speech_recognition/FunASR
1
0
Fork 0
mirror of https://github.com/modelscope/FunASR synced 2025-09-15 14:48:36 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Merge pull request #491 from alibaba-damo-academy/main

Browse Source 
update to main
This commit is contained in:
zhifu gao 2023-05-10 19:17:04 +08:00 committed by GitHub
commit 7fe37e0352
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
23 changed files with 543 additions and 190 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md
View File

@ -1 +1 @@
../TEMPLATE/README.md
../../TEMPLATE/README.md

1
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.sh
View File

@ -1 +0,0 @@
../TEMPLATE/infer.sh

103
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.sh Normal file
View File

@ -0,0 +1,103 @@
#!/usr/bin/env bash
set -e
set -u
set -o pipefail
stage=1
stop_stage=2
model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
data_dir="./data/test"
output_dir="./results"
batch_size=64
gpu_inference=true # whether to perform gpu decoding
gpuid_list="0,1" # set gpus, e.g., gpuid_list="0,1"
njob=64 # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob
checkpoint_dir=
checkpoint_name="valid.cer_ctc.ave.pb"
. utils/parse_options.sh || exit 1;
if ${gpu_inference} == "true"; then
nj=$(echo $gpuid_list | awk -F "," '{print NF}')
else
nj=$njob
batch_size=1
gpuid_list=""
for JOB in $(seq ${nj}); do
gpuid_list=$gpuid_list"-1,"
done
fi
mkdir -p $output_dir/split
split_scps=""
for JOB in $(seq ${nj}); do
split_scps="$split_scps $output_dir/split/wav.$JOB.scp"
done
perl utils/split_scp.pl ${data_dir}/wav.scp ${split_scps}
if [ -n "${checkpoint_dir}" ]; then
python utils/prepare_checkpoint.py ${model} ${checkpoint_dir} ${checkpoint_name}
model=${checkpoint_dir}/${model}
fi
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then
echo "Decoding ..."
gpuid_list_array=(${gpuid_list//,/ })
for JOB in $(seq ${nj}); do
{
id=$((JOB-1))
gpuid=${gpuid_list_array[$id]}
mkdir -p ${output_dir}/output.$JOB
python infer.py \
--model ${model} \
--audio_in ${output_dir}/split/wav.$JOB.scp \
--output_dir ${output_dir}/output.$JOB \
--batch_size ${batch_size} \
--gpuid ${gpuid}
}&
done
wait
mkdir -p ${output_dir}/1best_recog
for f in token score text; do
if [ -f "${output_dir}/output.1/1best_recog/${f}" ]; then
for i in $(seq "${nj}"); do
cat "${output_dir}/output.${i}/1best_recog/${f}"
done | sort -k1 >"${output_dir}/1best_recog/${f}"
fi
done
fi
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then
echo "Computing WER ..."
cp ${output_dir}/1best_recog/text ${output_dir}/1best_recog/text.proc
cp ${data_dir}/text ${output_dir}/1best_recog/text.ref
python utils/compute_wer.py ${output_dir}/1best_recog/text.ref ${output_dir}/1best_recog/text.proc ${output_dir}/1best_recog/text.cer
tail -n 3 ${output_dir}/1best_recog/text.cer
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ];then
echo "SpeechIO TIOBE textnorm"
echo "$0 --> Normalizing REF text ..."
./utils/textnorm_zh.py \
--has_key --to_upper \
${data_dir}/text \
${output_dir}/1best_recog/ref.txt
echo "$0 --> Normalizing HYP text ..."
./utils/textnorm_zh.py \
--has_key --to_upper \
${output_dir}/1best_recog/text.proc \
${output_dir}/1best_recog/rec.txt
grep -v $'\t$' ${output_dir}/1best_recog/rec.txt > ${output_dir}/1best_recog/rec_non_empty.txt
echo "$0 --> computing WER/CER and alignment ..."
./utils/error_rate_zh \
--tokenizer char \
--ref ${output_dir}/1best_recog/ref.txt \
--hyp ${output_dir}/1best_recog/rec_non_empty.txt \
${output_dir}/1best_recog/DETAILS.txt | tee ${output_dir}/1best_recog/RESULTS.txt
rm -rf ${output_dir}/1best_recog/rec.txt ${output_dir}/1best_recog/rec_non_empty.txt
fi

