![]() ![]() However, the company is now probing the leaking of recordings made by smart devices, including those made without device users’ knowledge or permission. Google insists that the capturing of voice and audio is for improving how these apps work. ![]() latency internal_state = ( delayed_time, past_buffers ) #. ![]() step ) output = SWF ( aggregation, resolution ) # we update the internal state delayed_time = real_time - self. sliding_window resolution = SlidingWindow ( start = required. and wrap it into a self-contained SlidingWindowFeature (SWF) instance resolution = current_buffer. past_buffers = + # we aggregate all past buffers (but only on the 'required' region of interest) intersection = np. we can get rid of the others as they # will no longer be needed as they are too far away in the past. latency ) # to compute more robust scores, we will combine all buffers that have a non-empty # temporal intersection with required time range. required = Segment ( delayed_time, real_time - self. end # because we are only allowed `self.latency` seconds of latency, this call should # return aggregated scores for time range. """ if internal_state is None : internal_state = ( 0.0, list ()) # previous call led to the emission of aggregated scores up to time `delayed_time` # `past_buffers` is a rolling list of past buffers that we are going to aggregate delayed_time, past_buffers = internal_state # real time is the current end time of the audio buffer # (here, estimated from the end time of the VAD buffer) real_time = current_buffer. current_buffer : SlidingWindowFeature New incoming score buffer. `past_buffers` is a rolling list of past buffers that we are going to aggregate. latency = latency def _call_ ( self, internal_state, current_buffer : SWF ) -> Tuple ], SWF ]: """Ingest new buffer and return aggregated output with delay Parameters - internal_state : (internal_time, past_buffers) tuple `internal_time` is a float such that previous call emitted aggregated scores up to time `delayed_time`. """ def _init_ ( self, latency = 0.0 ): self. max ( segmentation, axis =- 1, keepdims = True ) return SWF ( speech_probability, resolution )įrom typing import Tuple, List class Aggregation : """Aggregate multiple overlapping buffers with a Parameters - latency : float, optional Allowed latency, in seconds. step ) # pyannote/segmentation pretrained model actually does more than just voice activity detection # see for more details. frames # temporal shift to keep track of current buffer start time resolution = SlidingWindow ( start = current_buffer. ![]() numpy () # temporal resolution of the output of the model resolution = self. eval () def _call_ ( self, current_buffer : SWF ) -> SWF : # we start by applying the model on the current buffer with torch. from_pretrained ( "pyannote/segmentation" ) self. Import torch import numpy as np from dio import Model class VoiceActivityDetection : def _init_ ( self ): self. duration ) resolution = SlidingWindow ( start = chunk. step, end = duration ) for chunk in window : # for each position of the window, yield the corresponding audio buffer # as a SlidingWindowFeature instance waveform, sample_rate = self. get_duration ( file ) # slide a 5s window from the beginning to the end of the file window = SlidingWindow ( start = 0. step = step def _call_ ( self, file : AudioFile ): # duration of the whole audio file duration = self. _init_ ( sample_rate = sample_rate, mono = True ) self. Usage - > buffer = RollingAudioBuffer()("audio.wav") > current_buffer = next(buffer) """ def _init_ ( self, sample_rate = 16000, duration = 5.0, step = 1. step : float, optional Delay between two updates of the rolling buffer. From import Audio, AudioFile class RollingAudioBuffer ( Audio ): """Rolling audio buffer Parameters - sample_rate : int Sample rate duration : float, optional Duration of rolling buffer. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |