initial commit

FunASR/examples/industrial_data_pretraining/paraformer_streaming/demo.sh

+
+model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"
+
+
+python funasr/bin/inference.py \
++model=${model} \
++input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
++output_dir="./outputs/debug" \
++device="cpu" \
+

FunASR/examples/industrial_data_pretraining/paraformer_streaming/export.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)
+
+
+# method1, inference from model hub
+
+
+from funasr import AutoModel
+
+model = AutoModel(
+    model="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online",
+)
+
+res = model.export(type="onnx", quantize=False)
+print(res)
+
+
+# # method2, inference from local path
+# from funasr import AutoModel
+#
+#
+# model = AutoModel(
+#     model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online"
+# )
+#
+# res = model.export(type="onnx", quantize=False)
+# print(res)

FunASR/examples/industrial_data_pretraining/paraformer_streaming/export.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
+export HYDRA_FULL_ERROR=1
+
+model="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online"
+
+python -m funasr.bin.export \
+++model=${model} \
+++type="onnx" \
+++quantize=false \
+++device="cpu"
+
+
+## method2, inference from local path
+#model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online"
+#
+#python -m funasr.bin.export \
+#++model=${model} \
+#++type="onnx" \
+#++quantize=false \
+#++device="cpu" \
+#++debug=false

FunASR/examples/industrial_data_pretraining/paraformer_streaming/finetune.sh

+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+workspace=`pwd`
+
+# which gpu to train or finetune
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+# model_name from model_hub, or model_dir in local path
+
+## option 1, download model automatically
+model_name_or_model_dir="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online"
+
+
+## option 2, download model by git
+#local_path_root=${workspace}/modelscope_models
+#mkdir -p ${local_path_root}/${model_name_or_model_dir}
+#git clone https://www.modelscope.cn/${model_name_or_model_dir}.git ${local_path_root}/${model_name_or_model_dir}
+#model_name_or_model_dir=${local_path_root}/${model_name_or_model_dir}
+
+
+# data dir, which contains: train.json, val.json
+data_dir="../../../data/list"
+
+train_data="${data_dir}/train.jsonl"
+val_data="${data_dir}/val.jsonl"
+
+# generate train.jsonl and val.jsonl from wav.scp and text.txt
+scp2jsonl \
+++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt"]' \
+++data_type_list='["source", "target"]' \
+++jsonl_file_out="${train_data}"
+
+scp2jsonl \
+++scp_file_list='["../../../data/list/val_wav.scp", "../../../data/list/val_text.txt"]' \
+++data_type_list='["source", "target"]' \
+++jsonl_file_out="${val_data}"
+
+
+# exp output dir
+output_dir="./outputs"
+log_file="${output_dir}/log.txt"
+
+deepspeed_config=${workspace}/../../ds_stage1.json
+
+mkdir -p ${output_dir}
+echo "log_file: ${log_file}"
+
+DISTRIBUTED_ARGS="
+    --nnodes ${WORLD_SIZE:-1} \
+    --nproc_per_node $gpu_num \
+    --node_rank ${RANK:-0} \
+    --master_addr ${MASTER_ADDR:-127.0.0.1} \
+    --master_port ${MASTER_PORT:-26669}
+"
+
+echo $DISTRIBUTED_ARGS
+
+torchrun $DISTRIBUTED_ARGS \
+../../../funasr/bin/train_ds.py \
+++model="${model_name_or_model_dir}" \
+++train_data_set_list="${train_data}" \
+++valid_data_set_list="${val_data}" \
+++dataset="AudioDataset" \
+++dataset_conf.index_ds="IndexDSJsonl" \
+++dataset_conf.data_split_num=1 \
+++dataset_conf.batch_sampler="BatchSampler" \
+++dataset_conf.batch_size=6000  \
+++dataset_conf.sort_size=1024 \
+++dataset_conf.batch_type="token" \
+++dataset_conf.num_workers=4 \
+++train_conf.max_epoch=50 \
+++train_conf.log_interval=1 \
+++train_conf.resume=true \
+++train_conf.validate_interval=2000 \
+++train_conf.save_checkpoint_interval=2000 \
+++train_conf.keep_nbest_models=20 \
+++train_conf.avg_nbest_model=10 \
+++train_conf.use_deepspeed=false \
+++train_conf.deepspeed_config=${deepspeed_config} \
+++optim_conf.lr=0.0002 \
+++output_dir="${output_dir}" &> ${log_file}
\ No newline at end of file