2
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/README.md
View File

@ -1 +1 @@
../TEMPLATE/README.md
../../TEMPLATE/README.md

2
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/infer.py
View File

@ -1 +1 @@
../TEMPLATE/infer.py
../../TEMPLATE/infer.py

1
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/infer.sh
View File

@ -1 +0,0 @@
../TEMPLATE/infer.sh

103
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/infer.sh Normal file
View File

@ -0,0 +1,103 @@
#!/usr/bin/env bash
set -e
set -u
set -o pipefail
stage=1
stop_stage=2
model="damo/speech_paraformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch"
data_dir="./data/test"
output_dir="./results"
batch_size=64
gpu_inference=true # whether to perform gpu decoding
gpuid_list="0,1" # set gpus, e.g., gpuid_list="0,1"
njob=64 # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob
checkpoint_dir=
checkpoint_name="valid.cer_ctc.ave.pb"
. utils/parse_options.sh || exit 1;
if ${gpu_inference} == "true"; then
nj=$(echo $gpuid_list | awk -F "," '{print NF}')
else
nj=$njob
batch_size=1
gpuid_list=""
for JOB in $(seq ${nj}); do
gpuid_list=$gpuid_list"-1,"
done
fi
mkdir -p $output_dir/split
split_scps=""
for JOB in $(seq ${nj}); do
split_scps="$split_scps $output_dir/split/wav.$JOB.scp"
done
perl utils/split_scp.pl ${data_dir}/wav.scp ${split_scps}
if [ -n "${checkpoint_dir}" ]; then
python utils/prepare_checkpoint.py ${model} ${checkpoint_dir} ${checkpoint_name}
model=${checkpoint_dir}/${model}
fi
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then
echo "Decoding ..."
gpuid_list_array=(${gpuid_list//,/ })
for JOB in $(seq ${nj}); do
{
id=$((JOB-1))
gpuid=${gpuid_list_array[$id]}
mkdir -p ${output_dir}/output.$JOB
python infer.py \
--model ${model} \
--audio_in ${output_dir}/split/wav.$JOB.scp \
--output_dir ${output_dir}/output.$JOB \
--batch_size ${batch_size} \
--gpuid ${gpuid}
}&
done
wait
mkdir -p ${output_dir}/1best_recog
for f in token score text; do
if [ -f "${output_dir}/output.1/1best_recog/${f}" ]; then
for i in $(seq "${nj}"); do
cat "${output_dir}/output.${i}/1best_recog/${f}"
done | sort -k1 >"${output_dir}/1best_recog/${f}"
fi
done
fi
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then
echo "Computing WER ..."
cp ${output_dir}/1best_recog/text ${output_dir}/1best_recog/text.proc
cp ${data_dir}/text ${output_dir}/1best_recog/text.ref
python utils/compute_wer.py ${output_dir}/1best_recog/text.ref ${output_dir}/1best_recog/text.proc ${output_dir}/1best_recog/text.cer
tail -n 3 ${output_dir}/1best_recog/text.cer
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ];then
echo "SpeechIO TIOBE textnorm"
echo "$0 --> Normalizing REF text ..."
./utils/textnorm_zh.py \
--has_key --to_upper \
${data_dir}/text \
${output_dir}/1best_recog/ref.txt
echo "$0 --> Normalizing HYP text ..."
./utils/textnorm_zh.py \
--has_key --to_upper \
${output_dir}/1best_recog/text.proc \
${output_dir}/1best_recog/rec.txt
grep -v $'\t$' ${output_dir}/1best_recog/rec.txt > ${output_dir}/1best_recog/rec_non_empty.txt
echo "$0 --> computing WER/CER and alignment ..."
./utils/error_rate_zh \
--tokenizer char \
--ref ${output_dir}/1best_recog/ref.txt \
--hyp ${output_dir}/1best_recog/rec_non_empty.txt \
${output_dir}/1best_recog/DETAILS.txt | tee ${output_dir}/1best_recog/RESULTS.txt
rm -rf ${output_dir}/1best_recog/rec.txt ${output_dir}/1best_recog/rec_non_empty.txt
fi

