performance on MIK-1K

[Dream-High]

Recently, I evaluate crepe on MIR-1K dataset using the open model. The step_size=20, corresponding to MIR-1K label.
And transfer MIR-1K pitch label to frequency using f = np.power(2, (m-69)/12)*440. However, the performance is not very well.
Is there something bug in my code.

[xavriley]

@Dream-High to help with debugging, could you copy-paste the code into a comment here instead of using a screenshot?

[Dream-High]

@Dream-High to help with debugging, could you copy-paste the code into a comment here instead of using a screenshot?
Thanks very much.

Below is my code

import os
import crepe
import librosa
import json
import numpy as np
import pandas as pd
from mir_eval.melody import raw_pitch_accuracy, to_cent_voicing, voicing_measures

audio_file = r'D:\联合模型\Data\MIR-1K\Wavfile'
label_file = r'D:\联合模型\Data\MIR-1K\PitchLabel'

rpa_list = []
vr_list = []
vfa_list = []
with open('../split_details.json', 'r') as f:
    split_details = json.load(f)
test_dict = split_details['test']

for file in test_dict.values():
    audio, sr = librosa.load(os.path.join(audio_file, file), 16000, mono=False)
    # audio, sr = librosa.load(os.path.join(audio_file, file), 16000, mono=True)
    audio_v = np.array(audio[1])
    # audio_v = audio
    time, frequency, confidence, activation = crepe.predict(audio_v, sr, viterbi=True, step_size=10)
    df_p = pd.DataFrame(columns=['times', 'freqs', 'confi', 'cent', 'midi'])
    df_p['times'] = time
    df_p['freqs'] = frequency
    df_p['confi'] = confidence
    est_f = frequency[1:-1]
    ref = []
    with open(os.path.join(label_file, file.replace('.wav', '.pv')), 'r') as f:
        lines = f.readlines()
        for line in lines:
            ref.append(line)
    ref = np.array(ref).astype(float)
    ref_f = np.power(2, (ref-69)/12)*440
    assert len(est_f) == len(ref_f)
    ref_v, ref_c, est_v, est_c = to_cent_voicing(time[1:-1], ref_f, time[1:-1], est_f)
    vr, vfa = voicing_measures(ref_v, est_v)
    rpa = raw_pitch_accuracy(ref_v, ref_c, est_v, est_c)
    rpa_list.append(rpa)
    vr_list.append(vr)
    vfa_list.append(vfa)
    print(len(rpa_list), '\t', rpa, '\t', vr, '\t', vfa)

The following is my split details. I evaluate crepe on test dataset

split_details.json

{"train": {"0": "amy_11_01.wav", "1": "davidson_1_09.wav", "2": "leon_1_07.wav", "3": "davidson_3_14.wav", "4": "yifen_3_06.wav", "5": "leon_1_09.wav", "6": "davidson_2_01.wav", "7": "davidson_2_09.wav", "8": "leon_5_10.wav", "9": "leon_8_12.wav", "10": "geniusturtle_7_02.wav", "11": "amy_4_02.wav", "12": "jmzen_1_01.wav", "13": "amy_4_09.wav", "14": "bug_5_01.wav", "15": "amy_14_05.wav", "16": "bug_5_11.wav", "17": "fdps_2_07.wav", "18": "khair_2_02.wav", "19": "annar_3_02.wav", "20": "Kenshin_3_06.wav", "21": "yifen_1_15.wav", "22": "leon_5_07.wav", "23": "amy_12_02.wav", "24": "bug_3_06.wav", "25": "geniusturtle_3_01.wav", "26": "tammy_1_03.wav", "27": "yifen_1_06.wav", "28": "davidson_3_04.wav", "29": "leon_5_01.wav", "30": "titon_2_09.wav", "31": "davidson_3_11.wav", "32": "amy_6_06.wav", "33": "stool_3_01.wav", "34": "bobon_2_07.wav", "35": "amy_11_05.wav", "36": "khair_1_07.wav", "37": "leon_8_08.wav", "38": "khair_6_04.wav", "39": "leon_2_03.wav", "40": "fdps_2_02.wav", "41": 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