Python config.sample_rate方法代码示例

# 需要导入模块: import config [as 别名]
# 或者: from config import sample_rate [as 别名]
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax):
        '''Log mel feature extractor. 
        
        Args:
          sample_rate: int
          window_size: int
          hop_size: int
          mel_bins: int
          fmin: int, minimum frequency of mel filter banks
          fmax: int, maximum frequency of mel filter banks
        '''
        
        self.window_size = window_size
        self.hop_size = hop_size
        self.window_func = np.hanning(window_size)
        
        self.melW = librosa.filters.mel(
            sr=sample_rate, 
            n_fft=window_size, 
            n_mels=mel_bins, 
            fmin=fmin, 
            fmax=fmax).T
        '''(n_fft // 2 + 1, mel_bins)''' 

# 需要导入模块: import config [as 别名]
# 或者: from config import sample_rate [as 别名]
def write_audio(path, audio, sample_rate):
    """Write audio sequence to .wav file. 
    
    Args:
      path: string, path to write out .wav file. 
      data: ndarray, audio sequence to write out. 
      sample_rate: int, sample rate to write out. 
      
    Returns: 
      None. 
    """
    soundfile.write(file=path, data=audio, samplerate=sample_rate) 

# 需要导入模块: import config [as 别名]
# 或者: from config import sample_rate [as 别名]
def logmel(audio):
    """Calculate log Mel spectrogram of an audio sequence. 
    
    Args: 
      audio: 1darray, audio sequence. 
      
    Returns:
      x: ndarray, log Mel spectrogram (n_time, n_freq)
    """
    n_window = cfg.n_window
    n_overlap = cfg.n_overlap
    fs = cfg.sample_rate
    
    ham_win = np.hamming(n_window)
    [f, t, x] = signal.spectral.spectrogram(
                    audio, 
                    window=ham_win,
                    nperseg=n_window, 
                    noverlap=n_overlap, 
                    detrend=False, 
                    return_onesided=True, 
                    mode='magnitude') 
    x = x.T
                    
    if globals().get('melW') is None:
        global melW
        melW = librosa.filters.mel(sr=fs, 
                                n_fft=n_window, 
                                n_mels=229, 
                                fmin=0, 
                                fmax=fs / 2.)
    x = np.dot(x, melW.T)
    x = np.log(x + 1e-8)
    x = x.astype(np.float32)
    return x 

# 需要导入模块: import config [as 别名]
# 或者: from config import sample_rate [as 别名]
def extract_feature(input_file, feature='fbank', dim=80, cmvn=True, delta=False, delta_delta=False,
                    window_size=25, stride=10, save_feature=None):
    y, sr = librosa.load(input_file, sr=sample_rate)
    yt, _ = librosa.effects.trim(y, top_db=20)
    yt = normalize(yt)
    ws = int(sr * 0.001 * window_size)
    st = int(sr * 0.001 * stride)
    if feature == 'fbank':  # log-scaled
        feat = librosa.feature.melspectrogram(y=yt, sr=sr, n_mels=dim,
                                              n_fft=ws, hop_length=st)
        feat = np.log(feat + 1e-6)
    elif feature == 'mfcc':
        feat = librosa.feature.mfcc(y=yt, sr=sr, n_mfcc=dim, n_mels=26,
                                    n_fft=ws, hop_length=st)
        feat[0] = librosa.feature.rmse(yt, hop_length=st, frame_length=ws)

    else:
        raise ValueError('Unsupported Acoustic Feature: ' + feature)

    feat = [feat]
    if delta:
        feat.append(librosa.feature.delta(feat[0]))

    if delta_delta:
        feat.append(librosa.feature.delta(feat[0], order=2))
    feat = np.concatenate(feat, axis=0)
    if cmvn:
        feat = (feat - feat.mean(axis=1)[:, np.newaxis]) / (feat.std(axis=1) + 1e-16)[:, np.newaxis]
    if save_feature is not None:
        tmp = np.swapaxes(feat, 0, 1).astype('float32')
        np.save(save_feature, tmp)
        return len(tmp)
    else:
        return np.swapaxes(feat, 0, 1).astype('float32') 

# 需要导入模块: import config [as 别名]
# 或者: from config import sample_rate [as 别名]
def __init__(self, sample_rate, window_size, overlap, mel_bins):
        
        self.window_size = window_size
        self.overlap = overlap
        self.ham_win = np.hamming(window_size)
        
        self.melW = librosa.filters.mel(sr=sample_rate, 
                                        n_fft=window_size, 
                                        n_mels=mel_bins, 
                                        fmin=50., 
                                        fmax=sample_rate // 2).T 

# 需要导入模块: import config [as 别名]
# 或者: from config import sample_rate [as 别名]
def calculate_logmel(audio_path, sample_rate, feature_extractor):
    
    # Read audio
    (audio, fs) = read_audio(audio_path, target_fs=sample_rate)
    
    '''We do not divide the maximum value of an audio here because we assume 
    the low energy of an audio may also contain information of a scene. '''
    
    # Extract feature
    feature = feature_extractor.transform(audio)
    
    return feature 

# 需要导入模块: import config [as 别名]
# 或者: from config import sample_rate [as 别名]
def write_audio(path, audio, sample_rate):
    soundfile.write(file=path, data=audio, samplerate=sample_rate)

# Create an empty folder 

# 需要导入模块: import config [as 别名]
# 或者: from config import sample_rate [as 别名]
def calculate_features(args): 
    """Calculate and write out features & ground truth notes of all songs in MUS 
    directory of all pianos. 
    """
    dataset_dir = args.dataset_dir
    workspace = args.workspace
    feat_type = args.feat_type
    fs = cfg.sample_rate
    tr_pianos = cfg.tr_pianos
    te_pianos = cfg.te_pianos
    pitch_bgn = cfg.pitch_bgn
    pitch_fin = cfg.pitch_fin
    
    out_dir = os.path.join(workspace, "features", feat_type)
    create_folder(out_dir)
    
    # Calculate features for all 9 pianos. 
    cnt = 0
    for piano in tr_pianos + te_pianos:
        audio_dir = os.path.join(dataset_dir, piano, "MUS")
        wav_names = [na for na in os.listdir(audio_dir) if na.endswith('.wav')]
        
        for wav_na in wav_names:
            # Read audio. 
            bare_na = os.path.splitext(wav_na)[0]
            wav_path = os.path.join(audio_dir, wav_na)
            (audio, _) = read_audio(wav_path, target_fs=fs)
            
            # Calculate feature. 
            if feat_type == "spectrogram":
                x = spectrogram(audio)
            elif feat_type == "logmel":
                x = logmel(audio)
            else:
                raise Exception("Error!")
            
            # Read piano roll from txt file. 
            (n_time, n_freq) = x.shape
            txt_path = os.path.join(audio_dir, "%s.txt" % bare_na)
            roll = txt_to_midi_roll(txt_path, max_fr_len=n_time)    # (n_time, 128)
            y = roll[:, pitch_bgn : pitch_fin]      # (n_time, 88)
            
            # Write out data. 
            data = [x, y]
            out_path = os.path.join(out_dir, "%s.p" % bare_na)
            print(cnt, out_path, x.shape, y.shape)
            cPickle.dump(data, open(out_path, 'wb'), protocol=cPickle.HIGHEST_PROTOCOL)
            cnt += 1
        
### Pack features.