FunASR/examples/industrial_data_pretraining/qwen_audio/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)
+
+# To install requirements: pip3 install -U "funasr[llm]"
+
+from funasr import AutoModel
+
+model = AutoModel(model="Qwen-Audio")
+
+audio_in = "https://github.com/QwenLM/Qwen-Audio/raw/main/assets/audio/1272-128104-0000.flac"
+prompt = "<|startoftranscription|><|en|><|transcribe|><|en|><|notimestamps|><|wo_itn|>"
+
+res = model.generate(input=audio_in, prompt=prompt)
+print(res)

FunASR/examples/industrial_data_pretraining/qwen_audio/demo_chat.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)
+
+# To install requirements: pip3 install -U "funasr[llm]"
+
+from funasr import AutoModel
+
+model = AutoModel(model="Qwen/Qwen-Audio-Chat")
+
+audio_in = "https://github.com/QwenLM/Qwen-Audio/raw/main/assets/audio/1272-128104-0000.flac"
+
+# 1st dialogue turn
+prompt = "what does the person say?"
+cache = {"history": None}
+res = model.generate(input=audio_in, prompt=prompt, cache=cache)
+print(res)
+
+
+# 2nd dialogue turn
+prompt = 'Find the start time and end time of the word "middle classes"'
+res = model.generate(input=None, prompt=prompt, cache=cache)
+print(res)

FunASR/examples/industrial_data_pretraining/qwen_audio/demo_chat_from_local.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)
+
+# To install requirements: pip3 install -U "funasr[llm]"
+
+from funasr import AutoModel
+
+model = AutoModel(
+    model="Qwen-Audio-Chat",
+    model_path="/nfs/zhifu.gzf/init_model/qwen/Qwen-Audio-Chat",
+)
+
+audio_in = (
+    "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav"
+)
+
+# 1st dialogue turn
+prompt = "what does the person say?"
+cache = {"history": None}
+res = model.generate(input=audio_in, prompt=prompt, cache=cache)
+print(res)
+
+
+# 2nd dialogue turn
+prompt = 'Find the start time and end time of the word "middle classes"'
+res = model.generate(input=None, prompt=prompt, cache=cache)
+print(res)

FunASR/examples/industrial_data_pretraining/qwen_audio/demo_from_local.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)
+
+# To install requirements: pip3 install -U "funasr[llm]"
+
+from funasr import AutoModel
+
+model = AutoModel(
+    model="Qwen-Audio",
+    model_path="/nfs/zhifu.gzf/init_model/qwen/Qwen-Audio",
+)
+
+audio_in = "https://github.com/QwenLM/Qwen-Audio/raw/main/assets/audio/1272-128104-0000.flac"
+prompt = "<|startoftranscription|><|en|><|transcribe|><|en|><|notimestamps|><|wo_itn|>"
+
+res = model.generate(input=audio_in, prompt=prompt)
+print(res)

FunASR/examples/industrial_data_pretraining/sanm_kws/conf/sanm_6e_320_256_fdim40_t2602.yaml

+
+# network architecture
+model: SanmKWS
+model_conf:
+    ctc_weight: 1.0
+
+# encoder
+encoder: SANMEncoder
+encoder_conf:
+    output_size: 256    # dimension of attention
+    attention_heads: 4
+    linear_units: 320  # the number of units of position-wise feed forward
+    num_blocks: 6      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: pe
+    pos_enc_class: SinusoidalPositionEncoder
+    normalize_before: true
+    kernel_size: 11
+    sanm_shfit: 0
+    selfattention_layer_type: sanm
+
+# frontend related
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 40
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 7
+    lfr_n: 6
+
+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: 6
+    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: 20
+  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: 0.001
+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: 96000 # 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>
+
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin  # ctc_type: focalctc, builtin
+    reduce: true
+    ignore_nan_grad: true
+normalize: null

