Python librosa.load方法代碼示例

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def demo_plot():
    audio = './data/esc10/audio/Dog/1-30226-A.ogg'
    y, sr = librosa.load(audio, sr=44100)
    y_ps = librosa.effects.pitch_shift(y, sr, n_steps=6)   # n_steps控製音調變化尺度
    y_ts = librosa.effects.time_stretch(y, rate=1.2)   # rate控製時間維度的變換尺度
    plt.subplot(311)
    plt.plot(y)
    plt.title('Original waveform')
    plt.axis([0, 200000, -0.4, 0.4])
    # plt.axis([88000, 94000, -0.4, 0.4])
    plt.subplot(312)
    plt.plot(y_ts)
    plt.title('Time Stretch transformed waveform')
    plt.axis([0, 200000, -0.4, 0.4])
    plt.subplot(313)
    plt.plot(y_ps)
    plt.title('Pitch Shift transformed waveform')
    plt.axis([0, 200000, -0.4, 0.4])
    # plt.axis([88000, 94000, -0.4, 0.4])
    plt.tight_layout()
    plt.show() 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def read_audio(file_path):
    min_samples = int(config.min_seconds * config.sampling_rate)
    try:
        y, sr = librosa.load(file_path, sr=config.sampling_rate)
        trim_y, trim_idx = librosa.effects.trim(y)  # trim, top_db=default(60)

        if len(trim_y) < min_samples:
            center = (trim_idx[1] - trim_idx[0]) // 2
            left_idx = max(0, center - min_samples // 2)
            right_idx = min(len(y), center + min_samples // 2)
            trim_y = y[left_idx:right_idx]

            if len(trim_y) < min_samples:
                padding = min_samples - len(trim_y)
                offset = padding // 2
                trim_y = np.pad(trim_y, (offset, padding - offset), 'constant')
        return trim_y
    except BaseException as e:
        print(f"Exception while reading file {e}")
        return np.zeros(min_samples, dtype=np.float32) 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def compute_spectrograms(filename):
    out_rate = 12000
    N_FFT = 512
    HOP_LEN = 256

    frames, rate = librosa.load(filename, sr=out_rate, mono=True)
    if len(frames) < out_rate*3:
        # if less then 3 second - can't process
        raise Exception("Audio duration is too short")

    logam = librosa.power_to_db
    melgram = librosa.feature.melspectrogram
    x = logam(melgram(y=frames, sr=out_rate, hop_length=HOP_LEN,
                      n_fft=N_FFT, n_mels=N_MEL_BANDS) ** 2,
              ref=1.0)

    # now going through spectrogram with the stride of the segment duration
    for start_idx in range(0, x.shape[1] - SEGMENT_DUR + 1, SEGMENT_DUR):
        yield x[:, start_idx:start_idx + SEGMENT_DUR] 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def phase_enhance_pred(mix_STFT,pred_file, mode='STFT'):
    if mode=='wav':
        T_pred, _ = librosa.load(pred_file,sr=16000)
        F_pred = fast_stft(T_pred)
    if mode =='STFT':
        F_pred = pred_file
    M = np.sqrt(np.square(F_pred[:,:,0])+np.square(F_pred[:,:,1]))     #magnitude
    print('shape M:',M.shape)
    P = np.arctan(np.divide(mix_STFT[:,:,0],mix_STFT[:,:,1]))          #phase
    print('shape p:',P.shape)
    F_enhance = np.zeros_like(F_pred)
    print('shape enhance',F_enhance.shape)
    F_enhance[:,:,0] = np.multiply(M,np.cos(P))
    F_enhance[:,:,1] = np.multiply(M,np.sin(P))
    print('shape enhance', F_enhance.shape)
    T_enhance = fast_istft(F_enhance)
    return T_enhance

## test code part 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def load_generic_audio(directory, sample_rate):
    '''Generator that yields audio waveforms from the directory.'''
    files = find_files(directory)
    id_reg_exp = re.compile(FILE_PATTERN)
    print("files length: {}".format(len(files)))
    randomized_files = randomize_files(files)
    for filename in randomized_files:
        ids = id_reg_exp.findall(filename)
        if not ids:
            # The file name does not match the pattern containing ids, so
            # there is no id.
            category_id = None
        else:
            # The file name matches the pattern for containing ids.
            category_id = int(ids[0][0])
        audio, _ = librosa.load(filename, sr=sample_rate, mono=True)
        audio = audio.reshape(-1, 1)
        yield audio, filename, category_id 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def extract_features(files=None):
    if files is None:
        files = transcode_audio()

    audio = []
    labels = []

    for (wav, srt) in files:
        print("Processing audio:", wav)
        y, sr = librosa.load(wav, sr=FREQ)
        mfcc = librosa.feature.mfcc(y=y, sr=sr, hop_length=int(HOP_LEN), n_mfcc=int(N_MFCC))
        label = extract_labels(srt, len(mfcc[0]))
        audio.append(mfcc)
        labels.append(label)

    return audio, labels 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def __extract_melspec(audio_fpath, audio_fname):
    """
    Using librosa to calculate log mel spectrogram values
    and scipy.misc to draw and store them (in grayscale).

