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Merge pull request #2 from Jackwaterveg/cli_infer

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KP 3 years ago committed by GitHub
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paddlespeech/cli/asr/infer.py
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@ -18,17 +18,17 @@ from typing import List
from typing import Optional
from typing import Union
import librosa
import paddle
import soundfile
from yacs.config import CfgNode
from ..executor import BaseExecutor
from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import logger
from ..utils import MODEL_HOME
from paddlespeech.s2t.exps.u2.config import get_cfg_defaults
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.io.collator import SpeechCollator
from paddlespeech.s2t.transform.transformation import Transformation
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.s2t.utils.utility import UpdateConfig
@ -36,7 +36,7 @@ from paddlespeech.s2t.utils.utility import UpdateConfig
__all__ = ['ASRExecutor']
pretrained_models = {
"wenetspeech_zh": {
"wenetspeech_zh_16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/conformer.model.tar.gz',
'md5':
@ -73,7 +73,15 @@ class ASRExecutor(BaseExecutor):
default='wenetspeech',
help='Choose model type of asr task.')
self.parser.add_argument(
'--lang', type=str, default='zh', help='Choose model language.')
'--lang',
type=str,
default='zh',
help='Choose model language. zh or en')
self.parser.add_argument(
"--model_sample_rate",
type=int,
default=16000,
help='Choose the audio sample rate of the model. 8000 or 16000')
self.parser.add_argument(
'--config',
type=str,
@ -109,13 +117,16 @@ class ASRExecutor(BaseExecutor):
def _init_from_path(self,
model_type: str='wenetspeech',
lang: str='zh',
model_sample_rate: int=16000,
cfg_path: Optional[os.PathLike]=None,
ckpt_path: Optional[os.PathLike]=None):
ckpt_path: Optional[os.PathLike]=None,
device: str='cpu'):
"""
Init model and other resources from a specific path.
"""
if cfg_path is None or ckpt_path is None:
tag = model_type + '_' + lang
model_sample_rate_str = '16k' if model_sample_rate == 16000 else '8k'
tag = model_type + '_' + lang + '_' + model_sample_rate_str
res_path = self._get_pretrained_path(tag) # wenetspeech_zh
self.cfg_path = os.path.join(res_path,
pretrained_models[tag]['cfg_path'])
@ -130,40 +141,44 @@ class ASRExecutor(BaseExecutor):
res_path = os.path.dirname(
os.path.dirname(os.path.abspath(self.cfg_path)))
# Enter the path of model root
os.chdir(res_path)
#Init body.
parser_args = self.parser_args
paddle.set_device(parser_args.device)
self.config = get_cfg_defaults()
paddle.set_device(device)
self.config = CfgNode(new_allowed=True)
self.config.merge_from_file(self.cfg_path)
self.config.decoding.decoding_method = "attention_rescoring"
#self.config.freeze()
model_conf = self.config.model
logger.info(model_conf)
with UpdateConfig(model_conf):
if parser_args.model == "ds2_online" or parser_args.model == "ds2_offline":
if model_type == "ds2_online" or model_type == "ds2_offline":
from paddlespeech.s2t.io.collator import SpeechCollator
self.config.collator.vocab_filepath = os.path.join(
res_path, self.config.collator.vocab_filepath)
self.config.collator.vocab_filepath = os.path.join(
self.config.collator.mean_std_filepath = os.path.join(
res_path, self.config.collator.cmvn_path)
self.collate_fn_test = SpeechCollator.from_config(self.config)
model_conf.feat_size = self.collate_fn_test.feature_size
model_conf.dict_size = self.text_feature.vocab_size
elif parser_args.model == "conformer" or parser_args.model == "transformer" or parser_args.model == "wenetspeech":
text_feature = TextFeaturizer(
unit_type=self.config.collator.unit_type,
vocab_filepath=self.config.collator.vocab_filepath,
spm_model_prefix=self.config.collator.spm_model_prefix)
model_conf.input_dim = self.collate_fn_test.feature_size
model_conf.output_dim = text_feature.vocab_size
elif model_type == "conformer" or model_type == "transformer" or model_type == "wenetspeech":
self.config.collator.vocab_filepath = os.path.join(
res_path, self.config.collator.vocab_filepath)
self.text_feature = TextFeaturizer(
text_feature = TextFeaturizer(
unit_type=self.config.collator.unit_type,
vocab_filepath=self.config.collator.vocab_filepath,
spm_model_prefix=self.config.collator.spm_model_prefix)
model_conf.input_dim = self.config.collator.feat_dim
model_conf.output_dim = self.text_feature.vocab_size
model_conf.output_dim = text_feature.vocab_size
else:
raise Exception("wrong type")
model_class = dynamic_import(parser_args.model, model_alias)
self.config.freeze()
# Enter the path of model root
os.chdir(res_path)
model_class = dynamic_import(model_type, model_alias)
model = model_class.from_config(model_conf)
self.model = model
self.model.eval()
@ -173,75 +188,94 @@ class ASRExecutor(BaseExecutor):
model_dict = paddle.load(params_path)
self.model.set_state_dict(model_dict)
def preprocess(self, input: Union[str, os.PathLike]):
def preprocess(self, model_type: str, input: Union[str, os.PathLike]):
"""
Input preprocess and return paddle.Tensor stored in self.input.
Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
"""
parser_args = self.parser_args
config = self.config
audio_file = input
logger.info("audio_file" + audio_file)
logger.info("Preprocess audio_file:" + audio_file)
self.sr = config.collator.target_sample_rate
config_target_sample_rate = self.config.collator.target_sample_rate
# Get the object for feature extraction
if parser_args.model == "ds2_online" or parser_args.model == "ds2_offline":
audio, _ = collate_fn_test.process_utterance(
if model_type == "ds2_online" or model_type == "ds2_offline":
audio, _ = self.collate_fn_test.process_utterance(
audio_file=audio_file, transcript=" ")
audio_len = audio.shape[0]
audio = paddle.to_tensor(audio, dtype='float32')
self.audio_len = paddle.to_tensor(audio_len)
self.audio = paddle.unsqueeze(audio, axis=0)
self.vocab_list = collate_fn_test.vocab_list
logger.info(f"audio feat shape: {self.audio.shape}")
elif parser_args.model == "conformer" or parser_args.model == "transformer" or parser_args.model == "wenetspeech":
audio_len = paddle.to_tensor(audio_len)
audio = paddle.unsqueeze(audio, axis=0)
vocab_list = collate_fn_test.vocab_list
self._inputs["audio"] = audio
self._inputs["audio_len"] = audio_len
logger.info(f"audio feat shape: {audio.shape}")
elif model_type == "conformer" or model_type == "transformer" or model_type == "wenetspeech":
logger.info("get the preprocess conf")
preprocess_conf = os.path.join(
os.path.dirname(os.path.abspath(self.cfg_path)),
"preprocess.yaml")
cmvn_path: data / mean_std.json
logger.info(preprocess_conf)
preprocess_args = {"train": False}
preprocessing = Transformation(preprocess_conf)
logger.info("read the audio file")
audio, sample_rate = soundfile.read(
audio_file, dtype="int16", always_2d=True)
if sample_rate != self.sr:
if self.change_format:
if audio.shape[1] >= 2:
audio = audio.mean(axis=1)
else:
audio = audio[:, 0]
audio = audio.astype("float32")
audio = librosa.resample(audio, sample_rate,
self.target_sample_rate)
sample_rate = self.target_sample_rate
audio = audio.astype("int16")
else:
audio = audio[:, 0]
if sample_rate != config_target_sample_rate:
logger.error(
f"sample rate error: {sample_rate}, need {self.sr} ")
sys.exit(-1)
audio = audio[:, 0]
logger.info(f"audio shape: {audio.shape}")
# fbank
audio = preprocessing(audio, **preprocess_args)
self.audio_len = paddle.to_tensor(audio.shape[0])
self.audio = paddle.to_tensor(
audio, dtype='float32').unsqueeze(axis=0)
logger.info(f"audio feat shape: {self.audio.shape}")
audio_len = paddle.to_tensor(audio.shape[0])
audio = paddle.to_tensor(audio, dtype='float32').unsqueeze(axis=0)
text_feature = TextFeaturizer(
unit_type=self.config.collator.unit_type,
vocab_filepath=self.config.collator.vocab_filepath,
spm_model_prefix=self.config.collator.spm_model_prefix)
self._inputs["audio"] = audio
self._inputs["audio_len"] = audio_len
logger.info(f"audio feat shape: {audio.shape}")
else:
raise Exception("wrong type")
@paddle.no_grad()
def infer(self):
def infer(self, model_type: str):
"""
Model inference and result stored in self.output.
