From a7b34960396fa83398e0000e0273ef8e9e6371cc Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期三, 19 七月 2023 01:49:02 +0800
Subject: [PATCH] update
---
egs/callhome/eend_ola/run.sh | 2
egs/callhome/eend_ola/local/run_prepare_shared_eda.sh | 6
egs/callhome/eend_ola/run_test.sh | 257 ++++++++++++++++++++++++++++++++
egs/callhome/eend_ola/local/make_mixture.py | 2
funasr/modules/eend_ola/utils/kaldi_data.py | 162 ++++++++++++++++++++
5 files changed, 424 insertions(+), 5 deletions(-)
diff --git a/egs/callhome/eend_ola/local/make_mixture.py b/egs/callhome/eend_ola/local/make_mixture.py
index 82d03cd..6b15903 100755
--- a/egs/callhome/eend_ola/local/make_mixture.py
+++ b/egs/callhome/eend_ola/local/make_mixture.py
@@ -13,7 +13,7 @@
import argparse
import os
-from eend import kaldi_data
+from funasr.modules.eend_ola.utils import kaldi_data
import numpy as np
import math
import soundfile as sf
diff --git a/egs/callhome/eend_ola/local/run_prepare_shared_eda.sh b/egs/callhome/eend_ola/local/run_prepare_shared_eda.sh
index a256eda..5431ba1 100755
--- a/egs/callhome/eend_ola/local/run_prepare_shared_eda.sh
+++ b/egs/callhome/eend_ola/local/run_prepare_shared_eda.sh
@@ -9,7 +9,7 @@
# - data/simu_${simu_outputs}
# simulation mixtures generated with various options
-stage=0
+stage=1
# Modify corpus directories
# - callhome_dir
@@ -156,8 +156,8 @@
if [ $stage -le 1 ]; then
echo "simulation of mixture"
mkdir -p $simudir/.work
- local/random_mixture_cmd=random_mixture.py
- local/make_mixture_cmd=make_mixture.py
+ random_mixture_cmd=local/random_mixture.py
+ make_mixture_cmd=local/make_mixture.py
for ((i=0; i<${#simu_opts_sil_scale_array[@]}; ++i)); do
simu_opts_num_speaker=${simu_opts_num_speaker_array[i]}
diff --git a/egs/callhome/eend_ola/run.sh b/egs/callhome/eend_ola/run.sh
index 286fc29..b4f2739 100644
--- a/egs/callhome/eend_ola/run.sh
+++ b/egs/callhome/eend_ola/run.sh
@@ -31,7 +31,7 @@
stop_stage=-1
# exp tag
-tag="exp_fix"
+tag="exp1"
. local/parse_options.sh || exit 1;
diff --git a/egs/callhome/eend_ola/run_test.sh b/egs/callhome/eend_ola/run_test.sh
new file mode 100644
index 0000000..d004446
--- /dev/null
+++ b/egs/callhome/eend_ola/run_test.sh
@@ -0,0 +1,257 @@
+#!/usr/bin/env bash
+
+. ./path.sh || exit 1;
+
+# machines configuration
+CUDA_VISIBLE_DEVICES="7"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+count=1
+
+# general configuration
+simu_feats_dir="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/simu_data/data"
+simu_feats_dir_chunk2000="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/simu_data_chunk2000/data"
+callhome_feats_dir_chunk2000="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/callhome_chunk2000/data"
+simu_train_dataset=train
+simu_valid_dataset=dev
+callhome_train_dataset=callhome1_allspk
+callhome_valid_dataset=callhome2_allspk
+callhome2_wav_scp_file=wav.scp
+
+# model average
+simu_average_2spkr_start=91
+simu_average_2spkr_end=100
+simu_average_allspkr_start=16
+simu_average_allspkr_end=25
+callhome_average_start=91
+callhome_average_end=100
+
+exp_dir="."