2
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/README.md
View File

@ -1 +1 @@
../TEMPLATE/README.md
../../TEMPLATE/README.md

2
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/infer.py
View File

@ -1 +1 @@
../TEMPLATE/infer.py
../../TEMPLATE/infer.py

1
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/infer.sh
View File

@ -1 +0,0 @@
../TEMPLATE/infer.sh

103
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/infer.sh Normal file
View File

@ -0,0 +1,103 @@
#!/usr/bin/env bash
set -e
set -u
set -o pipefail
stage=1
stop_stage=2
model="damo/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch"
data_dir="./data/test"
output_dir="./results"
batch_size=64
gpu_inference=true # whether to perform gpu decoding
gpuid_list="0,1" # set gpus, e.g., gpuid_list="0,1"
njob=64 # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob
checkpoint_dir=
checkpoint_name="valid.cer_ctc.ave.pb"
. utils/parse_options.sh || exit 1;
if ${gpu_inference} == "true"; then
nj=$(echo $gpuid_list | awk -F "," '{print NF}')
else
nj=$njob
batch_size=1
gpuid_list=""
for JOB in $(seq ${nj}); do
gpuid_list=$gpuid_list"-1,"
done
fi
mkdir -p $output_dir/split
split_scps=""
for JOB in $(seq ${nj}); do
split_scps="$split_scps $output_dir/split/wav.$JOB.scp"
done
perl utils/split_scp.pl ${data_dir}/wav.scp ${split_scps}
if [ -n "${checkpoint_dir}" ]; then
python utils/prepare_checkpoint.py ${model} ${checkpoint_dir} ${checkpoint_name}
model=${checkpoint_dir}/${model}
fi
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then
echo "Decoding ..."
gpuid_list_array=(${gpuid_list//,/ })
for JOB in $(seq ${nj}); do
{
id=$((JOB-1))
gpuid=${gpuid_list_array[$id]}
mkdir -p ${output_dir}/output.$JOB
python infer.py \
--model ${model} \
--audio_in ${output_dir}/split/wav.$JOB.scp \
--output_dir ${output_dir}/output.$JOB \
--batch_size ${batch_size} \
--gpuid ${gpuid}
}&
done
wait
mkdir -p ${output_dir}/1best_recog
for f in token score text; do
if [ -f "${output_dir}/output.1/1best_recog/${f}" ]; then
for i in $(seq "${nj}"); do
cat "${output_dir}/output.${i}/1best_recog/${f}"
done | sort -k1 >"${output_dir}/1best_recog/${f}"
fi
done
fi
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then
echo "Computing WER ..."
cp ${output_dir}/1best_recog/text ${output_dir}/1best_recog/text.proc
cp ${data_dir}/text ${output_dir}/1best_recog/text.ref
python utils/compute_wer.py ${output_dir}/1best_recog/text.ref ${output_dir}/1best_recog/text.proc ${output_dir}/1best_recog/text.cer
tail -n 3 ${output_dir}/1best_recog/text.cer
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ];then
echo "SpeechIO TIOBE textnorm"
echo "$0 --> Normalizing REF text ..."
./utils/textnorm_zh.py \
--has_key --to_upper \
${data_dir}/text \
${output_dir}/1best_recog/ref.txt
echo "$0 --> Normalizing HYP text ..."
./utils/textnorm_zh.py \
--has_key --to_upper \
${output_dir}/1best_recog/text.proc \
${output_dir}/1best_recog/rec.txt
grep -v $'\t$' ${output_dir}/1best_recog/rec.txt > ${output_dir}/1best_recog/rec_non_empty.txt
echo "$0 --> computing WER/CER and alignment ..."
./utils/error_rate_zh \
--tokenizer char \
--ref ${output_dir}/1best_recog/ref.txt \
--hyp ${output_dir}/1best_recog/rec_non_empty.txt \
${output_dir}/1best_recog/DETAILS.txt | tee ${output_dir}/1best_recog/RESULTS.txt
rm -rf ${output_dir}/1best_recog/rec.txt ${output_dir}/1best_recog/rec_non_empty.txt
fi

