Fixed adversarial minimum words bug

examples/capture_activations_barco.py

@@ -43,7 +43,7 @@ parser.add_argument(
     "--output_dir",
     help="Where to save the audio that resulted in an activation",
     type=str,
-    default="positivedetections/meeting_test/june_19_standup/hey_barcoh_v1",
+    default="positivedetections/meeting_test/june_20_standup/hey_barcoh_v1",
     required=False
 )
 parser.add_argument(

examples/capture_activations_stryker.py

@@ -43,7 +43,7 @@ parser.add_argument(
     "--output_dir",
     help="Where to save the audio that resulted in an activation",
     type=str,
-    default="positivedetections/meeting_test/june_19_standup/hey_stryker_dipco_1",
+    default="positivedetections/meeting_test/june_20_standup/hey_stryker_dipco_1",
     required=False
 )
 parser.add_argument(

examples/capture_activations_voice_genie.py

@@ -0,0 +1,166 @@
+# Copyright 2022 David Scripka. 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.
+
+##################################
+
+# This example scripts runs openWakeWord continuously on a microphone stream,
+# and saves 5 seconds of audio immediately before the activation as WAV clips
+# in the specified output location.
+
+##################################
+
+# Imports
+import os
+import platform
+import collections
+import time
+if platform.system() == "Windows":
+    import pyaudiowpatch as pyaudio
+else:
+    import pyaudio
+import numpy as np
+from openwakeword.model import Model
+import openwakeword
+import scipy.io.wavfile
+import datetime
+import argparse
+from utils.beep import playBeep
+
+# Parse input arguments
+parser=argparse.ArgumentParser()
+parser.add_argument(
+    "--output_dir",
+    help="Where to save the audio that resulted in an activation",
+    type=str,
+    default="positivedetections/meeting_test/june_20_standup/voice_genie_v01",
+    required=False
+)
+parser.add_argument(
+    "--threshold",
+    help="The score threshold for an activation",
+    type=float,
+    default=0.5,
+    required=False
+)
+parser.add_argument(
+    "--vad_threshold",
+    help="""The threshold to use for voice activity detection (VAD) in the openWakeWord instance.
+            The default (0.0), disables VAD.""",
+    type=float,
+    default=0.0,
+    required=False
+)
+parser.add_argument(
+    "--noise_suppression",
+    help="Whether to enable speex noise suppression in the openWakeWord instance.",
+    type=bool,
+    default=False,
+    required=False
+)
+# parser=argparse.ArgumentParser()
+parser.add_argument(
+    "--chunk_size",
+    help="How much audio (in number of 16khz samples) to predict on at once",
+    type=int,
+    default=1280,
+    required=False
+)
+parser.add_argument(
+    "--model_path",
+    help="The path of a specific model to load",
+    type=str,
+    default="/Users/SAI/Documents/Code/wakeWord/wakeWordForked/Untitled/wakeword_models/voice_genie/voice_genie_v01.onnx",
+    required=False
+)
+parser.add_argument(
+    "--inference_framework",
+    help="The inference framework to use (either 'onnx' or 'tflite'",
+    type=str,
+    default='onnx',
+    required=False
+)
+parser.add_argument(
+    "--disable_activation_sound",
+    help="Disables the activation sound, clips are silently captured",
+    action='store_true',
+    required=False
+)
+
+args=parser.parse_args()
+
+# Get microphone stream
+FORMAT = pyaudio.paInt16
+CHANNELS = 1
+RATE = 16000
+CHUNK = args.chunk_size * 4
+audio = pyaudio.PyAudio()
+mic_stream = audio.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK)
+
+# Load pre-trained openwakeword models
+if args.model_path != "":
+    owwModel = Model(
+        wakeword_models=[args.model_path], 
+        enable_speex_noise_suppression=args.noise_suppression,
+        vad_threshold = args.vad_threshold,
+        inference_framework=args.inference_framework
+        )
+else:
+    owwModel = Model(inference_framework=args.inference_framework)
+
+
+# Set waiting period after activation before saving clip (to get some audio context after the activation)
+save_delay = 1  # seconds
+
+# Set cooldown period before another clip can be saved
+cooldown = 4  # seconds
+
+# Create output directory if it does not already exist
+if not os.path.exists(args.output_dir):
+    os.mkdir(args.output_dir)
+
+# Run capture loop, checking for hotwords
+if __name__ == "__main__":
+    # Predict continuously on audio stream
+    last_save = time.time()
+    activation_times = collections.defaultdict(list)
+
+    print("\n\nListening for wakewords...\n")
+    while True:
+        # Get audio
+        mic_audio = np.frombuffer(mic_stream.read(CHUNK), dtype=np.int16)
+
+        # Feed to openWakeWord model
+        prediction = owwModel.predict(mic_audio)
+
+        # Check for model activations (score above threshold), and save clips
+        for mdl in prediction.keys():
+            if prediction[mdl] >= args.threshold:
+                activation_times[mdl].append(time.time())
+
+            if activation_times.get(mdl) and (time.time() - last_save) >= cooldown \
+               and (time.time() - activation_times.get(mdl)[0]) >= save_delay:
+                last_save = time.time()
+                activation_times[mdl] = []
+                detect_time = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
+                
+                print(f'Detected activation from \"{mdl}\" model at time {detect_time}!')
+
+                # Capture total of 5 seconds, with the microphone audio associated with the
+                # activation around the ~4 second point
+                audio_context = np.array(list(owwModel.preprocessor.raw_data_buffer)[-16000*5:]).astype(np.int16)
+                fname = detect_time + f"_{mdl}.wav"
+                scipy.io.wavfile.write(os.path.join(os.path.abspath(args.output_dir), fname), 16000, audio_context)
+                
+                if not args.disable_activation_sound:
+                    playBeep(os.path.join(os.path.dirname(__file__), 'audio', 'activation.wav'), audio)

examples/capture_activations_zelda.py

@@ -43,7 +43,7 @@ parser.add_argument(
     "--output_dir",
     help="Where to save the audio that resulted in an activation",
     type=str,
-    default="positivedetections/meeting_test/june_19_standup/hey_zelda_multi_phrase",
+    default="positivedetections/meeting_test/june_20_standup/hey_zelda_multi_phrase",
     required=False
 )
 parser.add_argument(

examples/capture_activations_zelda_negative.py

@@ -43,7 +43,7 @@ parser.add_argument(
     "--output_dir",
     help="Where to save the audio that resulted in an activation",
     type=str,
-    default="positivedetections/meeting_test/june_19_standup/hey_zelda_multi_phrase",
+    default="positivedetections/meeting_test/june_20_standup/hey_zelda_negative",
     required=False
 )
 parser.add_argument(

openwakeword/data.py

@@ -988,10 +988,15 @@ def generate_adversarial_texts(input_text: str, N: int, include_partial_phrase:
 
         if include_partial_phrase is not None and len(input_text.split()) > 1 and np.random.random() <= include_partial_phrase:
             n_words = np.random.randint(1, len(input_text.split())+1)
-            adversarial_texts.append(" ".join(np.random.choice(txts, size=n_words, replace=False)))
+            if len(txts) >= n_words:
+                adversarial_texts.append(" ".join(np.random.choice(txts, size=n_words, replace=False)))
+            else:
+                # Handle the case where there are fewer words than required
+                adversarial_texts.append(" ".join(np.random.choice(txts, size=n_words, replace=True)))
         else:
             adversarial_texts.append(" ".join(txts))
 
+
     # Remove any exact matches to input phrase
     adversarial_texts = [i for i in adversarial_texts if i != input_text]