我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用librosa.get_duration()。
def transform_audio(self, y): '''Compute the tempogram Parameters ---------- y : np.ndarray Audio buffer Returns ------- data : dict data['tempogram'] : np.ndarray, shape=(n_frames, win_length) The tempogram ''' n_frames = self.n_frames(get_duration(y=y, sr=self.sr)) tgram = tempogram(y=y, sr=self.sr, hop_length=self.hop_length, win_length=self.win_length).astype(np.float32) tgram = fix_length(tgram, n_frames) return {'tempogram': tgram.T[self.idx]}
def _crossFadeRegion(self): # Computes the cross fade region for the mixed song Na = self.beats['in'].shape[0]-1 scores = [self._score(i, Na) for i in xrange(2, int(Na/4))] noBeats = np.argmax(scores)+2 inDuration = librosa.get_duration(y=self.Yin, sr=self.sr) fadeInStart = librosa.frames_to_time(self.beats['in'], sr=self.sr)[-int(noBeats/2)] fadeIn = inDuration - fadeInStart fadeOut = librosa.frames_to_time(self.beats['out'], sr=self.sr)[int(noBeats/2)] print "Best Power Corelation Scores=", np.max(scores) print "Number of beats in cross fade region=", noBeats print "fadeInStart=", fadeInStart print "fadeOutEnd=", fadeOut print "Cross Fade Time=", fadeIn+fadeOut self.crossFade = [fadeInStart*1000, fadeOut*1000] # In milliseconds
def duration_in_s(self) -> float: try: return librosa.get_duration(filename=str(self.audio_file)) except Exception as e: log("Failed to get duration of {}: {}".format(self.audio_file, e)) return 0
def predict(self, filename=None, y=None, sr=None, outputs=None): '''Predict annotations Parameters ---------- filename : str (optional) Path to audio file y, sr : (optional) Audio buffer and sample rate outputs : (optional) Pre-computed model outputs as produced by `CremaModel.outputs`. If provided, then predictions are derived from these instead of `filename` or `(y, sr)`. .. note:: At least one of `filename`, `y, sr` must be provided. Returns ------- jams.Annotation The predicted annotation ''' # Pump the input features output_key = self.model.output_names[0] if outputs is None: outputs = self.outputs(filename=filename, y=y, sr=sr) # Invert the prediction. This is always the first output layer. ann = self.pump[output_key].inverse(outputs[output_key]) # Populate the metadata ann.annotation_metadata.version = self.version ann.annotation_metadata.annotation_tools = 'CREMA {}'.format(version) ann.annotation_metadata.data_source = 'program' ann.duration = librosa.get_duration(y=y, sr=sr, filename=filename) return ann
def __init__(self, file: str, *, sample_rate: int = 44100): """ Parameters ---------- file Audio file to load """ self.file = file self.samples, self.sample_rate = librosa.load(file, sr=sample_rate) self.duration = librosa.get_duration(y=self.samples, sr=self.sample_rate)
def transform_audio(self, y): '''Compute the STFT magnitude and phase. Parameters ---------- y : np.ndarray The audio buffer Returns ------- data : dict data['mag'] : np.ndarray, shape=(n_frames, 1 + n_fft//2) STFT magnitude data['phase'] : np.ndarray, shape=(n_frames, 1 + n_fft//2) STFT phase ''' n_frames = self.n_frames(get_duration(y=y, sr=self.sr)) D = stft(y, hop_length=self.hop_length, n_fft=self.n_fft) D = fix_length(D, n_frames) mag, phase = magphase(D) if self.log: mag = amplitude_to_db(mag, ref=np.max) return {'mag': mag.T[self.idx].astype(np.float32), 'phase': np.angle(phase.T)[self.idx].astype(np.float32)}
def transform_audio(self, y): '''Compute the time position encoding Parameters ---------- y : np.ndarray Audio buffer Returns ------- data : dict data['relative'] = np.ndarray, shape=(n_frames, 2) data['absolute'] = np.ndarray, shape=(n_frames, 2) Relative and absolute time positional encodings. ''' duration = get_duration(y=y, sr=self.sr) n_frames = self.n_frames(duration) relative = np.zeros((n_frames, 2), dtype=np.float32) relative[:, 0] = np.cos(np.pi * np.linspace(0, 1, num=n_frames)) relative[:, 1] = np.sin(np.pi * np.linspace(0, 1, num=n_frames)) absolute = relative * np.sqrt(duration) return {'relative': relative[self.idx], 'absolute': absolute[self.idx]}
