From 0a6ff596c6b7a4508f322a39142d549f713fc506 Mon Sep 17 00:00:00 2001
From: 志浩 <neo.dzh@alibaba-inc.com>
Date: 星期五, 24 二月 2023 11:50:42 +0800
Subject: [PATCH] sond pipeline
---
egs/mars/sd/path.sh | 5
egs/mars/sd/scripts/calculate_shapes.py | 45 +++++++
funasr/models/e2e_diar_sond.py | 10 +
egs/mars/sd/local_run.sh | 171 ++++++++++++++++++++++++++++
funasr/tasks/diar.py | 2
funasr/models/encoder/ecapa_tdnn_encoder.py | 3
egs/mars/sd/conf/SOND_ECAPATDNN_None_Dot_SAN_L4N512_FSMN_L6N512_n16k2.yaml | 121 ++++++++++++++++++++
7 files changed, 351 insertions(+), 6 deletions(-)
diff --git a/egs/mars/sd/conf/SOND_ECAPATDNN_None_Dot_SAN_L4N512_FSMN_L6N512_n16k2.yaml b/egs/mars/sd/conf/SOND_ECAPATDNN_None_Dot_SAN_L4N512_FSMN_L6N512_n16k2.yaml
new file mode 100644
index 0000000..459a741
--- /dev/null
+++ b/egs/mars/sd/conf/SOND_ECAPATDNN_None_Dot_SAN_L4N512_FSMN_L6N512_n16k2.yaml
@@ -0,0 +1,121 @@
+model: sond
+model_conf:
+ lsm_weight: 0.0
+ length_normalized_loss: true
+ max_spk_num: 16
+
+# speech encoder
+encoder: ecapa_tdnn
+encoder_conf:
+ # pass by model, equal to feature dim
+ # input_size: 80
+ pool_size: 20
+ stride: 1
+speaker_encoder: conv
+speaker_encoder_conf:
+ input_units: 256
+ num_layers: 3
+ num_units: 256
+ kernel_size: 1
+ dropout_rate: 0.0
+ position_encoder: null
+ out_units: 256
+ out_norm: false
+ auxiliary_states: false
+ tf2torch_tensor_name_prefix_torch: speaker_encoder
+ tf2torch_tensor_name_prefix_tf: EAND/speaker_encoder
+ci_scorer: dot
+ci_scorer_conf: {}
+cd_scorer: san
+cd_scorer_conf:
+ input_size: 512
+ output_size: 512
+ out_units: 1
+ attention_heads: 4
+ linear_units: 1024
+ num_blocks: 4
+ dropout_rate: 0.0
+ positional_dropout_rate: 0.0
+ attention_dropout_rate: 0.0
+ # use string "null" to remove input layer
+ input_layer: "null"
+ pos_enc_class: null
+ normalize_before: true
+ tf2torch_tensor_name_prefix_torch: cd_scorer
+ tf2torch_tensor_name_prefix_tf: EAND/compute_distance_layer
+# post net
+decoder: fsmn
+decoder_conf:
+ in_units: 32
+ out_units: 2517
+ filter_size: 31
+ fsmn_num_layers: 6
+ dnn_num_layers: 1
+ num_memory_units: 512
+ ffn_inner_dim: 512
+ dropout_rate: 0.0
+ tf2torch_tensor_name_prefix_torch: decoder
+ tf2torch_tensor_name_prefix_tf: EAND/post_net
+frontend: wav_frontend
+frontend_conf:
+ fs: 16000
+ window: povey
+ n_mels: 80
+ frame_length: 25
+ frame_shift: 10
+ filter_length_min: -1
+ filter_length_max: -1
+ lfr_m: 1
+ lfr_n: 1
+ dither: 0.0
+ snip_edges: false
+
+# minibatch related
+batch_type: length
+# 16s * 16k * 16 samples
+batch_bins: 4096000
+num_workers: 8
+
+# optimization related
+accum_grad: 1
+grad_clip: 5
+max_epoch: 50
+val_scheduler_criterion:
+ - valid
+ - acc
+best_model_criterion:
+- - valid
+ - der
+ - min
+- - valid
+ - forward_steps
+ - max
+keep_nbest_models: 10
+
+optim: adam
+optim_conf:
+ lr: 0.001
+scheduler: warmuplr
+scheduler_conf:
+ warmup_steps: 10000
+
+# without spec aug
+specaug: null
+specaug_conf:
+ apply_time_warp: true
+ time_warp_window: 5
+ time_warp_mode: bicubic
+ apply_freq_mask: true
+ freq_mask_width_range:
+ - 0
+ - 30
+ num_freq_mask: 2
+ apply_time_mask: true
+ time_mask_width_range:
+ - 0
+ - 40
+ num_time_mask: 2
+
+log_interval: 50