FunASR/examples/industrial_data_pretraining/sanm_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_sanm_kws_phone-xiaoyun-commands-offline",
+    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/sanm_kws/export.sh

+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+config_path="/home/pengteng.spt/source/FunASR_KWS/examples/industrial_data_pretraining/sanm_kws/conf"
+config_path="/home/pengteng.spt/source/FunASR_KWS/examples/industrial_data_pretraining/sanm_kws/exp/20240914_xiaoyun_finetune_sanm_6e_320_256_feats_dim40_char_t2602_offline"
+
+config_file="sanm_6e_320_256_fdim40_t2602.yaml"
+config_file="config.yaml"
+
+model_path="./modelscope_models_kws/speech_charctc_kws_phone-xiaoyun/funasr/finetune_sanm_6e_320_256_fdim40_t2602_online_xiaoyun_commands.pt"
+
+python -m funasr.bin.export \
+    --config-path="${config_path}" \
+    --config-name="${config_file}" \
+    ++init_param=${model_path} \
+    ++type="onnx" \
+    ++quantize=true

FunASR/examples/industrial_data_pretraining/sanm_kws/finetune.sh

+#!/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="cpu" #"cpu"
+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_sanm_kws_phone-xiaoyun-commands-offline"
+
+## 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_sanm_kws_phone-xiaoyun-commands-offline.git ${model_name_or_model_dir}
+fi
+
+config=sanm_6e_320_256_fdim40_t2602.yaml
+token_list=${model_name_or_model_dir}/tokens_2602.txt
+lexicon_list=${model_name_or_model_dir}/lexicon.txt
+cmvn_file=${model_name_or_model_dir}/am.mvn.dim40_l3r3
+init_param="${model_name_or_model_dir}/basetrain_sanm_6e_320_256_fdim40_t2602_offline.pt"
+
+
+# data prepare
+# data dir, which contains: train.json, val.json
+data_dir=../../data
+
+train_data="${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
+          # ++batch_size="${inference_batch_size}"
+        }&
+
+    done
+    wait
+
+    for f in detect score; do
+        if [ -f "${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/sanm_kws/funasr

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

FunASR/examples/industrial_data_pretraining/sanm_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_sanm_kws_phone-xiaoyun-commands-offline"
+
+# 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/sanm_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_sanm_kws_phone-xiaoyun-commands-offline
+git clone https://www.modelscope.cn/iic/speech_sanm_kws_phone-xiaoyun-commands-offline.git ${local_path}
+
+device="cpu" # "cuda:0" for gpu0, "cuda:1" for gpu1, "cpu"
+
+config="inference_sanm_6e_320_256_fdim40_t2602_offline.yaml"
+tokens="${local_path}/tokens_2602.txt"
+seg_dict="${local_path}/lexicon.txt"
+init_param="${local_path}/finetune_sanm_6e_320_256_fdim40_t2602_offline_xiaoyun_commands.pt"
+cmvn_file="${local_path}/am.mvn.dim40_l3r3"
+
+keywords=(小云小云)
+keywords_string=$(IFS=,; echo "${keywords[*]}")
+echo "keywords: $keywords_string"
+
+python -m funasr.bin.inference \
+--config-path "${local_path}/" \
+--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/sanm_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/sanm_kws_streaming/conf/sanm_6e_320_256_fdim40_t2602.yaml

