Did you ever need a way to detect when an answering machine was detected on a voice call? no? Thats ok. I did!
This post assumed you know some Python as well as having a very basic understanding of machine learning.
A few weeks ago, I received a request from our of our sales engineers about a answering machine detection service for a client. They wanted a way to send a message to a answering machine when the call went to voicemail. When the answering machine picked up, they wanted to send a canned response into the voicemail. I've done some research on this, and it does seem possible, but I couldn't find anything on HOW this was done. So I decided to figure out..
The first thought was to build a machine learning model that is to detect when the beep sound in an answering machine is detected. In this post, we'll go over how we trained the model, built it, and deployed into a application.
Before we can start building a machine learning model, we need to have some data. For this problem, we need to have a bunch of audio files with the answering machine beep sound, like this:
[audio wav="https://www.nexmo.com/wp-content/uploads/2019/02/75ea624b-8ab9-4e17-9000-70e96166642a-1.wav"][/audio]
or this: [audio wav="https://www.nexmo.com/wp-content/uploads/2019/02/75ea624b-8ab9-4e17-9000-70e96166642a.wav"][/audio]
We also need to include samples that don't include the beep:
[audio wav="https://www.nexmo.com/wp-content/uploads/2019/02/26b25bb7-6825-43e7-b8bd-03a3884ed694.wav"][/audio] [audio wav="https://www.nexmo.com/wp-content/uploads/2019/02/5c082690-02f2-11e9-aa3d-ad1a095d8d72.wav"][/audio]
Since this kind of data doesn't seem to exist on the internet, the next step is to gather as many samples of beeps and other sounds from calls, in order to train our model. To do this, we built a webpage that allows anyone to record their voicemail greeting message. When you call the Nexmo number, the application will create an outbound call to the same number. When the call is received, you just need to send the call to voicemail. From there, we record the call and save the file into a Google Cloud Storage bucket.
After we have a bunch of samples, which took a week or 2 to gather, we now have some data when can look like and see if there is a difference between the beep sound, and other recordings
In any machine learning project, one of the first things to do (after gathering data) is to look at the data, and make sure its something we can work with. Since its audio, we can't look at it directly, but we can use a way to visualize the audio files using a mel-spectrogram,
A mel-spectrogram shows a range of frequencies (lowest at the bottom of the display, highest at the top) and shows how loud events are at different frequencies. In general loud events will appear bright and quiet events will appear dark.
We'll need to load a few sounds files of both beep sounds and non-beep sounds, plot them, and see how they look. To show the mel-spectrogram, we'll use a python package called librosa to load the audio recording, then plot the melspectrogram using matplotlib, another python package
import glob
import librosa
import matplotlib.pyplot as plt
%matplotlib inline
def plot_specgram(file_path):
y, sr = librosa.load(file_path)
S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128,fmax=8000)
plt.figure(figsize=(10, 4))
librosa.display.specshow(librosa.power_to_db(S,ref=np.max),y_axis='mel', fmax=8000,x_axis='time')
plt.colorbar(format='%+2.0f dB')
plt.title(file_path.split("/")[-2])
plt.tight_layout()
sound_file_paths = ["drive/My Drive/amd-training/answering-machine/07a3d677-0fdd-4155-a804-37679c039a8e.wav",
"drive/My Drive/amd-training/answering-machine/26b25bb7-6825-43e7-b8bd-03a3884ed694.wav",
"drive/My Drive/amd-training/answering-machine/2a685eda-8dd9-4a4d-b00e-4f43715f81a4.wav",
"drive/My Drive/amd-training/answering-machine/55b654e5-7d9f-4132-bc98-93e576b2d665.wav",
"drive/My Drive/amd-training/speech-recordings/110ac98e-34fa-42e7-bbc5-450c72851db5.wav",
"drive/My Drive/amd-training/speech-recordings/3840b850-02e6-11e9-aa3d-ad1a095d8d72.wav",
"drive/My Drive/amd-training/speech-recordings/55b654e5-7d9f-4132-bc98-93e576b2d665.wav",
"drive/My Drive/amd-training/speech-recordings/81270a2a-088b-4e3c-9f47-fd927a90b0ab.wav"
]
for file in sound_file_paths:
plot_specgram(file)Lets see what each audio file looks like.
