# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A module to record audio in a streaming basis."""
import threading
import numpy as np
try:
# pylint: disable=g-import-not-at-top
import sounddevice as sd
# pylint: enable=g-import-not-at-top
except OSError as oe:
sd = None
sd_error = oe
except ImportError as ie:
sd = None
sd_error = ie
class AudioRecord(object):
"""A class to record audio in a streaming basis."""
def __init__(self, channels: int, sampling_rate: int,
buffer_size: int) -> None:
"""Creates an AudioRecord instance.
Args:
channels: Number of input channels.
sampling_rate: Sampling rate in Hertz.
buffer_size: Size of the ring buffer in number of samples.
Raises:
ValueError: if any of the arguments is non-positive.
ImportError: if failed to import `sounddevice`.
OSError: if failed to load `PortAudio`.
"""
if sd is None:
raise sd_error
if channels <= 0:
raise ValueError('channels must be postive.')
if sampling_rate <= 0:
raise ValueError('sampling_rate must be postive.')
if buffer_size <= 0:
raise ValueError('buffer_size must be postive.')
self._audio_buffer = []
self._buffer_size = buffer_size
self._channels = channels
self._sampling_rate = sampling_rate
# Create a ring buffer to store the input audio.
self._buffer = np.zeros([buffer_size, channels], dtype=float)
self._lock = threading.Lock()
def audio_callback(data, *_):
"""A callback to receive recorded audio data from sounddevice."""
self._lock.acquire()
shift = len(data)
if shift > buffer_size:
self._buffer = np.copy(data[:buffer_size])
else:
self._buffer = np.roll(self._buffer, -shift, axis=0)
self._buffer[-shift:, :] = np.copy(data)
self._lock.release()
# Create an input stream to continuously capture the audio data.
self._stream = sd.InputStream(
channels=channels,
samplerate=sampling_rate,
callback=audio_callback,
)
@property
def channels(self) -> int:
return self._channels
@property
def sampling_rate(self) -> int:
return self._sampling_rate
@property
def buffer_size(self) -> int:
return self._buffer_size
def start_recording(self) -> None:
"""Starts the audio recording."""
# Clear the internal ring buffer.
self._buffer.fill(0)
# Start recording using sounddevice's InputStream.
self._stream.start()
def stop(self) -> None:
"""Stops the audio recording."""
self._stream.stop()
def read(self, size: int) -> np.ndarray:
"""Reads the latest audio data captured in the buffer.
Args:
size: Number of samples to read from the buffer.
Returns:
A NumPy array containing the audio data.
Raises:
ValueError: Raised if `size` is larger than the buffer size.
"""
if size > self._buffer_size:
raise ValueError('Cannot read more samples than the size of the buffer.')
elif size <= 0:
raise ValueError('Size must be positive.')
start_index = self._buffer_size - size
return np.copy(self._buffer[start_index:])
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