Add Silero VAD (#313)

python-api-examples/README.md

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+# File description
+
+- [./http_server.py](./http_server.py) It defines which files to server.
+  Files are saved in [./web](./web).
+- [non_streaming_server.py](./non_streaming_server.py) WebSocket server for
+  non-streaming models.
+- [vad-remove-non-speech-segments.py](./vad-remove-non-speech-segments.py) It uses
+  [silero-vad](https://github.com/snakers4/silero-vad) to remove non-speech
+  segments and concatenate all speech segments into a single one.

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

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+#!/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.
+
+Usage
+
+python3 ./vad-remove-non-speech-segments.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 sys
+import time
+from pathlib import Path
+
+import numpy as np
+import sherpa_onnx
+import soundfile as sf
+
+try:
+    import sounddevice as sd
+except ImportError:
+    print("Please install sounddevice first. You can use")
+    print()
+    print("  pip install sounddevice")
+    print()
+    print("to install it")
+    sys.exit(-1)
+
+
+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",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    devices = sd.query_devices()
+    if len(devices) == 0:
+        print("No microphone devices found")
+        sys.exit(0)
+
+    print(devices)
+    default_input_device_idx = sd.default.device[0]
+    print(f'Use default device: {devices[default_input_device_idx]["name"]}')
+
+    args = get_args()
+    assert_file_exists(args.silero_vad_model)
+
+    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")
+
+    try:
+        with sd.InputStream(channels=1, dtype="float32", samplerate=sample_rate) as s:
+            while True:
+                samples, _ = s.read(samples_per_read)  # a blocking read
+                samples = samples.reshape(-1)
+                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()