initial commit

FunASR/examples/industrial_data_pretraining/whisper/infer_from_local.sh

+# 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 openai-whisper
+
+# 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/ASR/test_audio/asr_example_zh.wav"
+
+output_dir="./outputs/debug"
+
+workspace=`pwd`
+
+# download model
+local_path_root=${workspace}/modelscope_models
+mkdir -p ${local_path_root}
+#Whisper-large-v2
+#local_path=${local_path_root}/speech_whisper-large_asr_multilingual
+#git clone https://www.modelscope.cn/iic/speech_whisper-large_asr_multilingual.git ${local_path}
+#init_param="${local_path}/large-v2.pt"
+#Whisper-large-v3
+local_path=${local_path_root}/Whisper-large-v3
+git clone https://www.modelscope.cn/iic/Whisper-large-v3.git ${local_path}
+init_param="${local_path}/large-v3.pt"
+
+device="cuda:0" # "cuda:0" for gpu0, "cuda:1" for gpu1, "cpu"
+
+config="config.yaml"
+
+
+python -m funasr.bin.inference \
+--config-path "${local_path}" \
+--config-name "${config}" \
+++init_param="${init_param}" \
+++input="${input}" \
+++output_dir="${output_dir}" \
+++device="${device}" \
+
+
+
+

FunASR/examples/industrial_data_pretraining/whisper/infer_from_openai.sh

+# 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 openai-whisper
+
+# method1, inference from model hub
+
+# for more input type, please ref to readme.md
+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav"
+
+output_dir="./outputs/debug"
+
+#model="Whisper-small"
+#model="Whisper-medium"
+model="Whisper-large-v2"
+#model="Whisper-large-v3"
+hub="openai"
+
+device="cuda:0" # "cuda:0" for gpu0, "cuda:1" for gpu1, "cpu"
+
+python -m funasr.bin.inference \
+++model=${model} \
+++hub=${hub} \
+++input="${input}" \
+++output_dir="${output_dir}" \
+++device="${device}" \

FunASR/examples/wenetspeech/conformer/README.md

+
+# Conformer Result
+
+## Training Config
+- Feature info: using 80 dims fbank, global cmvn, speed perturb(0.9, 1.0, 1.1), specaugment
+- Train info: lr 5e-4, batch_size 25000, 2 gpu(Tesla V100), acc_grad 1, 50 epochs
+- Train config: conf/train_asr_transformer.yaml
+- LM config: LM was not used
+- Model size: 46M
+
+## Results (CER)
+
+|   testset   | CER(%)  |
+|:-----------:|:-------:|
+|     dev     |  4.42   |
+|    test     |  4.87   |
\ No newline at end of file

FunASR/examples/wenetspeech/conformer/conf/conformer_12e_6d_2048_512.yaml

+# This is an example that demonstrates how to configure a model file.
+# You can modify the configuration according to your own requirements.
+
+# to print the register_table:
+# from funasr.register import tables
+# tables.print()
+
+# network architecture
+model: Conformer
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
+
+# encoder
+encoder: ConformerEncoder
+encoder_conf:
+    output_size: 512    # dimension of attention
+    attention_heads: 8
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder architecture type
+    normalize_before: true
+    rel_pos_type: latest
+    pos_enc_layer_type: rel_pos
+    selfattention_layer_type: rel_selfattn
+    activation_type: swish
+    macaron_style: true
+    use_cnn_module: true
+    cnn_module_kernel: 15
+
+# decoder
+decoder: TransformerDecoder
+decoder_conf:
+    attention_heads: 8
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
+
+
+# frontend related
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 1
+    lfr_n: 1
+
+specaug: SpecAug
+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
+
+train_conf:
+  accum_grad: 4
+  grad_clip: 5
+  max_epoch: 30
+  keep_nbest_models: 10
+  log_interval: 50
+
+optim: adam
+optim_conf:
+   lr: 0.0015
+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: 3200 # 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: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
+    buffer_size: 1024
+    shuffle: True
+    num_workers: 4
+    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
+    reduce: true
+    ignore_nan_grad: true
+normalize: null