"""
text_feature = TextFeaturizer(
unit_type=self.config.collator.unit_type,
vocab_filepath=self.config.collator.vocab_filepath,
spm_model_prefix=self.config.collator.spm_model_prefix)
cfg = self.config.decoding
parser_args = self.parser_args
audio = self.audio
audio_len = self.audio_len
if parser_args.model == "ds2_online" or parser_args.model == "ds2_offline":
vocab_list = self.vocab_list
audio = self._inputs["audio"]
audio_len = self._inputs["audio_len"]
if model_type == "ds2_online" or model_type == "ds2_offline":
result_transcripts = self.model.decode(
audio,
audio_len,
vocab_list,
text_feature.vocab_list,
decoding_method=cfg.decoding_method,
lang_model_path=cfg.lang_model_path,
beam_alpha=cfg.alpha,
@ -250,14 +284,13 @@ class ASRExecutor(BaseExecutor):
cutoff_prob=cfg.cutoff_prob,
cutoff_top_n=cfg.cutoff_top_n,
num_processes=cfg.num_proc_bsearch)
self.result_transcripts = result_transcripts[0]
self._outputs["result"] = result_transcripts[0]
elif parser_args.model == "conformer" or parser_args.model == "transformer" or parser_args.model == "wenetspeech":
text_feature = self.text_feature
elif model_type == "conformer" or model_type == "transformer" or model_type == "wenetspeech":
result_transcripts = self.model.decode(
audio,
audio_len,
text_feature=self.text_feature,
text_feature=text_feature,
decoding_method=cfg.decoding_method,
lang_model_path=cfg.lang_model_path,
beam_alpha=cfg.alpha,
@ -270,46 +303,110 @@ class ASRExecutor(BaseExecutor):
decoding_chunk_size=cfg.decoding_chunk_size,
num_decoding_left_chunks=cfg.num_decoding_left_chunks,
simulate_streaming=cfg.simulate_streaming)
self.result_transcripts = result_transcripts[0][0]
self._outputs["result"] = result_transcripts[0][0]
else:
raise Exception("invalid model name")
pass
def postprocess(self) -> Union[str, os.PathLike]:
"""
Output postprocess and return human-readable results such as texts and audio files.
"""
return self.result_transcripts
return self._outputs["result"]
def _check(self, audio_file: str, model_sample_rate: int):
self.target_sample_rate = model_sample_rate
if self.target_sample_rate != 16000 and self.target_sample_rate != 8000:
logger.error(
"please input --model_sample_rate 8000 or --model_sample_rate 16000"
)
raise Exception("invalid sample rate")
sys.exit(-1)
if not os.path.isfile(audio_file):
logger.error("Please input the right audio file path")
sys.exit(-1)
logger.info("checking the audio file format......")
try:
sig, sample_rate = soundfile.read(
audio_file, dtype="int16", always_2d=True)
except Exception as e:
logger.error(str(e))
logger.error(
"can not open the audio file, please check the audio file format is 'wav'. \n \
you can try to use sox to change the file format.\n \
For example: \n \
sample rate: 16k \n \
sox input_audio.xx --rate 16k --bits 16 --channels 1 output_audio.wav \n \
sample rate: 8k \n \
sox input_audio.xx --rate 8k --bits 16 --channels 1 output_audio.wav \n \
")
sys.exit(-1)
logger.info("The sample rate is %d" % sample_rate)
if sample_rate != self.target_sample_rate:
logger.warning(
"The sample rate of the input file is not {}.\n \
The program will resample the wav file to {}.\n \
If the result does not meet your expectations，\n \
Please input the 16k 16bit 1 channel wav file. \
"
.format(self.target_sample_rate, self.target_sample_rate))
while (True):
logger.info(
"Whether to change the sample rate and the channel. Y: change the sample. N: exit the prgream."
)
content = input("Input(Y/N):")
if content.strip() == "Y" or content.strip(
) == "y" or content.strip() == "yes" or content.strip() == "Yes":
logger.info(
"change the sampele rate, channel to 16k and 1 channel")
break
elif content.strip() == "N" or content.strip(
) == "n" or content.strip() == "no" or content.strip() == "No":
logger.info("Exit the program")
exit(1)
else:
logger.warning("Not regular input, please input again")
self.change_format = True
else:
logger.info("The audio file format is right")
self.change_format = False
def execute(self, argv: List[str]) -> bool:
"""
Command line entry.
"""
self.parser_args = self.parser.parse_args(argv)
parser_args = self.parser.parse_args(argv)
model = self.parser_args.model
lang = self.parser_args.lang
config = self.parser_args.config
ckpt_path = self.parser_args.ckpt_path
audio_file = os.path.abspath(self.parser_args.input)
device = self.parser_args.device
model = parser_args.model
lang = parser_args.lang
model_sample_rate = parser_args.model_sample_rate
config = parser_args.config
ckpt_path = parser_args.ckpt_path
audio_file = parser_args.input
device = parser_args.device
try:
res = self(model, lang, config, ckpt_path, audio_file, device)
res = self(model, lang, model_sample_rate, config, ckpt_path,
audio_file, device)
logger.info('ASR Result: {}'.format(res))
return True
except Exception as e:
print(e)
return False
def __call__(self, model, lang, config, ckpt_path, audio_file, device):
def __call__(self, model, lang, model_sample_rate, config, ckpt_path,
audio_file, device):
"""
Python API to call an executor.
"""
self._init_from_path(model, lang, config, ckpt_path)
self.preprocess(audio_file)
self.infer()
audio_file = os.path.abspath(audio_file)
self._check(audio_file, model_sample_rate)
self._init_from_path(model, lang, model_sample_rate, config, ckpt_path,
device)
self.preprocess(model, audio_file)
self.infer(model)
res = self.postprocess() # Retrieve result of asr.
return res

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