61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
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"""
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FFT tools to analyze frequency content of audio segments. This is not code for
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dealing with spectrogram images, but for analysis of waveforms.
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"""
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import struct
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import typing as T
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import numpy as np
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import plotly.graph_objects as go
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import pydub
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from scipy.fft import rfft, rfftfreq
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def plot_ffts(
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segments: T.Dict[str, pydub.AudioSegment],
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title: str = "FFT",
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min_frequency: float = 20,
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max_frequency: float = 20000,
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) -> None:
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"""
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Plot an FFT analysis of the given audio segments.
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"""
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ffts = {name: compute_fft(seg) for name, seg in segments.items()}
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fig = go.Figure(
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data=[go.Scatter(x=data[0], y=data[1], name=name) for name, data in ffts.items()],
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layout={"title": title},
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)
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fig.update_xaxes(
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range=[np.log(min_frequency) / np.log(10), np.log(max_frequency) / np.log(10)],
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type="log",
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title="Frequency",
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)
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fig.update_yaxes(title="Value")
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fig.show()
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def compute_fft(sound: pydub.AudioSegment) -> T.Tuple[np.ndarray, np.ndarray]:
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"""
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Compute the FFT of the given audio segment as a mono signal.
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Returns:
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frequencies: FFT computed frequencies
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amplitudes: Amplitudes of each frequency
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"""
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# Convert to mono if needed.
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if sound.channels > 1:
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sound = sound.set_channels(1)
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sample_rate = sound.frame_rate
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num_samples = int(sound.frame_count())
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samples = struct.unpack(f"{num_samples * sound.channels}h", sound.raw_data)
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fft_values = rfft(samples)
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amplitudes = np.abs(fft_values)
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frequencies = rfftfreq(n=num_samples, d=1 / sample_rate)
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return frequencies, amplitudes
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