+# without normalize
+normalize: None
diff --git a/egs/mars/sd/local_run.sh b/egs/mars/sd/local_run.sh
new file mode 100755
index 0000000..3b319f4
--- /dev/null
+++ b/egs/mars/sd/local_run.sh
@@ -0,0 +1,171 @@
+#!/usr/bin/env bash
+
+. ./path.sh || exit 1;
+
+# machines configuration
+CUDA_VISIBLE_DEVICES="6,7"
+gpu_num=2
+count=1
+gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
+# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
+njob=5
+train_cmd=utils/run.pl
+infer_cmd=utils/run.pl
+
+# general configuration
+feats_dir="." #feature output dictionary
+exp_dir="."
+lang=zh
+dumpdir=dump/raw
+feats_type=raw
+token_type=char
+scp=wav.scp
+type=kaldi_ark
+stage=3
+stop_stage=4
+
+# feature configuration
+feats_dim=
+sample_frequency=16000
+nj=32
+speed_perturb=
+
+# exp tag
+tag="exp1"
+
+. utils/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
+
+train_set=train
+valid_set=dev
+test_sets="dev test"
+
+asr_config=conf/train_asr_conformer.yaml
+model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
+
+inference_config=conf/decode_asr_transformer.yaml
+inference_asr_model=valid.acc.ave_10best.pth
+
+# you can set gpu num for decoding here
+gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default
+ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
+
+if ${gpu_inference}; then
+ inference_nj=$[${ngpu}*${njob}]
+ _ngpu=1
+else
+ inference_nj=$njob
+ _ngpu=0
+fi
+
+feat_train_dir=${feats_dir}/${dumpdir}/train; mkdir -p ${feat_train_dir}
+feat_dev_dir=${feats_dir}/${dumpdir}/dev; mkdir -p ${feat_dev_dir}
+feat_test_dir=${feats_dir}/${dumpdir}/test; mkdir -p ${feat_test_dir}
+
+# Training Stage
+world_size=$gpu_num # run on one machine
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+ echo "stage 3: Training"
+ mkdir -p ${exp_dir}/exp/${model_dir}
+ mkdir -p ${exp_dir}/exp/${model_dir}/log
+ INIT_FILE=${exp_dir}/exp/${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])
+ asr_train.py \
+ --gpu_id $gpu_id \
+ --use_preprocessor true \
+ --token_type char \
+ --token_list $token_list \
+ --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/${scp},speech,${type} \
+ --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/text,text,text \
+ --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/speech_shape \
+ --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/text_shape.char \
+ --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/${scp},speech,${type} \
+ --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/text,text,text \
+ --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/speech_shape \
+ --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/text_shape.char \
+ --resume true \
+ --output_dir ${exp_dir}/exp/${model_dir} \
+ --config $asr_config \
+ --input_size $feats_dim \
+ --ngpu $gpu_num \
+ --num_worker_count $count \
+ --multiprocessing_distributed true \
+ --dist_init_method $init_method \
+ --dist_world_size $world_size \
+ --dist_rank $rank \
+ --local_rank $local_rank 1> ${exp_dir}/exp/${model_dir}/log/train.log.$i 2>&1
+ } &
+ done
+ wait
+fi
+
+# Testing Stage
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+ echo "stage 4: Inference"
+ for dset in ${test_sets}; do
