![\"\"](\"https:\/\/taskhub.jp\/magazine\/wp-content\/themes\/the-thor\/img\/dummy.gif\")
<\/figure>\n\n\n\n\u30b0\u30e9\u30d5\u306e\u898b\u65b9\u3092\u306f\u3058\u3081\u3001\u97f3\u58f0\u306e\u5f37\u5ea6\u3084\u30d4\u30fc\u30af\u306b\u3064\u3044\u3066\u4e01\u5be7\u306b\u89e3\u8aac\u3057\u3066\u304f\u308c\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
Code Interpreter\u3067\u5b9f\u884c\u3057\u305fPython\u30b3\u30fc\u30c9\u306f\u3053\u3061\u3089\u3067\u3059\u3002<\/p>\n\n\n\n
import matplotlib.pyplot as plt\nimport numpy as np\nimport wave\nimport struct\n\n# Open the file in read mode and get the parameters\nfile = wave.open(\"\/mnt\/data\/loop1.wav\", \"r\")\nparams = file.getparams()\n\n# Number of samples, sample width, frame rate\nnchannels, sampwidth, framerate, nframes = params[:4]\n\n# Read the frames from the file\nstr_data = file.readframes(nframes)\n\n# Close the file\nfile.close()\n\n# Convert binary data to integers\nwave_data = np.frombuffer(str_data, dtype=np.short)\n\n# If stereo audio, average the two channels\nif nchannels > 1:\n wave_data.shape = -1, 2\n wave_data = np.mean(wave_data, axis=1)\n\n# Create a time array for the x-axis of the plot\ntime = np.arange(0, nframes) * (1.0 \/ framerate)\n\n# Create the plot\nplt.figure(figsize=(12, 4))\nplt.plot(time, wave_data, color=\"blue\")\n\n# Add labels and title\nplt.ylabel(\"Amplitude\")\nplt.xlabel(\"Time (s)\")\nplt.title(\"Waveform of Audio File\")\n\n# Show the plot\nplt.show()\n<\/code><\/pre>\n\n\n\n\u3082\u3046\u4e00\u3064\u306e\u97f3\u6e90\u306b\u3064\u3044\u3066\u3082\u6ce2\u5f62\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u3001\u7279\u5fb4\u3092\u6bd4\u8f03\u3057\u3066\u3082\u3089\u3044\u307e\u3059\u3002<\/p>\n\n\n\n<\/figure>\n\n\n\n\u540c\u69d8\u306b\u3001\u97f3\u58f0\u306e\u6ce2\u5f62\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u304f\u308c\u307e\u3057\u305f\u3002\u307e\u305f\u30012\u3064\u306e\u6ce2\u5f62\u3092\u4e26\u3079\u3066\u7279\u5fb4\u3092\u6bd4\u8f03\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
2\u756a\u76ee\u306e\u30d5\u30a1\u30a4\u30eb\uff08\u6697\u3081\u306a\u30c9\u30e9\u30e0\u97f3\u6e90\uff09\u306e\u65b9\u304c\u5f37\u5ea6\u304c\u9ad8\u304f\u3001\u632f\u52d5\u6570\u304c\u5927\u304d\u3044\u3053\u3068\u3092\u89e3\u8aac\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u30b9\u30da\u30af\u30c8\u30ed\u30b0\u30e9\u30e0\u3092\u4f5c\u6210\u3059\u308b<\/h3>\n\n\n\n
\u30d7\u30ed\u30f3\u30d7\u30c8\uff1a<\/p>\n\n\n\n
\u30b9\u30da\u30af\u30c8\u30ed\u30b0\u30e9\u30e0\u3092\u4f5c\u6210\u3057\u3001\u97f3\u58f0\u4fe1\u53f7\u3092\u5206\u6790\u3057\u3066\u56f3\u89e3\u3057\u3066\u304f\u3060\u3055\u3044\u3002\n<\/code><\/pre>\n\n\n\nGPT-4\u306e\u51fa\u529b\uff08Code Interpreter\uff09\uff1a<\/p>\n\n\n\n<\/figure>\n\n\n\n\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u6307\u793a\u3057\u305f\u901a\u308a\u3001\u30b9\u30da\u30af\u30c8\u30ed\u30b0\u30e9\u30e0\u304c\u51fa\u529b\u3055\u308c\u307e\u3057\u305f\u3002\u30b0\u30e9\u30d5\u306e\u898b\u65b9\u3068\u30b9\u30da\u30af\u30c8\u30ed\u30b0\u30e9\u30e0\u306e\u30e1\u30ea\u30c3\u30c8\u3092\u8aac\u660e\u3057\u3066\u304f\u308c\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
Code Interpreter\u3067\u5b9f\u884c\u3057\u305fPython\u30b3\u30fc\u30c9\u306f\u3053\u3061\u3089\u3067\u3059\u3002<\/p>\n\n\n\n
