Jaikinator
128 lines
3.7 KiB
Python
128 lines
3.7 KiB
Python
import numpy as np
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import torch
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from subprocess import CalledProcessError, run
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SAMPLE_RATE = 16000
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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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"""
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def __init__(self, waveform: torch.Tensor, sr : torch.Tensor,
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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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"""
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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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self.waveform = waveform.to(device)
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self.sr = sr
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if not isinstance(self.sr, int):
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raise ValueError("Sample rate should be a single value of type int," \
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f"not {len(self.sr)} and type {type(self.sr)}")
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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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"""
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audio, sr = cls.load_audio(file , *args, **kwargs)
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audio = torch.from_numpy(audio)
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return cls(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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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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@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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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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sr: int
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The sample rate to resample the audio if necessary
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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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"""
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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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# fmt: off
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cmd = [
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"ffmpeg",
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"-nostdin",
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"-threads", "0",
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"-i", file,
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"-f", "s16le",
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"-ac", "1",
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"-acodec", "pcm_s16le",
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"-ar", str(sr),
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"-"
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]
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# fmt: on
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try:
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out = run(cmd, capture_output=True, check=True).stdout
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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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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")) |