+input_size=345
+stage=5
+stop_stage=5
+
+# exp tag
+tag="exp1"
+
+. local/parse_options.sh || exit 1;
+
+# Set bash to 'debug' mode, it will exit on :
+# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
+set -e
+set -u
+set -o pipefail
+
+simu_2spkr_diar_config=conf/train_diar_eend_ola_simu_2spkr.yaml
+simu_allspkr_diar_config=conf/train_diar_eend_ola_simu_allspkr.yaml
+simu_allspkr_chunk2000_diar_config=conf/train_diar_eend_ola_simu_allspkr_chunk2000.yaml
+callhome_diar_config=conf/train_diar_eend_ola_callhome_chunk2000.yaml
+simu_2spkr_model_dir="baseline_$(basename "${simu_2spkr_diar_config}" .yaml)_${tag}"
+simu_allspkr_model_dir="baseline_$(basename "${simu_allspkr_diar_config}" .yaml)_${tag}"
+simu_allspkr_chunk2000_model_dir="baseline_$(basename "${simu_allspkr_chunk2000_diar_config}" .yaml)_${tag}"
+callhome_model_dir="baseline_$(basename "${callhome_diar_config}" .yaml)_${tag}"
+
+# simulate mixture data for training and inference
+if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
+ echo "stage -1: Simulate mixture data for training and inference"
+ echo "The detail can be found in https://github.com/hitachi-speech/EEND"
+ echo "Before running this step, you should download and compile kaldi and set KALDI_ROOT in this script and path.sh"
+ echo "This stage may take a long time, please waiting..."
+ KALDI_ROOT=
+ ln -s $KALDI_ROOT/egs/wsj/s5/steps steps
+ ln -s $KALDI_ROOT/egs/wsj/s5/utils utils
+ local/run_prepare_shared_eda.sh
+fi
+
+## Prepare data for training and inference
+#if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+# echo "stage 0: Prepare data for training and inference"
+# echo "The detail can be found in https://github.com/hitachi-speech/EEND"
+# . ./local/
+#fi
+#
+
+# Training on simulated two-speaker data
+world_size=$gpu_num
+simu_2spkr_ave_id=avg${simu_average_2spkr_start}-${simu_average_2spkr_end}
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+ echo "stage 1: Training on simulated two-speaker data"
+ mkdir -p ${exp_dir}/exp/${simu_2spkr_model_dir}
+ mkdir -p ${exp_dir}/exp/${simu_2spkr_model_dir}/log
+ INIT_FILE=${exp_dir}/exp/${simu_2spkr_model_dir}/ddp_init
+ if [ -f $INIT_FILE ];then
+ rm -f $INIT_FILE
+ fi
+ init_method=file://$(readlink -f $INIT_FILE)
+ echo "$0: init method is $init_method"
+ for ((i = 0; i < $gpu_num; ++i)); do
+ {
+ rank=$i
+ local_rank=$i
+ gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
+ train.py \
+ --task_name diar \
+ --gpu_id $gpu_id \
+ --use_preprocessor false \
+ --input_size $input_size \
+ --data_dir ${simu_feats_dir} \
+ --train_set ${simu_train_dataset} \
+ --valid_set ${simu_valid_dataset} \
+ --data_file_names "feats_2spkr.scp" \
+ --resume true \
+ --output_dir ${exp_dir}/exp/${simu_2spkr_model_dir} \
+ --config $simu_2spkr_diar_config \
+ --ngpu $gpu_num \
+ --num_worker_count $count \
+ --dist_init_method $init_method \
+ --dist_world_size $world_size \
+ --dist_rank $rank \
+ --local_rank $local_rank 1> ${exp_dir}/exp/${simu_2spkr_model_dir}/log/train.log.$i 2>&1
+ } &
+ done
+ wait
+ echo "averaging model parameters into ${exp_dir}/exp/$simu_2spkr_model_dir/$simu_2spkr_ave_id.pb"
+ models=`eval echo ${exp_dir}/exp/${simu_2spkr_model_dir}/{$simu_average_2spkr_start..$simu_average_2spkr_end}epoch.pb`
+ python local/model_averaging.py ${exp_dir}/exp/${simu_2spkr_model_dir}/$simu_2spkr_ave_id.pb $models
+fi
+
+# Training on simulated all-speaker data
+world_size=$gpu_num
+simu_allspkr_ave_id=avg${simu_average_allspkr_start}-${simu_average_allspkr_end}