+
+# network architecture
+model: SanmKWSStreaming
+model_conf:
+    ctc_weight: 1.0
+
+# encoder
+encoder: SANMEncoderChunkOpt
+encoder_conf:
+    output_size: 256    # dimension of attention
+    attention_heads: 4
+    linear_units: 320  # the number of units of position-wise feed forward
+    num_blocks: 6      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: pe_online
+    pos_enc_class: SinusoidalPositionEncoder
+    normalize_before: true
+    kernel_size: 11
+    sanm_shfit: 0
+    selfattention_layer_type: sanm
+    chunk_size:
+    - 16
+    - 20
+    stride:
+    - 8
+    - 10
+    pad_left:
+    - 4
+    - 5
+    encoder_att_look_back_factor:
+    - 0
+    - 0
+    decoder_att_look_back_factor:
+    - 0
+    - 0
+
+# frontend related
+frontend: WavFrontendOnline
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 40
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 7
+    lfr_n: 6
+
+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: 6
+    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: 20
+  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: 0.001
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 30000
+
+dataset: AudioDataset
+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]
+
+tokenizer: CharTokenizer
+tokenizer_conf:
+  unk_symbol: <unk>
+
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin  # ctc_type: focalctc, builtin
+    reduce: true
+    ignore_nan_grad: true
+normalize: null

FunASR/examples/industrial_data_pretraining/sanm_kws_streaming/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_sanm_kws_phone-xiaoyun-commands-online",
+    keywords="小云小云",
+    output_dir="./outputs/debug",
+    device='cpu',
+    chunk_size=[4, 8, 4],
+    encoder_chunk_look_back=0,
+    decoder_chunk_look_back=0,
+)
+
+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/sanm_kws_streaming/export.sh

+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+config_path="/home/pengteng.spt/source/FunASR_KWS/examples/industrial_data_pretraining/sanm_kws_streaming/conf"
+config_path="/home/pengteng.spt/source/FunASR_KWS/examples/industrial_data_pretraining/sanm_kws_streaming/exp/20240618_xiaoyun_finetune_sanm_6e_320_256_feats_dim40_char_t2602_online_6"
+
+config_file="sanm_6e_320_256_fdim40_t2602.yaml"
+config_file="config.yaml"
+
+model_path="./modelscope_models_kws/speech_charctc_kws_phone-xiaoyun/funasr/finetune_sanm_6e_320_256_fdim40_t2602_online_xiaoyun_commands.pt"
+
+python -m funasr.bin.export \
+    --config-path="${config_path}" \
+    --config-name="${config_file}" \
+    ++init_param=${model_path} \
+    ++type="onnx" \
+    ++quantize=true

FunASR/examples/industrial_data_pretraining/sanm_kws_streaming/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=4
+
+inference_device="cpu" #"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_sanm_kws_phone-xiaoyun-commands-online"
+
+## 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_sanm_kws_phone-xiaoyun-commands-online.git ${model_name_or_model_dir}
+fi
+
+config=sanm_6e_320_256_fdim40_t2602.yaml
+token_list=${model_name_or_model_dir}/tokens_2602.txt
+lexicon_list=${model_name_or_model_dir}/lexicon.txt
+cmvn_file=${model_name_or_model_dir}/am.mvn.dim40_l3r3
+init_param="${model_name_or_model_dir}/basetrain_sanm_6e_320_256_fdim40_t2602_online.pt"
+
+
+# data prepare
+# data dir, which contains: train.json, val.json
+data_dir=../../data
+
+train_data="${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 ] && [ ${stop_stage} -ge 3 ]; then
+  echo "stage 3: Inference chunk_size: [4, 8, 4]"
+  keywords=(小云小云)
+  keywords_string=$(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}/chunk-4-8-4_elb-0_dlb_0"
+    _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}/${JOB}" \
+          ++chunk_size='[4, 8, 4]' \
+          ++encoder_chunk_look_back=0 \
+          ++decoder_chunk_look_back=0 \
+          ++device="${inference_device}" \
+          ++ncpu=1 \
+          ++disable_log=true \
+          ++batch_size="${inference_batch_size}" &> ${_logdir}/log.${JOB}.txt
+        }&
+
+    done
+    wait
+
+    for f in detect score; 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
+
+
+# Testing Stage
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+  echo "stage 4: Inference chunk_size: [5, 10, 5]"
+  keywords=(小云小云)
+  keywords_string=$(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}/chunk-5-10-5_elb-0_dlb_0"
+    _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}/${JOB}" \
+          ++chunk_size='[5, 10, 5]' \
+          ++encoder_chunk_look_back=0 \
+          ++decoder_chunk_look_back=0 \
+          ++device="${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