From 4ebde3c4ac27c15ff39ffbd5aa601035d189497a Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 19 二月 2024 18:42:35 +0800
Subject: [PATCH] aishell example
---
examples/aishell/paraformer/utils/compute_wer.py | 157 ++++++++++++++++++++++++++
examples/aishell/paraformer/run.sh | 175 +++++++++++++---------------
examples/industrial_data_pretraining/paraformer/finetune.sh | 11 -
examples/industrial_data_pretraining/paraformer/infer_demo.sh | 3
4 files changed, 245 insertions(+), 101 deletions(-)
diff --git a/examples/aishell/paraformer/run.sh b/examples/aishell/paraformer/run.sh
index 3f485c2..410751a 100755
--- a/examples/aishell/paraformer/run.sh
+++ b/examples/aishell/paraformer/run.sh
@@ -39,23 +39,14 @@
valid_set=dev
test_sets="dev test"
-asr_config=train_asr_paraformer_conformer_12e_6d_2048_256.yaml
-model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
+config=train_asr_paraformer_conformer_12e_6d_2048_256.yaml
+model_dir="baseline_$(basename "${config}" .yaml)_${lang}_${token_type}_${tag}"
-#inference_config=conf/decode_asr_transformer_noctc_1best.yaml
-#inference_asr_model=valid.acc.ave_10best.pb
+inference_device="cuda" #"cpu"
+inference_checkpoint="model.pt"
+inference_scp="wav.scp"
-## you can set gpu num for decoding here
-#gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default
-#ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
-#
-#if ${gpu_inference}; then
-# inference_nj=$[${ngpu}*${njob}]
-# _ngpu=1
-#else
-# inference_nj=$njob
-# _ngpu=0
-#fi
+
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
echo "stage -1: Data Download"
@@ -85,10 +76,10 @@
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "stage 1: Feature and CMVN Generation"
-# utils/compute_cmvn.sh --fbankdir ${feats_dir}/data/${train_set} --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --config_file "$asr_config" --scale 1.0
+# utils/compute_cmvn.sh --fbankdir ${feats_dir}/data/${train_set} --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --config_file "$config" --scale 1.0
python ../../../funasr/bin/compute_audio_cmvn.py \
--config-path "${workspace}" \
- --config-name "${asr_config}" \
+ --config-name "${config}" \
++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
++cmvn_file="${feats_dir}/data/${train_set}/cmvn.json" \
++dataset_conf.num_workers=$nj
@@ -116,90 +107,84 @@
# ASR Training Stage
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
-echo "stage 4: ASR Training"
+ echo "stage 4: ASR Training"
+ log_file="${exp_dir}/exp/${model_dir}/train.log.txt"
+ echo "log_file: ${log_file}"
torchrun \
--nnodes 1 \
--nproc_per_node ${gpu_num} \
../../../funasr/bin/train.py \
--config-path "${workspace}" \
- --config-name "${asr_config}" \
+ --config-name "${config}" \
++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
- ++cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
- ++token_list="${token_list}" \
- ++output_dir="${exp_dir}/exp/${model_dir}"
+ ++tokenizer_conf.token_list="${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"
-# for dset in ${test_sets}; do
-# asr_exp=${exp_dir}/exp/${model_dir}
-# inference_tag="$(basename "${inference_config}" .yaml)"
-# _dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
-# _logdir="${_dir}/logdir"
-# if [ -d ${_dir} ]; then
-# echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
-# exit 0
-# fi
-# mkdir -p "${_logdir}"
-# _data="${feats_dir}/data/${dset}"
-# key_file=${_data}/${scp}
-# num_scp_file="$(<${key_file} wc -l)"
-# _nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
-# split_scps=
-# for n in $(seq "${_nj}"); do
-# split_scps+=" ${_logdir}/keys.${n}.scp"
-# done
-# # shellcheck disable=SC2086
-# utils/split_scp.pl "${key_file}" ${split_scps}
-# _opts=
-# if [ -n "${inference_config}" ]; then
-# _opts+="--config ${inference_config} "
-# fi
-# ${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
-# python -m funasr.bin.asr_inference_launch \
-# --batch_size 1 \
-# --ngpu "${_ngpu}" \
-# --njob ${njob} \
-# --gpuid_list ${gpuid_list} \
-# --data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
-# --cmvn_file ${feats_dir}/data/${train_set}/cmvn/am.mvn \
-# --key_file "${_logdir}"/keys.JOB.scp \
-# --asr_train_config "${asr_exp}"/config.yaml \
-# --asr_model_file "${asr_exp}"/"${inference_asr_model}" \
-# --output_dir "${_logdir}"/output.JOB \
-# --mode paraformer \
-# ${_opts}
-#
-# for f in token token_int score text; do
-# if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
-# for i in $(seq "${_nj}"); do
-# cat "${_logdir}/output.${i}/1best_recog/${f}"
-# done | sort -k1 >"${_dir}/${f}"
-# fi
-# done
-# python utils/proce_text.py ${_dir}/text ${_dir}/text.proc
-# python utils/proce_text.py ${_data}/text ${_data}/text.proc
-# python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
-# tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
-# cat ${_dir}/text.cer.txt
-# done
-#fi
-#
-## Prepare files for ModelScope fine-tuning and inference
-#if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
-# echo "stage 6: ModelScope Preparation"
-# cp ${feats_dir}/data/${train_set}/cmvn/am.mvn ${exp_dir}/exp/${model_dir}/am.mvn
-# vocab_size=$(cat ${token_list} | wc -l)
-# python utils/gen_modelscope_configuration.py \
-# --am_model_name $inference_asr_model \
-# --mode paraformer \
-# --model_name paraformer \
-# --dataset aishell \
-# --output_dir $exp_dir/exp/$model_dir \
-# --vocab_size $vocab_size \
-# --nat _nat \
-# --tag $tag
-#fi
\ No newline at end of file
+
+
+# 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
+ nj=$njob
+ batch_size=1
+ gpuid_list=""
+ for JOB in $(seq ${nj}); do
+ gpuid_list=CUDA_VISIBLE_DEVICES"-1,"
+ done
+ fi
+
+ for dset in ${test_sets}; do
+
+ inference_dir="${asr_exp}/${inference_checkpoint}/${dset}"
+ _logdir="${inference_dir}/logdir"
+
+ mkdir -p "${_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}
+
+ for JOB in $(seq ${nj}); do
+ {
+ python ../../../funasr/bin/inference.py \
+ --config-path="${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}"
+ }&
+
+ 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 ..."