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+ echo "stage 2: Training on simulated all-speaker data"
+ mkdir -p ${exp_dir}/exp/${simu_allspkr_model_dir}
+ mkdir -p ${exp_dir}/exp/${simu_allspkr_model_dir}/log
+ INIT_FILE=${exp_dir}/exp/${simu_allspkr_model_dir}/ddp_init
+ if [ -f $INIT_FILE ];then
+ rm -f $INIT_FILE
+ fi
+ init_method=file://$(readlink -f $INIT_FILE)
+ echo "$0: init method is $init_method"
+ for ((i = 0; i < $gpu_num; ++i)); do
+ {
+ rank=$i
+ local_rank=$i
+ gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
+ train.py \
+ --task_name diar \
+ --gpu_id $gpu_id \
+ --use_preprocessor false \
+ --input_size $input_size \
+ --data_dir ${simu_feats_dir} \
+ --train_set ${simu_train_dataset} \
+ --valid_set ${simu_valid_dataset} \
+ --data_file_names "feats.scp" \
+ --resume true \
+ --init_param ${exp_dir}/exp/${simu_2spkr_model_dir}/$simu_2spkr_ave_id.pb \
+ --output_dir ${exp_dir}/exp/${simu_allspkr_model_dir} \
+ --config $simu_allspkr_diar_config \
+ --ngpu $gpu_num \
+ --num_worker_count $count \
+ --dist_init_method $init_method \
+ --dist_world_size $world_size \
+ --dist_rank $rank \
+ --local_rank $local_rank 1> ${exp_dir}/exp/${simu_allspkr_model_dir}/log/train.log.$i 2>&1
+ } &
+ done
+ wait
+ echo "averaging model parameters into ${exp_dir}/exp/$simu_allspkr_model_dir/$simu_allspkr_ave_id.pb"
+ models=`eval echo ${exp_dir}/exp/${simu_allspkr_model_dir}/{$simu_average_allspkr_start..$simu_average_allspkr_end}epoch.pb`
+ python local/model_averaging.py ${exp_dir}/exp/${simu_allspkr_model_dir}/$simu_allspkr_ave_id.pb $models
+fi
+
+# Training on simulated all-speaker data with chunk_size=2000
+world_size=$gpu_num
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+ echo "stage 3: Training on simulated all-speaker data with chunk_size=2000"
+ mkdir -p ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}
+ mkdir -p ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}/log
+ INIT_FILE=${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}/ddp_init
+ if [ -f $INIT_FILE ];then
+ rm -f $INIT_FILE
+ fi
+ init_method=file://$(readlink -f $INIT_FILE)
+ echo "$0: init method is $init_method"
+ for ((i = 0; i < $gpu_num; ++i)); do
+ {
+ rank=$i
+ local_rank=$i
+ gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
+ train.py \
+ --task_name diar \
+ --gpu_id $gpu_id \
+ --use_preprocessor false \
+ --input_size $input_size \
+ --data_dir ${simu_feats_dir_chunk2000} \
+ --train_set ${simu_train_dataset} \
+ --valid_set ${simu_valid_dataset} \
+ --data_file_names "feats.scp" \
+ --resume true \
+ --init_param ${exp_dir}/exp/${simu_allspkr_model_dir}/$simu_allspkr_ave_id.pb \
+ --output_dir ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir} \
+ --config $simu_allspkr_chunk2000_diar_config \
+ --ngpu $gpu_num \
+ --num_worker_count $count \
+ --dist_init_method $init_method \
+ --dist_world_size $world_size \
+ --dist_rank $rank \
+ --local_rank $local_rank 1> ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}/log/train.log.$i 2>&1
+ } &
+ done
+ wait
+fi
+
+# Training on callhome all-speaker data with chunk_size=2000
+world_size=$gpu_num
+callhome_ave_id=avg${callhome_average_start}-${callhome_average_end}
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+ echo "stage 4: Training on callhome all-speaker data with chunk_size=2000"
+ mkdir -p ${exp_dir}/exp/${callhome_model_dir}
+ mkdir -p ${exp_dir}/exp/${callhome_model_dir}/log
+ INIT_FILE=${exp_dir}/exp/${callhome_model_dir}/ddp_init
+ if [ -f $INIT_FILE ];then
+ rm -f $INIT_FILE
+ fi
+ init_method=file://$(readlink -f $INIT_FILE)
