|
|
|
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
|
|
#
|
|
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
|
|
# you may not use this file except in compliance with the License.
|
|
|
|
# You may obtain a copy of the License at
|
|
|
|
#
|
|
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
#
|
|
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
|
|
# See the License for the specific language governing permissions and
|
|
|
|
# limitations under the License.
|
|
|
|
import io
|
|
|
|
import json
|
|
|
|
import os
|
|
|
|
import re
|
|
|
|
|
|
|
|
import numpy as np
|
|
|
|
import paddle
|
|
|
|
import soundfile
|
|
|
|
import websocket
|
|
|
|
|
|
|
|
from paddlespeech.cli.log import logger
|
|
|
|
from paddlespeech.server.engine.base_engine import BaseEngine
|
|
|
|
|
|
|
|
|
|
|
|
class ACSEngine(BaseEngine):
|
|
|
|
def __init__(self):
|
|
|
|
"""The ACSEngine Engine
|
|
|
|
"""
|
|
|
|
super(ACSEngine, self).__init__()
|
|
|
|
logger.info("Create the ACSEngine Instance")
|
|
|
|
self.word_list = []
|
|
|
|
|
|
|
|
def init(self, config: dict):
|
|
|
|
"""Init the ACSEngine Engine
|
|
|
|
|
|
|
|
Args:
|
|
|
|
config (dict): The server configuation
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
bool: The engine instance flag
|
|
|
|
"""
|
|
|
|
logger.info("Init the acs engine")
|
|
|
|
try:
|
|
|
|
self.config = config
|
|
|
|
self.device = self.config.get("device", paddle.get_device())
|
|
|
|
paddle.set_device(self.device)
|
|
|
|
logger.info(f"ACS 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"
|
|
|
|
)
|
|
|
|
logger.error("Initialize Text server engine Failed on device: %s." %
|
|
|
|
(self.device))
|
|
|
|
return False
|
|
|
|
|
|
|
|
self.read_search_words()
|
|
|
|
|
|
|
|
# init the asr url
|
|
|
|
self.url = "ws://" + self.config.asr_server_ip + ":" + str(
|
|
|
|
self.config.asr_server_port) + "/paddlespeech/asr/streaming"
|
|
|
|
|
|
|
|
logger.info("Init the acs engine successfully")
|
|
|
|
return True
|
|
|
|
|
|
|
|
def read_search_words(self):
|
|
|
|
word_list = self.config.word_list
|
|
|
|
if word_list is None:
|
|
|
|
logger.error(
|
|
|
|
"No word list file in config, please set the word list parameter"
|
|
|
|
)
|
|
|
|
return
|
|
|
|
|
|
|
|
if not os.path.exists(word_list):
|
|
|
|
logger.error("Please input correct word list file")
|
|
|
|
return
|
|
|
|
|
|
|
|
with open(word_list, 'r') as fp:
|
|
|
|
self.word_list = [line.strip() for line in fp.readlines()]
|
|
|
|
|
|
|
|
logger.info(f"word list: {self.word_list}")
|
|
|
|
|
|
|
|
def get_asr_content(self, audio_data):
|
|
|
|
"""Get the streaming asr result
|
|
|
|
|
|
|
|
Args:
|
|
|
|
audio_data (_type_): _description_
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
_type_: _description_
|
|
|
|
"""
|
|
|
|
logger.info("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()
|
|
|
|
ws.connect(self.url)
|
|
|
|
# with websocket.WebSocket.connect(self.url) as ws:
|
|
|
|
audio_info = json.dumps(
|
|
|
|
{
|
|
|
|
"name": "test.wav",
|
|
|
|
"signal": "start",
|
|
|
|
"nbest": 1
|
|
|
|
},
|
|
|
|
sort_keys=True,
|
|
|
|
indent=4,
|
|
|
|
separators=(',', ': '))
|
|
|
|
ws.send(audio_info)
|
|
|
|
msg = ws.recv()
|
|
|
|
logger.info("client receive msg={}".format(msg))
|
|
|
|
|
|
|
|
