Add Python example to show how to register speakers dynamically for speaker ID. (#986)

+#!/usr/bin/env python3
+
+"""
+This script shows how to use Python APIs for speaker identification with
+a microphone and a VAD model
+
+Usage:
+
+(1) Download a model for computing speaker embeddings
+
+Please visit
+https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-recongition-models
+to download a model. An example is given below:
+
+    wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-recongition-models/3dspeaker_speech_eres2net_large_sv_zh-cn_3dspeaker_16k.onnx
+
+Note that `zh` means Chinese, while `en` means English.
+
+(2) Download the VAD model
+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
+
+(3) Run this script
+
+python3 ./python-api-examples/speaker-identification-with-vad-dynamic.py \
+  --silero-vad-model=/path/to/silero_vad.onnx \
+  --model ./3dspeaker_speech_eres2net_large_sv_zh-cn_3dspeaker_16k.onnx
+"""
+import argparse
+import sys
+
+import numpy as np
+import sherpa_onnx
+
+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)
+
+g_sample_rate = 16000
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--model",
+        type=str,
+        required=True,
+        help="Path to the speaker embedding model file.",
+    )
+
+    parser.add_argument(
+        "--silero-vad-model",
+        type=str,
+        required=True,
+        help="Path to silero_vad.onnx",
+    )
+
+    parser.add_argument("--threshold", type=float, default=0.4)
+
+    parser.add_argument(
+        "--num-threads",
+        type=int,
+        default=1,
+        help="Number of threads for neural network computation",
+    )
+
+    parser.add_argument(
+        "--debug",
+        type=bool,
+        default=False,
+        help="True to show debug messages",
+    )
+
+    parser.add_argument(
+        "--provider",
+        type=str,
+        default="cpu",
+        help="Valid values: cpu, cuda, coreml",
+    )
+
+    return parser.parse_args()
+
+
+def load_speaker_embedding_model(args):
+    config = sherpa_onnx.SpeakerEmbeddingExtractorConfig(
+        model=args.model,
+        num_threads=args.num_threads,
+        debug=args.debug,
+        provider=args.provider,
+    )
+    if not config.validate():
+        raise ValueError(f"Invalid config. {config}")
+    extractor = sherpa_onnx.SpeakerEmbeddingExtractor(config)
+    return extractor
+
+
+def compute_speaker_embedding(
+    samples: np.ndarray,
+    extractor: sherpa_onnx.SpeakerEmbeddingExtractor,
+) -> np.ndarray:
+    """
+    Args:
+      samples:
+        A 1-D float32 array.
+      extractor:
+        The return value of function load_speaker_embedding_model().
+    Returns:
+      Return a 1-D float32 array.
+    """
+    if len(samples) < g_sample_rate:
+        print(f"Your input contains only {len(samples)} samples!")
+
+    stream = extractor.create_stream()
+    stream.accept_waveform(sample_rate=g_sample_rate, waveform=samples)
+    stream.input_finished()
+
+    assert extractor.is_ready(stream)
+    embedding = extractor.compute(stream)
+    embedding = np.array(embedding)
+    return embedding
+
+
+def main():
+    args = get_args()
+    print(args)
+
+    devices = sd.query_devices()
+    if len(devices) == 0:
+        print("No microphone devices found")
+        sys.exit(0)
+
+    print(devices)
+    # If you want to select a different device, please change
+    # sd.default.device[0]. For instance, if you want to select device 10,
+    # please use
+    #
+    #  sd.default.device[0] = 4
+    #  print(devices)
+    #
+
+    default_input_device_idx = sd.default.device[0]
+    print(f'Use default device: {devices[default_input_device_idx]["name"]}')
+
+    extractor = load_speaker_embedding_model(args)
+
+    manager = sherpa_onnx.SpeakerEmbeddingManager(extractor.dim)
+
+    vad_config = sherpa_onnx.VadModelConfig()
+    vad_config.silero_vad.model = args.silero_vad_model
+    vad_config.silero_vad.min_silence_duration = 0.25
+    vad_config.silero_vad.min_speech_duration = 1.0
+    vad_config.sample_rate = g_sample_rate
+
+    window_size = vad_config.silero_vad.window_size
+    vad = sherpa_onnx.VoiceActivityDetector(vad_config, buffer_size_in_seconds=100)
+
+    samples_per_read = int(0.1 * g_sample_rate)  # 0.1 second = 100 ms
+
+    print("Started! Please speak")
+
+    line_num = 0
+    speaker_id = 0
+    buffer = []
+    with sd.InputStream(channels=1, dtype="float32", samplerate=g_sample_rate) as s:
+        while True:
+            samples, _ = s.read(samples_per_read)  # a blocking read
+            samples = samples.reshape(-1)
+            buffer = np.concatenate([buffer, samples])
+            while len(buffer) > window_size:
+                vad.accept_waveform(buffer[:window_size])
+                buffer = buffer[window_size:]
+
+            while not vad.empty():
+                if len(vad.front.samples) < 0.5 * g_sample_rate:
+                    # this segment is too short, skip it
+                    vad.pop()
+                    continue
+                stream = extractor.create_stream()
+                stream.accept_waveform(
+                    sample_rate=g_sample_rate, waveform=vad.front.samples
+                )
+                vad.pop()
+                stream.input_finished()
+
+                embedding = extractor.compute(stream)
+                embedding = np.array(embedding)
+                name = manager.search(embedding, threshold=args.threshold)
+                if not name:
+                    # register it
+                    new_name = f"speaker_{speaker_id}"
+                    status = manager.add(new_name, embedding)
+                    if not status:
+                        raise RuntimeError(f"Failed to register speaker {new_name}")
+                    print(
+                        f"{line_num}: Detected new speaker. Register it as {new_name}"
+                    )
+                    speaker_id += 1
+                else:
+                    print(f"{line_num}: Detected existing speaker: {name}")
+                line_num += 1
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("\nCaught Ctrl + C. Exiting")