initial commit

FunASR/examples/industrial_data_pretraining/fsmn_kws/conf/fsmn_4e_l10r2_250_128_fdim80_t2599.yaml

+
+# network architecture
+model: FsmnKWS
+model_conf:
+    ctc_weight: 1.0
+
+# encoder related
+encoder: FSMN
+encoder_conf:
+    input_dim: 400
+    input_affine_dim: 140
+    fsmn_layers: 4
+    linear_dim: 250
+    proj_dim: 128
+    lorder: 10
+    rorder: 2
+    lstride: 1
+    rstride: 1
+    output_affine_dim: 140
+    output_dim: 2599
+    use_softmax: false
+
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 5
+    lfr_n: 3
+
+specaug: SpecAugLFR
+specaug_conf:
+    apply_time_warp: false
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 30
+    lfr_rate: 3
+    num_freq_mask: 1
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 12
+    num_time_mask: 1
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 100
+  keep_nbest_models: 10
+  avg_nbest_model: 10
+  avg_keep_nbest_models_type: loss
+  validate_interval: 50000
+  save_checkpoint_interval: 50000
+  avg_checkpoint_interval: 1000
+  log_interval: 50
+
+optim: adam
+optim_conf:
+   lr: 0.0005
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 10000
+
+dataset: AudioDataset
+dataset_conf:
+    index_ds: IndexDSJsonl
+    batch_sampler: EspnetStyleBatchSampler
+    batch_type: length # example or length
+    batch_size: 32000 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
+    max_token_length: 1600 # filter samples if source_token_len+target_token_len > max_token_length,
+    buffer_size: 2048
+    shuffle: true
+    num_workers: 8
+    preprocessor_speech: SpeechPreprocessSpeedPerturb
+    preprocessor_speech_conf:
+      speed_perturb: [0.9, 1.0, 1.1]
+
+tokenizer: CharTokenizer
+tokenizer_conf:
+    unk_symbol: <unk>
+    split_with_space: true
+
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: true
+    extra_linear: false
+
+normalize: null

FunASR/examples/industrial_data_pretraining/fsmn_kws/conf/fsmn_4e_l10r2_280_200_fdim40_t2602.yaml

+
+# network architecture
+model: FsmnKWS
+model_conf:
+    ctc_weight: 1.0
+
+# encoder related
+encoder: FSMN
+encoder_conf:
+    input_dim: 360
+    input_affine_dim: 280
+    fsmn_layers: 4
+    linear_dim: 280
+    proj_dim: 200
+    lorder: 10
+    rorder: 2
+    lstride: 1
+    rstride: 1
+    output_affine_dim: 400
+    output_dim: 2602
+    use_softmax: false
+
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 40
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 9
+    lfr_n: 3
+
+specaug: SpecAugLFR
+specaug_conf:
+    apply_time_warp: false
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 30
+    lfr_rate: 3
+    num_freq_mask: 1
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 12
+    num_time_mask: 1
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 100
+  keep_nbest_models: 10
+  avg_nbest_model: 10
+  avg_keep_nbest_models_type: loss
+  validate_interval: 50000
+  save_checkpoint_interval: 50000
+  avg_checkpoint_interval: 1000
+  log_interval: 50
+
+optim: adam
+optim_conf:
+   lr: 0.0005
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 10000
+
+dataset: AudioDataset
+dataset_conf:
+    index_ds: IndexDSJsonl
+    batch_sampler: EspnetStyleBatchSampler
+    batch_type: length # example or length
+    batch_size: 32000 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
+    max_token_length: 1600 # filter samples if source_token_len+target_token_len > max_token_length,
+    buffer_size: 2048
+    shuffle: true
+    num_workers: 8
+    preprocessor_speech: SpeechPreprocessSpeedPerturb
+    preprocessor_speech_conf:
+      speed_perturb: [0.9, 1.0, 1.1]
+
+tokenizer: CharTokenizer
+tokenizer_conf:
+    unk_symbol: <unk>
+    split_with_space: true
+
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: true
+    extra_linear: false
+
+normalize: null

