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#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
import os
from pathlib import Path
import sys
import tempfile
pwd = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(pwd, "../../"))
import librosa
import numpy as np
import sherpa
from scipy.io import wavfile
import torch
import torchaudio
from project_settings import project_path, temp_directory
from toolbox.k2_sherpa.utils import audio_convert
from toolbox.k2_sherpa import decode, models
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_dir",
default=(project_path / "pretrained_models/huggingface/csukuangfj/wenet-chinese-model").as_posix(),
type=str
)
parser.add_argument(
"--in_filename",
default=(project_path / "data/test_wavs/paraformer-zh/si_chuan_hua.wav").as_posix(),
type=str
)
parser.add_argument("--sample_rate", default=16000, type=int)
args = parser.parse_args()
return args
def main():
args = get_args()
# audio convert
signal, sample_rate = librosa.load(args.in_filename, sr=args.sample_rate)
signal *= 32768.0
signal = np.array(signal, dtype=np.int16)
temp_file = temp_directory / "temp.wav"
wavfile.write(
temp_file.as_posix(),
rate=args.sample_rate,
data=signal
)
# audio convert
# in_filename = Path(args.in_filename)
# out_filename = Path(tempfile.gettempdir()) / "asr" / in_filename.name
# out_filename.parent.mkdir(parents=True, exist_ok=True)
#
# audio_convert(in_filename=in_filename.as_posix(),
# out_filename=out_filename.as_posix(),
# )
# load recognizer
m_dict = models.model_map["Chinese"][0]
local_model_dir = Path(args.model_dir)
nn_model_file = local_model_dir / m_dict["nn_model_file"]
tokens_file = local_model_dir / m_dict["tokens_file"]
# recognizer = models.load_recognizer(
# repo_id=m_dict["repo_id"],
# nn_model_file=nn_model_file.as_posix(),
# tokens_file=tokens_file.as_posix(),
# sub_folder=m_dict["sub_folder"],
# local_model_dir=local_model_dir,
# recognizer_type=m_dict["recognizer_type"],
# decoding_method="greedy_search",
# num_active_paths=2,
# )
feat_config = sherpa.FeatureConfig(normalize_samples=False)
feat_config.fbank_opts.frame_opts.samp_freq = args.sample_rate
feat_config.fbank_opts.mel_opts.num_bins = 80
feat_config.fbank_opts.frame_opts.dither = 0
config = sherpa.OfflineRecognizerConfig(
nn_model=nn_model_file.as_posix(),
tokens=tokens_file.as_posix(),
use_gpu=False,
feat_config=feat_config,
decoding_method="greedy_search",
num_active_paths=2,
)
recognizer = sherpa.OfflineRecognizer(config)
# s = recognizer.create_stream()
# s.accept_wave_file(
# temp_file.as_posix()
# )
# recognizer.decode_stream(s)
# text = s.result.text.strip()
# text = text.lower()
# print("text: {}".format(text))
text = decode.decode_by_recognizer(recognizer=recognizer,
filename=temp_file.as_posix(),
)
print("text: {}".format(text))
return
if __name__ == "__main__":
main()
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