1 files changed, 0 insertions, 56 deletions
diff --git a/python/pyarmnn/examples/speech_recognition/audio_capture.py b/python/pyarmnn/examples/speech_recognition/audio_capture.py
deleted file mode 100644
index 0c899208a4..0000000000
--- a/python/pyarmnn/examples/speech_recognition/audio_capture.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# 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 = 47712  # (model_input_size-1)*stride + frame_len
-
-
-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