FunASR/examples/industrial_data_pretraining/fsmn_kws/convert.py

+from __future__ import print_function
+
+import argparse
+import copy
+import logging
+import os
+from shutil import copyfile
+
+import torch
+import yaml
+from typing import Union
+
+
+from funasr.models.fsmn_kws.model import FsmnKWSConvert
+
+
+def count_parameters(model):
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        description=
+        'load and convert network to each other between kaldi/pytorch format')
+    parser.add_argument('--config', required=True, help='config file')
+    parser.add_argument(
+        '--network_file',
+        default='',
+        required=True,
+        help='input network, support kaldi.txt/pytorch.pt')
+    parser.add_argument('--model_dir', required=True, help='save model dir')
+    parser.add_argument('--model_name', required=True, help='save model name')
+    parser.add_argument('--convert_to',
+                        default='kaldi',
+                        required=True,
+                        help='target network type, kaldi/pytorch')
+
+    args = parser.parse_args()
+    return args
+
+
+def convert_to_kaldi(
+    configs,
+    network_file,
+    model_dir,
+    model_name="convert.kaldi.txt"
+):
+    copyfile(network_file, os.path.join(model_dir, 'origin.torch.pt'))
+
+    model = FsmnKWSConvert(
+            vocab_size=configs['encoder_conf']['output_dim'],
+            encoder='FSMNConvert',
+            encoder_conf=configs['encoder_conf'],
+            ctc_conf=configs['ctc_conf'],
+    )
+    print(model)
+    num_params = count_parameters(model)
+    print('the number of model params: {}'.format(num_params))
+
+    states= torch.load(network_file, map_location='cpu')
+    model.load_state_dict(states["state_dict"])
+
+    kaldi_text = os.path.join(model_dir, model_name)
+    with open(kaldi_text, 'w', encoding='utf8') as fout:
+        nnet_desp = model.to_kaldi_net()
+        fout.write(nnet_desp)
+    fout.close()
+
+
+def convert_to_pytorch(
+    configs,
+    network_file,
+    model_dir,
+    model_name="convert.torch.pt"
+):
+    model = FsmnKWSConvert(
+            vocab_size=configs['encoder_conf']['output_dim'],
+            frontend=None,
+            specaug=None,
+            normalize=None,
+            encoder='FSMNConvert',
+            encoder_conf=configs['encoder_conf'],
+            ctc_conf=configs['ctc_conf'],
+    )
+
+    num_params = count_parameters(model)
+    print('the number of model params: {}'.format(num_params))
+
+    copyfile(network_file, os.path.join(model_dir, 'origin.kaldi.txt'))
+    model.to_pytorch_net(network_file)
+
+    save_model_path = os.path.join(model_dir, model_name)
+    torch.save({"model": model.state_dict()}, save_model_path)
+
+    print('convert torch format back to kaldi')
+    kaldi_text = os.path.join(model_dir, 'convert.kaldi.txt')
+    with open(kaldi_text, 'w', encoding='utf8') as fout:
+        nnet_desp = model.to_kaldi_net()
+        fout.write(nnet_desp)
+    fout.close()
+
+    print('Done!')
+
+
+def main():
+    args = get_args()
+    logging.basicConfig(level=logging.DEBUG,
+                        format='%(asctime)s %(levelname)s %(message)s')
+    print(args)
+    with open(args.config, 'r') as fin:
+        configs = yaml.load(fin, Loader=yaml.FullLoader)
+
+    if args.convert_to == 'pytorch':
+        print('convert kaldi net to pytorch...')
+        convert_to_pytorch(
+            configs,
+            args.network_file,
+            args.model_dir,
+            args.model_name
+        )
+    elif args.convert_to == 'kaldi':
+        print('convert pytorch net to kaldi...')
+        convert_to_kaldi(
+            configs,
+            args.network_file,
+            args.model_dir,
+            args.model_name
+        )
+    else:
+        print('unsupported target network type: {}'.format(args.convert_to))
+
+
+if __name__ == '__main__':
+    main()

FunASR/examples/industrial_data_pretraining/fsmn_kws/convert.sh

+
+workspace=`pwd`
+
+# download model
+local_path_root=${workspace}/modelscope_models_kws
+mkdir -p ${local_path_root}
+
+local_path=${local_path_root}/speech_charctc_kws_phone-xiaoyun
+if [ ! -d "$local_path" ]; then
+    git clone https://www.modelscope.cn/iic/speech_charctc_kws_phone-xiaoyun.git ${local_path}
+fi
+
+export PATH=${local_path}/runtime:$PATH
+export LD_LIBRARY_PATH=${local_path}/runtime:$LD_LIBRARY_PATH
+
+config=./conf/fsmn_4e_l10r2_250_128_fdim80_t2599.yaml
+torch_nnet=exp/finetune_outputs/model.pt.avg10
+out_dir=exp/finetune_outputs
+
+if [ ! -d "$out_dir" ]; then
+    mkdir -p $out_dir
+fi
+
+python convert.py --config $config --network_file $torch_nnet --model_dir $out_dir --model_name "convert.kaldi.txt" --convert_to kaldi
+
+nnet-copy --binary=true ${out_dir}/convert.kaldi.txt ${out_dir}/convert.kaldi.net

FunASR/examples/industrial_data_pretraining/fsmn_kws/demo.py

+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+from funasr import AutoModel
+
+model = AutoModel(
+    model="iic/speech_charctc_kws_phone-xiaoyun",
+    keywords="小云小云",
+    output_dir="./outputs/debug",
+    device='cpu'
+)
+
+test_wav = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/KWS/pos_testset/kws_xiaoyunxiaoyun.wav"
+
+res = model.generate(input=test_wav, cache={},)
+print(res)