# Create a spectrogram of the audio data\nfrequencies, times, spectrogram = signal.spectrogram(wave_data, framerate)\n\n# Create the plot\nplt.figure(figsize=(12, 6))\nplt.imshow(spectrogram, aspect='auto', cmap='hot_r', origin='lower', extent=[times.min(), times.max(), frequencies.min(), frequencies.max()])\nplt.colorbar(format=\"%+2.0f dB\")\nplt.title('Spectrogram of Audio File')\nplt.ylabel('Frequency [Hz]')\nplt.xlabel('Time [sec]')\nplt.ylim([frequencies.min(), frequencies.max()]) # Set y-axis limits to frequency range\nplt.tight_layout()\nplt.show()\n<\/code><\/pre>\n\n\n\n<\/figure>\n\n\n\n2\u3064\u76ee\u306e\u97f3\u6e90\u306b\u3064\u3044\u3066\u3082\u30b9\u30da\u30af\u30c8\u30ed\u30b0\u30e9\u30e0\u3092\u4f5c\u6210\u3057\u3066\u30012\u3064\u306e\u97f3\u6e90\u3092\u6bd4\u8f03\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u4eca\u56de\u306f\u8996\u899a\u7684\u306b\u898b\u3065\u3089\u304f\u306a\u3063\u3066\u3044\u307e\u3059\u304c\u30012\u756a\u76ee\u306e\u97f3\u6e90\u306e\u65b9\u304c\u3001\u5e83\u3044\u7bc4\u56f2\u306e\u5468\u6ce2\u6570\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
\u307e\u3068\u3081<\/h2>\n\n\n\n
\u4eca\u56dePROMPTY\u3067\u306f\u3001ChatGPT\u306eCode Interpreter\u3092\u6d3b\u7528\u3057\u3066\u3001\u6b21\u306e\u3088\u3046\u306a\u97f3\u58f0\u30d5\u30a1\u30a4\u30eb\u306e\u51e6\u7406\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
\n- \u97f3\u58f0\u30d5\u30a1\u30a4\u30eb\u306e\u30c8\u30ea\u30df\u30f3\u30b0\u3092\u3059\u308b<\/li>\n\n\n\n
- \u97f3\u58f0\u30d5\u30a1\u30a4\u30eb\u306e\u518d\u751f\u901f\u5ea6\u3092\u5909\u66f4\u3059\u308b<\/li>\n\n\n\n
- \u97f3\u6e90\u306e\u6ce2\u5f62\u3092\u8abf\u3079\u308b<\/li>\n\n\n\n
- \u97f3\u6e90\u306e\u30b9\u30da\u30af\u30c8\u30ed\u30b0\u30e9\u30e0\u3092\u4f5c\u6210\u3059\u308b<\/li>\n<\/ul>\n\n\n\n
\u97f3\u58f0\u30d5\u30a1\u30a4\u30eb\u3092\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3059\u308b\u3060\u3051\u3067\u3001\u30b3\u30fc\u30c9\u3092\u66f8\u304f\u5fc5\u8981\u304c\u306a\u304f\u6240\u671b\u306e\u51e6\u7406\u304c\u3067\u304d\u308b\u3053\u3068\u304c\u3001ChatGPT Code Interpreter\u306e\u5927\u304d\u306a\u5f37\u307f\u3068\u8a00\u3048\u308b\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
\u4eca\u56de\u306e\u4f8b\u3092\u53c2\u8003\u306b\u3001\u7686\u3055\u307e\u3082ChatGPT\u306eCode Interpreter\u3092\u4f7f\u3063\u305f\u97f3\u58f0\u30d5\u30a1\u30a4\u30eb\u306e\u51e6\u7406\u3092\u8a66\u3057\u3066\u307f\u3066\u306f\u3044\u304b\u304c\u3067\u3057\u3087\u3046\u304b\u3002<\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
\u300cChatGPT\u306eCode Interpreter\u306e\u6d3b\u7528\u65b9\u6cd5\u3092\u77e5\u308a\u305f\u3044\u300d\u300cChatGPT\u306eCode Interpreter\u3092\u4f7f\u3063\u3066\u97f3\u58f0\u30d5\u30a1\u30a4\u30eb\u306e\u51e6\u7406\u304c\u3067\u304d\u308b\u306e\uff1f\u300d\u305d\u3046\u601d\u3046\u65b9\u3082\u3044\u308b\u306e\u3067\u306f\u306a\u3044\u3067\u3057\u3087\u3046\u304b\u3002 […]<\/p>\n","protected":false},"author":9,"featured_media":7138,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":""},"categories":[46],"tags":[],"_links":{"self":[{"href":"https:\/\/taskhub.jp\/magazine\/wp-json\/wp\/v2\/posts\/7114"}],"collection":[{"href":"https:\/\/taskhub.jp\/magazine\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/taskhub.jp\/magazine\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/taskhub.jp\/magazine\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/taskhub.jp\/magazine\/wp-json\/wp\/v2\/comments?post=7114"}],"version-history":[{"count":5,"href":"https:\/\/taskhub.jp\/magazine\/wp-json\/wp\/v2\/posts\/7114\/revisions"}],"predecessor-version":[{"id":7132,"href":"https:\/\/taskhub.jp\/magazine\/wp-json\/wp\/v2\/posts\/7114\/revisions\/7132"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/taskhub.jp\/magazine\/wp-json\/wp\/v2\/media\/7138"}],"wp:attachment":[{"href":"https:\/\/taskhub.jp\/magazine\/wp-json\/wp\/v2\/media?parent=7114"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/taskhub.jp\/magazine\/wp-json\/wp\/v2\/categories?post=7114"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/taskhub.jp\/magazine\/wp-json\/wp\/v2\/tags?post=7114"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}