Jaikinator
110 lines
3.2 KiB
Python
110 lines
3.2 KiB
Python
import os
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from warnings import warn
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import numpy as np
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import torch
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import ffmpeg
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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) -> 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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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 compatibility
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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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try:
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# This launches a subprocess to decode audio while down-mixing
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# and resampling as necessary.
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# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
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out, _ = (
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ffmpeg.input(file, threads=0)
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.output("-", format="s16le", acodec="pcm_s16le",
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ac=1, ar=sr)
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.run(cmd=["ffmpeg", "-nostdin"],
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capture_stdout=True, capture_stderr=True)
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)
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except ffmpeg.Error 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")) |