You can clearly tell which audio file is a beep, and which is just speech. Next, we'll gather our data, and train a simple model.
Before we train our model, we will take all the recordings that we have, for both beeps, and non-beeps, and convert each recording into a matrix of numbers, since our model, will only accept numbers, rather than an image.
To compute the matrix, we'll use the Mel-frequency cepstral coefficients (MFCCs) of each sample. Then, we'll save this value into a Pandas Dataframe, so that we can re-use later without having to re-compute the MFCC's over again.
For each audio sample, the dataframe will contain the path to the audio sample, the type of audio sample it is(beep, or non-beep), the MFCC, the duration of the audio sample (using the get_duration function in librosa). We also tried a few other audio characteristics including chroma, contrast and tonnetz). However, these features were not used in the latest version of the model.
Let's now take a look at the first 5 rows of the Dataframe, just to see what the data looks like.
Each row contains a 1 dimension matrix of each of the audio features. This is what we'll use to train our model.
Now we'll take this Dataframe, and train it. We'll be using Scikit learn package to do our training. Scikit learn is a great package that allows you to build simple machine learning models without having to be a machine learning expert.
There are many types of models in scikit-learn that you can use right out of the box. A good part of our time when building out the model is to try a bunch of models on the data, and see what works best. In our research, we tried the following models and included their accuracy. Accuracy is just a percentage on how well the model did.
For each model, we took our dataframe, which contained the label of each audio file, (beep, speech), with the MFCC for each sample, split it into a train and test dataset, and ran each model through the data.
def train(features, model):
X, y = generateFeaturesLabels(features)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
model.fit(X_train, y_train)
print("Score:",model.score(X_test, y_test))
cross_val_scores = cross_val_score(model, X, y, cv=5, scoring='f1_macro')
print("cross_val_scores:", cross_val_scores)
print("Accuracy: %0.2f (+/- %0.2f)" % (cross_val_scores.mean(), cross_val_scores.std() * 2))
predictions = model.predict(X_test)
cm = metrics.confusion_matrix(y_test, predictions)
plot_confusion_matrix(cm, class_names)
return modelThe function train, takes a list of features that we want to use, which is just MFCC of the audio sample, as well as the model we want to train on. Then, we print our score, which is how well the performed. We also print the cross validation score. This makes sure that our model was trained correctly. The plot_confusion_matrix function plots a confusion matrix that shows exactly what the model got correct and incorrect
We trained a few models on our data. These models, as well as their accuracy(how well the model performed) follows:
- RandomForestClassifier 97% accuracy
- LogisticRegression 96% accuracy
- Support Vector Machines 84% accuracy
- Gaussian Naive Bayes 98%
All these models performed very well, except Support Vector Machines. The best was Gaussian Naive Bayes, so we will use that model. In our Confusion Matrix from above, out of the 67 examples, 40 samples that were predicted as a beep, were actually beeps, and 22 samples that were predicted to be speech, were in fact, speech examples. However, 1 example that was predicted to be a beep, was actually speech
After we have our model, we need to save it to a file, then import this model into our VAPI application.
import pickle
filename = "model.pkl"
pickle.dump(model, open(filename, 'wb'))The last part is to now integrate our model into a VAPI application. Source
We'll build an application that lets a user dial a Nexmo Number. We'll then ask the user to enter a phone number to call. Once that number is entered, we'll connect that call into the current conversation, and connect to our websocket. Using Nexmo websockets, we are able to stream the audio call into our application.
When the user first dials the Nexmo number, we return a NCCO with the following:
class EnterPhoneNumberHandler(tornado.web.RequestHandler):
@tornado.web.asynchronous
def get(self):
ncco = [
{
"action": "talk",
"text": "Please enter a phone number to dial"
},
{
"action": "input",
"eventUrl": ["https://3c66cdfa.ngrok.io/ivr"],
"timeOut":10,
"maxDigits":12,
"submitOnHash":True
}
]
self.write(json.dumps(ncco))
self.set_header("Content-Type", 'application/json; charset="utf-8"')
self.finish()We first send a Text-To-Speech action into the call asking the user to enter a phone number. When the phone number is entered, we get those digits from the https://3c66cdfa.ngrok.io/ivr url.