FunASR/examples/industrial_data_pretraining/fsmn_kws/finetune.sh

+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+#!/usr/bin/env bash
+
+# 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
+
+. ./path.sh
+workspace=`pwd`
+
+CUDA_VISIBLE_DEVICES="0,1"
+
+stage=2
+stop_stage=3
+
+inference_device="cuda" #"cpu"
+inference_checkpoint="model.pt.avg10"
+inference_scp="wav.scp"
+inference_batch_size=32
+nj=32
+test_sets="test"
+
+# model_name from model_hub, or model_dir in local path
+
+## option 1, download model automatically, unsupported currently
+model_name_or_model_dir="iic/speech_charctc_kws_phone-xiaoyun"
+
+## option 2, download model by git
+local_path_root=${workspace}/modelscope_models
+model_name_or_model_dir=${local_path_root}/${model_name_or_model_dir}
+if [ ! -d $model_name_or_model_dir ]; then
+  mkdir -p ${model_name_or_model_dir}
+  git clone https://www.modelscope.cn/iic/speech_charctc_kws_phone-xiaoyun.git ${model_name_or_model_dir}
+fi
+
+config=fsmn_4e_l10r2_250_128_fdim80_t2599.yaml
+token_list=${model_name_or_model_dir}/funasr/tokens_2599.txt
+lexicon_list=${model_name_or_model_dir}/funasr/lexicon.txt
+cmvn_file=${model_name_or_model_dir}/funasr/am.mvn.dim80_l2r2
+init_param="${model_name_or_model_dir}/funasr/basetrain_fsmn_4e_l10r2_250_128_fdim80_t2599.pt"
+
+
+# data prepare
+# data dir, which contains: train.json, val.json
+data_dir=../../data
+
+train_data="${data_dir}/train.jsonl"
+val_data="${data_dir}/val.jsonl"
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+  echo "stage 1: Generate audio json list"
+  # generate train.jsonl and val.jsonl from wav.scp and text.txt
+  python $FUNASR_DIR/funasr/datasets/audio_datasets/scp2jsonl.py \
+  ++scp_file_list='['''${data_dir}/train_wav.scp''', '''${data_dir}/train_text.txt''']' \
+  ++data_type_list='["source", "target"]' \
+  ++jsonl_file_out="${train_data}"
+
+  python $FUNASR_DIR/funasr/datasets/audio_datasets/scp2jsonl.py \
+  ++scp_file_list='['''${data_dir}/val_wav.scp''', '''${data_dir}/val_text.txt''']' \
+  ++data_type_list='["source", "target"]' \
+  ++jsonl_file_out="${val_data}"
+fi
+
+# exp output dir
+output_dir="${workspace}/exp/finetune_outputs"
+
+# Training Stage
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+  echo "stage 2: KWS Training"
+
+  mkdir -p ${output_dir}
+  current_time=$(date "+%Y-%m-%d_%H-%M")
+  log_file="${output_dir}/train.log.txt.${current_time}"
+  echo "log_file: ${log_file}"
+	echo "finetune use basetrain model: ${init_param}"
+
+  export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES
+  gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+  torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
+  ../../../funasr/bin/train.py \
+  --config-path "${workspace}/conf" \
+  --config-name "${config}" \
+  ++init_param="${init_param}" \
+  ++disable_update=true \
+  ++train_data_set_list="${train_data}" \
+  ++valid_data_set_list="${val_data}" \
+  ++tokenizer_conf.token_list="${token_list}" \
+  ++tokenizer_conf.seg_dict="${lexicon_list}" \
+  ++frontend_conf.cmvn_file="${cmvn_file}" \
+  ++output_dir="${output_dir}" &> ${log_file}
+fi
+
+
+# Testing Stage
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+  echo "stage 3: Inference"
+  keywords=(小云小云)
+  keywords_string=$(IFS=,; echo "${keywords[*]}")
+  echo "keywords: $keywords_string"
+
+  if [ ${inference_device} == "cuda" ]; then
+      nj=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+  else
+      inference_batch_size=1
+      CUDA_VISIBLE_DEVICES=""
+      for JOB in $(seq ${nj}); do
+          CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"-1,"
+      done
+  fi
+
+  for dset in ${test_sets}; do
+    inference_dir="${output_dir}/inference-${inference_checkpoint}/${dset}"
+    _logdir="${inference_dir}/logdir"
+    echo "inference_dir: ${inference_dir}"
+
+    mkdir -p "${_logdir}"
+    test_data_dir="${data_dir}/${dset}"
+    key_file=${test_data_dir}/${inference_scp}
+
+    split_scps=
+    for JOB in $(seq "${nj}"); do
+        split_scps+=" ${_logdir}/keys.${JOB}.scp"
+    done
+    $FUNASR_DIR/examples/aishell/paraformer/utils/split_scp.pl "${key_file}" ${split_scps}
+
+    gpuid_list_array=(${CUDA_VISIBLE_DEVICES//,/ })
+    for JOB in $(seq ${nj}); do
+        {
+          id=$((JOB-1))
+          gpuid=${gpuid_list_array[$id]}
+
+          echo "${output_dir}"
+
+          export CUDA_VISIBLE_DEVICES=${gpuid}
+          python ../../../funasr/bin/inference.py \
+          --config-path="${output_dir}" \
+          --config-name="config.yaml" \
+          ++init_param="${output_dir}/${inference_checkpoint}" \
+          ++tokenizer_conf.token_list="${token_list}" \
+          ++tokenizer_conf.seg_dict="${lexicon_list}" \
+          ++frontend_conf.cmvn_file="${cmvn_file}" \
+          ++keywords="\"$keywords_string"\" \
+          ++input="${_logdir}/keys.${JOB}.scp" \
+          ++output_dir="${inference_dir}/${JOB}" \
+          ++device="${inference_device}" \
+          ++ncpu=1 \
+          ++disable_log=true \
+          ++batch_size="${inference_batch_size}" &> ${_logdir}/log.${JOB}.txt
+        }&
+
+    done
+    wait
+
+    for f in detect; do
+        if [ -f "${inference_dir}/${JOB}/${f}" ]; then
+          for JOB in $(seq "${nj}"); do
+              cat "${inference_dir}/${JOB}/${f}"
+          done | sort -k1 >"${inference_dir}/${f}"
+        fi
+    done
+
+    python funasr/utils/compute_det_ctc.py \
+        --keywords ${keywords_string} \
+        --test_data ${test_data_dir}/wav.scp \
+        --trans_data ${test_data_dir}/text \
+        --score_file ${inference_dir}/detect \
+        --stats_dir ${inference_dir}
+  done
+
+fi

