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# 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
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