From f8d1c79fe355efb18ae49e4363307dfec3ab89ce Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期一, 07 八月 2023 16:14:11 +0800
Subject: [PATCH] Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into main
---
egs/callhome/eend_ola/local/make_mixture.py | 120 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 120 insertions(+), 0 deletions(-)
diff --git a/egs/callhome/eend_ola/local/make_mixture.py b/egs/callhome/eend_ola/local/make_mixture.py
new file mode 100755
index 0000000..6b15903
--- /dev/null
+++ b/egs/callhome/eend_ola/local/make_mixture.py
@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+
+# Copyright 2019 Hitachi, Ltd. (author: Yusuke Fujita)
+# Licensed under the MIT license.
+#
+# This script generates simulated multi-talker mixtures for diarization
+#
+# common/make_mixture.py \
+# mixture.scp \
+# data/mixture \
+# wav/mixture
+
+
+import argparse
+import os
+from funasr.modules.eend_ola.utils import kaldi_data
+import numpy as np
+import math
+import soundfile as sf
+import json
+
+parser = argparse.ArgumentParser()
+parser.add_argument('script',
+ help='list of json')
+parser.add_argument('out_data_dir',
+ help='output data dir of mixture')
+parser.add_argument('out_wav_dir',
+ help='output mixture wav files are stored here')
+parser.add_argument('--rate', type=int, default=16000,
+ help='sampling rate')
+args = parser.parse_args()
+
+# open output data files
+segments_f = open(args.out_data_dir + '/segments', 'w')
+utt2spk_f = open(args.out_data_dir + '/utt2spk', 'w')
+wav_scp_f = open(args.out_data_dir + '/wav.scp', 'w')
+
+# "-R" forces the default random seed for reproducibility
+resample_cmd = "sox -R -t wav - -t wav - rate {}".format(args.rate)
+
+for line in open(args.script):
+ recid, jsonstr = line.strip().split(None, 1)
+ indata = json.loads(jsonstr)
+ wavfn = indata['recid']
+ # recid now include out_wav_dir
+ recid = os.path.join(args.out_wav_dir, wavfn).replace('/','_')
+ noise = indata['noise']
+ noise_snr = indata['snr']
+ mixture = []
+ for speaker in indata['speakers']:
+ spkid = speaker['spkid']
+ utts = speaker['utts']
+ intervals = speaker['intervals']
+ rir = speaker['rir']
+ data = []
+ pos = 0
+ for interval, utt in zip(intervals, utts):
+ # append silence interval data
+ silence = np.zeros(int(interval * args.rate))
+ data.append(silence)
+ # utterance is reverberated using room impulse response
+ preprocess = "wav-reverberate --print-args=false " \
+ " --impulse-response={} - -".format(rir)
+ if isinstance(utt, list):
+ rec, st, et = utt
+ st = np.rint(st * args.rate).astype(int)
+ et = np.rint(et * args.rate).astype(int)
+ else:
+ rec = utt
+ st = 0
+ et = None
+ if rir is not None:
+ wav_rxfilename = kaldi_data.process_wav(rec, preprocess)
+ else:
+ wav_rxfilename = rec
+ wav_rxfilename = kaldi_data.process_wav(
+ wav_rxfilename, resample_cmd)
+ speech, _ = kaldi_data.load_wav(wav_rxfilename, st, et)
+ data.append(speech)
+ # calculate start/end position in samples
+ startpos = pos + len(silence)
+ endpos = startpos + len(speech)
+ # write segments and utt2spk
+ uttid = '{}_{}_{:07d}_{:07d}'.format(
+ spkid, recid, int(startpos / args.rate * 100),
+ int(endpos / args.rate * 100))
+ print(uttid, recid,
+ startpos / args.rate, endpos / args.rate, file=segments_f)
+ print(uttid, spkid, file=utt2spk_f)
+ # update position for next utterance
+ pos = endpos
+ data = np.concatenate(data)
+ mixture.append(data)
+
+ # fitting to the maximum-length speaker data, then mix all speakers
+ maxlen = max(len(x) for x in mixture)
+ mixture = [np.pad(x, (0, maxlen - len(x)), 'constant') for x in mixture]
+ mixture = np.sum(mixture, axis=0)
+ # noise is repeated or cutted for fitting to the mixture data length
+ noise_resampled = kaldi_data.process_wav(noise, resample_cmd)
+ noise_data, _ = kaldi_data.load_wav(noise_resampled)
+ if maxlen > len(noise_data):
+ noise_data = np.pad(noise_data, (0, maxlen - len(noise_data)), 'wrap')
+ else:
+ noise_data = noise_data[:maxlen]
+ # noise power is scaled according to selected SNR, then mixed
+ signal_power = np.sum(mixture**2) / len(mixture)
+ noise_power = np.sum(noise_data**2) / len(noise_data)
+ scale = math.sqrt(
+ math.pow(10, - noise_snr / 10) * signal_power / noise_power)
+ mixture += noise_data * scale
+ # output the wav file and write wav.scp
+ outfname = '{}.wav'.format(wavfn)
+ outpath = os.path.join(args.out_wav_dir, outfname)
+ sf.write(outpath, mixture, args.rate)
+ print(recid, os.path.abspath(outpath), file=wav_scp_f)
+
+wav_scp_f.close()
+segments_f.close()
+utt2spk_f.close()
--
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