2
funasr/runtime/grpc/paraformer-server.cc
View File

@ -137,7 +137,7 @@ grpc::Status ASRServicer::Recognize(
stream->Write(res);
}
else {
FUNASR_RESULT Result= FunOfflineRecogPCMBuffer(AsrHanlde, tmp_data.c_str(), data_len_int, 16000, RASR_NONE, NULL);
FUNASR_RESULT Result= FunOfflineInferBuffer(AsrHanlde, tmp_data.c_str(), data_len_int, RASR_NONE, NULL, 16000);
std::string asr_result = ((FUNASR_RECOG_RESULT*)Result)->msg;
auto end_time = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::system_clock::now().time_since_epoch()).count();

28
funasr/runtime/onnxruntime/include/funasrruntime.h
View File

@ -46,15 +46,20 @@ typedef enum {
FUNASR_MODEL_PARAFORMER = 3,
}FUNASR_MODEL_TYPE;
typedef enum
{
FSMN_VAD_OFFLINE=0,
FSMN_VAD_ONLINE = 1,
}FSMN_VAD_MODE;
typedef void (* QM_CALLBACK)(int cur_step, int n_total); // n_total: total steps; cur_step: Current Step.
// ASR
_FUNASRAPI FUNASR_HANDLE FunASRInit(std::map<std::string, std::string>& model_path, int thread_num);
_FUNASRAPI FUNASR_RESULT FunASRRecogBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len, FUNASR_MODE mode, QM_CALLBACK fn_callback);
_FUNASRAPI FUNASR_RESULT FunASRRecogPCMBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len, int sampling_rate, FUNASR_MODE mode, QM_CALLBACK fn_callback);
_FUNASRAPI FUNASR_RESULT FunASRRecogPCMFile(FUNASR_HANDLE handle, const char* sz_filename, int sampling_rate, FUNASR_MODE mode, QM_CALLBACK fn_callback);
_FUNASRAPI FUNASR_RESULT FunASRRecogFile(FUNASR_HANDLE handle, const char* sz_wavfile, FUNASR_MODE mode, QM_CALLBACK fn_callback);
// buffer
_FUNASRAPI FUNASR_RESULT FunASRInferBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len, FUNASR_MODE mode, QM_CALLBACK fn_callback, int sampling_rate=16000);
// file, support wav & pcm
_FUNASRAPI FUNASR_RESULT FunASRInfer(FUNASR_HANDLE handle, const char* sz_filename, FUNASR_MODE mode, QM_CALLBACK fn_callback, int sampling_rate=16000);
_FUNASRAPI const char* FunASRGetResult(FUNASR_RESULT result,int n_index);
_FUNASRAPI const int FunASRGetRetNumber(FUNASR_RESULT result);
@ -63,9 +68,12 @@ _FUNASRAPI void FunASRUninit(FUNASR_HANDLE handle);
_FUNASRAPI const float FunASRGetRetSnippetTime(FUNASR_RESULT result);
// VAD
_FUNASRAPI FUNASR_HANDLE FsmnVadInit(std::map<std::string, std::string>& model_path, int thread_num);
_FUNASRAPI FUNASR_HANDLE FsmnVadInit(std::map<std::string, std::string>& model_path, int thread_num, FSMN_VAD_MODE mode=FSMN_VAD_OFFLINE);
// buffer
_FUNASRAPI FUNASR_RESULT FsmnVadInferBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len, FSMN_VAD_MODE mode, QM_CALLBACK fn_callback, int sampling_rate=16000);
// file, support wav & pcm
_FUNASRAPI FUNASR_RESULT FsmnVadInfer(FUNASR_HANDLE handle, const char* sz_filename, FSMN_VAD_MODE mode, QM_CALLBACK fn_callback, int sampling_rate=16000);
_FUNASRAPI FUNASR_RESULT FsmnVadWavFile(FUNASR_HANDLE handle, const char* sz_wavfile, FUNASR_MODE mode, QM_CALLBACK fn_callback);
_FUNASRAPI std::vector<std::vector<int>>* FsmnVadGetResult(FUNASR_RESULT result,int n_index);
_FUNASRAPI void FsmnVadFreeResult(FUNASR_RESULT result);
_FUNASRAPI void FsmnVadUninit(FUNASR_HANDLE handle);
@ -78,8 +86,10 @@ _FUNASRAPI void CTTransformerUninit(FUNASR_HANDLE handle);
//OfflineStream
_FUNASRAPI FUNASR_HANDLE FunOfflineInit(std::map<std::string, std::string>& model_path, int thread_num);
_FUNASRAPI FUNASR_RESULT FunOfflineRecogFile(FUNASR_HANDLE handle, const char* sz_wavfile, FUNASR_MODE mode, QM_CALLBACK fn_callback);
_FUNASRAPI FUNASR_RESULT FunOfflineRecogPCMBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len, int sampling_rate, FUNASR_MODE mode, QM_CALLBACK fn_callback);
// buffer
_FUNASRAPI FUNASR_RESULT FunOfflineInferBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len, FUNASR_MODE mode, QM_CALLBACK fn_callback, int sampling_rate=16000);
// file, support wav & pcm
_FUNASRAPI FUNASR_RESULT FunOfflineInfer(FUNASR_HANDLE handle, const char* sz_filename, FUNASR_MODE mode, QM_CALLBACK fn_callback, int sampling_rate=16000);
_FUNASRAPI void FunOfflineUninit(FUNASR_HANDLE handle);
#ifdef __cplusplus

