Audio

normalize_loudness(wav)

Normalize the loudness of an audio waveform.

Parameters:

Name	Type	Description	Default
wav	Tensor	The input waveform.	required

Returns:

Type	Description
Tensor	torch.Tensor: The normalized waveform.

Examples:

>>> wav = np.array([1.0, 2.0, 3.0])
>>> normalize_loudness(wav)
tensor([0.33333333, 0.66666667, 1.  ])

Source code in training/preprocess/audio.py

def normalize_loudness(wav: torch.Tensor) -> torch.Tensor:
    r"""Normalize the loudness of an audio waveform.

    Args:
        wav (torch.Tensor): The input waveform.

    Returns:
        torch.Tensor: The normalized waveform.

    Examples:
        >>> wav = np.array([1.0, 2.0, 3.0])
        >>> normalize_loudness(wav)
        tensor([0.33333333, 0.66666667, 1.  ])
    """
    return wav / torch.max(torch.abs(wav))

preprocess_audio(audio, sr_actual, sr)

Preprocesses audio by converting stereo to mono, resampling if necessary, and returning the audio tensor and sample rate.

Parameters:

Name	Type	Description	Default
audio	Tensor	The audio tensor to preprocess.	required
sr_actual	int	The actual sample rate of the audio.	required
sr	Union[int, None]	The target sample rate to resample the audio to, if necessary.	required

Returns:

Type	Description
Tuple[Tensor, int]	Tuple[torch.Tensor, int]: The preprocessed audio tensor and sample rate.

Source code in training/preprocess/audio.py

def preprocess_audio(
    audio: torch.Tensor, sr_actual: int, sr: Union[int, None],
) -> Tuple[torch.Tensor, int]:
    r"""Preprocesses audio by converting stereo to mono, resampling if necessary, and returning the audio tensor and sample rate.

    Args:
        audio (torch.Tensor): The audio tensor to preprocess.
        sr_actual (int): The actual sample rate of the audio.
        sr (Union[int, None]): The target sample rate to resample the audio to, if necessary.

    Returns:
        Tuple[torch.Tensor, int]: The preprocessed audio tensor and sample rate.
    """
    try:
        if audio.shape[0] > 0:
            audio = stereo_to_mono(audio)
        audio = audio.squeeze(0)
        if sr_actual != sr and sr is not None:
            audio_np = resample(audio.numpy(), orig_sr=sr_actual, target_sr=sr)
            # Convert back to torch tensor
            audio = torch.from_numpy(audio_np)
            sr_actual = sr
    except Exception as e:
        raise type(e)(
            f"The following error happened while processing the audio ... \n {e!s}",
        ).with_traceback(sys.exc_info()[2])

    return audio, sr_actual

resample(wav, orig_sr, target_sr)

Resamples an audio waveform from the original sampling rate to the target sampling rate.

Parameters:

Name	Type	Description	Default
wav	ndarray	The audio waveform to be resampled.	required
orig_sr	int	The original sampling rate of the audio waveform.	required
target_sr	int	The target sampling rate to resample the audio waveform to.	required

Returns:

Type	Description
ndarray	np.ndarray: The resampled audio waveform.

Source code in training/preprocess/audio.py

def resample(wav: np.ndarray, orig_sr: int, target_sr: int) -> np.ndarray:
    r"""Resamples an audio waveform from the original sampling rate to the target sampling rate.

    Args:
        wav (np.ndarray): The audio waveform to be resampled.
        orig_sr (int): The original sampling rate of the audio waveform.
        target_sr (int): The target sampling rate to resample the audio waveform to.

    Returns:
        np.ndarray: The resampled audio waveform.
    """
    return librosa.resample(wav, orig_sr=orig_sr, target_sr=target_sr)

safe_load(path, sr)

Load an audio file from disk and return its content as a numpy array.

Parameters:

Name	Type	Description	Default
path	str	The path to the audio file.	required
sr	int or None	The target sampling rate. If None, the original sampling rate is used.	required

Returns:

Type	Description
Tuple[ndarray, int]	Tuple[np.ndarray, int]: A tuple containing the audio content as a numpy array and the actual sampling rate.

Source code in training/preprocess/audio.py

def safe_load(path: str, sr: Union[int, None]) -> Tuple[np.ndarray, int]:
    r"""Load an audio file from disk and return its content as a numpy array.

    Args:
        path (str): The path to the audio file.
        sr (int or None): The target sampling rate. If None, the original sampling rate is used.

    Returns:
        Tuple[np.ndarray, int]: A tuple containing the audio content as a numpy array and the actual sampling rate.
    """
    try:
        audio, sr_actual = torchaudio.load(path) # type: ignore
        if audio.shape[0] > 0:
            audio = stereo_to_mono(audio)
        audio = audio.squeeze(0)
        if sr_actual != sr and sr is not None:
            audio = resample(audio.numpy(), orig_sr=sr_actual, target_sr=sr)
            sr_actual = sr
        else:
            audio = audio.numpy()
    except Exception as e:
        raise type(e)(
            f"The following error happened loading the file {path} ... \n" + str(e),
        ).with_traceback(sys.exc_info()[2])

    return audio, sr_actual

stereo_to_mono(audio)

Converts a stereo audio tensor to mono by taking the mean across channels.

Parameters:

Name	Type	Description	Default
audio	Tensor	Input audio tensor of shape (channels, samples).	required

Returns:

Type	Description
Tensor	torch.Tensor: Mono audio tensor of shape (1, samples).

Source code in training/preprocess/audio.py

def stereo_to_mono(audio: torch.Tensor) -> torch.Tensor:
    r"""Converts a stereo audio tensor to mono by taking the mean across channels.

    Args:
        audio (torch.Tensor): Input audio tensor of shape (channels, samples).

    Returns:
        torch.Tensor: Mono audio tensor of shape (1, samples).
    """
    return torch.mean(audio, 0, True)