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Diffstat (limited to 'python/pyarmnn/examples/speech_recognition/audio_capture.py')
-rw-r--r-- | python/pyarmnn/examples/speech_recognition/audio_capture.py | 56 |
1 files changed, 56 insertions, 0 deletions
diff --git a/python/pyarmnn/examples/speech_recognition/audio_capture.py b/python/pyarmnn/examples/speech_recognition/audio_capture.py new file mode 100644 index 0000000000..9f28d1006e --- /dev/null +++ b/python/pyarmnn/examples/speech_recognition/audio_capture.py @@ -0,0 +1,56 @@ +# Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +# SPDX-License-Identifier: MIT + +"""Contains AudioCapture class for capturing chunks of audio data from file.""" + +from typing import Generator + +import numpy as np +import soundfile as sf + + +class ModelParams: + def __init__(self, model_file_path: str): + """Defines sampling parameters for model used. + + Args: + model_file_path: Path to ASR model to use. + """ + self.path = model_file_path + self.mono = True + self.dtype = np.float32 + self.samplerate = 16000 + self.min_samples = 167392 + + +class AudioCapture: + def __init__(self, model_params): + """Sampling parameters for model used.""" + self.model_params = model_params + + def from_audio_file(self, audio_file_path, overlap=31712) -> Generator[np.ndarray, None, None]: + """Creates a generator that yields audio data from a file. Data is padded with + zeros if necessary to make up minimum number of samples. + + Args: + audio_file_path: Path to audio file provided by user. + overlap: The overlap with previous buffer. We need the offset to be the same as the inner context + of the mfcc output, which is sized as 100 x 39. Each mfcc compute produces 1 x 39 vector, + and consumes 160 audio samples. The default overlap is then calculated to be 47712 - (160 x 100) + where 47712 is the min_samples needed for 1 inference of wav2letter. + + Yields: + Blocks of audio data of minimum sample size. + """ + with sf.SoundFile(audio_file_path) as audio_file: + for block in audio_file.blocks( + blocksize=self.model_params.min_samples, + dtype=self.model_params.dtype, + always_2d=True, + fill_value=0, + overlap=overlap + ): + # Convert to mono if specified + if self.model_params.mono and block.shape[0] > 1: + block = np.mean(block, axis=1) + yield block |