Home assistants require special phrases called hotwords to get activated(eg:"ok google"). EfficientWord-Net is a hotword detection engine based on few-shot learning that allows developers to add custom hotwords to their programs without extra charges. The library is purely written in Python and uses Google's Tflite implementation for faster real-time inference. It is inspired by FaceNet's Siamese Network Architecture and performs the best when 3-4 hotword samples are collected directly from the user.
EfficientWord-Net.mp4
Training File to access the training file.
Here are the links: https://drive.google.com/file/d/1f6dp72D9WxErXvaZP6KIBLv4-eKpvLZa/view?usp=sharing, https://drive.google.com/file/d/19QUTiAZvF1pFy5BeaV_rc83MeDu8yojv/view?usp=sharing
Research Paper to access the research paper.
This Library works between python versions:
3.6 to 3.9
Before running the pip installation command for the library, few dependencies need to be installed manually.
- PyAudio (depends on PortAudio)
- Tflite (tensorflow lightweight binaries)
- Librosa (Binaries might not be available for certain systems) Mac OS M* and Raspberry Pi users might have to compile these dependecies.
tflite package cannot be listed in requirements.txt hence will be automatically installed when the package is initialized in the system.
librosa package is not required for inference only cases , however when generate_reference is called , will be automatically installed.
Run the following pip command
pip install EfficientWord-Net
and to import running
import eff_word_net
After installing the packages, you can run the Demo script inbuilt with library (ensure you have a working mic).
Accesss Documentation from : https://ant-brain.github.io/EfficientWord-Net/
Command to run demo
python -m eff_word_net.engine
For any new hotword, the library needs information about the hotword, this information is obtained from a file called {wakeword}_ref.json.
Eg: For the wakeword 'alexa', the library would need the file called alexa_ref.json
These files can be generated with the following procedure:
One needs to collect few 4 to 10 uniquely sounding pronunciations of a given wakeword. Then put them into a seperate folder, which doesnt contain anything else.
Or one could use the following command to generate audio files for a given word, uses ibm neural tts demo api. Kindly dont over use it for our sake.
python -m eff_word_net.ibm_generateFinally run this command, it will ask for the input folder's location (containing the audio files) and the output folder (where _ref.json file will be stored).
python -m eff_word_net.generate_reference
The pathname of the generated wakeword needs to passed to the HotwordDetector detector instance.
HotwordDetector(
hotword="hello",
model = Resnet_50_Arc_loss(),
reference_file = "/full/path/name/of/hello_ref.json"),
threshold=0.9, #min confidence required to consider a trigger
relaxation_time = 0.8 #default value ,in seconds
)The model variable can receive an instance of Resnet_50_Arc_loss or First_Iteration_Siamese.
Relaxation time parameter is used to determine the min time between any 2 triggers, any potential triggers before the relaxation_time will be cancelled. The detector operates on a sliding widow approach resulting in multiple triggers for single utterance of a hotword, the relaxation_time parameter can used to control the multiple triggers, in most cases 0.8sec(default) will do.
Few wakewords such as Mycroft, Google, Firefox, Alexa, Mobile, Siri the library has predefined embeddings readily available in the library installation directory, its path is readily available in the following variable
from eff_word_net import samples_locimport os
from eff_word_net.streams import SimpleMicStream
from eff_word_net.engine import HotwordDetector
from eff_word_net.audio_processing import Resnet50_Arc_loss
from eff_word_net import samples_loc
base_model = Resnet50_Arc_loss()
mycroft_hw = HotwordDetector(
hotword="mycroft",
model = base_model,
reference_file=os.path.join(samples_loc, "mycroft_ref.json"),
threshold=0.7,
relaxation_time=2
)
mic_stream = SimpleMicStream(
window_length_secs=1.5,
sliding_window_secs=0.75,
)
mic_stream.start_stream()
print("Say Mycroft ")
while True :
frame = mic_stream.getFrame()
result = mycroft_hw.scoreFrame(frame)
if result==None :
#no voice activity
continue
if(result["match"]):
print("Wakeword uttered",result["confidence"])The library provides a computation friendly way
to detect multiple hotwords from a given stream, instead of running scoreFrame() of each wakeword individually
import os
from eff_word_net.streams import SimpleMicStream
from eff_word_net import samples_loc
print(samples_loc)
base_model = Resnet50_Arc_loss()
mycroft_hw = HotwordDetector(
hotword="mycroft",
model = base_model,
reference_file=os.path.join(samples_loc,"mycroft_ref.json"),
threshold=0.7,
relaxation_time=2
)
alexa_hw = HotwordDetector(
hotword="alexa",
model=base_model,
reference_file=os.path.join(samples_loc,"alexa_ref.json"),
threshold=0.7,
relaxation_time=2,
#verbose=True
)
computer_hw = HotwordDetector(
hotword="computer",
model=base_model,
reference_file=os.path.join(samples_loc,"computer_ref.json"),
threshold=0.7,
relaxation_time=2,
#verbose=True
)
multi_hotword_detector = MultiHotwordDetector(
[mycroft_hw, alexa_hw, computer_hw],
model=base_model,
continuous=True,
)
mic_stream = SimpleMicStream(window_length_secs=1.5, sliding_window_secs=0.75)
mic_stream.start_stream()
print("Say ", " / ".join([x.hotword for x in multi_hotword_detector.detector_collection]))
while True :
frame = mic_stream.getFrame()
result = multi_hotword_detector.findBestMatch(frame)
if(None not in result):
print(result[0],f",Confidence {result[1]:0.4f}")
Access documentation of the library from here : https://ant-brain.github.io/EfficientWord-Net/
Trained a new model from scratch using a modified distilled dataset from MLCommons, used Arcloss function instead of triplet loss function.
The resultant model created is stored resnet_50_arcloss.
The newer model is show casing much better resilience towards background noise and requires fewer samples for good accuracy.
Minor changes in the api flow to facilitate easy addition of newer models.
Newer model can handle a fixed window length of 1.5 seconds.
The old model can still be accessed through first_iteration_siamese.
Major changes to replace complex friking logic of handling poly triggers per utterance into more simpler logic and more simpler api for programmers.
Introduces breaking changes
- Trained on single words , hence may result in bizare behaviour on using phrases like "Hey xxx".
- Audio processing window limited to 1 sec. Hence will not work effectively for longer hotwords.
- Hotword Perfomance is bad : if you are having some issue like this , feel to ask the same in discussions
- Can it run on FPGAs like arduino? : No , new model Resnet_50_Arcloss is too heavy to run on arduino (Roughly 88Mb) in size, soon we will add support of pruned versions of the model so that it can become light enough to run on tiny devices, for now it should be able to run in Raspberry pi like devices
- If you have an ideas to make the project better, feel free to ping us in discussions.
- The current logmelcalc.tflite graph can convert only 1 audio frame to Log Mel Spectrogram at a time. It will be of a great help if tensorflow guru's outthere help us out with this.
- Add audio file handler in streams. PR's are welcome.
- Remove librosa requirement to encourage generating reference files directly in edge devices.
- Add more detailed documentation explaining slider window concept.
- Add model finetuning support.
- Add support for sparse and finegrained pruning where the resultant models could be used for finetuning (already working on the same).
Our hotword detector's performance is notably low when compared to Porcupine. We have thought about better NN architectures for the engine and hope to outperform Porcupine. This has been our undergrad project. Hence your support and encouragement will motivate us to develop the engine. If you loved this project recommend this to your peers, give us a 🌟 in Github and a clap 👏 in medium.
Update: Your stars encouraged us to create a new model which is far better , lets make this community grow