    :param audio_fpath:
    :param audio_fname:
    :return:
    """
    # Load sound file
    y, sr = librosa.load(audio_fpath, sr=12000)

    # Let's make and display a mel-scaled power (energy-squared) spectrogram
    S = librosa.feature.melspectrogram(y, sr=sr, hop_length=256, n_mels=96)

    # Convert to log scale (dB). We'll use the peak power as reference.
    log_S = librosa.logamplitude(S, ref_power=np.max)

    spectr_fname = audio_fname + '.png'
    subdir_path = __get_subdir(spectr_fname)

    # Draw log values matrix in grayscale
    scipy.misc.toimage(log_S).save(subdir_path.format(spectr_fname)) 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def triangle(t, randfunc=np.random.rand, t0_fac=None): # ramp up then down
    height = (0.4 * randfunc() + 0.4) * np.random.choice([-1,1])
    width = randfunc()/4 * t[-1]     # half-width actually
    t0 = 2*width + 0.4 * randfunc()*t[-1] if t0_fac is None else t0_fac*t[-1]
    x = height * (1 - np.abs(t-t0)/width)
    x[np.where(t < (t0-width))] = 0
    x[np.where(t > (t0+width))] = 0
    amp_n = (0.1*randfunc()+0.02)   # add noise
    return x + amp_n*pinknoise(t.shape[0])


# Prelude to read_audio_file
# Tried lots of ways of doing this.. most are slow.
#signal, rate = librosa.load(filename, sr=sr, mono=True, res_type='kaiser_fast') # Librosa's reader is incredibly slow. do not use
#signal, rate = torchaudio.load(filename)#, normalization=True)   # Torchaudio's reader is pretty fast but normalization is a problem
#signal = signal.numpy().flatten()
#reader = io_methods.AudioIO   # Stylios' file reader. Haven't gotten it working yet
#signal, rate = reader.audioRead(filename, mono=True)
#signal, rate = sf.read('existing_file.wav') 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def __call__(self, pkg, cached_file=None):
        pkg = format_package(pkg)
        wav = pkg['chunk']
        if torch.is_tensor(wav):
            wav = wav.data.numpy().astype(np.float32)
        max_frames = wav.shape[0] // self.hop
        if cached_file is not None:
            # load pre-computed data
            X = torch.load(cached_file)
            beg_i = pkg['chunk_beg_i'] // self.hop
            end_i = pkg['chunk_end_i'] // self.hop
            X = X[:, beg_i:end_i]
            pkg[self.name] = X
        else:
            wav = self.frame_signal(wav, self.window)
            #print('wav shape: ', wav.shape)
            lpc = pysptk.sptk.lpc(wav, order=self.order)
            #print('lpc: ', lpc.shape)
            pkg[self.name] = torch.FloatTensor(lpc)
        # Overwrite resolution to hop length
        pkg['dec_resolution'] = self.hop
        return pkg 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def load_IR(self, ir_file, ir_fmt):
        ir_file = os.path.join(self.data_root, ir_file)
        # print('loading ir_file: ', ir_file)
        if hasattr(self, 'cache') and ir_file in self.cache:
            return self.cache[ir_file]
        else:
            if ir_fmt == 'mat':
                IR = loadmat(ir_file, squeeze_me=True, struct_as_record=False)
                IR = IR['risp_imp']
            elif ir_fmt == 'imp' or ir_fmt == 'txt':
                IR = np.loadtxt(ir_file)
            elif ir_fmt == 'npy':
                IR = np.load(ir_file)
            elif ir_fmt == 'wav':
                IR, _ = sf.read(ir_file)
            else:
                raise TypeError('Unrecognized IR format: ', ir_fmt)
            IR = IR[:self.max_reverb_len]
            if np.max(IR)>0:
                IR = IR / np.abs(np.max(IR))
            p_max = np.argmax(np.abs(IR))
            if hasattr(self, 'cache'):
                self.cache[ir_file] = (IR, p_max)
            return IR, p_max 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def load_filter(self, filt_file, filt_fmt):

        filt_file = os.path.join(self.data_root, filt_file)

