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https://github.com/modelscope/FunASR
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嘉渊
parent
fa618d7634
commit
0109889f1c
@ -1,10 +1,11 @@
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import argparse
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import os
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import numpy as np
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from kaldiio import WriteHelper
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import funasr.modules.eend_ola.utils.feature as feature
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import funasr.modules.eend_ola.utils.kaldi_data as kaldi_data
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from funasr.modules.eend_ola.utils.kaldi_data import load_segments_rechash, load_utt2spk, load_wav_scp, load_reco2dur, \
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load_spk2utt, load_wav
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def _count_frames(data_len, size, step):
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@ -24,10 +25,34 @@ def _gen_frame_indices(
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yield (i + 1) * step, data_length
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class KaldiData:
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def __init__(self, data_dir, idx):
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self.data_dir = data_dir
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segment_file = os.path.join(self.data_dir, 'segments.{}'.format(idx))
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self.segments = load_segments_rechash(segment_file)
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utt2spk_file = os.path.join(self.data_dir, 'utt2spk.{}'.format(idx))
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self.utt2spk = load_utt2spk(utt2spk_file)
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wav_file = os.path.join(self.data_dir, 'wav.scp.{}'.format(idx))
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self.wavs = load_wav_scp(wav_file)
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reco2dur_file = os.path.join(self.data_dir, 'reco2dur.{}'.format(idx))
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self.reco2dur = load_reco2dur(reco2dur_file)
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spk2utt_file = os.path.join(self.data_dir, 'spk2utt.{}'.format(idx))
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self.spk2utt = load_spk2utt(spk2utt_file)
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def load_wav(self, recid, start=0, end=None):
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data, rate = load_wav(self.wavs[recid], start, end)
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return data, rate
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class KaldiDiarizationDataset():
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def __init__(
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self,
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data_dir,
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index,
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chunk_size=2000,
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context_size=0,
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frame_size=1024,
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@ -40,6 +65,7 @@ class KaldiDiarizationDataset():
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n_speakers=None,
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):
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self.data_dir = data_dir
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self.index = index
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self.chunk_size = chunk_size
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self.context_size = context_size
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self.frame_size = frame_size
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@ -50,9 +76,8 @@ class KaldiDiarizationDataset():
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self.chunk_indices = []
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self.label_delay = label_delay
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self.data = kaldi_data.KaldiData(self.data_dir)
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self.data = KaldiData(self.data_dir, index)
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# make chunk indices: filepath, start_frame, end_frame
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for rec, path in self.data.wavs.items():
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data_len = int(self.data.reco2dur[rec] * rate / frame_shift)
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data_len = int(data_len / self.subsampling)
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@ -66,62 +91,54 @@ class KaldiDiarizationDataset():
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def convert(args):
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f = open(out_wav_file, 'w')
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dataset = KaldiDiarizationDataset(
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data_dir=args.data_dir,
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index=args.index,
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chunk_size=args.num_frames,
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context_size=args.context_size,
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input_transform=args.input_transform,
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input_transform="logmel23_mn",
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frame_size=args.frame_size,
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frame_shift=args.frame_shift,
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subsampling=args.subsampling,
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rate=8000,
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use_last_samples=True,
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)
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length = len(dataset.chunk_indices)
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for idx, (rec, path, st, ed) in enumerate(dataset.chunk_indices):
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Y, T = feature.get_labeledSTFT(
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dataset.data,
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rec,
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st,
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ed,
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dataset.frame_size,
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dataset.frame_shift,
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dataset.n_speakers)
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Y = feature.transform(Y, dataset.input_transform)
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Y_spliced = feature.splice(Y, dataset.context_size)
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Y_ss, T_ss = feature.subsample(Y_spliced, T, dataset.subsampling)
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st = '{:0>7d}'.format(st)
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ed = '{:0>7d}'.format(ed)
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suffix = '_' + st + '_' + ed
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parts = os.readlink('/'.join(path.split('/')[:-1])).split('/')
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# print('parts: ', parts)
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parts = parts[:4] + ['numpy_data'] + parts[4:]