+ cp ${inference_dir}/1best_recog/text ${inference_dir}/1best_recog/text.proc
+ cp ${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
diff --git a/examples/aishell/paraformer/utils/compute_wer.py b/examples/aishell/paraformer/utils/compute_wer.py
new file mode 100755
index 0000000..26a9f49
--- /dev/null
+++ b/examples/aishell/paraformer/utils/compute_wer.py
@@ -0,0 +1,157 @@
+import os
+import numpy as np
+import sys
+
+def compute_wer(ref_file,
+ hyp_file,
+ cer_detail_file):
+ rst = {
+ 'Wrd': 0,
+ 'Corr': 0,
+ 'Ins': 0,
+ 'Del': 0,
+ 'Sub': 0,
+ 'Snt': 0,
+ 'Err': 0.0,
+ 'S.Err': 0.0,
+ 'wrong_words': 0,
+ 'wrong_sentences': 0
+ }
+
+ hyp_dict = {}
+ ref_dict = {}
+ with open(hyp_file, 'r') as hyp_reader:
+ for line in hyp_reader:
+ key = line.strip().split()[0]
+ value = line.strip().split()[1:]
+ hyp_dict[key] = value
+ with open(ref_file, 'r') as ref_reader:
+ for line in ref_reader:
+ key = line.strip().split()[0]
+ value = line.strip().split()[1:]
+ ref_dict[key] = value
+
+ cer_detail_writer = open(cer_detail_file, 'w')
+ for hyp_key in hyp_dict:
+ if hyp_key in ref_dict:
+ out_item = compute_wer_by_line(hyp_dict[hyp_key], ref_dict[hyp_key])
+ rst['Wrd'] += out_item['nwords']
+ rst['Corr'] += out_item['cor']
+ rst['wrong_words'] += out_item['wrong']
+ rst['Ins'] += out_item['ins']
+ rst['Del'] += out_item['del']
+ rst['Sub'] += out_item['sub']
+ rst['Snt'] += 1
+ if out_item['wrong'] > 0:
+ rst['wrong_sentences'] += 1
+ cer_detail_writer.write(hyp_key + print_cer_detail(out_item) + '\n')
+ cer_detail_writer.write("ref:" + '\t' + " ".join(list(map(lambda x: x.lower(), ref_dict[hyp_key]))) + '\n')
+ cer_detail_writer.write("hyp:" + '\t' + " ".join(list(map(lambda x: x.lower(), hyp_dict[hyp_key]))) + '\n')
+
+ if rst['Wrd'] > 0:
+ rst['Err'] = round(rst['wrong_words'] * 100 / rst['Wrd'], 2)
+ if rst['Snt'] > 0:
+ rst['S.Err'] = round(rst['wrong_sentences'] * 100 / rst['Snt'], 2)
+
+ cer_detail_writer.write('\n')
+ cer_detail_writer.write("%WER " + str(rst['Err']) + " [ " + str(rst['wrong_words'])+ " / " + str(rst['Wrd']) +
+ ", " + str(rst['Ins']) + " ins, " + str(rst['Del']) + " del, " + str(rst['Sub']) + " sub ]" + '\n')
+ cer_detail_writer.write("%SER " + str(rst['S.Err']) + " [ " + str(rst['wrong_sentences']) + " / " + str(rst['Snt']) + " ]" + '\n')
+ cer_detail_writer.write("Scored " + str(len(hyp_dict)) + " sentences, " + str(len(hyp_dict) - rst['Snt']) + " not present in hyp." + '\n')
+
+
+def compute_wer_by_line(hyp,
+ ref):
+ hyp = list(map(lambda x: x.lower(), hyp))
+ ref = list(map(lambda x: x.lower(), ref))
+
+ len_hyp = len(hyp)
+ len_ref = len(ref)
+
+ cost_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int16)
+
+ ops_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int8)
+
+ for i in range(len_hyp + 1):
+ cost_matrix[i][0] = i
+ for j in range(len_ref + 1):
+ cost_matrix[0][j] = j
+
+ for i in range(1, len_hyp + 1):
+ for j in range(1, len_ref + 1):
+ if hyp[i - 1] == ref[j - 1]:
+ cost_matrix[i][j] = cost_matrix[i - 1][j - 1]
+ else:
+ substitution = cost_matrix[i - 1][j - 1] + 1
+ insertion = cost_matrix[i - 1][j] + 1
+ deletion = cost_matrix[i][j - 1] + 1
+
+ compare_val = [substitution, insertion, deletion]
+
