|
|
@ -11,29 +11,184 @@
|
|
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
|
|
# See the License for the specific language governing permissions and
|
|
|
|
# See the License for the specific language governing permissions and
|
|
|
|
# limitations under the License.
|
|
|
|
# limitations under the License.
|
|
|
|
|
|
|
|
import io
|
|
|
|
|
|
|
|
import os
|
|
|
|
|
|
|
|
from typing import List
|
|
|
|
|
|
|
|
from typing import Optional
|
|
|
|
|
|
|
|
from typing import Union
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
import librosa
|
|
|
|
|
|
|
|
import paddle
|
|
|
|
|
|
|
|
import soundfile
|
|
|
|
from engine.base_engine import BaseEngine
|
|
|
|
from engine.base_engine import BaseEngine
|
|
|
|
|
|
|
|
|
|
|
|
from utils.log import logger
|
|
|
|
from paddlespeech.cli.asr.infer import ASRExecutor
|
|
|
|
|
|
|
|
from paddlespeech.cli.log import logger
|
|
|
|
|
|
|
|
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
|
|
|
|
|
|
|
|
from paddlespeech.s2t.transform.transformation import Transformation
|
|
|
|
|
|
|
|
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
|
|
|
|
|
|
|
|
from paddlespeech.s2t.utils.utility import UpdateConfig
|
|
|
|
from utils.config import get_config
|
|
|
|
from utils.config import get_config
|
|
|
|
|
|
|
|
|
|
|
|
__all__ = ['ASREngine']
|
|
|
|
__all__ = ['ASREngine']
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class ASRServerExecutor(ASRExecutor):
|
|
|
|
|
|
|
|
def __init__(self):
|
|
|
|
|
|
|
|
super().__init__()
|
|
|
|
|
|
|
|
pass
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def _check(self, audio_file: str, sample_rate: int, force_yes: bool):
|
|
|
|
|
|
|
|
self.sample_rate = sample_rate
|
|
|
|
|
|
|
|
if self.sample_rate != 16000 and self.sample_rate != 8000:
|
|
|
|
|
|
|
|
logger.error("please input --sr 8000 or --sr 16000")
|
|
|
|
|
|
|
|
return False
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
logger.info("checking the audio file format......")
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
|
|
|
audio, audio_sample_rate = soundfile.read(
|
|
|
|
|
|
|
|
audio_file, dtype="int16", always_2d=True)
|
|
|
|
|
|
|
|
except Exception as e:
|
|
|
|
|
|
|
|
logger.exception(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 \
|
|
|
|
|
|
|
|
")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
logger.info("The sample rate is %d" % audio_sample_rate)
|
|
|
|
|
|
|
|
if audio_sample_rate != self.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 16 bit 1 channel wav file. \
|
|
|
|
|
|
|
|
".format(self.sample_rate, self.sample_rate))
|
|
|
|
|
|
|
|
self.change_format = True
|
|
|
|
|
|
|
|
else:
|
|
|
|
|
|
|
|
logger.info("The audio file format is right")
|
|
|
|
|
|
|
|
self.change_format = False
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
return True
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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).
|
|
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
audio_file = input
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# Get the object for feature extraction
|
|
|
|
|
|
|
|
if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
|
|
|
|
|
|
|
|
audio, _ = self.collate_fn_test.process_utterance(
|
|
|
|
|
|
|
|
audio_file=audio_file, transcript=" ")
|
|
|
|
|
|
|
|
audio_len = audio.shape[0]
|
|
|
|
|
|
|
|
audio = paddle.to_tensor(audio, dtype='float32')
|
|
|
|
|
|
|
|
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 "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
|
|
|
|
|
|
|
|
logger.info("get the preprocess conf")
|
|
|
|
|
|
|
|
preprocess_conf = self.config.preprocess_config
|
|
|
|
|
|
|
|
preprocess_args = {"train": False}
|
|
|
|
|
|
|
|
preprocessing = Transformation(preprocess_conf)
|
|
|
|
|
|
|
|
logger.info("read the audio file")
|
|
|
|
|
|
|
|
audio, audio_sample_rate = soundfile.read(
|
|
|
|
|
|
|
|
audio_file, dtype="int16", always_2d=True)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if self.change_format:
