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Jaikinator
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import os
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from warnings import warn
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import torch
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from pydub import AudioSegment
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from torchaudio import load, save
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class AudioProcessor:
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def __init__(self, audio_file:str):
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self.audio = AudioSegment.from_file(audio_file,
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format=audio_file.split('.')[-1])
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self.audio_file_path = audio_file
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self.waveform = self.pydub_to_tensor[0]
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self.sr = self.pydub_to_tensor[1]
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@property
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def pydub_to_tensor(self):
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"""
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Converts pydub audio segment into np.float32 of shape
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[duration_in_seconds*sample_rate, channels],
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where each value is in range [-1.0, 1.0].
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Returns tuple (audio_np_array, sample_rate).
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"""
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audio = self.audio
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x = torch.Tensor(audio.get_array_of_samples()
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).reshape((-1, audio.channels))
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y = (1 << (8 * audio.sample_width - 1))
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return x / y, audio.frame_rate
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def convert_audio(self, path: str, remove_orginal: bool = False,
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*args, **kwargs) -> None:
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"""
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Convert and saves video file or other audio files to a different file type,
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Can be used to ensure that the audio file is in the correct format
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for the Whisper model.
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:param path : path to save file
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:param remove_orginal: remove original file
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:param args: arguments for pydub.AudioSegment.export
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:param kwargs: keyword arguments for pydub.AudioSegment.export
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e.g. format
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:return: None
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"""
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self.audio.export(path, *args, **kwargs)
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if remove_orginal:
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os.remove(self.audio_file_path)
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print(f'File {self.audio_file_path} removed')
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self.audio_file_path = path
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def to_mp3(self, *args, **kwargs) -> None:
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"""
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Convert audio file to mp3 file
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:param file: audio file
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:param remove_orginal: remove original file
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:return: mp3 file path
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"""
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warn(DeprecationWarning, "This function is deprecated," \
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"please use convert_audio instead")
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if "mp3" not in kwargs["format"]:
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kwargs["format"] = "mp3"
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self.convert_audio(*args, **kwargs)
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def to_wav(self,*args, **kwargs) -> None:
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"""
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Convert audio file to wav file
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:param file: audio file
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:param remove_orginal: remove original file
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:return: wav file path
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"""
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warn(DeprecationWarning, "This function is deprecated," \
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"please use convert_audio instead")
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if "wav" not in kwargs["format"]:
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kwargs["format"] = "wav"
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self.convert_audio(*args, **kwargs)
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def slower_mp3(self, path: str,
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speed: float = 0.75,
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type: str = "mp3") -> None:
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"""
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Slow down mp3 file
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:param file: mp3 file
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:param speed: speed
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:return: None
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"""
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sound = self.audio_file
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slow_sound = sound._spawn(sound.raw_data, overrides={
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"frame_rate": int(sound.frame_rate * speed)
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})
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slow_sound.export(path, format=type)
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return slow_sound
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class TorchAudioProcessor:
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"""
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Audio Processor using PyTorchaudio instead of PyDub
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"""
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def __init__(self, waveform: torch.Tensor, sr : torch.Tensor) -> 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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"""
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self.waveform = waveform
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self.sr = sr
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@classmethod
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def from_file(cls, file: str, *args, **kwargs) -> 'TorchAudioProcessor':
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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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"""
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if not os.path.exists(file):
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raise FileNotFoundError(f'File {file} not found')
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if "format" not in kwargs:
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kwargs["format"] = file.split('.')[-1]
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audio, sr = load(file , *args, **kwargs)
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return cls(audio, sr)
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@classmethod
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def from_ffmpeg(cls, file: str, *args, **kwargs) -> 'TorchAudioProcessor':
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"""
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Initialise audio processor using pydub audio segment.
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pydub uses ffmped instead of SoX (which is used by torchaudio)
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:param file: audio file
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:return: TorchAudioProcessor
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"""
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audio = AudioProcessor(file)
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return cls(audio.waveform, audio.sr)
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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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"""
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start = int(start / self.sr)
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end = torch.ceil(end / self.sr)
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return self.waveform[:, start:end]
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def save(self, path: str, *args, **kwargs) -> None:
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"""
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Save audio file
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:param path: path to save file
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:return: None
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"""
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if "format" not in kwargs:
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kwargs["format"] = path.split('.')[-1]
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save(path, self.waveform, self.sr, *args, **kwargs)
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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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