4
funasr/runtime/onnxruntime/include/vad-model.h
View File

@ -16,7 +16,7 @@ class VadModel {
virtual void LoadConfigFromYaml(const char* filename)=0;
virtual void FbankKaldi(float sample_rate, std::vector<std::vector<float>> &vad_feats,
const std::vector<float> &waves)=0;
virtual std::vector<std::vector<float>> &LfrCmvn(std::vector<std::vector<float>> &vad_feats)=0;
virtual void LfrCmvn(std::vector<std::vector<float>> &vad_feats)=0;
virtual void Forward(
const std::vector<std::vector<float>> &chunk_feats,
std::vector<std::vector<float>> *out_prob)=0;
@ -24,6 +24,6 @@ class VadModel {
virtual void InitCache()=0;
};
VadModel *CreateVadModel(std::map<std::string, std::string>& model_path, int thread_num);
VadModel *CreateVadModel(std::map<std::string, std::string>& model_path, int thread_num, int mode);
} // namespace funasr
#endif

51
funasr/runtime/onnxruntime/readme.md
View File

@ -43,11 +43,10 @@ make
### funasr-onnx-offline
```shell
./funasr-onnx-offline [--wav-scp <string>] [--wav-path <string>]
[--punc-quant <string>] [--punc-dir <string>]
[--vad-quant <string>] [--vad-dir <string>]
[--quantize <string>] --model-dir <string>
[--] [--version] [-h]
./funasr-onnx-offline --model-dir <string> [--quantize <string>]
[--vad-dir <string>] [--vad-quant <string>]
[--punc-dir <string>] [--punc-quant <string>]
--wav-path <string> [--] [--version] [-h]
Where:
--model-dir <string>
(required) the asr model path, which contains model.onnx, config.yaml, am.mvn
@ -64,12 +63,13 @@ Where:
--punc-quant <string>
false (Default), load the model of model.onnx in punc_dir. If set true, load the model of model_quant.onnx in punc_dir
--wav-scp <string>
wave scp path
--wav-path <string>
wave file path
(required) the input could be:
wav_path, e.g.: asr_example.wav;
pcm_path, e.g.: asr_example.pcm;
wav.scp, kaldi style wav list (wav_id \t wav_path)
Required: --model-dir <string>
Required: --model-dir <string> --wav-path <string>
If use vad, please add: --vad-dir <string>
If use punc, please add: --punc-dir <string>
@ -84,20 +84,20 @@ For example:
### funasr-onnx-offline-vad
```shell
./funasr-onnx-offline-vad [--wav-scp <string>] [--wav-path <string>]
[--quantize <string>] --model-dir <string>
[--] [--version] [-h]
./funasr-onnx-offline-vad --model-dir <string> [--quantize <string>]
--wav-path <string> [--] [--version] [-h]
Where:
--model-dir <string>
(required) the vad model path, which contains model.onnx, vad.yaml, vad.mvn
--quantize <string>
false (Default), load the model of model.onnx in model_dir. If set true, load the model of model_quant.onnx in model_dir
--wav-scp <string>
wave scp path
--wav-path <string>
wave file path
(required) the input could be:
wav_path, e.g.: asr_example.wav;
pcm_path, e.g.: asr_example.pcm;
wav.scp, kaldi style wav list (wav_id \t wav_path)
Required: --model-dir <string>
