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aishell example
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LauraGPT committed Feb 19, 2024
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179 changes: 82 additions & 97 deletions examples/aishell/paraformer/run.sh
Original file line number Diff line number Diff line change
Expand Up @@ -39,23 +39,14 @@ train_set=train
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}"

#inference_config=conf/decode_asr_transformer_noctc_1best.yaml
#inference_asr_model=valid.acc.ave_10best.pb

## 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=$njob
# _ngpu=0
#fi
config=train_asr_paraformer_conformer_12e_6d_2048_256.yaml
model_dir="baseline_$(basename "${config}" .yaml)_${lang}_${token_type}_${tag}"

inference_device="cuda" #"cpu"
inference_checkpoint="model.pt"
inference_scp="wav.scp"



if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
echo "stage -1: Data Download"
Expand Down Expand Up @@ -85,10 +76,10 @@ fi

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
Expand Down Expand Up @@ -116,90 +107,84 @@ fi

# 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


# 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
157 changes: 157 additions & 0 deletions examples/aishell/paraformer/utils/compute_wer.py
Original file line number Diff line number Diff line change
@@ -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)
11 changes: 5 additions & 6 deletions examples/industrial_data_pretraining/paraformer/finetune.sh
Original file line number Diff line number Diff line change
Expand Up @@ -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 \
Expand All @@ -24,5 +24,4 @@ python funasr/bin/train.py \
++dataset_conf.batch_type="example" \
++train_conf.max_epoch=2 \
++dataset_conf.num_workers=4 \
+output_dir="outputs/debug/ckpt/funasr2/exp2" \
+debug="true"
+output_dir="outputs/debug/ckpt/funasr2/exp2"
Original file line number Diff line number Diff line change
Expand Up @@ -9,3 +9,6 @@ python funasr/bin/inference.py \
+output_dir="./outputs/debug" \
+device="cpu" \




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