FunASR/examples/wenetspeech/conformer/demo_infer.sh

+../paraformer/demo_infer.sh
\ No newline at end of file

FunASR/examples/wenetspeech/conformer/demo_train_or_finetune.sh

+../paraformer/demo_train_or_finetune.sh
\ No newline at end of file

FunASR/examples/wenetspeech/conformer/local/aishell_data_prep.sh

+#!/bin/bash
+
+# Copyright 2017 Xingyu Na
+# Apache 2.0
+
+#. ./path.sh || exit 1;
+
+if [ $# != 3 ]; then
+  echo "Usage: $0 <audio-path> <text-path> <output-path>"
+  echo " $0 /export/a05/xna/data/data_aishell/wav /export/a05/xna/data/data_aishell/transcript data"
+  exit 1;
+fi
+
+aishell_audio_dir=$1
+aishell_text=$2/aishell_transcript_v0.8.txt
+output_dir=$3
+
+train_dir=$output_dir/data/local/train
+dev_dir=$output_dir/data/local/dev
+test_dir=$output_dir/data/local/test
+tmp_dir=$output_dir/data/local/tmp
+
+mkdir -p $train_dir
+mkdir -p $dev_dir
+mkdir -p $test_dir
+mkdir -p $tmp_dir
+
+# data directory check
+if [ ! -d $aishell_audio_dir ] || [ ! -f $aishell_text ]; then
+  echo "Error: $0 requires two directory arguments"
+  exit 1;
+fi
+
+# find wav audio file for train, dev and test resp.
+find $aishell_audio_dir -iname "*.wav" > $tmp_dir/wav.flist
+n=`cat $tmp_dir/wav.flist | wc -l`
+[ $n -ne 141925 ] && \
+  echo Warning: expected 141925 data data files, found $n
+
+grep -i "wav/train" $tmp_dir/wav.flist > $train_dir/wav.flist || exit 1;
+grep -i "wav/dev" $tmp_dir/wav.flist > $dev_dir/wav.flist || exit 1;
+grep -i "wav/test" $tmp_dir/wav.flist > $test_dir/wav.flist || exit 1;
+
+rm -r $tmp_dir
+
+# Transcriptions preparation
+for dir in $train_dir $dev_dir $test_dir; do
+  echo Preparing $dir transcriptions
+  sed -e 's/\.wav//' $dir/wav.flist | awk -F '/' '{print $NF}' > $dir/utt.list
+  paste -d' ' $dir/utt.list $dir/wav.flist > $dir/wav.scp_all
+  utils/filter_scp.pl -f 1 $dir/utt.list $aishell_text > $dir/transcripts.txt
+  awk '{print $1}' $dir/transcripts.txt > $dir/utt.list
+  utils/filter_scp.pl -f 1 $dir/utt.list $dir/wav.scp_all | sort -u > $dir/wav.scp
+  sort -u $dir/transcripts.txt > $dir/text
+done
+
+mkdir -p $output_dir/data/train $output_dir/data/dev $output_dir/data/test
+
+for f in wav.scp text; do
+  cp $train_dir/$f $output_dir/data/train/$f || exit 1;
+  cp $dev_dir/$f $output_dir/data/dev/$f || exit 1;
+  cp $test_dir/$f $output_dir/data/test/$f || exit 1;
+done
+
+echo "$0: AISHELL data preparation succeeded"
+exit 0;

