| # 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) |