FunASR/examples/industrial_data_pretraining/fsmn_kws/funasr

+../../../funasr
\ No newline at end of file

FunASR/examples/industrial_data_pretraining/fsmn_kws/infer.sh

+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+# method1, inference from model hub
+
+model="iic/speech_charctc_kws_phone-xiaoyun"
+
+# for more input type, please ref to readme.md
+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/KWS/pos_testset/kws_xiaoyunxiaoyun.wav"
+
+keywords=(小云小云)
+keywords_string=$(IFS=,; echo "${keywords[*]}")
+echo "keywords: $keywords_string"
+
+python funasr/bin/inference.py \
++model=${model} \
++input=${input} \
++output_dir="./outputs/debug" \
++device="cpu" \
+++keywords="\"$keywords_string"\"

FunASR/examples/industrial_data_pretraining/fsmn_kws/infer_from_local.sh

+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+# method2, inference from local model
+
+# for more input type, please ref to readme.md
+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/KWS/pos_testset/kws_xiaoyunxiaoyun.wav"
+
+output_dir="./outputs/debug"
+
+workspace=`pwd`
+
+# download model
+local_path_root=${workspace}/modelscope_models
+mkdir -p ${local_path_root}
+local_path=${local_path_root}/speech_charctc_kws_phone-xiaoyun
+git clone https://www.modelscope.cn/iic/speech_charctc_kws_phone-xiaoyun.git ${local_path}
+
+device="cuda:0" # "cuda:0" for gpu0, "cuda:1" for gpu1, "cpu"
+
+config="inference_fsmn_4e_l10r2_250_128_fdim80_t2599.yaml"
+tokens="${local_path}/funasr/tokens_2599.txt"
+seg_dict="${local_path}/funasr/lexicon.txt"
+init_param="${local_path}/funasr/finetune_fsmn_4e_l10r2_250_128_fdim80_t2599_xiaoyun_xiaoyun.pt"
+cmvn_file="${local_path}/funasr/am.mvn.dim80_l2r2"
+
+keywords=(小云小云)
+keywords_string=$(IFS=,; echo "${keywords[*]}")
+echo "keywords: $keywords_string"
+
+python -m funasr.bin.inference \
+--config-path "${local_path}/funasr" \
+--config-name "${config}" \
+++init_param="${init_param}" \
+++frontend_conf.cmvn_file="${cmvn_file}" \
+++tokenizer_conf.token_list="${tokens}" \
+++tokenizer_conf.seg_dict="${seg_dict}" \
+++input="${input}" \
+++output_dir="${output_dir}" \
+++device="${device}" \
+++keywords="\"$keywords_string"\"

FunASR/examples/industrial_data_pretraining/fsmn_kws/path.sh

+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

FunASR/examples/industrial_data_pretraining/fsmn_kws_mt/conf/fsmn_4e_l10r2_250_128_fdim80_t2599_t4.yaml

