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# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
# SPDX-License-Identifier: MIT
"""Automatic speech recognition with PyArmNN demo for processing audio clips to text."""
import sys
import os
from argparse import ArgumentParser
script_dir = os.path.dirname(__file__)
sys.path.insert(1, os.path.join(script_dir, '..', 'common'))
from network_executor import ArmnnNetworkExecutor
from utils import dict_labels
from preprocess import MFCCParams, Preprocessor, MFCC
from audio_capture import AudioCapture, ModelParams
from audio_utils import decode_text, prepare_input_tensors, display_text
def parse_args():
parser = ArgumentParser(description="ASR with PyArmNN")
parser.add_argument(
"--audio_file_path",
required=True,
type=str,
help="Path to the audio file to perform ASR",
)
parser.add_argument(
"--model_file_path",
required=True,
type=str,
help="Path to ASR model to use",
)
parser.add_argument(
"--labels_file_path",
required=True,
type=str,
help="Path to text file containing labels to map to model output",
)
parser.add_argument(
"--preferred_backends",
type=str,
nargs="+",
default=["CpuAcc", "CpuRef"],
help="""List of backends in order of preference for optimizing
subgraphs, falling back to the next backend in the list on unsupported
layers. Defaults to [CpuAcc, CpuRef]""",
)
return parser.parse_args()
def main(args):
# Read command line args
audio_file = args.audio_file_path
model = ModelParams(args.model_file_path)
labels = dict_labels(args.labels_file_path)
# Create the ArmNN inference runner
network = ArmnnNetworkExecutor(model.path, args.preferred_backends)
audio_capture = AudioCapture(model)
buffer = audio_capture.from_audio_file(audio_file)
# Create the preprocessor
mfcc_params = MFCCParams(sampling_freq=16000, num_fbank_bins=128, mel_lo_freq=0, mel_hi_freq=8000,
num_mfcc_feats=13, frame_len=512, use_htk_method=False, n_FFT=512)
mfcc = MFCC(mfcc_params)
preprocessor = Preprocessor(mfcc, model_input_size=296, stride=160)
text = ""
current_r_context = ""
is_first_window = True
print("Processing Audio Frames...")
for audio_data in buffer:
# Prepare the input Tensors
input_tensors = prepare_input_tensors(audio_data, network.input_binding_info, preprocessor)
# Run inference
output_result = network.run(input_tensors)
# Slice and Decode the text, and store the right context
current_r_context, text = decode_text(is_first_window, labels, output_result)
is_first_window = False
display_text(text)
print(current_r_context, flush=True)
if __name__ == "__main__":
args = parse_args()
main(args)
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