MLECO-2079 Adding the python KWS example
Signed-off-by: Eanna O Cathain <eanna.ocathain@arm.com>
Change-Id: Ie1463aaeb5e3cade22df8f560ae99a8e1c4a9c17
diff --git a/python/pyarmnn/examples/speech_recognition/run_audio_file.py b/python/pyarmnn/examples/speech_recognition/run_audio_file.py
index 942de20..0430f68 100644
--- a/python/pyarmnn/examples/speech_recognition/run_audio_file.py
+++ b/python/pyarmnn/examples/speech_recognition/run_audio_file.py
@@ -1,20 +1,29 @@
-# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+# Copyright © 2021 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
+import numpy as np
script_dir = os.path.dirname(__file__)
sys.path.insert(1, os.path.join(script_dir, '..', 'common'))
+from argparse import ArgumentParser
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
+from utils import prepare_input_tensors
+from audio_capture import AudioCaptureParams, capture_audio
+from audio_utils import decode_text, display_text
+from wav2letter_mfcc import Wav2LetterMFCC, W2LAudioPreprocessor
+from mfcc import MFCCParams
+
+# Model Specific Labels
+labels = {0: 'a', 1: 'b', 2: 'c', 3: 'd', 4: 'e', 5: 'f', 6: 'g', 7: 'h', 8: 'i', 9: 'j', 10: 'k', 11: 'l', 12: 'm',
+ 13: 'n',
+ 14: 'o', 15: 'p', 16: 'q', 17: 'r', 18: 's', 19: 't', 20: 'u', 21: 'v', 22: 'w', 23: 'x', 24: 'y',
+ 25: 'z',
+ 26: "'", 27: ' ', 28: '$'}
def parse_args():
@@ -32,12 +41,6 @@
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="+",
@@ -52,22 +55,23 @@
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)
+ network = ArmnnNetworkExecutor(args.model_file_path, args.preferred_backends)
- audio_capture = AudioCapture(model)
- buffer = audio_capture.from_audio_file(audio_file)
+ # Specify model specific audio data requirements
+ audio_capture_params = AudioCaptureParams(dtype=np.float32, overlap=31712, min_samples=47712, sampling_freq=16000,
+ mono=True)
- # Create the preprocessor
+ buffer = capture_audio(audio_file, audio_capture_params)
+
+ # Extract features and 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)
+ num_mfcc_feats=13, frame_len=512, use_htk_method=False, n_fft=512)
- text = ""
+ wmfcc = Wav2LetterMFCC(mfcc_params)
+ preprocessor = W2LAudioPreprocessor(wmfcc, model_input_size=296, stride=160)
current_r_context = ""
is_first_window = True