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/dump_feature.py | 144 ++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 144 insertions(+), 0 deletions(-)
diff --git a/egs/callhome/eend_ola/local/dump_feature.py b/egs/callhome/eend_ola/local/dump_feature.py
new file mode 100644
index 0000000..5d7a061
--- /dev/null
+++ b/egs/callhome/eend_ola/local/dump_feature.py
@@ -0,0 +1,144 @@
+import argparse
+import os
+
+from kaldiio import WriteHelper
+
+import funasr.modules.eend_ola.utils.feature as feature
+from funasr.modules.eend_ola.utils.kaldi_data import load_segments_rechash, load_utt2spk, load_wav_scp, load_reco2dur, \
+ load_spk2utt, load_wav
+
+
+def _count_frames(data_len, size, step):
+ return int((data_len - size + step) / step)
+
+
+def _gen_frame_indices(
+ data_length, size=2000, step=2000,
+ use_last_samples=False,
+ label_delay=0,
+ subsampling=1):
+ i = -1
+ for i in range(_count_frames(data_length, size, step)):
+ yield i * step, i * step + size
+ if use_last_samples and i * step + size < data_length:
+ if data_length - (i + 1) * step - subsampling * label_delay > 0:
+ yield (i + 1) * step, data_length
+
+
+class KaldiData:
+ def __init__(self, data_dir, idx):
+ self.data_dir = data_dir
+ segment_file = os.path.join(self.data_dir, 'segments.{}'.format(idx))
+ self.segments = load_segments_rechash(segment_file)
+
+ utt2spk_file = os.path.join(self.data_dir, 'utt2spk.{}'.format(idx))
+ self.utt2spk = load_utt2spk(utt2spk_file)
+
+ wav_file = os.path.join(self.data_dir, 'wav.scp.{}'.format(idx))
+ self.wavs = load_wav_scp(wav_file)
+
+ reco2dur_file = os.path.join(self.data_dir, 'reco2dur.{}'.format(idx))
+ self.reco2dur = load_reco2dur(reco2dur_file)
+
+ spk2utt_file = os.path.join(self.data_dir, 'spk2utt.{}'.format(idx))
+ self.spk2utt = load_spk2utt(spk2utt_file)
+
+ def load_wav(self, recid, start=0, end=None):
+ data, rate = load_wav(self.wavs[recid], start, end)
+ return data, rate
+
+
+class KaldiDiarizationDataset():
+ def __init__(
+ self,
+ data_dir,
+ index,
+ chunk_size=2000,
+ context_size=0,
+ frame_size=1024,
+ frame_shift=256,
+ subsampling=1,
+ rate=16000,
+ input_transform=None,
+ use_last_samples=False,
+ label_delay=0,
+ n_speakers=None,
+ ):
+ self.data_dir = data_dir
+ self.index = index
+ self.chunk_size = chunk_size
+ self.context_size = context_size
+ self.frame_size = frame_size
+ self.frame_shift = frame_shift
+ self.subsampling = subsampling
+ self.input_transform = input_transform
+ self.n_speakers = n_speakers
+ self.chunk_indices = []
+ self.label_delay = label_delay
+
+ self.data = KaldiData(self.data_dir, index)
+
+ for rec, path in self.data.wavs.items():
+ data_len = int(self.data.reco2dur[rec] * rate / frame_shift)
+ data_len = int(data_len / self.subsampling)
+ for st, ed in _gen_frame_indices(
+ data_len, chunk_size, chunk_size, use_last_samples,
+ label_delay=self.label_delay,
+ subsampling=self.subsampling):
+ self.chunk_indices.append(
+ (rec, path, st * self.subsampling, ed * self.subsampling))
+ print(len(self.chunk_indices), " chunks")
+
+
+def convert(args):
+ dataset = KaldiDiarizationDataset(
+ data_dir=args.data_dir,
+ index=args.index,
+ chunk_size=args.num_frames,
+ context_size=args.context_size,
+ input_transform="logmel23_mn",
+ frame_size=args.frame_size,
+ frame_shift=args.frame_shift,
+ subsampling=args.subsampling,
+ rate=8000,
+ use_last_samples=True,
+ )
+
+ feature_ark_file = os.path.join(args.output_dir, "feature.ark.{}".format(args.index))
+ feature_scp_file = os.path.join(args.output_dir, "feature.scp.{}".format(args.index))
+ label_ark_file = os.path.join(args.output_dir, "label.ark.{}".format(args.index))
+ label_scp_file = os.path.join(args.output_dir, "label.scp.{}".format(args.index))
+ with WriteHelper('ark,scp:{},{}'.format(feature_ark_file, feature_scp_file)) as feature_writer, \
+ WriteHelper('ark,scp:{},{}'.format(label_ark_file, label_scp_file)) as label_writer:
+ for idx, (rec, path, st, ed) in enumerate(dataset.chunk_indices):
+ Y, T = feature.get_labeledSTFT(
+ dataset.data,
+ rec,
+ st,
+ ed,
+ dataset.frame_size,
+ dataset.frame_shift,
+ dataset.n_speakers)
+ Y = feature.transform(Y, dataset.input_transform)
+ Y_spliced = feature.splice(Y, dataset.context_size)
+ Y_ss, T_ss = feature.subsample(Y_spliced, T, dataset.subsampling)
+ st = '{:0>7d}'.format(st)
+ ed = '{:0>7d}'.format(ed)
+ key = "{}_{}_{}".format(rec, st, ed)
+ feature_writer(key, Y_ss)
+ label_writer(key, T_ss.reshape(-1))
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--data_dir", type=str)
+ parser.add_argument("--output_dir", type=str)
+ parser.add_argument("--index", type=str)
+ parser.add_argument("--num_frames", type=int, default=500)
+ parser.add_argument("--context_size", type=int, default=7)
+ parser.add_argument("--frame_size", type=int, default=200)
+ parser.add_argument("--frame_shift", type=int, default=80)
+ parser.add_argument("--subsampling", type=int, default=10)
+
+ args = parser.parse_args()
+ convert(args)
--
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