+ echo "$0: init method is $init_method"
+ for ((i = 0; i < $gpu_num; ++i)); do
+ {
+ rank=$i
+ local_rank=$i
+ gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
+ train.py \
+ --task_name diar \
+ --gpu_id $gpu_id \
+ --use_preprocessor false \
+ --input_size $input_size \
+ --data_dir ${callhome_feats_dir_chunk2000} \
+ --train_set ${callhome_train_dataset} \
+ --valid_set ${callhome_valid_dataset} \
+ --data_file_names "feats.scp" \
+ --resume true \
+ --init_param ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}/1epoch.pb \
+ --output_dir ${exp_dir}/exp/${callhome_model_dir} \
+ --config $callhome_diar_config \
+ --ngpu $gpu_num \
+ --num_worker_count $count \
+ --dist_init_method $init_method \
+ --dist_world_size $world_size \
+ --dist_rank $rank \
+ --local_rank $local_rank 1> ${exp_dir}/exp/${callhome_model_dir}/log/train.log.$i 2>&1
+ } &
+ done
+ wait
+ echo "averaging model parameters into ${exp_dir}/exp/$callhome_model_dir/$callhome_ave_id.pb"
+ models=`eval echo ${exp_dir}/exp/${callhome_model_dir}/{$callhome_average_start..$callhome_average_end}epoch.pb`
+ python local/model_averaging.py ${exp_dir}/exp/${callhome_model_dir}/$callhome_ave_id.pb $models
+fi
+
+# inference
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
+ echo "Inference"
+ mkdir -p ${exp_dir}/exp/${callhome_model_dir}/inference/log
+ CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES python local/infer.py \
+ --config_file ${exp_dir}/exp/${callhome_model_dir}/config.yaml \
+ --model_file ${exp_dir}/exp/${callhome_model_dir}/$callhome_ave_id.pb \
+ --output_rttm_file ${exp_dir}/exp/${callhome_model_dir}/inference/rttm \
+ --wav_scp_file ${callhome_feats_dir_chunk2000}/${callhome_valid_dataset}/${callhome2_wav_scp_file} 1> ${exp_dir}/exp/${callhome_model_dir}/inference/log/infer.log 2>&1
+fi
\ No newline at end of file
diff --git a/funasr/modules/eend_ola/utils/kaldi_data.py b/funasr/modules/eend_ola/utils/kaldi_data.py
new file mode 100644
index 0000000..42f6d5e
--- /dev/null
+++ b/funasr/modules/eend_ola/utils/kaldi_data.py
@@ -0,0 +1,162 @@
+# Copyright 2019 Hitachi, Ltd. (author: Yusuke Fujita)
+# Licensed under the MIT license.
+#
+# This library provides utilities for kaldi-style data directory.
+
+
+from __future__ import print_function
+import os
+import sys
+import numpy as np
+import subprocess
+import soundfile as sf
+import io
+from functools import lru_cache
+
+
+def load_segments(segments_file):
+ """ load segments file as array """
+ if not os.path.exists(segments_file):
+ return None
+ return np.loadtxt(
+ segments_file,
+ dtype=[('utt', 'object'),
+ ('rec', 'object'),
+ ('st', 'f'),
+ ('et', 'f')],
+ ndmin=1)
+
+
+def load_segments_hash(segments_file):
+ ret = {}
+ if not os.path.exists(segments_file):
+ return None
+ for line in open(segments_file):
+ utt, rec, st, et = line.strip().split()
+ ret[utt] = (rec, float(st), float(et))
+ return ret
+
+
+def load_segments_rechash(segments_file):
+ ret = {}
+ if not os.path.exists(segments_file):
+ return None
+ for line in open(segments_file):
+ utt, rec, st, et = line.strip().split()
+ if rec not in ret:
+ ret[rec] = []
+ ret[rec].append({'utt':utt, 'st':float(st), 'et':float(et)})
+ return ret
+
+
+def load_wav_scp(wav_scp_file):
+ """ return dictionary { rec: wav_rxfilename } """
+ lines = [line.strip().split(None, 1) for line in open(wav_scp_file)]
+ return {x[0]: x[1] for x in lines}
+
+
+@lru_cache(maxsize=1)
+def load_wav(wav_rxfilename, start=0, end=None):
+ """ This function reads audio file and return data in numpy.float32 array.
