我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用librosa.resample()。
def transform(self, y, sr): '''Transform an audio signal Parameters ---------- y : np.ndarray The audio signal sr : number > 0 The native sampling rate of y Returns ------- dict Data dictionary containing features extracted from y See Also -------- transform_audio ''' if sr != self.sr: y = resample(y, sr, self.sr) return self.merge([self.transform_audio(y)])
def wav_data_to_samples(wav_data, sample_rate): """Read PCM-formatted WAV data and return a NumPy array of samples. Uses scipy to read and librosa to process WAV data. Audio will be converted to mono if necessary. Args: wav_data: WAV audio data to read. sample_rate: The number of samples per second at which the audio will be returned. Resampling will be performed if necessary. Returns: A numpy array of audio samples, single-channel (mono) and sampled at the specified rate, in float32 format. Raises: AudioIOReadException: If scipy is unable to read the WAV data. AudioIOException: If audio processing fails. """ try: # Read the wav file, converting sample rate & number of channels. native_sr, y = scipy.io.wavfile.read(six.BytesIO(wav_data)) except Exception as e: # pylint: disable=broad-except raise AudioIOReadException(e) if y.dtype != np.int16: raise AudioIOException('WAV file not 16-bit PCM, unsupported') try: # Convert to float, mono, and the desired sample rate. y = int16_samples_to_float32(y) if y.ndim == 2 and y.shape[1] == 2: y = y.T y = librosa.to_mono(y) if native_sr != sample_rate: y = librosa.resample(y, native_sr, sample_rate) except Exception as e: # pylint: disable=broad-except raise AudioIOException(e) return y
def load_data(data_dict, file_list): for f in file_list: data, samplerate = sf.read(f) print(f) try: assert samplerate == 16000 and len(data.shape) == 1 except AssertionError: data = librosa.resample(data, samplerate, SR) data_dict.update({f.split('/')[-1][:-4]:data})
def __call__(self, y): """Resample a time series. Parameters ---------- y : np.ndarray [shape=(n,) or shape=(2, n)] audio time series. Can be mono or stereo. Returns ------- y_hat : np.ndarray [shape=(n * target_sr / orig_sr,)] """ return librosa.resample(y, **self.__dict__) # Feature
def process_music_data(data_in, is_fft, is_energy, n_output_bins, n_fft, is_visual): # length is len(data_in)/4 data_np = np.fromstring(data_in, 'Float32') # visualizer if is_visual: visualizer(data_np) # energy if is_energy: energy = np.abs(data_np) ** 2 energy = energy.sum() energy *= 2**5 energy_output = energy.astype(np.uint16) else: energy_output = np.zeros(2).astype(np.uint16) # fft if is_fft: global sample_rate # down-sample by 4, with filtering, energy not scaled data_np = librosa.resample(data_np, sample_rate, sample_rate/4, res_type='kaiser_fast') # short time fft over n_fft samples fft_data = librosa.stft(data_np, n_fft, hop_length=n_fft, center=False) fft_data_mag = np.abs(fft_data[0:n_fft//2]) ** 2 # magnitude scaling fft_data_mag *= 2**3 fft_output = get_output_fft_bins(fft_data_mag, n_output_bins) fft_output = fft_output.astype(np.uint8) else: fft_output = np.zeros(n_output_bins).astype(np.uint8) return fft_output, energy_output
def load_audio(path, target_sr=16000): data, orig_sr = sf.read(path) if orig_sr != target_sr: return librosa.resample(data, orig_sr, target_sr) return data # def _update_progress(progress): # print("\rProgress: [{0:50s}] {1:.1f}%".format('#' * int(progress * 50), # progress * 100), end="") # # # def create_manifest(data_path, manifest_path, ordered=True): # file_paths = [] # wav_files = [os.path.join(dirpath, f) # for dirpath, dirnames, files in os.walk(data_path) # for f in fnmatch.filter(files, '*.wav')] # size = len(wav_files) # counter = 0 # for file_path in wav_files: # file_paths.append(file_path.strip()) # counter += 1 # _update_progress(counter / float(size)) # print('\n') # if ordered: # _order_files(file_paths) # counter = 0 # with io.FileIO(manifest_path, "w") as file: # for wav_path in file_paths: # transcript_path = wav_path.replace( # '/wav/', '/txt/').replace('.wav', '.txt') # sample = os.path.abspath(wav_path) + ',' + \ # os.path.abspath(transcript_path) + '\n' # file.write(sample) # counter += 1 # _update_progress(counter / float(size)) # print('\n') # # # def _order_files(file_paths): # print("Sorting files by length...") # # def func(element): # output = subprocess.check_output( # ['soxi -D %s' % element.strip()], # shell=True # ) # return float(output) # # file_paths.sort(key=func)