FunASR/examples/wenetspeech/conformer/local/download_and_untar.sh

+#!/usr/bin/env bash
+
+# Copyright   2014  Johns Hopkins University (author: Daniel Povey)
+#             2017  Xingyu Na
+# Apache 2.0
+
+remove_archive=false
+
+if [ "$1" == --remove-archive ]; then
+  remove_archive=true
+  shift
+fi
+
+if [ $# -ne 3 ]; then
+  echo "Usage: $0 [--remove-archive] <data-base> <url-base> <corpus-part>"
+  echo "e.g.: $0 /export/a05/xna/data www.openslr.org/resources/33 data_aishell"
+  echo "With --remove-archive it will remove the archive after successfully un-tarring it."
+  echo "<corpus-part> can be one of: data_aishell, resource_aishell."
+fi
+
+data=$1
+url=$2
+part=$3
+
+if [ ! -d "$data" ]; then
+  echo "$0: no such directory $data"
+  exit 1;
+fi
+
+part_ok=false
+list="data_aishell resource_aishell"
+for x in $list; do
+  if [ "$part" == $x ]; then part_ok=true; fi
+done
+if ! $part_ok; then
+  echo "$0: expected <corpus-part> to be one of $list, but got '$part'"
+  exit 1;
+fi
+
+if [ -z "$url" ]; then
+  echo "$0: empty URL base."
+  exit 1;
+fi
+
+if [ -f $data/$part/.complete ]; then
+  echo "$0: data part $part was already successfully extracted, nothing to do."
+  exit 0;
+fi
+
+# sizes of the archive files in bytes.
+sizes="15582913665 1246920"
+
+if [ -f $data/$part.tgz ]; then
+  size=$(/bin/ls -l $data/$part.tgz | awk '{print $5}')
+  size_ok=false
+  for s in $sizes; do if [ $s == $size ]; then size_ok=true; fi; done
+  if ! $size_ok; then
+    echo "$0: removing existing file $data/$part.tgz because its size in bytes $size"
+    echo "does not equal the size of one of the archives."
+    rm $data/$part.tgz
+  else
+    echo "$data/$part.tgz exists and appears to be complete."
+  fi
+fi
+
+if [ ! -f $data/$part.tgz ]; then
+  if ! command -v wget >/dev/null; then
+    echo "$0: wget is not installed."
+    exit 1;
+  fi
+  full_url=$url/$part.tgz
+  echo "$0: downloading data from $full_url.  This may take some time, please be patient."
+
+  cd $data || exit 1
+  if ! wget --no-check-certificate $full_url; then
+    echo "$0: error executing wget $full_url"
+    exit 1;
+  fi
+fi
+
+cd $data || exit 1
+
+if ! tar -xvzf $part.tgz; then
+  echo "$0: error un-tarring archive $data/$part.tgz"
+  exit 1;
+fi
+
+touch $data/$part/.complete
+
+if [ $part == "data_aishell" ]; then
+  cd $data/$part/wav || exit 1
+  for wav in ./*.tar.gz; do
+    echo "Extracting wav from $wav"
+    tar -zxf $wav && rm $wav
+  done
+fi
+
+echo "$0: Successfully downloaded and un-tarred $data/$part.tgz"
+
+if $remove_archive; then
+  echo "$0: removing $data/$part.tgz file since --remove-archive option was supplied."
+  rm $data/$part.tgz
+fi
+
+exit 0;

