +94,7 @@ def pad_sequence(sequences: List[paddle.Tensor],
for i, tensor in enumerate(sequences): for i, tensor in enumerate(sequences):
length = tensor.shape[0] length = tensor.shape[0]
# use index notation to prevent duplicate references to the tensor # use index notation to prevent duplicate references to the tensor
logger.info( logger.debug(
f"length {length}, out_tensor {out_tensor.shape}, tensor {tensor.shape}" f"length {length}, out_tensor {out_tensor.shape}, tensor {tensor.shape}"
) )
if batch_first: if batch_first:

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