        if filt_fmt == 'mat':
            filt_coeff = loadmat(filt_file, squeeze_me=True, struct_as_record=False)
            filt_coeff = filt_coeff['filt_coeff']

        elif filt_fmt == 'imp' or filt_fmt == 'txt':
            filt_coeff = np.loadtxt(filt_file)
        elif filt_fmt == 'npy':
            filt_coeff = np.load(filt_file)
        else:
            raise TypeError('Unrecognized filter format: ', filt_fmt)

        filt_coeff = filt_coeff / np.abs(np.max(filt_coeff))

        return filt_coeff 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def read_file_pair(filename_pair, mono=True):
    """
    given a pair of file names, read in both waveforms and upsample (through
    librosa's default interpolation) the downsampled waveform
    assumes the file name pair is of the form ("original", "downsampled")
    mono selects whether to read in mono or stereo formatted waveforms

    returns a pair of numpy arrays representing the original and upsampled
    waveform
    """
    channel = 1 if mono else 2
    true_waveform, true_br = librosa.load(filename_pair[0], sr=None,
                                          mono=mono)
    ds_waveform, _ = librosa.load(filename_pair[1], sr=true_br, mono=mono)
    # truth, example
    return true_waveform.reshape((-1, channel)), \
        ds_waveform.reshape((-1, channel)) 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def test_compute(self):
        test_file_path = resources.sample_wav_file('wav_1.wav')
        y, sr = librosa.load(test_file_path, sr=None)
        frames = librosa.util.frame(y, frame_length=2048, hop_length=1024).T

        # EXPECTED
        S = np.abs(librosa.stft(y, center=False, n_fft=2048, hop_length=1024)) ** 2
        S = librosa.feature.melspectrogram(S=S, n_mels=128, sr=sr)
        S = librosa.power_to_db(S)
        onsets = librosa.onset.onset_strength(S=S, center=False)
        exp_tgram = librosa.feature.tempogram(onset_envelope=onsets, sr=sr, win_length=11, center=True).T

        # ACTUAL
        tgram_step = pipeline.Tempogram(win_length=11)
        tgrams = tgram_step.process_frames(frames, sr, last=True)

        assert np.allclose(tgrams, exp_tgram) 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def test_compute_online(self):
        # Data: 41523 samples, 16 kHz
        # yields 40 frames with frame-size 2048 and hop-size 1024
        test_file_path = resources.sample_wav_file('wav_1.wav')
        y, sr = librosa.load(test_file_path, sr=None)

        # EXPECTED
        y_pad = np.pad(y, (0, 1024), mode='constant', constant_values=0)
        S = np.abs(librosa.stft(y_pad, center=False, n_fft=2048, hop_length=1024)) ** 2
        S = librosa.feature.melspectrogram(S=S, n_mels=128, sr=sr)
        S = librosa.power_to_db(S)
        onsets = librosa.onset.onset_strength(S=S, center=False)
        exp_tgram = librosa.feature.tempogram(onset_envelope=onsets, sr=sr, win_length=4, center=True).T

        # ACTUAL
        test_file = tracks.FileTrack('idx', test_file_path)
        tgram_step = pipeline.Tempogram(win_length=4)
        tgram_gen = tgram_step.process_track_online(test_file, 2048, 1024, chunk_size=5)

        chunks = list(tgram_gen)
        tgrams = np.vstack(chunks)

        assert np.allclose(tgrams, exp_tgram) 

# 需要導入模塊: import librosa [as 別名]
# 或者: from librosa import load [as 別名]
def test_compute_cleanup_after_one_utterance(self):
        test_file_path = resources.sample_wav_file('wav_1.wav')
        y, sr = librosa.load(test_file_path, sr=None)
        frames = librosa.util.frame(y, frame_length=2048, hop_length=1024).T

        # EXPECTED
        S = np.abs(librosa.stft(y, center=False, n_fft=2048, hop_length=1024)) ** 2
        S = librosa.feature.melspectrogram(S=S, n_mels=128, sr=sr)
        S = librosa.power_to_db(S)
        onsets = librosa.onset.onset_strength(S=S, center=False)
        exp_tgram = librosa.feature.tempogram(onset_envelope=onsets, sr=sr, win_length=11, center=True).T

        # ACTUAL
        tgram_step = pipeline.Tempogram(win_length=11)

        # FIRST RUN
        tgrams = tgram_step.process_frames(frames, sr, last=True)

        assert np.allclose(tgrams, exp_tgram)

        # SECOND RUN
        tgrams = tgram_step.process_frames(frames, sr, last=True)

        assert np.allclose(tgrams, exp_tgram)