FunASR/examples/wenetspeech/conformer/run.sh

+#!/usr/bin/env bash
+
+
+CUDA_VISIBLE_DEVICES="0,1"
+
+# general configuration
+feats_dir="../DATA" #feature output dictionary
+exp_dir=`pwd`
+lang=zh
+token_type=char
+stage=0
+stop_stage=5
+
+# feature configuration
+nj=32
+
+inference_device="cuda" #"cpu", "cuda:0", "cuda:1"
+inference_checkpoint="model.pt.avg10"
+inference_scp="wav.scp"
+inference_batch_size=1
+
+# data
+raw_data=../raw_data
+data_url=www.openslr.org/resources/33
+
+# exp tag
+tag="exp1"
+workspace=`pwd`
+
+master_port=12345
+
+. 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
+
+set=L
+train_set=train_l
+valid_set=dev
+test_sets="dev test_net test_meeting"
+
+config=conformer_12e_6d_2048_512.yaml
+model_dir="baseline_$(basename "${config}" .yaml)_${lang}_${token_type}_${tag}"
+
+
+
+if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
+    echo "For downloading data, please refer to https://github.com/wenet-e2e/WenetSpeech."
+    exit 0;
+fi
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    echo "stage 0: Data preparation"
+    # Data preparation
+    local/data.sh --set ${set} --nj $nj --data_dir $feats_dir --WENETSPEECH $raw_data --train_cmd $train_cmd
+    mkdir $feats_dir/data
+    mv $feats_dir/$train_set $feats_dir/data/$train_set
+    for x in $test_sets; do
+        mv $feats_dir/$x $feats_dir/data/
+
+        # convert wav.scp text to jsonl
+        scp_file_list_arg="++scp_file_list='[\"${feats_dir}/data/${x}/wav.scp\",\"${feats_dir}/data/${x}/text\"]'"
+        python ../../../funasr/datasets/audio_datasets/scp2jsonl.py \
+        ++data_type_list='["source", "target"]' \
+        ++jsonl_file_out=${feats_dir}/data/${x}/audio_datasets.jsonl \
+        ${scp_file_list_arg}
+    done
+fi
+
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    echo "stage 1: Feature and CMVN Generation"
+    python ../../../funasr/bin/compute_audio_cmvn.py \
+    --config-path "${workspace}/conf" \
+    --config-name "${config}" \
+    ++scale=0.1 \
+    ++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
+    ++cmvn_file="${feats_dir}/data/${train_set}/cmvn.json" \
+
+fi
+
+token_list=${feats_dir}/data/${lang}_token_list/$token_type/tokens.txt
+echo "dictionary: ${token_list}"
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    echo "stage 2: Dictionary Preparation"
+    mkdir -p ${feats_dir}/data/${lang}_token_list/$token_type/
+   
+    echo "make a dictionary"
+    echo "<blank>" > ${token_list}
+    echo "<s>" >> ${token_list}
+    echo "</s>" >> ${token_list}
+    utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/$train_set/text | cut -f 2- -d" " | tr " " "\n" \
+        | sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list}
+    echo "<unk>" >> ${token_list}
+fi
+
+# LM Training Stage
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    echo "stage 3: LM Training"
+fi
+
+# ASR Training Stage
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+  echo "stage 4: ASR Training"
+
+  mkdir -p ${exp_dir}/exp/${model_dir}
+  current_time=$(date "+%Y-%m-%d_%H-%M")
+  log_file="${exp_dir}/exp/${model_dir}/train.log.txt.${current_time}"
+  echo "log_file: ${log_file}"
+
+  export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES
+  gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+  torchrun \
+  --nnodes 1 \
+  --nproc_per_node ${gpu_num} \
+  --master_port ${master_port} \
+  ../../../funasr/bin/train.py \
+  --config-path "${workspace}/conf" \
+  --config-name "${config}" \
+  ++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
+  ++valid_data_set_list="${feats_dir}/data/${valid_set}/audio_datasets.jsonl" \
+  ++tokenizer_conf.token_list="${token_list}" \
+  ++frontend_conf.cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
+  ++output_dir="${exp_dir}/exp/${model_dir}" &> ${log_file}
+fi
+
+
+
+# Testing Stage
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
+  echo "stage 5: Inference"
+
+  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 ${test_sets}; do
+
+    inference_dir="${exp_dir}/exp/${model_dir}/inference-${inference_checkpoint}/${dset}"
+    _logdir="${inference_dir}/logdir"
+    echo "inference_dir: ${inference_dir}"
+
+    mkdir -p "${_logdir}"
+    data_dir="${feats_dir}/data/${dset}"
+    key_file=${data_dir}/${inference_scp}
+
+    split_scps=
+    for JOB in $(seq "${nj}"); do
+        split_scps+=" ${_logdir}/keys.${JOB}.scp"
+    done
+    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]}
+
+          export CUDA_VISIBLE_DEVICES=${gpuid}
+          python ../../../funasr/bin/inference.py \
+          --config-path="${exp_dir}/exp/${model_dir}" \
+          --config-name="config.yaml" \
+          ++init_param="${exp_dir}/exp/${model_dir}/${inference_checkpoint}" \
+          ++tokenizer_conf.token_list="${token_list}" \
+          ++frontend_conf.cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
+          ++input="${_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
+
+    mkdir -p ${inference_dir}/1best_recog
+    for f in token score text; do
+        if [ -f "${inference_dir}/${JOB}/1best_recog/${f}" ]; then
+          for JOB in $(seq "${nj}"); do
+              cat "${inference_dir}/${JOB}/1best_recog/${f}"
+          done | sort -k1 >"${inference_dir}/1best_recog/${f}"
+        fi
+    done
+
+    echo "Computing WER ..."
+    python utils/postprocess_text_zh.py ${inference_dir}/1best_recog/text ${inference_dir}/1best_recog/text.proc
+    python utils/postprocess_text_zh.py  ${data_dir}/text ${inference_dir}/1best_recog/text.ref
+    python utils/compute_wer.py ${inference_dir}/1best_recog/text.ref ${inference_dir}/1best_recog/text.proc ${inference_dir}/1best_recog/text.cer
+    tail -n 3 ${inference_dir}/1best_recog/text.cer
+  done
+
+fi

