From 45a42abc78328b5be7f717d53d61b8b7c69bea32 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 07 六月 2024 22:42:38 +0800
Subject: [PATCH] fix bug

---
 /dev/null                             |    1 -
 examples/wenetspeech/conformer/run.sh |    2 +-
 2 files changed, 1 insertions(+), 2 deletions(-)

diff --git a/examples/wenetspeech/conformer/run.sh b/examples/wenetspeech/conformer/run.sh
index 0503a9e..6ae995a 100755
--- a/examples/wenetspeech/conformer/run.sh
+++ b/examples/wenetspeech/conformer/run.sh
@@ -92,7 +92,7 @@
     echo "<blank>" > ${token_list}
     echo "<s>" >> ${token_list}
     echo "</s>" >> ${token_list}
-    utils/text2token.py -s 1 -n 1 --space "" --text_format "jsonl" ${feats_dir}/data/$train_set/audio_datasets.jsonl | cut -f 2- -d" " | tr " " "\n" \
+    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
diff --git a/examples/wenetspeech/transformer/README.md b/examples/wenetspeech/transformer/README.md
deleted file mode 100644
index 2435b55..0000000
--- a/examples/wenetspeech/transformer/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
-
-# 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     |  4.97  |
-|    test     |  5.37  |
\ No newline at end of file
diff --git a/examples/wenetspeech/transformer/conf/transformer_12e_6d_2048_256.yaml b/examples/wenetspeech/transformer/conf/transformer_12e_6d_2048_256.yaml
deleted file mode 100644
index efcf593..0000000
--- a/examples/wenetspeech/transformer/conf/transformer_12e_6d_2048_256.yaml
+++ /dev/null
@@ -1,104 +0,0 @@
-# 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: Transformer
-model_conf:
-    ctc_weight: 0.3
-    lsm_weight: 0.1     # label smoothing option
-    length_normalized_loss: false
-
-# encoder
-encoder: TransformerEncoder
-encoder_conf:
-    output_size: 256    # dimension of attention
-    attention_heads: 4
-    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
-
-# decoder
-decoder: TransformerDecoder
-decoder_conf:
-    attention_heads: 4
-    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: 1
-  grad_clip: 5
-  max_epoch: 150
-  keep_nbest_models: 10
-  log_interval: 50
-
-optim: adam
-optim_conf:
-   lr: 0.002
-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: 25000 # 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
diff --git a/examples/wenetspeech/transformer/demo_infer.sh b/examples/wenetspeech/transformer/demo_infer.sh
deleted file mode 120000
index 9d0a7a9..0000000
--- a/examples/wenetspeech/transformer/demo_infer.sh
+++ /dev/null
@@ -1 +0,0 @@
-../paraformer/demo_infer.sh
\ No newline at end of file
diff --git a/examples/wenetspeech/transformer/demo_train_or_finetune.sh b/examples/wenetspeech/transformer/demo_train_or_finetune.sh
deleted file mode 120000
index bbabdbe..0000000
--- a/examples/wenetspeech/transformer/demo_train_or_finetune.sh
+++ /dev/null
@@ -1 +0,0 @@
-../paraformer/demo_train_or_finetune.sh
\ No newline at end of file
diff --git a/examples/wenetspeech/transformer/local/aishell_data_prep.sh b/examples/wenetspeech/transformer/local/aishell_data_prep.sh
deleted file mode 100755
index 83f489b..0000000
--- a/examples/wenetspeech/transformer/local/aishell_data_prep.sh
+++ /dev/null
@@ -1,66 +0,0 @@
-#!/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;
diff --git a/examples/wenetspeech/transformer/local/download_and_untar.sh b/examples/wenetspeech/transformer/local/download_and_untar.sh
deleted file mode 100755
index d982559..0000000
--- a/examples/wenetspeech/transformer/local/download_and_untar.sh
+++ /dev/null
@@ -1,105 +0,0 @@
-#!/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;
diff --git a/examples/wenetspeech/transformer/run.sh b/examples/wenetspeech/transformer/run.sh
deleted file mode 100755
index 3fb8465..0000000
--- a/examples/wenetspeech/transformer/run.sh
+++ /dev/null
@@ -1,203 +0,0 @@
-#!/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"
-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
-
-train_set=train
-valid_set=dev
-test_sets="dev test"
-
-config=transformer_12e_6d_2048_256.yaml
-model_dir="baseline_$(basename "${config}" .yaml)_${lang}_${token_type}_${tag}"
-
-
-
-if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
-    echo "stage -1: Data Download"
-    mkdir -p ${raw_data}
-    local/download_and_untar.sh ${raw_data} ${data_url} data_aishell
-    local/download_and_untar.sh ${raw_data} ${data_url} resource_aishell
-fi
-
-if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
-    echo "stage 0: Data preparation"
-    # Data preparation
-    local/aishell_data_prep.sh ${raw_data}/data_aishell/wav ${raw_data}/data_aishell/transcript ${feats_dir}
-    for x in train dev test; do
-        cp ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org
-        paste -d " " <(cut -f 1 -d" " ${feats_dir}/data/${x}/text.org) <(cut -f 2- -d" " ${feats_dir}/data/${x}/text.org | tr -d " ") \
-            > ${feats_dir}/data/${x}/text
-        utils/text2token.py -n 1 -s 1 ${feats_dir}/data/${x}/text > ${feats_dir}/data/${x}/text.org
-        mv ${feats_dir}/data/${x}/text.org ${feats_dir}/data/${x}/text
-
-        # 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 "${config}" \
-    ++train_data_set_list="${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
\ No newline at end of file
diff --git a/examples/wenetspeech/transformer/utils b/examples/wenetspeech/transformer/utils
deleted file mode 120000
index 1f2ce9d..0000000
--- a/examples/wenetspeech/transformer/utils
+++ /dev/null
@@ -1 +0,0 @@
-../paraformer/utils
\ No newline at end of file

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