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#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
import os
from pathlib import Path
import sys
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
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(
"--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()
model_dir = Path(args.model_dir)
model_filename = model_dir / "final.zip"
tokens_filename = model_dir / "units.txt"
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=model_filename.as_posix(),
tokens=tokens_filename.as_posix(),
use_gpu=False,
feat_config=feat_config,
decoding_method="greedy_search",
num_active_paths=2,
)
recognizer = sherpa.OfflineRecognizer(config)
signal, sample_rate = librosa.load(args.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
)
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))
return
if __name__ == "__main__":
main()
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