使用sherpa-onnx遇到一个奇怪的问题

[AHPUymhd]

我的代码里面是将 你好军哥 转化成 n ǐ h ǎo j ūn g ē @你好军哥，但是应该在keyword_spotter = sherpa_onnx.KeywordSpotter这行代码的时候出现了错误，导致不能运行，请问这是哪里出来问题，非常期待您的回复！！！我的代码如下：

[AHPUymhd]

@pkufool @csukuangfj 可以麻烦您们帮我解答一下疑问吗

[AHPUymhd]

我下载的model是这种格式的文件，请问有问题吗，我是windows系统

[AHPUymhd]

[csukuangfj]

请直接贴文字代码

[AHPUymhd]

#!/usr/bin/env python3

"""
This file demonstrates how to use sherpa-onnx Python API to do keyword spotting
from wave file(s).

Please refer to
https://k2-fsa.github.io/sherpa/onnx/kws/pretrained_models/index.html
to download pre-trained models.
"""
import argparse
import time
import wave
from pathlib import Path
from typing import List, Tuple

import numpy as np
import sherpa_onnx

sound_files = "C:/Users/X/Desktop/sherpa-onnx-master/python-api-examples/action_done.wav"
tokens = "C:/Users/X/Documents/Tencent Files/1848795229/FileRecv/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/tokens.txt"
encoder = "C:/Users/X/Documents/Tencent Files/1848795229/FileRecv/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/encoder-epoch-12-avg-2-chunk-16-left-64.onnx"
decoder = "C:/Users/X/Documents/Tencent Files/1848795229/FileRecv/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/decoder-epoch-12-avg-2-chunk-16-left-64.onnx"
joiner = "C:/Users/X/Documents/Tencent Files/1848795229/FileRecv/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/joiner-epoch-12-avg-2-chunk-16-left-64.onnx"
key_file = "C:/Users/X/Documents/Tencent Files/1848795229/FileRecv/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/keywords_raw.txt"

def get_args():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)

parser.add_argument(
    "--tokens",
    type=str,
    default=tokens,
    help="Path to tokens.txt",
)

parser.add_argument(
    "--encoder",
    default=encoder,
    type=str,
    help="Path to the transducer encoder model",
)

parser.add_argument(
    "--decoder",
    default=decoder,
    type=str,
    help="Path to the transducer decoder model",
)

parser.add_argument(
    "--joiner",
    default=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-trailing-blanks",
    type=int,
    default=1,
    help="""The number of trailing 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=key_file,
    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=1.0,
    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=0.25,
    help="""
    The trigger threshold (i.e. probability) of the keyword. The larger the
    harder to trigger.
    """,
)

parser.add_argument(
    "--sound_files",
    type=str,
    nargs="+",
    default=sound_files,
    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/kws/pretrained_models/index.html to download it"
)

def read_wave(wave_filename: str) -> Tuple[np.ndarray, int]:

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.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_trailing_blanks=args.num_trailing_blanks,
    provider=args.provider,
)

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 = keyword_spotter.create_stream()

    s.accept_waveform(sample_rate, samples)

    tail_paddings = np.zeros(int(0.66 * sample_rate), dtype=np.float32)
    s.accept_waveform(sample_rate, tail_paddings)

    s.input_finished()

    streams.append(s)

results = [""] * len(streams)
while True:
    ready_list = []
    for i, s in enumerate(streams):
        if keyword_spotter.is_ready(s):
            ready_list.append(s)
        r = keyword_spotter.get_result(s)
        if r:
            results[i] += f"{r}/"
            print(f"{r} is detected.")
    if len(ready_list) == 0:
        break
    keyword_spotter.decode_streams(ready_list)
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 / total_duration
print(f"num_threads: {args.num_threads}")
print(f"Wave duration: {total_duration:.3f} s")
print(f"Elapsed time: {elapsed_seconds:.3f} s")
print(
    f"Real time factor (RTF): {elapsed_seconds:.3f}/{total_duration:.3f} = {rtf:.3f}"
)

if name == "main":
main()

[csukuangfj]

key_file = "C:/Users/X/Documents/Tencent Files/1848795229/FileRecv/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/keywords_raw.txt"

这个文件，包含什么？请把这个文件发过来。

[csukuangfj]

你是不是应该要传 keywords.txt 而不是 keywords_raw.txt ?

[AHPUymhd]

你是不是应该要传 keywords.txt 而不是 keywords_raw.txt ?

结果都一样的错误

[AHPUymhd]

jiu's就是这个

你是不是应该要传 keywords.txt 而不是 keywords_raw.txt ?

[csukuangfj]

你是不是应该要传 keywords.txt 而不是 keywords_raw.txt ?

结果都一样的错误

不应该，请贴使用 keywords.txt 的 error log

[csukuangfj]

jiu's就是这个

你是不是应该要传 keywords.txt 而不是 keywords_raw.txt ?

你这个错了。不要把这个给这个 python脚本，你要用 keywords.txt, 不要用keywords_raw.txt, 我应该表述清楚了吧？

[AHPUymhd]

改成了之后是这个错误

[AHPUymhd]

请问是模型没下载对吗

[csukuangfj]

你看，现在是文件名不对。不是刚次那个错误了。这种低级错误，你自己解决？

[AHPUymhd]

你看，现在是文件名不对。不是刚次那个错误了。这种低级错误，你自己解决？

好滴，非常谢谢您

[csukuangfj]

解决了没有呢？

[AHPUymhd]

解决了没有呢？

实在不好意思哈，已经解决了，非常感谢您的帮助。