+ min_val = min(compare_val)
+ operation_idx = compare_val.index(min_val) + 1
+ cost_matrix[i][j] = min_val
+ ops_matrix[i][j] = operation_idx
+
+ match_idx = []
+ i = len_hyp
+ j = len_ref
+ rst = {
+ 'nwords': len_ref,
+ 'cor': 0,
+ 'wrong': 0,
+ 'ins': 0,
+ 'del': 0,
+ 'sub': 0
+ }
+ while i >= 0 or j >= 0:
+ i_idx = max(0, i)
+ j_idx = max(0, j)
+
+ if ops_matrix[i_idx][j_idx] == 0: # correct
+ if i - 1 >= 0 and j - 1 >= 0:
+ match_idx.append((j - 1, i - 1))
+ rst['cor'] += 1
+
+ i -= 1
+ j -= 1
+
+ elif ops_matrix[i_idx][j_idx] == 2: # insert
+ i -= 1
+ rst['ins'] += 1
+
+ elif ops_matrix[i_idx][j_idx] == 3: # delete
+ j -= 1
+ rst['del'] += 1
+
+ elif ops_matrix[i_idx][j_idx] == 1: # substitute
+ i -= 1
+ j -= 1
+ rst['sub'] += 1
+
+ if i < 0 and j >= 0:
+ rst['del'] += 1
+ elif j < 0 and i >= 0:
+ rst['ins'] += 1
+
+ match_idx.reverse()
+ wrong_cnt = cost_matrix[len_hyp][len_ref]
+ rst['wrong'] = wrong_cnt
+
+ return rst
+
+def print_cer_detail(rst):
+ return ("(" + "nwords=" + str(rst['nwords']) + ",cor=" + str(rst['cor'])
+ + ",ins=" + str(rst['ins']) + ",del=" + str(rst['del']) + ",sub="
+ + str(rst['sub']) + ") corr:" + '{:.2%}'.format(rst['cor']/rst['nwords'])
+ + ",cer:" + '{:.2%}'.format(rst['wrong']/rst['nwords']))
+
+if __name__ == '__main__':
+ if len(sys.argv) != 4:
+ print("usage : python compute-wer.py test.ref test.hyp test.wer")
+ sys.exit(0)
+
+ ref_file = sys.argv[1]
+ hyp_file = sys.argv[2]
+ cer_detail_file = sys.argv[3]
+ compute_wer(ref_file, hyp_file, cer_detail_file)
diff --git a/examples/industrial_data_pretraining/paraformer/finetune.sh b/examples/industrial_data_pretraining/paraformer/finetune.sh
index 394861b..8bdd8da 100644
--- a/examples/industrial_data_pretraining/paraformer/finetune.sh
+++ b/examples/industrial_data_pretraining/paraformer/finetune.sh
@@ -6,10 +6,10 @@
#git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}
## generate jsonl from wav.scp and text.txt
-python funasr/datasets/audio_datasets/scp2jsonl.py \
-++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
-++data_type_list='["source", "target"]' \
-++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl
+#python funasr/datasets/audio_datasets/scp2jsonl.py \
+#++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
+#++data_type_list='["source", "target"]' \
+#++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl
# torchrun \
@@ -24,5 +24,4 @@
++dataset_conf.batch_type="example" \
++train_conf.max_epoch=2 \
++dataset_conf.num_workers=4 \
-+output_dir="outputs/debug/ckpt/funasr2/exp2" \
-+debug="true"
\ No newline at end of file
++output_dir="outputs/debug/ckpt/funasr2/exp2"
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/paraformer/infer.sh b/examples/industrial_data_pretraining/paraformer/infer_demo.sh
similarity index 98%
rename from examples/industrial_data_pretraining/paraformer/infer.sh
rename to examples/industrial_data_pretraining/paraformer/infer_demo.sh
index 7491e98..f9a03f9 100644
--- a/examples/industrial_data_pretraining/paraformer/infer.sh
+++ b/examples/industrial_data_pretraining/paraformer/infer_demo.sh
@@ -9,3 +9,6 @@
+output_dir="./outputs/debug" \
+device="cpu" \
+
+
+
--
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