def segmental_log_spectrum_distance(s, shat, winsize, winfunc): size = min(len(s), len(shat)) nf = size / (winsize / 2) - 1 ret = [] for no in xrange(nf): s_i = get_frame(s, winsize, no) shat_i = get_frame(shat, winsize, no) ret.append(log_spectrum_distance(s_i, shat_i, winfunc)) return ret
def padded_append(self, last_token, token, text_buffer): if last_token and last_token.span: spaces = token.span.start - last_token.span.stop elif last_token: spaces = 1 else: spaces = 0 for i in xrange(spaces): text_buffer.append(' ') text_buffer.append(token.norm)
def run_processor(process, args, doc_class=None, automagic=False, dealer_url='inproc://workers'): if any(getattr(args, a, None) for a in ('serve_url', 'worker_url')): context = zmq.Context(1) if args.serve_url: clients = context.socket(zmq.ROUTER) clients.bind(args.serve_url) workers = context.socket(zmq.DEALER) workers.bind(dealer_url) else: dealer_url = args.worker_url run = lambda: process(zmq_coroutine(context, dealer_url, doc_class, automagic)) threads = [threading.Thread(target=run) for i in xrange(args.nthreads)] for thread in threads: thread.start() if args.serve_url: zmq.device(zmq.QUEUE, clients, workers) else: process(stream_coroutine(sys.stdin, sys.stdout, doc_class, automagic))
int32_ary_sound = sp.int32(ary_sound) int32_ary_noise = sp.int32(ary_noise) ary2 = sp.int16(int32_ary_sound + int32_ary_noise) data2 = ary2.tostring() synth.writeframes(data2) remain = remain - s sound.close() noise.close() synth.close() infile = 'tools/sound/noisy.wav' signal, params = read_signal(infile, WINSIZE) nf = len(signal) / (WINSIZE / 2) - 1 sig_out = sp.zeros(len(signal), sp.float32) window = sp.hanning(WINSIZE) ms = MinimumStatistics(WINSIZE, window, params[2]) NP_lambda = compute_avgpowerspectrum(signal[0:WINSIZE * int(params[2] / float(WINSIZE) / 3.0)], WINSIZE, window) ms.init_noise_profile(NP_lambda) ss = JointMap(WINSIZE, window) for no in xrange(nf): frame = get_frame(signal, WINSIZE, no) n_pow = ms.compute(frame, no) res = ss.compute_by_noise_pow(frame, n_pow) add_signal(sig_out, res, WINSIZE, no) ms.show_debug_result() write_signal("tools/sound/noise_reduction.wav", params, sig_out)
int32_ary_sound = sp.int32(ary_sound) int32_ary_noise = sp.int32(ary_noise) ary2 = sp.int16(int32_ary_sound + int32_ary_noise) data2 = ary2.tostring() synth.writeframes(data2) remain = remain - s sound.close() noise.close() synth.close() infile = 'tools/sound/noisy.wav' signal, params = read_signal(infile, WINSIZE) nf = len(signal) / (WINSIZE / 2) - 1 sig_out = sp.zeros(len(signal), sp.float32) window = sp.hanning(WINSIZE) ms = MinimumStatistics(WINSIZE, window, params[2]) NP_lambda = compute_avgpowerspectrum( signal[0:WINSIZE * int(params[2] / float(WINSIZE) / 3.0)], WINSIZE, window) ms.init_noise_profile(NP_lambda) ss = JointMap(WINSIZE, window) for no in xrange(nf): frame = get_frame(signal, WINSIZE, no) n_pow = ms.compute(frame, no) res = ss.compute_by_noise_pow(frame, n_pow) add_signal(sig_out, res, WINSIZE, no) ms.show_debug_result() write_signal("tools/sound/noise_reduction.wav", params, sig_out)