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Zero
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import numpy as np
import matplotlib.pyplot as plt
import librosa.display
import librosa
def calculate_features(y, sr):
stft = np.abs(librosa.stft(y))
duration = librosa.get_duration(y=y, sr=sr)
cent = librosa.feature.spectral_centroid(S=stft, sr=sr)[0]
bw = librosa.feature.spectral_bandwidth(S=stft, sr=sr)[0]
rolloff = librosa.feature.spectral_rolloff(S=stft, sr=sr)[0]
return stft, duration, cent, bw, rolloff
def plot_title(title):
plt.suptitle(title, fontsize=16, fontweight="bold")
def plot_spectrogram(y, sr, stft, duration, cmap="inferno"):
plt.subplot(3, 1, 1)
plt.imshow(
librosa.amplitude_to_db(stft, ref=np.max),
origin="lower",
extent=[0, duration, 0, sr / 1000],
aspect="auto",
cmap=cmap, # Change the colormap here
)
plt.colorbar(format="%+2.0f dB")
plt.xlabel("Time (s)")
plt.ylabel("Frequency (kHz)")
plt.title("Spectrogram")
def plot_waveform(y, sr, duration):
plt.subplot(3, 1, 2)
librosa.display.waveshow(y, sr=sr)
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")
plt.title("Waveform")
def plot_features(times, cent, bw, rolloff, duration):
plt.subplot(3, 1, 3)
plt.plot(times, cent, label="Spectral Centroid (kHz)", color="b")
plt.plot(times, bw, label="Spectral Bandwidth (kHz)", color="g")
plt.plot(times, rolloff, label="Spectral Rolloff (kHz)", color="r")
plt.xlabel("Time (s)")
plt.title("Spectral Features")
plt.legend()
def analyze_audio(audio_file, save_plot_path="logs/audio_analysis.png"):
y, sr = librosa.load(audio_file)
stft, duration, cent, bw, rolloff = calculate_features(y, sr)
plt.figure(figsize=(12, 10))
plot_title("Audio Analysis" + " - " + audio_file.split("/")[-1])
plot_spectrogram(y, sr, stft, duration)
plot_waveform(y, sr, duration)
plot_features(librosa.times_like(cent), cent, bw, rolloff, duration)
plt.tight_layout()
if save_plot_path:
plt.savefig(save_plot_path, bbox_inches="tight", dpi=300)
plt.close()
audio_info = f"""Sample Rate: {sr}\nDuration: {(
str(round(duration, 2)) + " seconds"
if duration < 60
else str(round(duration / 60, 2)) + " minutes"
)}\nNumber of Samples: {len(y)}\nBits per Sample: {librosa.get_samplerate(audio_file)}\nChannels: {"Mono (1)" if y.ndim == 1 else "Stereo (2)"}"""
return audio_info, save_plot_path
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