Add Python API (#31)

python-api-examples/decode-file.py

@@ -0,0 +1,73 @@
+#!/usr/bin/env python3
+
+"""
+This file demonstrates how to use sherpa-onnx Python API to recognize
+a single file.
+
+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 wave
+import time
+
+import numpy as np
+import sherpa_onnx
+
+
+def main():
+    sample_rate = 16000
+    num_threads = 4
+    recognizer = sherpa_onnx.OnlineRecognizer(
+        tokens="./sherpa-onnx-lstm-en-2023-02-17/tokens.txt",
+        encoder="./sherpa-onnx-lstm-en-2023-02-17/encoder-epoch-99-avg-1.onnx",
+        decoder="./sherpa-onnx-lstm-en-2023-02-17/decoder-epoch-99-avg-1.onnx",
+        joiner="./sherpa-onnx-lstm-en-2023-02-17/joiner-epoch-99-avg-1.onnx",
+        num_threads=num_threads,
+        sample_rate=sample_rate,
+        feature_dim=80,
+    )
+    filename = "./sherpa-onnx-lstm-en-2023-02-17/test_wavs/1089-134686-0001.wav"
+    with wave.open(filename) as f:
+        assert f.getframerate() == sample_rate, f.getframerate()
+        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
+
+    duration = len(samples_float32) / sample_rate
+
+    start_time = time.time()
+    print("Started!")
+
+    stream = recognizer.create_stream()
+
+    stream.accept_waveform(sample_rate, samples_float32)
+
+    tail_paddings = np.zeros(int(0.2 * sample_rate), dtype=np.float32)
+    stream.accept_waveform(sample_rate, tail_paddings)
+
+    stream.input_finished()
+
+    while recognizer.is_ready(stream):
+        recognizer.decode_stream(stream)
+
+    print(recognizer.get_result(stream))
+
+    print("Done!")
+    end_time = time.time()
+    elapsed_seconds = end_time - start_time
+    rtf = elapsed_seconds / duration
+    print(f"num_threads: {num_threads}")
+    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()