"""
Audio Processor Module
=======================
This module provides the AudioProcessor class, utilizing PyTorchaudio for handling audio files.
It includes functionalities to load, cut, and manage audio waveforms, offering efficient and
flexible audio processing.
Available Classes:
- AudioProcessor: Processes audio waveforms and provides methods for loading,
cutting, and handling audio.
Usage:
from .audio_import AudioProcessor
processor = AudioProcessor.from_file("path/to/audiofile.wav")
cut_waveform = processor.cut(start=1.0, end=5.0)
Constants:
- SAMPLE_RATE (int): Default sample rate for processing.
- NORMALIZATION_FACTOR (float): Normalization factor for audio waveform.
"""
from subprocess import CalledProcessError, run
import numpy as np
import torch
SAMPLE_RATE = 16000
NORMALIZATION_FACTOR = 32768.0
[docs]
class AudioProcessor:
"""
Audio Processor class that leverages PyTorchaudio to provide functionalities
for loading, cutting, and handling audio waveforms.
Attributes:
waveform: torch.Tensor
The audio waveform tensor.
sr: int
The sample rate of the audio.
"""
[docs]
def __init__(self, waveform: torch.Tensor, sr : int = SAMPLE_RATE,
*args, **kwargs) -> None:
"""
Initialize the AudioProcessor object.
Args:
waveform (torch.Tensor): The audio waveform tensor.
sr (int, optional): The sample rate of the audio. Defaults to SAMPLE_RATE.
args: Additional arguments.
kwargs: Additional keyword arguments, e.g., device to use for processing.
If CUDA is available, it defaults to CUDA.
Raises:
ValueError: If the provided sample rate is not of type int.
"""
device = kwargs.get("device", "cuda" if torch.cuda.is_available() else "cpu")
self.waveform = waveform.to(device)
self.sr = sr
if not isinstance(self.sr, int):
raise ValueError("Sample rate should be a single value of type int," \
f"not {len(self.sr)} and type {type(self.sr)}")
[docs]
@classmethod
def from_file(cls, file: str, *args, **kwargs) -> 'AudioProcessor':
"""
Create an AudioProcessor instance from an audio file.
Args:
file (str): The audio file path.
Returns:
AudioProcessor: An instance of the AudioProcessor class containing the loaded audio.
"""
audio, sr = cls.load_audio(file , *args, **kwargs)
audio = torch.from_numpy(audio)
return cls(audio, sr)
[docs]
def cut(self, start: float, end: float) -> torch.Tensor:
"""
Cut a segment from the audio waveform between the specified start and end times.
Args:
start (float): Start time in seconds.
end (float): End time in seconds.
Returns:
torch.Tensor: The cut waveform segment.
"""
start = int(start * self.sr)
if (isinstance(end, float) or isinstance(end, int)) and isinstance(self.sr, int):
end = int(np.ceil(end * self.sr))
else:
end = int(torch.ceil(end * self.sr))
return self.waveform[start:end]
[docs]
@staticmethod
def load_audio(file: str, sr: int = SAMPLE_RATE):
"""
Open an audio file and read it as a mono waveform, resampling if necessary.
This method ensures compatibility with pyannote.audio
and requires the ffmpeg CLI in PATH.
Args:
file (str): The audio file to open.
sr (int, optional): The desired sample rate. Defaults to SAMPLE_RATE.
Returns:
tuple: A NumPy array containing the audio waveform in float32 dtype
and the sample rate.
Raises:
RuntimeError: If failed to load audio.
"""
# This launches a subprocess to decode audio while down-mixing
# and resampling as necessary. Requires the ffmpeg CLI in PATH.
# fmt: off
cmd = [
"ffmpeg",
"-nostdin",
"-threads", "0",
"-i", file,
"-f", "s16le",
"-ac", "1",
"-acodec", "pcm_s16le",
"-ar", str(sr),
"-"
]
# fmt: on
try:
out = run(cmd, capture_output=True, check=True).stdout
except CalledProcessError as e:
raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
out = np.frombuffer(out, np.int16).flatten().astype(np.float32) / NORMALIZATION_FACTOR
return out , sr
def __repr__(self) -> str:
return f'TorchAudioProcessor(waveform={len(self.waveform)}, sr={int(self.sr)})'