Add VAD examples using ALSA for recording (#739)

python-api-examples/vad-remove-non-speech-segments-alsa.py

@@ -0,0 +1,138 @@
+#!/usr/bin/env python3
+
+"""
+This file shows how to remove non-speech segments
+and merge all speech segments into a large segment
+and save it to a file.
+
+Different from ./vad-remove-non-speech-segments.py, this file supports only
+Linux.
+
+Usage
+
+python3 ./vad-remove-non-speech-segments-alsa.py \
+        --silero-vad-model silero_vad.onnx
+
+Please visit
+https://github.com/snakers4/silero-vad/blob/master/files/silero_vad.onnx
+to download silero_vad.onnx
+
+For instance,
+
+wget https://github.com/snakers4/silero-vad/raw/master/files/silero_vad.onnx
+"""
+
+import argparse
+import time
+from pathlib import Path
+
+import numpy as np
+import sherpa_onnx
+import soundfile as sf
+
+
+def assert_file_exists(filename: str):
+    assert Path(filename).is_file(), (
+        f"{filename} does not exist!\n"
+        "Please refer to "
+        "https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html to download it"
+    )
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--silero-vad-model",
+        type=str,
+        required=True,
+        help="Path to silero_vad.onnx",
+    )
+
+    parser.add_argument(
+        "--device-name",
+        type=str,
+        required=True,
+        help="""
+The device name specifies which microphone to use in case there are several
+on your system. You can use
+
+  arecord -l
+
+to find all available microphones on your computer. For instance, if it outputs
+
+**** List of CAPTURE Hardware Devices ****
+card 3: UACDemoV10 [UACDemoV1.0], device 0: USB Audio [USB Audio]
+  Subdevices: 1/1
+  Subdevice #0: subdevice #0
+
+and if you want to select card 3 and the device 0 on that card, please use:
+
+  plughw:3,0
+
+as the device_name.
+        """,
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    assert_file_exists(args.silero_vad_model)
+
+    device_name = args.device_name
+    print(f"device_name: {device_name}")
+    alsa = sherpa_onnx.Alsa(device_name)
+
+    sample_rate = 16000
+    samples_per_read = int(0.1 * sample_rate)  # 0.1 second = 100 ms
+
+    config = sherpa_onnx.VadModelConfig()
+    config.silero_vad.model = args.silero_vad_model
+    config.sample_rate = sample_rate
+
+    window_size = config.silero_vad.window_size
+
+    buffer = []
+    vad = sherpa_onnx.VoiceActivityDetector(config, buffer_size_in_seconds=30)
+
+    all_samples = []
+
+    print("Started! Please speak. Press Ctrl C to exit")
+
+    try:
+        while True:
+            samples = alsa.read(samples_per_read)  # a blocking read
+            samples = np.array(samples)
+
+            buffer = np.concatenate([buffer, samples])
+
+            all_samples = np.concatenate([all_samples, samples])
+
+            while len(buffer) > window_size:
+                vad.accept_waveform(buffer[:window_size])
+                buffer = buffer[window_size:]
+    except KeyboardInterrupt:
+        print("\nCaught Ctrl + C. Saving & Exiting")
+
+        speech_samples = []
+        while not vad.empty():
+            speech_samples.extend(vad.front.samples)
+            vad.pop()
+
+        speech_samples = np.array(speech_samples, dtype=np.float32)
+
+        filename_for_speech = time.strftime("%Y%m%d-%H%M%S-speech.wav")
+        sf.write(filename_for_speech, speech_samples, samplerate=sample_rate)
+
+        filename_for_all = time.strftime("%Y%m%d-%H%M%S-all.wav")
+        sf.write(filename_for_all, all_samples, samplerate=sample_rate)
+
+        print(f"Saved to {filename_for_speech} and {filename_for_all}")
+
+
+if __name__ == "__main__":
+    main()