FunASR/examples/wenetspeech/conformer/utils

+../../aishell/paraformer/utils
\ No newline at end of file

FunASR/fun_text_processing/README.md

+**Fundamental Text Processing (FunTextProcessing)**
+==========================
+
+### Introduction
+
+FunTextProcessing is a Python toolkit for fundamental text processing in ASR including text processing , inverse text processing, num2words, which is included in the `FunASR`.
+
+### Highlights
+
+- FunTextProcessing supports inverse text processing (ITN), text processing (TN), number to words (num2words).
+- FunTextProcessing supports multilingual, 10+ languages for ITN, 5 languages for TN, 50+ languages for num2words.
+
+
+### Example
+#### Inverse Text Processing (ITN)
+Given text inputs, such as speech recognition results, use `fun_text_processing/inverse_text_normalization/inverse_normalize.py` to output ITN results. You may refer to the following example scripts.
+
+```
+test_file=fun_text_processing/inverse_text_normalization/id/id_itn_test_input.txt
+
+python fun_text_processing/inverse_text_normalization/inverse_normalize.py --input_file $test_file --cache_dir ./itn_model/ --output_file output.txt --language=id
+```
+
+
+### Acknowledge
+1. We borrowed a lot of codes from [NeMo](https://github.com/NVIDIA/NeMo).
+2. We refered the codes from [WeTextProcessing](https://github.com/wenet-e2e/WeTextProcessing) for Chinese inverse text normalization. 
+3. We borrowed a lot of codes from  [num2words](https://pypi.org/project/num2words/) library for convert the number to words function in some languages.
+
+### License
+
+This project is licensed under the [The MIT License](https://opensource.org/licenses/MIT). FunTextProcessing also contains various third-party components and some code modified from other repos under other open source licenses. 

FunASR/fun_text_processing/__init__.py

+

FunASR/fun_text_processing/install_pynini.sh

+#!/bin/bash
+if [[ $OSTYPE == 'darwin'* ]]; then
+  conda install -c conda-forge -y pynini=2.1.5
+else
+  pip install pynini==2.1.5
+fi
\ No newline at end of file

FunASR/fun_text_processing/inverse_text_normalization/__init__.py

+

FunASR/fun_text_processing/inverse_text_normalization/de/__init__.py

+from fun_text_processing.inverse_text_normalization.en.taggers.tokenize_and_classify import (
+    ClassifyFst,
+)
+from fun_text_processing.inverse_text_normalization.en.verbalizers.verbalize import VerbalizeFst
+from fun_text_processing.inverse_text_normalization.en.verbalizers.verbalize_final import (
+    VerbalizeFinalFst,
+)

FunASR/fun_text_processing/inverse_text_normalization/de/taggers/cardinal.py

+import pynini
+from fun_text_processing.text_normalization.en.graph_utils import DAMO_SIGMA, GraphFst
+from pynini.lib import pynutil
+
+
+class CardinalFst(GraphFst):
+    """
+    Finite state transducer for classifying cardinals. Numbers below ten are not converted.
+    Allows both compound numeral strings or separated by whitespace.
+    "und" (en: "and") can be inserted between "hundert" and following number or "tausend" and following single or double digit number.
+
+        e.g. minus drei und zwanzig -> cardinal { negative: "-" integer: "23" } }
+        e.g. minus dreiundzwanzig -> cardinal { integer: "23" } }
+        e.g. dreizehn -> cardinal { integer: "13" } }
+        e.g. ein hundert -> cardinal { integer: "100" } }
+        e.g. einhundert -> cardinal { integer: "100" } }
+        e.g. ein tausend -> cardinal { integer: "1000" } }
+        e.g. eintausend -> cardinal { integer: "1000" } }
+        e.g. ein tausend zwanzig -> cardinal { integer: "1020" } }
+
+    Args:
+        tn_cardinal_tagger: TN cardinal tagger
+    """
+
+    def __init__(self, tn_cardinal_tagger: GraphFst, deterministic: bool = True):
+        super().__init__(name="cardinal", kind="classify", deterministic=deterministic)
+
+        # add_space_between_chars = pynini.cdrewrite(pynini.closure(insert_space, 0, 1), DAMO_CHAR, DAMO_CHAR, DAMO_SIGMA)
+        optional_delete_space = pynini.closure(DAMO_SIGMA | pynutil.delete(" "))
+
+        graph = (tn_cardinal_tagger.graph @ optional_delete_space).invert().optimize()
+        self.graph_hundred_component_at_least_one_none_zero_digit = (
+            (
+                tn_cardinal_tagger.graph_hundred_component_at_least_one_none_zero_digit
+                @ optional_delete_space
+            )
+            .invert()
+            .optimize()
+        )
+
+        self.graph_ties = (
+            (tn_cardinal_tagger.two_digit_non_zero @ optional_delete_space).invert().optimize()
+        )
+        # this is to make sure if there is an ambiguity with decimal, decimal is chosen, e.g. 1000000 vs. 1 million
+        graph = pynutil.add_weight(graph, weight=0.001)
+        self.graph_no_exception = graph
+        self.digit = (
+            pynini.arcmap(tn_cardinal_tagger.digit, map_type="rmweight").invert().optimize()
+        )
+        graph_exception = pynini.project(self.digit, "input")
+        self.graph = (pynini.project(graph, "input") - graph_exception.arcsort()) @ graph
+
+        self.optional_minus_graph = pynini.closure(
+            pynutil.insert("negative: ") + pynini.cross("minus ", '"-" '), 0, 1
+        )
+
+        final_graph = (
+            self.optional_minus_graph
+            + pynutil.insert('integer: "')
+            + self.graph
+            + pynutil.insert('"')
+        )
+
+        final_graph = self.add_tokens(final_graph)
+        self.fst = final_graph.optimize()

