<jupyter_start><jupyter_text>tags disponíveis no treinamento do modelo> The MTT dataset considers this 50-tags vocabulary: guitar, classical, slow, techno, strings, drums, electronic, rock, fast, piano, ambient, beat, violin, vocal, synth, female, indian, opera, male, singing, vocals, no vocals, harpsichord, loud, quiet, flute, woman, male vocal, no vocal, pop, soft, sitar, solo, man, classic, choir, voice, new age, dance, male voice, female vocal, beats, harp, cello, no voice, weird, country, metal, female voice, choral.  calcinha_preta-cobertor.mp3<jupyter_code>cp_audio = './audios/calcinha_preta-cobertor.mp3'
x, fs = librosa.load(cp_audio, duration=60)
ipd.Audio(x, rate=fs)
tags = top_tags(cp_audio, model='MTT_musicnn', topN=10)
top_tags_msd = top_tags('./audios/calcinha_preta-cobertor.mp3', model='MSD_musicnn', topN=10)
taggram, tags, features = extractor(cp_audio, model='MTT_musicnn')
plot_taggram(taggram)<jupyter_output><empty_output><jupyter_text>gloria_groove-coisa_boa.mp3<jupyter_code>gg_audio = './audios/gloria_groove-coisa_boa.mp3'
x, fs = librosa.load(gg_audio, offset=5, duration=60)
ipd.Audio(x, rate=fs)
top_tags(gg_audio, model='MTT_musicnn', topN=10)
top_tags(gg_audio, model='MSD_musicnn', topN=10)
taggram, tags, features = extractor(gg_audio, model='MTT_musicnn')
plot_taggram(taggram)<jupyter_output><empty_output><jupyter_text>mc_rebecca-ao_som_do_150.mp3<jupyter_code>mc_audio = './audios/mc_rebecca-ao_som_do_150.mp3'
x, fs = librosa.load(mc_audio, offset=60, duration=60)
ipd.Audio(x, rate=fs)
top_tags(mc_audio, model='MTT_musicnn', topN=10)
taggram, tags, features = extractor(mc_audio, model='MTT_musicnn')
plot_taggram(taggram)<jupyter_output><empty_output><jupyter_text>tom_jobim-garota_ipanema.mp3<jupyter_code>tj_audio = './audios/tom_jobim-garota_ipanema.mp3'
x, fs = librosa.load(tj_audio, offset=60, duration=60)
ipd.Audio(x, rate=fs)
top_tags(tj_audio, model='MTT_musicnn', topN=10)
taggram, tags, features = extractor(tj_audio, model='MTT_musicnn')
plot_taggram(taggram)
def plot_taggram(taggram):
    fontsize = 12
    in_length = 3
    
    fig, ax = plt.subplots()

    # title
    ax.title.set_text('Taggram')
    ax.title.set_fontsize(fontsize)

    # x-axis title
    ax.set_xlabel('(seconds)', fontsize=fontsize)

    # y-axis
    y_pos = np.arange(len(tags))
    ax.set_yticks(y_pos)
    ax.set_yticklabels(tags, fontsize=fontsize-1)

    # x-axis
    x_pos = np.arange(taggram.shape[0])
    x_label = np.arange(in_length/2, in_length*taggram.shape[0], 3)
    ax.set_xticks(x_pos)
    ax.set_xticklabels(x_label, fontsize=fontsize)

    # depict taggram
    ax.imshow(taggram.T, interpolation=None, aspect="auto")
    plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13718278778962853, 'fraction_numerical': 0.018389113644722323, 'mean_word_length': 6.4114441416893735, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 18, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 9, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '663351', 'license': 'MIT', 'n_tokens_mistral': 1141, 'n_tokens_neox': 1091, 'n_words': 185, 'path': 'autotaggers.ipynb', 'repo_name': 'giovana-morais/pybr2020', 'size': '2719'}