+
+# network architecture
+model: FsmnKWSMT
+model_conf:
+    ctc_weight: 1.0
+
+# encoder related
+encoder: FSMNMT
+encoder_conf:
+    input_dim: 400
+    input_affine_dim: 140
+    fsmn_layers: 4
+    linear_dim: 250
+    proj_dim: 128
+    lorder: 10
+    rorder: 2
+    lstride: 1
+    rstride: 1
+    output_affine_dim: 140
+    output_dim: 2599
+    output_dim2: 4
+    use_softmax: false
+
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 5
+    lfr_n: 3
+
+specaug: SpecAugLFR
+specaug_conf:
+    apply_time_warp: false
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 30
+    lfr_rate: 3
+    num_freq_mask: 1
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 12
+    num_time_mask: 1
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 100
+  keep_nbest_models: 100
+  avg_nbest_model: 10
+  avg_keep_nbest_models_type: loss
+  log_interval: 50
+
+optim: adam
+optim_conf:
+   lr: 0.001
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 10000
+
+dataset: KwsMTDataset
+dataset_conf:
+    index_ds: IndexDSJsonl
+    batch_sampler: EspnetStyleBatchSampler
+    batch_type: length # example or length
+    batch_size: 64000 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
+    max_token_length: 1600 # filter samples if source_token_len+target_token_len > max_token_length,
+    buffer_size: 2048
+    shuffle: true
+    num_workers: 8
+    preprocessor_speech: SpeechPreprocessSpeedPerturb
+    preprocessor_speech_conf:
+      speed_perturb: [0.9, 1.0, 1.1]
+    dataloader: DataloaderMapStyle
+
+tokenizer:
+    - CharTokenizer
+    - CharTokenizer
+
+tokenizer_conf:
+    - unk_symbol: <unk>
+      split_with_space: true
+      token_list: null
+      seg_dict: null
+    - unk_symbol: <unk>
+      split_with_space: true
+      token_list: null
+      seg_dict: null
+
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin  # ctc_type: focalctc, builtin
+    reduce: true
+    ignore_nan_grad: true
+    extra_linear: false
+
+normalize: null

FunASR/examples/industrial_data_pretraining/fsmn_kws_mt/conf/fsmn_4e_l10r2_280_200_fdim40_t2602_t4.yaml

+
+# network architecture
+model: FsmnKWSMT
+model_conf:
+    ctc_weight: 1.0
+
+# encoder related
+encoder: FSMNMT
+encoder_conf:
+    input_dim: 360
+    input_affine_dim: 280
+    fsmn_layers: 4
+    linear_dim: 280
+    proj_dim: 200
+    lorder: 10
+    rorder: 2
+    lstride: 1
+    rstride: 1
+    output_affine_dim: 400
+    output_dim: 2602
+    output_dim2: 4
+    use_softmax: false
+
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 40
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 9
+    lfr_n: 3
+
+specaug: SpecAugLFR
+specaug_conf:
+    apply_time_warp: false
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 30
+    lfr_rate: 3
+    num_freq_mask: 1
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 12
+    num_time_mask: 1
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 100
+  keep_nbest_models: 100
+  avg_nbest_model: 10
+  avg_keep_nbest_models_type: loss
+  log_interval: 50
+
+optim: adam
+optim_conf:
+   lr: 0.001
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 10000
+
+dataset: KwsMTDataset
+dataset_conf:
+    index_ds: IndexDSJsonl
+    batch_sampler: EspnetStyleBatchSampler
+    batch_type: length # example or length
+    batch_size: 64000 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
+    max_token_length: 1600 # filter samples if source_token_len+target_token_len > max_token_length,
+    buffer_size: 2048
+    shuffle: true
+    num_workers: 8
+    preprocessor_speech: SpeechPreprocessSpeedPerturb
+    preprocessor_speech_conf:
+      speed_perturb: [0.9, 1.0, 1.1]
+    dataloader: DataloaderMapStyle
+
+tokenizer:
+    - CharTokenizer
+    - CharTokenizer
+
+tokenizer_conf:
+    - unk_symbol: <unk>
+      split_with_space: true
+      token_list: null
+      seg_dict: null
+    - unk_symbol: <unk>
+      split_with_space: true
+      token_list: null
+      seg_dict: null
+
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin  # ctc_type: focalctc, builtin
+    reduce: true
+    ignore_nan_grad: true
+    extra_linear: false
+
+normalize: null