+ asr_exp=${exp_dir}/exp/${model_dir}
+ inference_tag="$(basename "${inference_config}" .yaml)"
+ _dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
+ _logdir="${_dir}/logdir"
+ if [ -d ${_dir} ]; then
+ echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
+ exit 0
+ fi
+ mkdir -p "${_logdir}"
+ _data="${feats_dir}/${dumpdir}/${dset}"
+ key_file=${_data}/${scp}
+ num_scp_file="$(<${key_file} wc -l)"
+ _nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
+ split_scps=
+ for n in $(seq "${_nj}"); do
+ split_scps+=" ${_logdir}/keys.${n}.scp"
+ done
+ # shellcheck disable=SC2086
+ utils/split_scp.pl "${key_file}" ${split_scps}
+ _opts=
+ if [ -n "${inference_config}" ]; then
+ _opts+="--config ${inference_config} "
+ fi
+ ${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1: "${_nj}" "${_logdir}"/asr_inference.JOB.log \
+ python -m funasr.bin.asr_inference_launch \
+ --batch_size 1 \
+ --ngpu "${_ngpu}" \
+ --njob ${njob} \
+ --gpuid_list ${gpuid_list} \
+ --data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
+ --key_file "${_logdir}"/keys.JOB.scp \
+ --asr_train_config "${asr_exp}"/config.yaml \
+ --asr_model_file "${asr_exp}"/"${inference_asr_model}" \
+ --output_dir "${_logdir}"/output.JOB \
+ --mode asr \
+ ${_opts}
+
+ for f in token token_int score text; do
+ if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
+ for i in $(seq "${_nj}"); do
+ cat "${_logdir}/output.${i}/1best_recog/${f}"
+ done | sort -k1 >"${_dir}/${f}"
+ fi
+ done
+ python utils/proce_text.py ${_dir}/text ${_dir}/text.proc
+ python utils/proce_text.py ${_data}/text ${_data}/text.proc
+ python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
+ tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
+ cat ${_dir}/text.cer.txt
+ done
+fi
+
diff --git a/egs/mars/sd/path.sh b/egs/mars/sd/path.sh
new file mode 100755
index 0000000..7972642
--- /dev/null
+++ b/egs/mars/sd/path.sh
@@ -0,0 +1,5 @@
+export FUNASR_DIR=$PWD/../../..
+
+# NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8
+export PATH=$FUNASR_DIR/funasr/bin:$PATH
diff --git a/egs/mars/sd/scripts/calculate_shapes.py b/egs/mars/sd/scripts/calculate_shapes.py
new file mode 100644
index 0000000..b207f2d
--- /dev/null
+++ b/egs/mars/sd/scripts/calculate_shapes.py
@@ -0,0 +1,45 @@
+import logging
+import numpy as np
+import soundfile
+import kaldiio
+from funasr.utils.job_runner import MultiProcessRunnerV3
+from funasr.utils.misc import load_scp_as_list, load_scp_as_dict
+import os
+import argparse
+from collections import OrderedDict
+
+
+class MyRunner(MultiProcessRunnerV3):
+
+ def prepare(self, parser: argparse.ArgumentParser):
+ parser.add_argument("--input_scp", type=str, required=True)
+ parser.add_argument("--out_path")
+ args = parser.parse_args()
+
+ if not os.path.exists(os.path.dirname(args.out_path)):
+ os.makedirs(os.path.dirname(args.out_path))
+
+ task_list = load_scp_as_list(args.input_scp)
+ return task_list, None, args
+
+ def post(self, result_list, args):
+ fd = open(args.out_path, "wt", encoding="utf-8")
+ for results in result_list:
+ for uttid, shape in results:
+ fd.write("{} {}\n".format(uttid, ",".join(shape)))
+ fd.close()
+
+
+def process(task_args):