FunASR/fun_text_processing/inverse_text_normalization/de/taggers/date.py

+import pynini
+from fun_text_processing.text_normalization.en.graph_utils import (
+    DAMO_DIGIT,
+    DAMO_NOT_QUOTE,
+    DAMO_SIGMA,
+    GraphFst,
+    convert_space,
+)
+from pynini.lib import pynutil
+
+
+class DateFst(GraphFst):
+    """
+    Finite state transducer for classifying date, in the form of (day) month (year) or year
+        e.g. vierundzwanzigster juli zwei tausend dreizehn -> tokens { name: "24. Jul. 2013" }
+        e.g. neunzehnachtzig -> tokens { name: "1980" }
+        e.g. vierzehnter januar -> tokens { name: "14. Jan." }
+        e.g. zweiter dritter -> tokens { name: "02.03." }
+        e.g. januar neunzehnachtzig -> tokens { name: "Jan. 1980" }
+        e.g. zwanzigzwanzig -> tokens { name: "2020" }
+
+    Args:
+        itn_cardinal_tagger: ITN cardinal tagger
+        tn_date_tagger: TN date tagger
+        tn_date_verbalizer: TN date verbalizer
+    """
+
+    def __init__(
+        self,
+        itn_cardinal_tagger: GraphFst,
+        tn_date_tagger: GraphFst,
+        tn_date_verbalizer: GraphFst,
+        deterministic: bool = True,
+    ):
+        super().__init__(name="date", kind="classify", deterministic=deterministic)
+
+        add_leading_zero_to_double_digit = (DAMO_DIGIT + DAMO_DIGIT) | (
+            pynutil.insert("0") + DAMO_DIGIT
+        )
+        optional_delete_space = pynini.closure(DAMO_SIGMA | pynutil.delete(" ", weight=0.0001))
+        tagger = tn_date_verbalizer.graph.invert().optimize()
+
+        delete_day_marker = (
+            pynutil.delete('day: "') + pynini.closure(DAMO_NOT_QUOTE, 1) + pynutil.delete('"')
+        ) @ itn_cardinal_tagger.graph_no_exception
+
+        month_as_number = (
+            pynutil.delete('month: "')
+            + itn_cardinal_tagger.graph_no_exception
+            + pynutil.delete('"')
+        )
+        month_as_string = (
+            pynutil.delete('month: "') + tn_date_tagger.month_abbr.invert() + pynutil.delete('"')
+        )
+
+        convert_year = (tn_date_tagger.year @ optional_delete_space).invert().optimize()
+        delete_year_marker = (
+            pynutil.delete('year: "') + pynini.closure(DAMO_NOT_QUOTE, 1) + pynutil.delete('"')
+        ) @ convert_year
+
+        # day. month as string (year)
+        verbalizer = (
+            pynini.closure(delete_day_marker + pynutil.insert(".") + pynini.accep(" "), 0, 1)
+            + month_as_string
+            + pynini.closure(pynini.accep(" ") + delete_year_marker, 0, 1)
+        )
+
+        # day. month as number (year)
+        verbalizer |= (
+            delete_day_marker @ add_leading_zero_to_double_digit
+            + pynutil.insert(".")
+            + pynutil.delete(" ")
+            + month_as_number @ add_leading_zero_to_double_digit
+            + pynutil.insert(".")
+            + pynini.closure(pynutil.delete(" ") + delete_year_marker, 0, 1)
+        )
+
+        # year
+        verbalizer |= delete_year_marker
+
+        final_graph = tagger @ verbalizer
+
+        graph = pynutil.insert('name: "') + convert_space(final_graph) + pynutil.insert('"')
+        self.fst = graph.optimize()