FunASR/examples/industrial_data_pretraining/fsmn_kws_mt/convert.py

+from __future__ import print_function
+
+import argparse
+import copy
+import logging
+import os
+from shutil import copyfile
+
+import torch
+import yaml
+from typing import Union
+from funasr.models.fsmn_kws_mt.encoder import FSMNMTConvert
+from funasr.models.fsmn_kws_mt.model import FsmnKWSMTConvert
+
+
+def count_parameters(model):
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        description=
+        'load and convert network to each other between kaldi/pytorch format')
+    parser.add_argument('--config', required=True, help='config file')
+    parser.add_argument(
+        '--network_file',
+        default='',
+        required=True,
+        help='input network, support kaldi.txt/pytorch.pt')
+    parser.add_argument('--model_dir', required=True, help='save model dir')
+    parser.add_argument('--model_name', required=True, help='save model name')
+    parser.add_argument('--model_name2', required=True, help='save model name')
+    parser.add_argument('--convert_to',
+                        default='kaldi',
+                        required=True,
+                        help='target network type, kaldi/pytorch')
+
+    args = parser.parse_args()
+    return args
+
+
+def convert_to_kaldi(
+    configs,
+    network_file,
+    model_dir,
+    model_name="convert.kaldi.txt",
+    model_name2="convert.kaldi2.txt"
+):
+    copyfile(network_file, os.path.join(model_dir, 'origin.torch.pt'))
+
+    model = FsmnKWSMTConvert(
+        encoder='FSMNMTConvert',
+        encoder_conf=configs['encoder_conf'],
+        ctc_conf=configs['ctc_conf'],
+    )
+    print(model)
+    num_params = count_parameters(model)
+    print('the number of model params: {}'.format(num_params))
+
+    states= torch.load(network_file, map_location='cpu')
+    model.load_state_dict(states["state_dict"])
+
+    kaldi_text = os.path.join(model_dir, model_name)
+    with open(kaldi_text, 'w', encoding='utf8') as fout:
+        nnet_desp = model.to_kaldi_net()
+        fout.write(nnet_desp)
+    fout.close()
+
+    kaldi_text2 = os.path.join(model_dir, model_name2)
+    with open(kaldi_text2, 'w', encoding='utf8') as fout:
+        nnet_desp2 = model.to_kaldi_net2()
+        fout.write(nnet_desp2)
+    fout.close()
+
+
+def convert_to_pytorch(
+    configs,
+    network_file,
+    model_dir,
+    model_name="convert.torch.pt"
+):
+    model = FsmnKWSMTConvert(
+        encoder='FSMNMTConvert',
+        encoder_conf=configs['encoder_conf'],
+        ctc_conf=configs['ctc_conf'],
+    )
+
+    num_params = count_parameters(model)
+    print('the number of model params: {}'.format(num_params))
+
+    copyfile(network_file, os.path.join(model_dir, 'origin.kaldi.txt'))
+    model.to_pytorch_net(network_file)
+
+    save_model_path = os.path.join(model_dir, model_name)
+    torch.save({"model": model.state_dict()}, save_model_path)
+
+    print('convert torch format back to kaldi')
+    kaldi_text = os.path.join(model_dir, 'convert.kaldi.txt')
+    with open(kaldi_text, 'w', encoding='utf8') as fout:
+        nnet_desp = model.to_kaldi_net()
+        fout.write(nnet_desp)
+    fout.close()
+
+    print('Done!')
+
+
+def main():
+    args = get_args()
+    logging.basicConfig(level=logging.DEBUG,
+                        format='%(asctime)s %(levelname)s %(message)s')
+    print(args)
+    with open(args.config, 'r') as fin:
+        configs = yaml.load(fin, Loader=yaml.FullLoader)
+
+    if args.convert_to == 'pytorch':
+        print('convert kaldi net to pytorch...')
+        convert_to_pytorch(
+            configs,
+            args.network_file,
+            args.model_dir,
+            args.model_name,
+            args.model_name2,
+        )
+    elif args.convert_to == 'kaldi':
+        print('convert pytorch net to kaldi...')
+        convert_to_kaldi(
+            configs,
+            args.network_file,
+            args.model_dir,
+            args.model_name
+        )
+    else:
+        print('unsupported target network type: {}'.format(args.convert_to))
+
+
+if __name__ == '__main__':
+    main()

FunASR/examples/industrial_data_pretraining/fsmn_kws_mt/convert.sh

+
+workspace=`pwd`
+
+# download model
+local_path_root=${workspace}/modelscope_models
+mkdir -p ${local_path_root}
+
+local_path=${local_path_root}/speech_charctc_kws_phone-xiaoyun_mt
+if [ ! -d "$local_path" ]; then
+    git clone https://www.modelscope.cn/iic/speech_charctc_kws_phone-xiaoyun_mt.git ${local_path}
+fi
+
+export PATH=${local_path}/runtime:$PATH
+export LD_LIBRARY_PATH=${local_path}/runtime:$LD_LIBRARY_PATH
+
+# finetune config file
+config=./conf/fsmn_4e_l10r2_250_128_fdim80_t2599_t4.yaml
+
+# finetune output checkpoint
+torch_nnet=exp/finetune_outputs/model.pt.avg10
+
+out_dir=exp/finetune_outputs
+
+if [ ! -d "$out_dir" ]; then
+    mkdir -p $out_dir
+fi
+
+python convert.py --config $config \
+	--network_file $torch_nnet \
+	--model_dir $out_dir \
+	--model_name "convert.kaldi.txt" \
+	--model_name2 "convert.kaldi2.txt" \
+	--convert_to kaldi
+
+nnet-copy --binary=true ${out_dir}/convert.kaldi.txt ${out_dir}/convert.kaldi.net
+nnet-copy --binary=true ${out_dir}/convert.kaldi2.txt ${out_dir}/convert.kaldi2.net

FunASR/examples/industrial_data_pretraining/fsmn_kws_mt/demo.py

+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+from funasr import AutoModel
+
+model = AutoModel(
+    model="iic/speech_charctc_kws_phone-xiaoyun_mt",
+    keywords="小云小云",
+    output_dir="./outputs/debug",
+    device='cpu'
+)
+
+test_wav = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/KWS/pos_testset/kws_xiaoyunxiaoyun.wav"
+
+res = model.generate(input=test_wav, cache={},)
+print(res)