+ "lru_cache" holds recently loaded audio so that can be called
+ many times on the same audio file.
+ OPTIMIZE: controls lru_cache size for random access,
+ considering memory size
+ """
+ if wav_rxfilename.endswith('|'):
+ # input piped command
+ p = subprocess.Popen(wav_rxfilename[:-1], shell=True,
+ stdout=subprocess.PIPE)
+ data, samplerate = sf.read(io.BytesIO(p.stdout.read()),
+ dtype='float32')
+ # cannot seek
+ data = data[start:end]
+ elif wav_rxfilename == '-':
+ # stdin
+ data, samplerate = sf.read(sys.stdin, dtype='float32')
+ # cannot seek
+ data = data[start:end]
+ else:
+ # normal wav file
+ data, samplerate = sf.read(wav_rxfilename, start=start, stop=end)
+ return data, samplerate
+
+
+def load_utt2spk(utt2spk_file):
+ """ returns dictionary { uttid: spkid } """
+ lines = [line.strip().split(None, 1) for line in open(utt2spk_file)]
+ return {x[0]: x[1] for x in lines}
+
+
+def load_spk2utt(spk2utt_file):
+ """ returns dictionary { spkid: list of uttids } """
+ if not os.path.exists(spk2utt_file):
+ return None
+ lines = [line.strip().split() for line in open(spk2utt_file)]
+ return {x[0]: x[1:] for x in lines}
+
+
+def load_reco2dur(reco2dur_file):
+ """ returns dictionary { recid: duration } """
+ if not os.path.exists(reco2dur_file):
+ return None
+ lines = [line.strip().split(None, 1) for line in open(reco2dur_file)]
+ return {x[0]: float(x[1]) for x in lines}
+
+
+def process_wav(wav_rxfilename, process):
+ """ This function returns preprocessed wav_rxfilename
+ Args:
+ wav_rxfilename: input
+ process: command which can be connected via pipe,
+ use stdin and stdout
+ Returns:
+ wav_rxfilename: output piped command
+ """
+ if wav_rxfilename.endswith('|'):
+ # input piped command
+ return wav_rxfilename + process + "|"
+ else:
+ # stdin "-" or normal file
+ return "cat {} | {} |".format(wav_rxfilename, process)
+
+
+def extract_segments(wavs, segments=None):
+ """ This function returns generator of segmented audio as
+ (utterance id, numpy.float32 array)
+ TODO?: sampling rate is not converted.
+ """
+ if segments is not None:
+ # segments should be sorted by rec-id
+ for seg in segments:
+ wav = wavs[seg['rec']]
+ data, samplerate = load_wav(wav)
+ st_sample = np.rint(seg['st'] * samplerate).astype(int)
+ et_sample = np.rint(seg['et'] * samplerate).astype(int)
+ yield seg['utt'], data[st_sample:et_sample]
+ else:
+ # segments file not found,
+ # wav.scp is used as segmented audio list
+ for rec in wavs:
+ data, samplerate = load_wav(wavs[rec])
+ yield rec, data
+
+
+class KaldiData:
+ def __init__(self, data_dir):
+ self.data_dir = data_dir
+ self.segments = load_segments_rechash(
+ os.path.join(self.data_dir, 'segments'))
+ self.utt2spk = load_utt2spk(
+ os.path.join(self.data_dir, 'utt2spk'))
+ self.wavs = load_wav_scp(
+ os.path.join(self.data_dir, 'wav.scp'))
+ self.reco2dur = load_reco2dur(
+ os.path.join(self.data_dir, 'reco2dur'))
+ self.spk2utt = load_spk2utt(
+ os.path.join(self.data_dir, 'spk2utt'))
+
+ def load_wav(self, recid, start=0, end=None):
+ data, rate = load_wav(
+ self.wavs[recid], start, end)
+ return data, rate
--
Gitblit v1.9.1