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# SPDX-License-Identifier: Apache-2.0
#
# Copyright (c) 2023 Malik Talha
#
# 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 gi
import vosk
gi.require_version('Gst', '1.0')
from gi.repository import Gst, GLib
Gst.init(None)
GLib.threads_init()
class WakeWordDetector:
def __init__(self, wake_word, stt_model, channels=1, sample_rate=16000, bits_per_sample=16):
self.loop = GLib.MainLoop()
self.pipeline = None
self.bus = None
self.wake_word = wake_word
self.wake_word_detected = False
self.sample_rate = sample_rate
self.channels = channels
self.bits_per_sample = bits_per_sample
self.frame_size = int(self.sample_rate * 0.02)
self.stt_model = stt_model # Speech to text model recognizer
self.recognizer_uuid = stt_model.setup_recognizer()
self.buffer_duration = 1 # Buffer audio for atleast 1 second
self.audio_buffer = bytearray()
def get_wake_word_status(self):
return self.wake_word_detected
def create_pipeline(self):
print("Creating pipeline for Wake Word Detection...")
self.pipeline = Gst.Pipeline()
autoaudiosrc = Gst.ElementFactory.make("autoaudiosrc", None)
queue = Gst.ElementFactory.make("queue", None)
audioconvert = Gst.ElementFactory.make("audioconvert", None)
wavenc = Gst.ElementFactory.make("wavenc", None)
capsfilter = Gst.ElementFactory.make("capsfilter", None)
caps = Gst.Caps.new_empty_simple("audio/x-raw")
caps.set_value("format", "S16LE")
caps.set_value("rate", self.sample_rate)
caps.set_value("channels", self.channels)
capsfilter.set_property("caps", caps)
appsink = Gst.ElementFactory.make("appsink", None)
appsink.set_property("emit-signals", True)
appsink.set_property("sync", False) # Set sync property to False to enable async processing
appsink.connect("new-sample", self.on_new_buffer, None)
self.pipeline.add(autoaudiosrc)
self.pipeline.add(queue)
self.pipeline.add(audioconvert)
self.pipeline.add(wavenc)
self.pipeline.add(capsfilter)
self.pipeline.add(appsink)
autoaudiosrc.link(queue)
queue.link(audioconvert)
audioconvert.link(capsfilter)
capsfilter.link(appsink)
self.bus = self.pipeline.get_bus()
self.bus.add_signal_watch()
self.bus.connect("message", self.on_bus_message)
def on_new_buffer(self, appsink, data) -> Gst.FlowReturn:
sample = appsink.emit("pull-sample")
buffer = sample.get_buffer()
data = buffer.extract_dup(0, buffer.get_size())
self.audio_buffer.extend(data)
if len(self.audio_buffer) >= self.sample_rate * self.buffer_duration * self.channels * self.bits_per_sample // 8:
self.process_audio_buffer()
return Gst.FlowReturn.OK
def process_audio_buffer(self):
# Process the accumulated audio data using the audio model
audio_data = bytes(self.audio_buffer)
if self.stt_model.init_recognition(self.recognizer_uuid, audio_data):
stt_result = self.stt_model.recognize(self.recognizer_uuid)
print("STT Result: ", stt_result)
if self.wake_word in stt_result["text"]:
self.wake_word_detected = True
print("Wake word detected!")
self.pipeline.send_event(Gst.Event.new_eos())
self.audio_buffer.clear() # Clear the buffer
def send_eos(self):
self.pipeline.send_event(Gst.Event.new_eos())
self.audio_buffer.clear()
def start_listening(self):
self.pipeline.set_state(Gst.State.PLAYING)
print("Listening for Wake Word...")
self.loop.run()
def stop_listening(self):
self.cleanup_pipeline()
self.loop.quit()
def on_bus_message(self, bus, message):
if message.type == Gst.MessageType.EOS:
print("End-of-stream message received")
self.stop_listening()
elif message.type == Gst.MessageType.ERROR:
err, debug_info = message.parse_error()
print(f"Error received from element {message.src.get_name()}: {err.message}")
print(f"Debugging information: {debug_info}")
self.stop_listening()
elif message.type == Gst.MessageType.WARNING:
err, debug_info = message.parse_warning()
print(f"Warning received from element {message.src.get_name()}: {err.message}")
print(f"Debugging information: {debug_info}")
elif message.type == Gst.MessageType.STATE_CHANGED:
if isinstance(message.src, Gst.Pipeline):
old_state, new_state, pending_state = message.parse_state_changed()
print(("Pipeline state changed from %s to %s." %
(old_state.value_nick, new_state.value_nick)))
def cleanup_pipeline(self):
if self.pipeline is not None:
print("Cleaning up pipeline...")
self.pipeline.set_state(Gst.State.NULL)
self.bus.remove_signal_watch()
print("Pipeline cleanup complete!")
self.bus = None
self.pipeline = None
self.stt_model.cleanup_recognizer(self.recognizer_uuid)
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