class AcceptNumberHandler(tornado.web.RequestHandler):
@tornado.web.asynchronous
def post(self):
data = json.loads(self.request.body)
print(data)
ncco = [
{
"action": "talk",
"text": "Thanks. Connecting you now"
},
{
"action": "record",
"eventUrl": ["https://3c66cdfa.ngrok.io/recording"],
},
{
"action": "connect",
"eventUrl": ["https://3c66cdfa.ngrok.io"/event"],
"from": NEXMO_NUMBER,
"endpoint": [
{
"type": "phone",
"number": data["dtmf"]
}
]
},
{
"action": "connect",
"eventUrl": ["https://3c66cdfa.ngrok.io/event"],
"from": NEXMO_NUMBER,
"endpoint": [
{
"type": "websocket",
"uri" : "ws://3c66cdfa.ngrok.io/socket",
"content-type": "audio/l16;rate=16000"
}
]
}
]
self.write(json.dumps(ncco))
self.set_header("Content-Type", 'application/json; charset="utf-8"')
self.finish()After the phone number is entered, we will receive a callback from the https://3c66cdfa.ngrok.io/ivr url. Here, we take the phone number the user entered from data["dtmf"], and perform a connect action to that phone number, then perform another connect action into our websocket. Now, our websocket is able to listen in on the call.
As the call is streamed into the websocket, we need to capturing chunks of speech using Voice Activity Detection, save into a wave file, and make our predictions on that wav file, using our trained model.
class AudioProcessor(object):
def __init__(self, path, rate, clip_min, uuid):
self.rate = rate
self.bytes_per_frame = rate/25
self._path = path
self.clip_min_frames = clip_min // MS_PER_FRAME
self.uuid = uuid
def process(self, count, payload, id):
if count > self.clip_min_frames: # If the buffer is less than CLIP_MIN_MS, ignore it
fn = "{}rec-{}-{}.wav".format('', id, datetime.datetime.now().strftime("%Y%m%dT%H%M%S"))
output = wave.open(fn, 'wb')
output.setparams((1, 2, self.rate, 0, 'NONE', 'not compressed'))
output.writeframes(payload)
output.close()
debug('File written {}'.format(fn))
self.process_file(fn)
info('Processing {} frames for {}'.format(str(count), id))
self.removeFile(fn)
else:
info('Discarding {} frames'.format(str(count)))
def process_file(self, wav_file):
if loaded_model != None:
print("load file {}".format(wav_file))
X, sample_rate = librosa.load(wav_file, res_type='kaiser_fast')
mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0)
X = [mfccs]
prediction = loaded_model.predict(X)
print("prediction",prediction)
if prediction[0] == 0:
beep_captured = True
print("beep detected")
else:
beep_captured = False
for client in clients:
client.write_message({"uuids":uuids, "beep_detected":beep_captured})
else:
print("model not loaded")
def removeFile(self, wav_file):
os.remove(wav_file)Once we have a wav file, we use librosa.load to load in the file, and then use the librosa.feature.mfcc function to generate the MFCC of the sample. We then call loaded_model.predict([mfccs]) to make our prediction. If the output of this function is 0, a beep was detected. If it outputs 1, then its speech. We then generate a JSON payload of wether a beep was detected, as the a uuids, of the conversation. This way, our client application, can send a TTS into the call, using the uuids.
The final step is to build a client that connects to the websocket, observes when a beep is detected, and send a TTS into the call, when the voicemail is detected.
First, we need to connect to the websocket.
ws = websocket.WebSocketApp("ws://3c66cdfa.ngrok.io/socket",
on_message = on_message,
on_error = on_error,
on_close = on_close)
ws.on_open = on_open
ws.run_forever()Next, we just listen for any incoming message from our websocket.
def on_message(ws, message):
data = json.loads(message)
if data["beep_detected"] == True:
for id in data["uuids"]:
response = client.send_speech(id, text='Answering Machine Detected')
time.sleep(4)
for id in data["uuids"]:
try:
client.update_call(id, action='hangup')
except:
passWe'll parse the incoming message as JSON, then check the beep_detected property is True. If it is, then a beep was detected. We will then send a TTS into the call saying 'Answering Machine Detected', then perform a hangup action into the call.
We've shown how we built a answering machine detection model with 96% accuracy, with using a few audio samples of beeps and speech in order to train our model. Hopefully, we've shown how you can use machine learning in your projects. Enjoy!