| from datasets import load_dataset | |
| import librosa | |
| import IPython.display as ipd | |
| from IPython.display import Audio, display | |
| import random | |
| from concurrent.futures import ProcessPoolExecutor | |
| import numpy as np | |
| import json | |
| ds0 = load_dataset('espnet/yodas', 'ja000') | |
| print("finished loading ja000") | |
| def wada_snr(wav): | |
| # Direct blind estimation of the SNR of a speech signal. | |
| # | |
| # Paper on WADA SNR: | |
| # http://www.cs.cmu.edu/~robust/Papers/KimSternIS08.pdf | |
| # | |
| # This function was adapted from this matlab code: | |
| # https://labrosa.ee.columbia.edu/projects/snreval/#9 | |
| # init | |
| eps = 1e-10 | |
| # next 2 lines define a fancy curve derived from a gamma distribution -- see paper | |
| db_vals = np.arange(-20, 101) | |
| g_vals = np.array([0.40974774, 0.40986926, 0.40998566, 0.40969089, 0.40986186, 0.40999006, 0.41027138, 0.41052627, 0.41101024, 0.41143264, 0.41231718, 0.41337272, 0.41526426, 0.4178192 , 0.42077252, 0.42452799, 0.42918886, 0.43510373, 0.44234195, 0.45161485, 0.46221153, 0.47491647, 0.48883809, 0.50509236, 0.52353709, 0.54372088, 0.56532427, 0.58847532, 0.61346212, 0.63954496, 0.66750818, 0.69583724, 0.72454762, 0.75414799, 0.78323148, 0.81240985, 0.84219775, 0.87166406, 0.90030504, 0.92880418, 0.95655449, 0.9835349 , 1.01047155, 1.0362095 , 1.06136425, 1.08579312, 1.1094819 , 1.13277995, 1.15472826, 1.17627308, 1.19703503, 1.21671694, 1.23535898, 1.25364313, 1.27103891, 1.28718029, 1.30302865, 1.31839527, 1.33294817, 1.34700935, 1.3605727 , 1.37345513, 1.38577122, 1.39733504, 1.40856397, 1.41959619, 1.42983624, 1.43958467, 1.44902176, 1.45804831, 1.46669568, 1.47486938, 1.48269965, 1.49034339, 1.49748214, 1.50435106, 1.51076426, 1.51698915, 1.5229097 , 1.528578 , 1.53389835, 1.5391211 , 1.5439065 , 1.54858517, 1.55310776, 1.55744391, 1.56164927, 1.56566348, 1.56938671, 1.57307767, 1.57654764, 1.57980083, 1.58304129, 1.58602496, 1.58880681, 1.59162477, 1.5941969 , 1.59693155, 1.599446 , 1.60185011, 1.60408668, 1.60627134, 1.60826199, 1.61004547, 1.61192472, 1.61369656, 1.61534074, 1.61688905, 1.61838916, 1.61985374, 1.62135878, 1.62268119, 1.62390423, 1.62513143, 1.62632463, 1.6274027 , 1.62842767, 1.62945532, 1.6303307 , 1.63128026, 1.63204102]) | |
| # peak normalize, get magnitude, clip lower bound | |
| wav = np.array(wav) | |
| max_val = np.abs(wav).max() | |
| if max_val == 0: | |
| max_val = eps | |
| wav = wav / max_val | |
| abs_wav = np.abs(wav) | |
| abs_wav[abs_wav < eps] = eps | |
| # calcuate statistics | |
| # E[|z|] | |
| v1 = max(eps, abs_wav.mean()) | |
| # E[log|z|] | |
| v2 = np.log(abs_wav).mean() | |
| # log(E[|z|]) - E[log(|z|)] | |
| v3 = np.log(v1) - v2 | |
| # table interpolation | |
| wav_snr_idx = None | |
| if any(g_vals < v3): | |
| wav_snr_idx = np.where(g_vals < v3)[0].max() | |
| # handle edge cases or interpolate | |
| if wav_snr_idx is None: | |
| wav_snr = db_vals[0] | |
| elif wav_snr_idx == len(db_vals) - 1: | |
| wav_snr = db_vals[-1] | |
| else: | |
| wav_snr = db_vals[wav_snr_idx] + \ | |
| (v3-g_vals[wav_snr_idx]) / (g_vals[wav_snr_idx+1] - \ | |
| g_vals[wav_snr_idx]) * (db_vals[wav_snr_idx+1] - db_vals[wav_snr_idx]) | |
| # Calculate SNR | |
| dEng = sum(wav**2) | |
| dFactor = 10**(wav_snr / 10) | |
| dNoiseEng = dEng / (1 + dFactor) # Noise energy | |
| dSigEng = dEng * dFactor / (1 + dFactor) # Signal energy | |
| snr = 10 * np.log10(dSigEng / dNoiseEng) | |
| return snr | |
| def preprocess_audio(data): | |
| # �?ータが整数型�?�場合、浮動小数点型に変換 | |
| if data.dtype == np.int16: | |
| data = data.astype(np.float32) / np.iinfo(np.int16).max | |
| elif data.dtype == np.int32: | |
| data = data.astype(np.float32) / np.iinfo(np.int32).max | |
| # ス�?レオをモノラルに変換?���?要があれば?�? | |
| if len(data.shape) == 2: | |
| data = data.mean(axis=1) | |
| return data | |
| # 音声データの前処理とSNR計算を行う関数 | |
| def process_audio_data(item): | |
| # 音声データの前処理 | |
| audio_data = item['audio']['array'] | |
| # 音声データが空でないことを確認 | |
| if len(audio_data) == 0: | |
| return None | |
| preprocessed_data = preprocess_audio(audio_data) | |
| # WADA-SNRを計算 | |
| snr = wada_snr(preprocessed_data) | |
| # データからidを取得 | |
| uuid = item['utt_id'] | |
| transcription = item['text'] | |
| return { | |
| "ファイル名": uuid, | |
| "SNR値": snr, | |
| "トランスクリプション": transcription | |
| } | |
| import os | |
| if __name__ == '__main__': | |
| ds = load_dataset('espnet/yodas', 'ja000', trust_remote_code=True) | |
| print("データ数: ", ds['train'].dataset_size) | |
| # CPUのコア数を取得 | |
| cpu_count = os.cpu_count() | |
| # 並列�?��?で関数を実�? | |
| with ProcessPoolExecutor(max_workers=cpu_count) as executor: | |
| results = list(executor.map(process_audio_data, ds['train'])) | |
| # Noneを除去 | |
| results = [result for result in results if result is not None] | |
| # 結果をJSONファイルに保存 | |
| with open('audio_analysis_results.json', 'w') as f: | |
| json.dump(results, f, ensure_ascii=False, indent=4) | |
| print("JSONファイルが保存されました") |