+ task_idx, task_list, _, args = task_args
+ rst = []
+ for uttid, file_path in task_list:
+ data = kaldiio.load_mat(file_path)
+ shape = [str(x) for x in data.shape]
+ rst.append((uttid, shape))
+ return rst
+
+
+if __name__ == '__main__':
+ my_runner = MyRunner(process)
+ my_runner.run()
diff --git a/funasr/models/e2e_diar_sond.py b/funasr/models/e2e_diar_sond.py
index f55bbf6..ad54723 100644
--- a/funasr/models/e2e_diar_sond.py
+++ b/funasr/models/e2e_diar_sond.py
@@ -90,6 +90,7 @@
self.int_token_arr = torch.from_numpy(np.array(self.token_list).astype(int)[np.newaxis, np.newaxis, :])
self.speaker_discrimination_loss_weight = speaker_discrimination_loss_weight
self.inter_score_loss_weight = inter_score_loss_weight
+ self.forward_steps = 0
def generate_pse_embedding(self):
embedding = np.zeros((len(self.token_list), self.max_spk_num), dtype=np.float)
@@ -123,7 +124,7 @@
"""
assert speech.shape[0] == binary_labels.shape[0], (speech.shape, binary_labels.shape)
batch_size = speech.shape[0]
-
+ self.forward_steps = self.forward_steps + 1
# 1. Network forward
pred, inter_outputs = self.prediction_forward(
speech, speech_lengths,
@@ -198,6 +199,7 @@
cf=cf,
acc=acc,
der=der,
+ forward_steps=self.forward_steps,
)
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
@@ -262,8 +264,10 @@
self,
speech: torch.Tensor,
speech_lengths: torch.Tensor,
- spk_labels: torch.Tensor = None,
- spk_labels_lengths: torch.Tensor = None,
+ profile: torch.Tensor = None,
+ profile_lengths: torch.Tensor = None,
+ binary_labels: torch.Tensor = None,
+ binary_labels_lengths: torch.Tensor = None,
) -> Dict[str, torch.Tensor]:
feats, feats_lengths = self._extract_feats(speech, speech_lengths)
return {"feats": feats, "feats_lengths": feats_lengths}
diff --git a/funasr/models/encoder/ecapa_tdnn_encoder.py b/funasr/models/encoder/ecapa_tdnn_encoder.py
index 3a75e5c..878a3c0 100644
--- a/funasr/models/encoder/ecapa_tdnn_encoder.py
+++ b/funasr/models/encoder/ecapa_tdnn_encoder.py
@@ -528,8 +528,6 @@
Arguments
---------
- device : str
- Device used, e.g., "cpu" or "cuda".
activation : torch class
A class for constructing the activation layers.
channels : list of ints
@@ -555,7 +553,6 @@
def __init__(
self,
input_size,
- device="cpu",
lin_neurons=192,
activation=torch.nn.ReLU,
channels=[512, 512, 512, 512, 1536],
diff --git a/funasr/tasks/diar.py b/funasr/tasks/diar.py
index 9a43945..7f154ef 100644
--- a/funasr/tasks/diar.py
+++ b/funasr/tasks/diar.py
@@ -24,6 +24,7 @@
from funasr.layers.label_aggregation import LabelAggregate
from funasr.models.ctc import CTC
from funasr.models.encoder.resnet34_encoder import ResNet34Diar
+from funasr.models.encoder.ecapa_tdnn_encoder import ECAPA_TDNN
from funasr.models.encoder.opennmt_encoders.conv_encoder import ConvEncoder
from funasr.models.encoder.opennmt_encoders.fsmn_encoder import FsmnEncoder
from funasr.models.encoder.opennmt_encoders.self_attention_encoder import SelfAttentionEncoder
@@ -123,6 +124,7 @@
resnet34=ResNet34Diar,
sanm_chunk_opt=SANMEncoderChunkOpt,
data2vec_encoder=Data2VecEncoder,
+ epaca_dtnn=ECAPA_TDNN,
),
type_check=AbsEncoder,
default="resnet34",
--
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