初めに
WADA SNRを使って音声の評価ができるみたいなので、以下を使って実際に試してみます
デモ
実行すると以下のようなログが表示されます
Calculated SNR: 13.775574879980502
開発環境
準備
以下のライブラリを入れます
pip install numpy scipy datasets pip install soundfile pip install librosa
実装
計測するためのコードは以下です
from datasets import load_dataset import numpy as np 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) wav = wav / abs(wav).max() abs_wav = 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 # データセットをロード ds = load_dataset("reazon-research/reazonspeech", "tiny", trust_remote_code=True) # 最初の音声データを取得 audio_data = ds['train'][0]['audio']['array'] # データを前処理するための関数。ファイルパスを必要としないため、少し変更します。 def preprocess_audio(data): # データが整数型の場合、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 # ステレオをモノラルに変換（必要があれば） if len(data.shape) == 2: data = data.mean(axis=1) return data # 音声データを前処理 preprocessed_data = preprocess_audio(audio_data) # WADA SNRを計算 snr = wada_snr(preprocessed_data) print(f'Calculated SNR: {snr}')