# send the total audio data
|
|
|
|
for chunk_data in self.read_wave(audio_data):
|
|
|
|
ws.send_binary(chunk_data.tobytes())
|
|
|
|
msg = ws.recv()
|
|
|
|
msg = json.loads(msg)
|
|
|
|
logger.info(f"audio result: {msg}")
|
|
|
|
|
|
|
|
# 3. send chunk audio data to engine
|
|
|
|
logger.info("send the end signal")
|
|
|
|
audio_info = json.dumps(
|
|
|
|
{
|
|
|
|
"name": "test.wav",
|
|
|
|
"signal": "end",
|
|
|
|
"nbest": 1
|
|
|
|
},
|
|
|
|
sort_keys=True,
|
|
|
|
indent=4,
|
|
|
|
separators=(',', ': '))
|
|
|
|
ws.send(audio_info)
|
|
|
|
msg = ws.recv()
|
|
|
|
msg = json.loads(msg)
|
|
|
|
|
|
|
|
logger.info(f"the final result: {msg}")
|
|
|
|
ws.close()
|
|
|
|
|
|
|
|
return msg
|
|
|
|
|
|
|
|
def read_wave(self, audio_data: str):
|
|
|
|
"""read the audio file from specific wavfile path
|
|
|
|
|
|
|
|
Args:
|
|
|
|
audio_data (str): the audio data,
|
|
|
|
we assume that audio sample rate matches the model
|
|
|
|
|
|
|
|
Yields:
|
|
|
|
numpy.array: the samall package audio pcm data
|
|
|
|
"""
|
|
|
|
samples, sample_rate = soundfile.read(audio_data, dtype='int16')
|
|
|
|
x_len = len(samples)
|
|
|
|
assert sample_rate == 16000
|
|
|
|
|
|
|
|
chunk_size = int(85 * sample_rate / 1000) # 85ms, sample_rate = 16kHz
|
|
|
|
|
|
|
|
if x_len % chunk_size != 0:
|
|
|
|
padding_len_x = chunk_size - x_len % chunk_size
|
|
|
|
else:
|
|
|
|
padding_len_x = 0
|
|
|
|
|
|
|
|
padding = np.zeros((padding_len_x), dtype=samples.dtype)
|
|
|
|
padded_x = np.concatenate([samples, padding], axis=0)
|
|
|
|
|
|
|
|
assert (x_len + padding_len_x) % chunk_size == 0
|
|
|
|
num_chunk = (x_len + padding_len_x) / chunk_size
|
|
|
|
num_chunk = int(num_chunk)
|
|
|
|
for i in range(0, num_chunk):
|
|
|
|
start = i * chunk_size
|
|
|
|
end = start + chunk_size
|
|
|
|
x_chunk = padded_x[start:end]
|
|
|
|
yield x_chunk
|
|
|
|
|
|
|
|
def get_macthed_word(self, msg):
|
|
|
|
"""Get the matched info in msg
|
|
|
|
|
|
|
|
Args:
|
|
|
|
msg (dict): the asr info, including the asr result and time stamp
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
acs_result, asr_result: the acs result and the asr result
|
|
|
|
"""
|
|
|
|
asr_result = msg['result']
|
|
|
|
time_stamp = msg['times']
|
|
|
|
acs_result = []
|
|
|
|
|
|
|
|
# search for each word in self.word_list
|
|
|
|
offset = self.config.offset
|
|
|
|
max_ed = time_stamp[-1]['ed']
|
|
|
|
for w in self.word_list:
|
|
|
|
# search the w in asr_result and the index in asr_result
|
|
|
|
for m in re.finditer(w, asr_result):
|
|
|
|
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}')
|
|
|
|
acs_result.append({'w': w, 'bg': start, 'ed': end})
|
|
|
|
|
|
|
|
return acs_result, asr_result
|
|
|
|
|
|
|
|
def run(self, audio_data):
|
|
|
|
"""process the audio data in acs engine
|
|
|
|
the engine does not store any data, so all the request use the self.run api
|
|
|
|
|
|
|
|
Args:
|
|
|
|
audio_data (str): the audio data
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
acs_result, asr_result: the acs result and the asr result
|
|
|
|
"""
|
|
|
|
logger.info("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)
|
|
|
|
logger.info(f'the asr result {asr_result}')
|
|
|
|
logger.info(f'the acs result: {acs_result}')
|
|
|
|
return acs_result, asr_result
|