FunASR/fun_text_processing/inverse_text_normalization/de/taggers/decimal.py

+import pynini
+from fun_text_processing.text_normalization.de.taggers.decimal import get_quantity, quantities
+from fun_text_processing.text_normalization.en.graph_utils import DAMO_SIGMA, GraphFst
+from pynini.lib import pynutil
+
+
+class DecimalFst(GraphFst):
+    """
+    Finite state transducer for classifying decimal
+        e.g. minus elf komma zwei null null sechs billionen -> decimal { negative: "true" integer_part: "11"  fractional_part: "2006" quantity: "billionen" }
+        e.g. eine billion -> decimal { integer_part: "1" quantity: "billion" }
+    Args:
+        itn_cardinal_tagger: ITN Cardinal tagger
+        tn_decimal_tagger: TN decimal tagger
+    """
+
+    def __init__(
+        self, itn_cardinal_tagger: GraphFst, tn_decimal_tagger: GraphFst, deterministic: bool = True
+    ):
+        super().__init__(name="decimal", kind="classify", deterministic=deterministic)
+
+        self.graph = tn_decimal_tagger.graph.invert().optimize()
+
+        delete_point = pynutil.delete(" komma")
+
+        allow_spelling = pynini.cdrewrite(
+            pynini.cross("eine ", "eins ") + quantities, "[BOS]", "[EOS]", DAMO_SIGMA
+        )
+
+        graph_fractional = pynutil.insert('fractional_part: "') + self.graph + pynutil.insert('"')
+        graph_integer = (
+            pynutil.insert('integer_part: "')
+            + itn_cardinal_tagger.graph_no_exception
+            + pynutil.insert('"')
+        )
+        final_graph_wo_sign = graph_integer + delete_point + pynini.accep(" ") + graph_fractional
+
+        self.final_graph_wo_negative = (
+            allow_spelling
+            @ (
+                final_graph_wo_sign
+                | get_quantity(
+                    final_graph_wo_sign,
+                    itn_cardinal_tagger.graph_hundred_component_at_least_one_none_zero_digit,
+                )
+            ).optimize()
+        )
+
+        final_graph = itn_cardinal_tagger.optional_minus_graph + self.final_graph_wo_negative
+        final_graph += pynutil.insert(" preserve_order: true")
+        final_graph = self.add_tokens(final_graph)
+        self.fst = final_graph.optimize()

FunASR/fun_text_processing/inverse_text_normalization/de/taggers/electronic.py

+import pynini
+from fun_text_processing.text_normalization.en.graph_utils import GraphFst
+from pynini.lib import pynutil
+
+
+class ElectronicFst(GraphFst):
+    """
+    Finite state transducer for classifying electronic: email addresses, etc.
+        e.g. c d f eins at a b c punkt e d u -> tokens { name: "cdf1.abc.edu" }
+
+    Args:
+        tn_electronic_tagger: TN eletronic tagger
+        tn_electronic_verbalizer: TN eletronic verbalizer
+    """
+
+    def __init__(
+        self,
+        tn_electronic_tagger: GraphFst,
+        tn_electronic_verbalizer: GraphFst,
+        deterministic: bool = True,
+    ):
+        super().__init__(name="electronic", kind="classify", deterministic=deterministic)
+
+        tagger = pynini.invert(tn_electronic_verbalizer.graph).optimize()
+        verbalizer = pynini.invert(tn_electronic_tagger.graph).optimize()
+        final_graph = tagger @ verbalizer
+
+        graph = pynutil.insert('name: "') + final_graph + pynutil.insert('"')
+        self.fst = graph.optimize()