FunASR/examples/industrial_data_pretraining/fsmn_kws_mt/finetune.sh

+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+#!/usr/bin/env bash
+
+# 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
+
+. ./path.sh
+workspace=`pwd`
+
+CUDA_VISIBLE_DEVICES="0,1"
+
+stage=2
+stop_stage=3
+
+inference_device="cuda" #"cpu"
+inference_checkpoint="model.pt.avg10"
+inference_scp="wav.scp"
+inference_batch_size=32
+nj=32
+test_sets="test"
+
+# model_name from model_hub, or model_dir in local path
+
+## option 1, download model automatically, unsupported currently
+model_name_or_model_dir="iic/speech_charctc_kws_phone-xiaoyun_mt"
+
+## option 2, download model by git
+local_path_root=${workspace}/modelscope_models
+model_name_or_model_dir=${local_path_root}/${model_name_or_model_dir}
+if [ ! -d $model_name_or_model_dir ]; then
+  mkdir -p ${model_name_or_model_dir}
+  git clone https://www.modelscope.cn/iic/speech_charctc_kws_phone-xiaoyun_mt.git ${model_name_or_model_dir}
+fi
+
+config=fsmn_4e_l10r2_250_128_fdim80_t2599_t4.yaml
+token_list=${model_name_or_model_dir}/funasr/tokens_2599.txt
+token_list2=${model_name_or_model_dir}/funasr/tokens_xiaoyun.txt
+lexicon_list=${model_name_or_model_dir}/funasr/lexicon.txt
+cmvn_file=${model_name_or_model_dir}/funasr/am.mvn.dim80_l2r2
+init_param="${model_name_or_model_dir}/funasr/basetrain_fsmn_4e_l10r2_250_128_fdim80_t2599.pt"
+
+
+# data prepare
+# data dir, which contains: train.json, val.json
+data_dir=../../data
+
+train_data="${data_dir}/train.jsonl"
+val_data="${data_dir}/val.jsonl"
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+  echo "stage 1: Generate audio json list"
+  # generate train.jsonl and val.jsonl from wav.scp and text.txt
+  python $FUNASR_DIR/funasr/datasets/audio_datasets/scp2jsonl.py \
+  ++scp_file_list='['''${data_dir}/train_wav.scp''', '''${data_dir}/train_text.txt''']' \
+  ++data_type_list='["source", "target"]' \
+  ++jsonl_file_out="${train_data}"
+
+  python $FUNASR_DIR/funasr/datasets/audio_datasets/scp2jsonl.py \
+  ++scp_file_list='['''${data_dir}/val_wav.scp''', '''${data_dir}/val_text.txt''']' \
+  ++data_type_list='["source", "target"]' \
+  ++jsonl_file_out="${val_data}"
+fi
+
+# exp output dir
+output_dir="${workspace}/exp/finetune_outputs"
+
+
+# Training Stage
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+  echo "stage 2: KWS Training"
+
+  mkdir -p ${output_dir}
+  current_time=$(date "+%Y-%m-%d_%H-%M")
+  log_file="${output_dir}/train.log.txt.${current_time}"
+  echo "log_file: ${log_file}"
+  echo "finetune use basetrain model: ${init_param}"
+
+  export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES
+  gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+  torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
+  ../../../funasr/bin/train.py \
+  --config-path "${workspace}/conf" \
+  --config-name "${config}" \
+  ++init_param="${init_param}" \
+  ++token_lists='['''${token_list}''', '''${token_list2}''']' \
+  ++seg_dicts='['''${lexicon_list}''', '''${lexicon_list}''']' \
+  ++disable_update=true \
+  ++train_data_set_list="${train_data}" \
+  ++valid_data_set_list="${val_data}" \
+  ++frontend_conf.cmvn_file="${cmvn_file}" \
+  ++output_dir="${output_dir}" &> ${log_file}
+fi
+
+
+# Testing Stage
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+  echo "stage 3: Inference"
+  keywords=(小云小云)
+  keywords_string=$(IFS=,; echo "${keywords[*]}")
+  echo "keywords: $keywords_string"
+
+  if [ ${inference_device} == "cuda" ]; then
+      nj=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+  else
+      inference_batch_size=1
+      CUDA_VISIBLE_DEVICES=""
+      for JOB in $(seq ${nj}); do
+          CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"-1,"
+      done
+  fi
+
+  for dset in ${test_sets}; do
+    inference_dir="${output_dir}/inference-${inference_checkpoint}/${dset}"
+    _logdir="${inference_dir}/logdir"
+    echo "inference_dir: ${inference_dir}"
+
+    mkdir -p "${_logdir}"
+    test_data_dir="${data_dir}/${dset}"
+    key_file=${test_data_dir}/${inference_scp}
+
+    split_scps=
+    for JOB in $(seq "${nj}"); do
+        split_scps+=" ${_logdir}/keys.${JOB}.scp"
+    done
+    $FUNASR_DIR/examples/aishell/paraformer/utils/split_scp.pl "${key_file}" ${split_scps}
+
+    gpuid_list_array=(${CUDA_VISIBLE_DEVICES//,/ })
+    for JOB in $(seq ${nj}); do
+        {
+          id=$((JOB-1))
+          gpuid=${gpuid_list_array[$id]}
+
+          echo "${output_dir}"
+
+          export CUDA_VISIBLE_DEVICES=${gpuid}
+          python ../../../funasr/bin/inference.py \
+          --config-path="${output_dir}" \
+          --config-name="config.yaml" \
+          ++init_param="${output_dir}/${inference_checkpoint}" \
+          ++token_lists='['''${token_list}''', '''${token_list2}''']' \
+          ++seg_dicts='['''${lexicon_list}''', '''${lexicon_list}''']' \
+          ++frontend_conf.cmvn_file="${cmvn_file}" \
+          ++keywords="\"$keywords_string"\" \
+          ++input="${_logdir}/keys.${JOB}.scp" \
+          ++output_dir="${inference_dir}/${JOB}" \
+          ++device="${inference_device}" \
+          ++ncpu=1 \
+          ++disable_log=true \
+          ++batch_size="${inference_batch_size}" &> ${_logdir}/log.${JOB}.txt
+        }&
+
+    done
+    wait
+
+    for f in detect detect2; do
+        if [ -f "${inference_dir}/${JOB}/${f}" ]; then
+          for JOB in $(seq "${nj}"); do
+              cat "${inference_dir}/${JOB}/${f}"
+          done | sort -k1 >"${inference_dir}/${f}"
+        fi
+    done
+
+    mkdir -p ${inference_dir}/task1
+    python funasr/utils/compute_det_ctc.py \
+        --keywords ${keywords_string} \
+        --test_data ${test_data_dir}/wav.scp \
+        --trans_data ${test_data_dir}/text \
+        --score_file ${inference_dir}/detect \
+        --stats_dir ${inference_dir}/task1
+
+    mkdir -p ${inference_dir}/task2
+    python funasr/utils/compute_det_ctc.py \
+        --keywords ${keywords_string} \
+        --test_data ${test_data_dir}/wav.scp \
+        --trans_data ${test_data_dir}/text \
+        --score_file ${inference_dir}/detect2 \
+        --stats_dir ${inference_dir}/task2
+  done
+
+fi

