Refactor python examples (#67)

@@ -9,7 +9,7 @@ log() {
 }
 
 
-repo_url=https://huggingface.co/csukuangfj/sherpa-onnx-lstm-en-2023-02-17
+repo_url=https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20
 
 log "Start testing ${repo_url}"
 repo=$(basename $repo_url)
@@ -30,4 +30,9 @@ ls -lh
 
 ls -lh $repo
 
-python3 python-api-examples/decode-file.py
+python3 ./python-api-examples/decode-file.py \
+  --tokens=$repo/tokens.txt \
+  --encoder=$repo/encoder-epoch-99-avg-1.onnx \
+  --decoder=$repo/decoder-epoch-99-avg-1.onnx \
+  --joiner=$repo/joiner-epoch-99-avg-1.onnx \
+  --wave-filename=$repo/test_wavs/4.wav

@@ -33,3 +33,4 @@ decode-file
 *.dylib
 tokens.txt
 *.onnx
+log.txt

 cmake_minimum_required(VERSION 3.13 FATAL_ERROR)
 project(sherpa-onnx)
 
-set(SHERPA_ONNX_VERSION "1.1")
+set(SHERPA_ONNX_VERSION "1.2")
 
 # Disable warning about
 #

@@ -9,27 +9,83 @@ 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 argparse
 import time
+import wave
+from pathlib import Path
 
 import numpy as np
 import sherpa_onnx
 
 
+def assert_file_exists(filename: str):
+    assert Path(
+        filename
+    ).is_file(), f"{filename} does not exist!\nPlease 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(
+        "--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(
+        "--wave-filename",
+        type=str,
+        help="""Path to the wave filename. Must be 16 kHz,
+        mono with 16-bit samples""",
+    )
+
+    return parser.parse_args()
+
+
 def main():
     sample_rate = 16000
-    num_threads = 4
+    num_threads = 2
+
+    args = get_args()
+    assert_file_exists(args.encoder)
+    assert_file_exists(args.decoder)
+    assert_file_exists(args.joiner)
+    assert_file_exists(args.tokens)
+    if not Path(args.wave_filename).is_file():
+        print(f"{args.wave_filename} does not exist!")
+        return
+
     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",
+        tokens=args.tokens,
+        encoder=args.encoder,
+        decoder=args.decoder,
+        joiner=args.joiner,
         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:
+    with wave.open(args.wave_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

@@ -7,7 +7,9 @@
 # https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
 # to download pre-trained models
 
+import argparse
 import sys
+from pathlib import Path
 
 try:
     import sounddevice as sd
@@ -22,18 +24,65 @@ except ImportError as e:
 import sherpa_onnx
 
 
+def assert_file_exists(filename: str):
+    assert Path(
+        filename
+    ).is_file(), f"{filename} does not exist!\nPlease 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(
+        "--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(
+        "--wave-filename",
+        type=str,
+        help="""Path to the wave filename. Must be 16 kHz,
+        mono with 16-bit samples""",
+    )
+
+    return parser.parse_args()
+
+
 def create_recognizer():
+    args = get_args()
+    assert_file_exists(args.encoder)
+    assert_file_exists(args.decoder)
+    assert_file_exists(args.joiner)
+    assert_file_exists(args.tokens)
     # Please replace the model files if needed.
     # See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
     # for download links.
     recognizer = sherpa_onnx.OnlineRecognizer(
-        tokens="./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt",
-        encoder="./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.onnx",
-        decoder="./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx",
-        joiner="./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.onnx",
-        num_threads=4,
-        sample_rate=16000,
-        feature_dim=80,
+        tokens=args.tokens,
+        encoder=args.encoder,
+        decoder=args.decoder,
+        joiner=args.joiner,
         enable_endpoint_detection=True,
         rule1_min_trailing_silence=2.4,
         rule2_min_trailing_silence=1.2,

@@ -6,7 +6,9 @@
 # https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
 # to download pre-trained models
 
+import argparse
 import sys
+from pathlib import Path
 
 try:
     import sounddevice as sd
@@ -21,15 +23,65 @@ except ImportError as e:
 import sherpa_onnx
 
 
+def assert_file_exists(filename: str):
+    assert Path(
+        filename
+    ).is_file(), f"{filename} does not exist!\nPlease 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(
+        "--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(
+        "--wave-filename",
+        type=str,
+        help="""Path to the wave filename. Must be 16 kHz,
+        mono with 16-bit samples""",
+    )
+
+    return parser.parse_args()
+
+
 def create_recognizer():
+    args = get_args()
+    assert_file_exists(args.encoder)
+    assert_file_exists(args.decoder)
+    assert_file_exists(args.joiner)
+    assert_file_exists(args.tokens)
     # Please replace the model files if needed.
     # See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
     # for download links.
     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",
+        tokens=args.tokens,
+        encoder=args.encoder,
+        decoder=args.decoder,
+        joiner=args.joiner,
         num_threads=4,
         sample_rate=16000,
         feature_dim=80,

@@ -3,6 +3,7 @@
 // Copyright (c)  2023  Xiaomi Corporation
 #include "sherpa-onnx/csrc/onnx-utils.h"
 
+#include <algorithm>
 #include <fstream>
 #include <string>
 #include <vector>