def transform_audio(self, y): '''Compute the Mel spectrogram Parameters ---------- y : np.ndarray The audio buffer Returns ------- data : dict data['mag'] : np.ndarray, shape=(n_frames, n_mels) The Mel spectrogram ''' n_frames = self.n_frames(get_duration(y=y, sr=self.sr)) mel = np.sqrt(melspectrogram(y=y, sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_mels=self.n_mels, fmax=self.fmax)).astype(np.float32) mel = fix_length(mel, n_frames) if self.log: mel = amplitude_to_db(mel, ref=np.max) return {'mag': mel.T[self.idx]}
def transform_audio(self, y): '''Compute the CQT Parameters ---------- y : np.ndarray The audio buffer Returns ------- data : dict data['mag'] : np.ndarray, shape = (n_frames, n_bins) The CQT magnitude data['phase']: np.ndarray, shape = mag.shape The CQT phase ''' n_frames = self.n_frames(get_duration(y=y, sr=self.sr)) C = cqt(y=y, sr=self.sr, hop_length=self.hop_length, fmin=self.fmin, n_bins=(self.n_octaves * self.over_sample * 12), bins_per_octave=(self.over_sample * 12)) C = fix_length(C, n_frames) cqtm, phase = magphase(C) if self.log: cqtm = amplitude_to_db(cqtm, ref=np.max) return {'mag': cqtm.T.astype(np.float32)[self.idx], 'phase': np.angle(phase).T.astype(np.float32)[self.idx]}
def analyze(filename=None, y=None, sr=None): '''Analyze a recording for all tasks. Parameters ---------- filename : str, optional Path to audio file y : np.ndarray, optional sr : number > 0, optional Audio buffer and sampling rate .. note:: At least one of `filename` or `y, sr` must be provided. Returns ------- jam : jams.JAMS a JAMS object containing all estimated annotations Examples -------- >>> from crema.analyze import analyze >>> import librosa >>> jam = analyze(filename=librosa.util.example_audio_file()) >>> jam <JAMS(file_metadata=<FileMetadata(...)>, annotations=[1 annotation], sandbox=<Sandbox(...)>)> >>> # Get the chord estimates >>> chords = jam.annotations['chord', 0] >>> chords.to_dataframe().head(5) time duration value confidence 0 0.000000 0.092880 E:maj 0.336977 1 0.092880 0.464399 E:7 0.324255 2 0.557279 1.021678 E:min 0.448759 3 1.578957 2.693515 E:maj 0.501462 4 4.272472 1.486077 E:min 0.287264 ''' _load_models() jam = jams.JAMS() # populate file metadata jam.file_metadata.duration = librosa.get_duration(y=y, sr=sr, filename=filename) for model in __MODELS__: jam.annotations.append(model.predict(filename=filename, y=y, sr=sr)) return jam
def generate_example(spec_filename, audio_filename): with open(spec_filename) as f: spec = json.load(f) spec_duration = (spec['audio_source']['end_time'] - spec['audio_source']['start_time']) sample_duration = librosa.get_duration(filename=audio_filename) if not math.isclose(spec_duration, sample_duration): print("Warning: sample duration is {} but spec says {}".format( sample_duration, spec_duration)) sample, sampling_rate = librosa.load(audio_filename, sr=44100) if sampling_rate != 44100: print("Warning: sampling rate is {}".format(sampling_rate)) return tf.train.SequenceExample( context=tf.train.Features(feature={ 'data_source': tf.train.Feature( bytes_list=tf.train.BytesList( value=[bytes(spec['data_source'], 'utf-8')])), 'tonic': tf.train.Feature( int64_list=tf.train.Int64List( value=[spec['key']['tonic']])), 'mode': tf.train.Feature( int64_list=tf.train.Int64List( value=[spec['key']['mode']])), 'beats': tf.train.Feature( int64_list=tf.train.Int64List( value=[spec['meter']['beats']])), 'beats_per_measure': tf.train.Feature( int64_list=tf.train.Int64List( value=[spec['meter']['beats_per_measure']])), }), feature_lists=tf.train.FeatureLists(feature_list={ 'audio': tf.train.FeatureList( feature=[tf.train.Feature( float_list=tf.train.FloatList(value=sample.tolist()))]), 'melody': tf.train.FeatureList( feature=[tf.train.Feature( int64_list=tf.train.Int64List(value=[]))]), 'harmony': tf.train.FeatureList( feature=[tf.train.Feature( int64_list=tf.train.Int64List(value=[]))]), }))