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cur_path = '/'.join(parts)
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# print('cur path: ', cur_path)
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out_path = os.path.join(cur_path, path.split('/')[-1].split('.')[0] + suffix + '.npz')
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# print(out_path)
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# print(cur_path)
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if not os.path.exists(cur_path):
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os.makedirs(cur_path)
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np.savez(out_path, Y=Y_ss, T=T_ss)
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if idx == length - 1:
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f.write(rec + suffix + ' ' + out_path)
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else:
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f.write(rec + suffix + ' ' + out_path + '\n')
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feature_ark_file = os.path.join(args.output_dir, "feature.ark.{}".format(args.index))
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feature_scp_file = os.path.join(args.output_dir, "feature.scp.{}".format(args.index))
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label_ark_file = os.path.join(args.output_dir, "label.ark.{}".format(args.index))
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label_scp_file = os.path.join(args.output_dir, "label.scp.{}".format(args.index))
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with WriteHelper('ark,scp:{},{}'.format(feature_ark_file, feature_scp_file)) as feature_writer, \
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WriteHelper('ark,scp:{},{}'.format(label_ark_file, label_scp_file)) as label_writer:
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for idx, (rec, path, st, ed) in enumerate(dataset.chunk_indices):
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Y, T = feature.get_labeledSTFT(
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dataset.data,
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rec,
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st,
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ed,
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dataset.frame_size,
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dataset.frame_shift,
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dataset.n_speakers)
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Y = feature.transform(Y, dataset.input_transform)
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Y_spliced = feature.splice(Y, dataset.context_size)
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Y_ss, T_ss = feature.subsample(Y_spliced, T, dataset.subsampling)
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st = '{:0>7d}'.format(st)
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ed = '{:0>7d}'.format(ed)
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key = "{}_{}_{}".format(rec, st, ed)
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feature_writer(key, Y_ss)
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label_writer(key, T_ss.reshape(-1))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("data_dir")
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parser.add_argument("num_frames")
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parser.add_argument("context_size")
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parser.add_argument("frame_size")
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parser.add_argument("frame_shift")
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parser.add_argument("subsampling")
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parser.add_argument("output_dir")
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parser.add_argument("index")
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parser.add_argument("num_frames", default=500)
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parser.add_argument("context_size", default=7)
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parser.add_argument("frame_size", default=200)
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parser.add_argument("frame_shift", default=80)
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parser.add_argument("subsampling", default=10)
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args = parser.parse_args()
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convert(args)
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@ -78,17 +78,26 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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for dset in swb_sre_tr swb_sre_cv; do
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if [ "$dset" == "swb_sre_tr" ]; then
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n_mixtures=${simu_opts_num_train}
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dataset=train
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else
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n_mixtures=500
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dataset=dev
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fi
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simu_data_dir=${dset}_ns"$(IFS="n"; echo "${simu_opts_num_speaker_array[*]}")"_beta"$(IFS="n"; echo "${simu_opts_sil_scale_array[*]}")"_${n_mixtures}
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mkdir -p ${data_dir}/simu/data/${simu_data_dir}/.work
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split_scps=
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for n in $(seq $nj); do
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split_scps="$split_scps ${data_dir}/simu/data/${simu_data_dir}/.work/wav.$n.scp"
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done
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utils/split_scp.pl "${data_dir}/simu/data/${simu_data_dir}/wav.scp" $split_scps || exit 1
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python local/split.py ${data_dir}/simu/data/${simu_data_dir}
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# mkdir -p ${data_dir}/simu/data/${simu_data_dir}/.work
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# split_scps=
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# for n in $(seq $nj); do
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# split_scps="$split_scps ${data_dir}/simu/data/${simu_data_dir}/.work/wav.$n.scp"
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# done
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# utils/split_scp.pl "${data_dir}/simu/data/${simu_data_dir}/wav.scp" $split_scps || exit 1
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# python local/split.py ${data_dir}/simu/data/${simu_data_dir}
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output_dir=${data_dir}/ark_data/dump/simu_data/$dataset
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mkdir -p $output_dir/.logs
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$dump_cmd --max-jobs-run $nj JOB=1:$nj $output_dir/.logs/dump.JOB.log \
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python local/dump_feature.py \
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--data_dir ${data_dir}/simu/data/${simu_data_dir}/.work \
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--output_dir ${data_dir}/ark_data/dump/simu_data/$dataset \
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--index JOB
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done
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fi
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