|
|
|
|
|
|
|
|
if audio.shape[1] >= 2:
|
|
|
|
|
|
|
|
audio = audio.mean(axis=1, dtype=np.int16)
|
|
|
|
|
|
|
|
else:
|
|
|
|
|
|
|
|
audio = audio[:, 0]
|
|
|
|
|
|
|
|
# pcm16 -> pcm 32
|
|
|
|
|
|
|
|
audio = self._pcm16to32(audio)
|
|
|
|
|
|
|
|
audio = librosa.resample(audio, audio_sample_rate,
|
|
|
|
|
|
|
|
self.sample_rate)
|
|
|
|
|
|
|
|
audio_sample_rate = self.sample_rate
|
|
|
|
|
|
|
|
# pcm32 -> pcm 16
|
|
|
|
|
|
|
|
audio = self._pcm32to16(audio)
|
|
|
|
|
|
|
|
else:
|
|
|
|
|
|
|
|
audio = audio[:, 0]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
logger.info(f"audio shape: {audio.shape}")
|
|
|
|
|
|
|
|
# fbank
|
|
|
|
|
|
|
|
audio = preprocessing(audio, **preprocess_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
audio_len = paddle.to_tensor(audio.shape[0])
|
|
|
|
|
|
|
|
audio = paddle.to_tensor(audio, dtype='float32').unsqueeze(axis=0)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
self._inputs["audio"] = audio
|
|
|
|
|
|
|
|
self._inputs["audio_len"] = audio_len
|
|
|
|
|
|
|
|
logger.info(f"audio feat shape: {audio.shape}")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
else:
|
|
|
|
|
|
|
|
raise Exception("wrong type")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class ASREngine(BaseEngine):
|
|
|
|
class ASREngine(BaseEngine):
|
|
|
|
|
|
|
|
"""ASR server engine
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
|
|
|
metaclass: Defaults to Singleton.
|
|
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
|
|
def __init__(self):
|
|
|
|
def __init__(self):
|
|
|
|
super(ASREngine, self).__init__()
|
|
|
|
super(ASREngine, self).__init__()
|
|
|
|
|
|
|
|
|
|
|
|
def init(self, config_file: str):
|
|
|
|
def init(self, config_file: str) -> bool:
|
|
|
|
self.config_file = config_file
|
|
|
|
"""init engine resource
|
|
|
|
self.executor = None
|
|
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
|
|
|
config_file (str): config file
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
|
|
|
bool: init failed or success
|
|
|
|
|
|
|
|
"""
|
|
|
|
self.input = None
|
|
|
|
self.input = None
|
|
|
|
self.output = None
|
|
|
|
self.output = None
|
|
|
|
config = get_config(self.config_file)
|
|
|
|
self.executor = ASRServerExecutor()
|
|
|
|
pass
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def postprocess(self):
|
|
|
|
try:
|
|
|
|
pass
|
|
|
|
self.config = get_config(config_file)
|
|
|
|
|
|
|
|
paddle.set_device(paddle.get_device())
|
|
|
|
|
|
|
|
self.executor._init_from_path(
|
|
|
|
|
|
|
|
self.config.model, self.config.lang, self.config.sample_rate,
|
|
|
|
|
|
|
|
self.config.cfg_path, self.config.decode_method,
|
|
|
|
|
|
|
|
self.config.ckpt_path)
|
|
|
|
|
|
|
|
except:
|
|
|
|
|
|
|
|
logger.info("Initialize ASR server engine Failed.")
|
|
|
|
|
|
|
|
return False
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
logger.info("Initialize ASR server engine successfully.")
|
|
|
|
|
|
|
|
return True
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def run(self, audio_data):
|
|
|
|
|
|
|
|
"""engine run
|
|
|
|
|
|
|
|
|
|
|
|
def run(self):
|
|
|
|
Args:
|
|
|
|
|
|
|
|
audio_data (bytes): base64.b64decode
|
|
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
if self.executor._check(
|
|
|
|
|
|
|
|
io.BytesIO(audio_data), self.config.sample_rate,
|
|
|
|
|
|
|
|
self.config.force_yes):
|
|
|
|
logger.info("start run asr engine")
|
|
|
|
logger.info("start run asr engine")
|
|
|
|
return "hello world"
|
|
|
|
self.executor.preprocess(self.config.model, io.BytesIO(audio_data))
|
|
|
|
|
|
|
|
self.executor.infer(self.config.model)
|
|
|
|
|
|
|
|
self.output = self.executor.postprocess() # Retrieve result of asr.
|
|
|
|
|
|
|
|
else:
|
|
|
|
|
|
|
|
logger.info("file check failed!")
|
|
|
|
|
|
|
|
self.output = None
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def postprocess(self):
|
|
|
|
|
|
|
|
"""postprocess
|
|
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
return self.output
|
|
|
|