Add Python API for keyword spotting

python-api-examples/keyword-spotter-from-microphone.py

@@ -0,0 +1,205 @@
+#!/usr/bin/env python3
+
+# Real-time keyword spotting from a microphone with sherpa-onnx Python API
+#
+# Please refer to
+# https://k2-fsa.github.io/sherpa/onnx/kws/pretrained_models/index.html
+# to download pre-trained models
+
+import argparse
+import sys
+from pathlib import Path
+
+from typing import List
+
+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)
+
+import sherpa_onnx
+
+
+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/kws/pretrained_models/index.html to download it"
+    )
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        help="Path to tokens.txt",
+    )
+
+    parser.add_argument(
+        "--encoder",
+        type=str,
+        help="Path to the transducer encoder model",
+    )
+
+    parser.add_argument(
+        "--decoder",
+        type=str,
+        help="Path to the transducer decoder model",
+    )
+
+    parser.add_argument(
+        "--joiner",
+        type=str,
+        help="Path to the transducer joiner model",
+    )
+
+    parser.add_argument(
+        "--num-threads",
+        type=int,
+        default=1,
+        help="Number of threads for neural network computation",
+    )
+
+    parser.add_argument(
+        "--provider",
+        type=str,
+        default="cpu",
+        help="Valid values: cpu, cuda, coreml",
+    )
+
+    parser.add_argument(
+        "--max-active-paths",
+        type=int,
+        default=4,
+        help="""
+        It specifies number of active paths to keep during decoding.
+        """,
+    )
+
+    parser.add_argument(
+        "--num-tailing-blanks",
+        type=int,
+        default=1,
+        help="""The number of tailing blanks a keyword should be followed. Setting
+        to a larger value (e.g. 8) when your keywords has overlapping tokens
+        between each other.
+        """,
+    )
+
+    parser.add_argument(
+        "--keywords-file",
+        type=str,
+        default="",
+        help="""
+        The file containing keywords, one words/phrases per line, and for each
+        phrase the bpe/cjkchar/pinyin are separated by a space. For example:
+
+        ▁HE LL O ▁WORLD
+        x iǎo ài t óng x ué 
+        """,
+    )
+
+    parser.add_argument(
+        "--keywords-score",
+        type=float,
+        default=1.5,
+        help="""
+        The boosting score of each token for keywords. The larger the easier to
+        survive beam search.
+        """,
+    )
+
+    parser.add_argument(
+        "--keywords-threshold",
+        type=float,
+        default=0.35,
+        help="""
+        The trigger threshold (i.e. probability) of the keyword. The larger the
+        harder to trigger.
+        """,
+    )
+
+    parser.add_argument(
+        "sound_files",
+        type=str,
+        nargs="+",
+        help="The input sound file(s) to decode. Each file must be of WAVE"
+        "format with a single channel, and each sample has 16-bit, "
+        "i.e., int16_t. "
+        "The sample rate of the file can be arbitrary and does not need to "
+        "be 16 kHz",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+
+    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"]}')
+
+    assert_file_exists(args.tokens)
+    assert_file_exists(args.encoder)
+    assert_file_exists(args.decoder)
+    assert_file_exists(args.joiner)
+
+    assert Path(
+        args.keywords_file
+    ).is_file(), (
+        f"keywords_file : {args.keywords_file} not exist, please provide a valid path."
+    )
+
+    keyword_spotter = sherpa_onnx.KeywordSpotter(
+        tokens=args.tokens,
+        encoder=args.encoder,
+        decoder=args.decoder,
+        joiner=args.joiner,
+        num_threads=args.num_threads,
+        max_active_paths=args.max_active_paths,
+        keywords_file=args.keywords_file,
+        keywords_score=args.keywords_score,
+        keywords_threshold=args.keywords_threshold,
+        num_tailing_blanks=args.num_tailing_blanks,
+        provider=args.provider,
+    )
+
+    print("Started! Please speak")
+
+    sample_rate = 16000
+    samples_per_read = int(0.1 * sample_rate)  # 0.1 second = 100 ms
+    last_result = ""
+    stream = keyword_spotter.create_stream()
+    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)
+            stream.accept_waveform(sample_rate, samples)
+            while keyword_spotter.is_ready(stream):
+                keyword_spotter.decode_stream(stream)
+            result = keyword_spotter.get_result(stream)
+            if last_result != result:
+                last_result = result
+                print("\r{}".format(result), end="", flush=True)
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("\nCaught Ctrl + C. Exiting")