FunASR/examples/industrial_data_pretraining/fsmn_kws_mt/funasr

+../../../funasr
\ No newline at end of file

FunASR/examples/industrial_data_pretraining/fsmn_kws_mt/infer.sh

+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+# method1, inference from model hub
+
+model="iic/speech_charctc_kws_phone-xiaoyun_mt"
+
+# for more input type, please ref to readme.md
+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/KWS/pos_testset/kws_xiaoyunxiaoyun.wav"
+
+keywords=(小云小云)
+keywords_string=$(IFS=,; echo "${keywords[*]}")
+echo "keywords: $keywords_string"
+
+python funasr/bin/inference.py \
++model=${model} \
++input=${input} \
++output_dir="./outputs/debug" \
++device="cpu" \
+++keywords="\"$keywords_string"\"

FunASR/examples/industrial_data_pretraining/fsmn_kws_mt/infer_from_local.sh

+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+# method2, inference from local model
+
+# for more input type, please ref to readme.md
+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/KWS/pos_testset/kws_xiaoyunxiaoyun.wav"
+
+output_dir="./outputs/debug"
+
+workspace=`pwd`
+
+# download model
+local_path_root=${workspace}/modelscope_models
+mkdir -p ${local_path_root}
+local_path=${local_path_root}/speech_charctc_kws_phone-xiaoyun_mt
+git clone https://www.modelscope.cn/iic/speech_charctc_kws_phone-xiaoyun_mt.git ${local_path}
+
+device="cuda:0" # "cuda:0" for gpu0, "cuda:1" for gpu1, "cpu"
+
+config="inference_fsmn_4e_l10r2_280_200_fdim40_t2602_t4.yaml"
+tokens="${local_path}/funasr/tokens_2602.txt"
+tokens2="${local_path}/funasr/tokens_xiaoyun.txt"
+seg_dict="${local_path}/funasr/lexicon.txt"
+init_param="${local_path}/funasr/finetune_fsmn_4e_l10r2_280_200_fdim40_t2602_t4_xiaoyun_xiaoyun.pt"
+cmvn_file="${local_path}/funasr/am.mvn.dim40_l4r4"
+
+keywords=(小云小云)
+keywords_string=$(IFS=,; echo "${keywords[*]}")
+echo "keywords: $keywords_string"
+
+python -m funasr.bin.inference \
+--config-path "${local_path}/funasr" \
+--config-name "${config}" \
+++init_param="${init_param}" \
+++frontend_conf.cmvn_file="${cmvn_file}" \
+++token_lists='['''${tokens}''', '''${tokens2}''']' \
+++seg_dicts='['''${seg_dict}''', '''${seg_dict}''']' \
+++input="${input}" \
+++output_dir="${output_dir}" \
+++device="${device}" \
+++keywords="\"$keywords_string"\"

FunASR/examples/industrial_data_pretraining/fsmn_kws_mt/path.sh

+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