add offline websocket server/client (#98)

python-api-examples/online-decode-files.py

@@ -0,0 +1,184 @@
+#!/usr/bin/env python3
+
+"""
+This file demonstrates how to use sherpa-onnx Python API to transcribe
+file(s) with a streaming model.
+
+Usage:
+    ./online-decode-files.py \
+      /path/to/foo.wav \
+      /path/to/bar.wav \
+      /path/to/16kHz.wav \
+      /path/to/8kHz.wav
+
+Please refer to
+https://k2-fsa.github.io/sherpa/onnx/index.html
+to install sherpa-onnx and to download the pre-trained models
+used in this file.
+"""
+import argparse
+import time
+import wave
+from pathlib import Path
+from typing import Tuple
+
+import numpy as np
+import sherpa_onnx
+
+
+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 encoder model",
+    )
+
+    parser.add_argument(
+        "--decoder",
+        type=str,
+        help="Path to the decoder model",
+    )
+
+    parser.add_argument(
+        "--joiner",
+        type=str,
+        help="Path to the joiner model",
+    )
+
+    parser.add_argument(
+        "--num-threads",
+        type=int,
+        default=1,
+        help="Number of threads for neural network computation",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="Valid values are greedy_search and modified_beam_search",
+    )
+
+    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 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 read_wave(wave_filename: str) -> Tuple[np.ndarray, int]:
+    """
+    Args:
+      wave_filename:
+        Path to a wave file. It should be single channel and each sample should
+        be 16-bit. Its sample rate does not need to be 16kHz.
+    Returns:
+      Return a tuple containing:
+       - A 1-D array of dtype np.float32 containing the samples, which are
+       normalized to the range [-1, 1].
+       - sample rate of the wave file
+    """
+
+    with wave.open(wave_filename) as f:
+        assert f.getnchannels() == 1, f.getnchannels()
+        assert f.getsampwidth() == 2, f.getsampwidth()  # it is in bytes
+        num_samples = f.getnframes()
+        samples = f.readframes(num_samples)
+        samples_int16 = np.frombuffer(samples, dtype=np.int16)
+        samples_float32 = samples_int16.astype(np.float32)
+
+        samples_float32 = samples_float32 / 32768
+        return samples_float32, f.getframerate()
+
+
+def main():
+    args = get_args()
+    assert_file_exists(args.encoder)
+    assert_file_exists(args.decoder)
+    assert_file_exists(args.joiner)
+    assert_file_exists(args.tokens)
+
+    recognizer = sherpa_onnx.OnlineRecognizer(
+        tokens=args.tokens,
+        encoder=args.encoder,
+        decoder=args.decoder,
+        joiner=args.joiner,
+        num_threads=args.num_threads,
+        sample_rate=16000,
+        feature_dim=80,
+        decoding_method=args.decoding_method,
+    )
+
+    print("Started!")
+    start_time = time.time()
+
+    streams = []
+    total_duration = 0
+    for wave_filename in args.sound_files:
+        assert_file_exists(wave_filename)
+        samples, sample_rate = read_wave(wave_filename)
+        duration = len(samples) / sample_rate
+        total_duration += duration
+
+        s = recognizer.create_stream()
+        s.accept_waveform(sample_rate, samples)
+
+        tail_paddings = np.zeros(int(0.2 * sample_rate), dtype=np.float32)
+        s.accept_waveform(sample_rate, tail_paddings)
+
+        s.input_finished()
+
+        streams.append(s)
+
+    while True:
+        ready_list = []
+        for s in streams:
+            if recognizer.is_ready(s):
+                ready_list.append(s)
+        if len(ready_list) == 0:
+            break
+        recognizer.decode_streams(ready_list)
+    results = [recognizer.get_result(s) for s in streams]
+    end_time = time.time()
+    print("Done!")
+
+    for wave_filename, result in zip(args.sound_files, results):
+        print(f"{wave_filename}\n{result}")
+        print("-" * 10)
+
+    elapsed_seconds = end_time - start_time
+    rtf = elapsed_seconds / duration
+    print(f"num_threads: {args.num_threads}")
+    print(f"decoding_method: {args.decoding_method}")
+    print(f"Wave duration: {duration:.3f} s")
+    print(f"Elapsed time: {elapsed_seconds:.3f} s")
+    print(f"Real time factor (RTF): {elapsed_seconds:.3f}/{duration:.3f} = {rtf:.3f}")
+
+
+if __name__ == "__main__":
+    main()