unifyed documentation
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Jaikinator
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"""
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Audio Processor Module
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=======================
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This module provides the AudioProcessor class, utilizing PyTorchaudio for handling audio files.
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It includes functionalities to load, cut, and manage audio waveforms, offering efficient and
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flexible audio processing.
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Available Classes:
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- AudioProcessor: Processes audio waveforms and provides methods for loading,
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cutting, and handling audio.
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Usage:
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from .audio_import AudioProcessor
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processor = AudioProcessor.from_file("path/to/audiofile.wav")
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cut_waveform = processor.cut(start=1.0, end=5.0)
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Constants:
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- SAMPLE_RATE (int): Default sample rate for processing.
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- NORMALIZATION_FACTOR (float): Normalization factor for audio waveform.
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"""
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from subprocess import CalledProcessError, run
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import numpy as np
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import torch
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from subprocess import CalledProcessError, run
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from typing import Union
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SAMPLE_RATE = 16000
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NORMALIZATION_FACTOR = 32768.0
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class AudioProcessor:
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"""
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Audio Processor using PyTorchaudio instead of PyDub
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Audio Processor class that leverages PyTorchaudio to provide functionalities
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for loading, cutting, and handling audio waveforms.
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Attributes:
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waveform: torch.Tensor
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The audio waveform tensor.
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sr: int
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The sample rate of the audio.
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"""
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def __init__(self, waveform: torch.Tensor, sr : int = SAMPLE_RATE,
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*args, **kwargs) -> None:
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"""
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Initialise audio processor
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:param waveform: waveform
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:param sr: sample rate
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:param args: additional arguments
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:param kwargs: additional keyword arguments
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example:
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- device: device to use for processing
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if cuda is available, cuda is used
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Initialize the AudioProcessor object.
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Args:
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waveform (torch.Tensor): The audio waveform tensor.
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sr (int, optional): The sample rate of the audio. Defaults to SAMPLE_RATE.
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args: Additional arguments.
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kwargs: Additional keyword arguments, e.g., device to use for processing.
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If CUDA is available, it defaults to CUDA.
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Raises:
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ValueError: If the provided sample rate is not of type int.
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"""
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if "device" in kwargs:
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device = kwargs["device"]
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else:
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if torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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device = kwargs.get("device", "cuda" if torch.cuda.is_available() else "cpu")
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self.waveform = waveform.to(device)
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self.sr = sr
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@@ -40,9 +69,13 @@ class AudioProcessor:
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@classmethod
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def from_file(cls, file: str, *args, **kwargs) -> 'AudioProcessor':
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"""
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Load audio file
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:param file: audio file
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:return: AudioProcessor
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Create an AudioProcessor instance from an audio file.
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Args:
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file (str): The audio file path.
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Returns:
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AudioProcessor: An instance of the AudioProcessor class containing the loaded audio.
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"""
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audio, sr = cls.load_audio(file , *args, **kwargs)
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@@ -54,42 +87,37 @@ class AudioProcessor:
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def cut(self, start: float, end: float) -> torch.Tensor:
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"""
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Cut audio file
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:param start: start time in seconds
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:param end: end time in seconds
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:return: AudioProcessor
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Cut a segment from the audio waveform between the specified start and end times.
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Args:
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start (float): Start time in seconds.
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end (float): End time in seconds.
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Returns:
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torch.Tensor: The cut waveform segment.
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"""
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if isinstance(start, float):
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start = torch.Tensor([start])
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if isinstance(end, float):
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end = torch.Tensor([end])
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sr = torch.Tensor([self.sr])
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start = int(start * sr)
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end = torch.ceil(end * sr)
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return self.waveform[start:end.to(int)]
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start = int(start * self.sr)
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end = int(torch.ceil(end * self.sr))
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return self.waveform[start:end]
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@staticmethod
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def load_audio(file: str, sr: int = SAMPLE_RATE):
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"""
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Open an audio file and read as mono waveform, resampling as necessary
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Open an audio file and read it as a mono waveform, resampling if necessary.
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This method ensures compatibility with pyannote.audio
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and requires the ffmpeg CLI in PATH.
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Changed from original function at whisper.audio.load_audio to ensure
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compatibility with pyannote.audio
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Parameters
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----------
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file: str
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The audio file to open
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Args:
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file (str): The audio file to open.
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sr (int, optional): The desired sample rate. Defaults to SAMPLE_RATE.
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sr: int
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The sample rate to resample the audio if necessary
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Returns:
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tuple: A NumPy array containing the audio waveform in float32 dtype
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and the sample rate.
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Returns
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-------
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A NumPy array containing the audio waveform, in float32 dtype.
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Raises:
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RuntimeError: If failed to load audio.
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"""
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# This launches a subprocess to decode audio while down-mixing
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# and resampling as necessary. Requires the ffmpeg CLI in PATH.
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@@ -111,18 +139,9 @@ class AudioProcessor:
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except CalledProcessError as e:
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raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
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out = np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
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out = np.frombuffer(out, np.int16).flatten().astype(np.float32) / NORMALIZATION_FACTOR
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return out , sr
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def __repr__(self) -> str:
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return f'TorchAudioProcessor(waveform={len(self.waveform)}, sr={int(self.sr)})'
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def __str__(self) -> str:
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return f'TorchAudioProcessor(waveform={len(self.waveform)}, sr={int(self.sr)})'
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if __name__ == "__main__":
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print("Testing AudioProcessor")
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print(AudioProcessor.from_file("tests/test.wav"))
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return f'TorchAudioProcessor(waveform={len(self.waveform)}, sr={int(self.sr)})'
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