Required: --model-dir <string> --wav-path <string>
For example:
./funasr-onnx-offline-vad \
@ -107,17 +107,17 @@ For example:
### funasr-onnx-offline-punc
```shell
./funasr-onnx-offline-punc [--txt-path <string>] [--quantize <string>]
--model-dir <string> [--] [--version] [-h]
./funasr-onnx-offline-punc --model-dir <string> [--quantize <string>]
--txt-path <string> [--] [--version] [-h]
Where:
--model-dir <string>
(required) the punc model path, which contains model.onnx, punc.yaml
--quantize <string>
false (Default), load the model of model.onnx in model_dir. If set true, load the model of model_quant.onnx in model_dir
--txt-path <string>
txt file path, one sentence per line
(required) txt file path, one sentence per line
Required: --model-dir <string>
Required: --model-dir <string> --txt-path <string>
For example:
./funasr-onnx-offline-punc \
@ -126,8 +126,8 @@ For example:
```
### funasr-onnx-offline-rtf
```shell
./funasr-onnx-offline-rtf --thread-num <int32_t> --wav-scp <string>
[--quantize <string>] --model-dir <string>
./funasr-onnx-offline-rtf --model-dir <string> [--quantize <string>]
--wav-path <string> --thread-num <int32_t>
[--] [--version] [-h]
Where:
--thread-num <int32_t>
@ -136,8 +136,11 @@ Where:
(required) the model path, which contains model.onnx, config.yaml, am.mvn
--quantize <string>
false (Default), load the model of model.onnx in model_dir. If set true, load the model of model_quant.onnx in model_dir
--wav-scp <string>
(required) wave scp path
--wav-path <string>
(required) the input could be:
wav_path, e.g.: asr_example.wav;
pcm_path, e.g.: asr_example.pcm;
wav.scp, kaldi style wav list (wav_id \t wav_path)
For example:
./funasr-onnx-offline-rtf \

5
funasr/runtime/onnxruntime/src/fsmn-vad.cpp
View File

@ -225,7 +225,7 @@ void FsmnVad::LoadCmvn(const char *filename)
}
}
std::vector<std::vector<float>> &FsmnVad::LfrCmvn(std::vector<std::vector<float>> &vad_feats) {
void FsmnVad::LfrCmvn(std::vector<std::vector<float>> &vad_feats) {
std::vector<std::vector<float>> out_feats;
int T = vad_feats.size();
@ -264,7 +264,6 @@ std::vector<std::vector<float>> &FsmnVad::LfrCmvn(std::vector<std::vector<float>
}
}
vad_feats = out_feats;
return vad_feats;
}
std::vector<std::vector<int>>
@ -272,7 +271,7 @@ FsmnVad::Infer(const std::vector<float> &waves) {
std::vector<std::vector<float>> vad_feats;
std::vector<std::vector<float>> vad_probs;
FbankKaldi(vad_sample_rate_, vad_feats, waves);
vad_feats = LfrCmvn(vad_feats);
LfrCmvn(vad_feats);
Forward(vad_feats, &vad_probs);
E2EVadModel vad_scorer = E2EVadModel();

2
funasr/runtime/onnxruntime/src/fsmn-vad.h
View File

@ -36,7 +36,7 @@ private:
void FbankKaldi(float sample_rate, std::vector<std::vector<float>> &vad_feats,
const std::vector<float> &waves);
std::vector<std::vector<float>> &LfrCmvn(std::vector<std::vector<float>> &vad_feats);
void LfrCmvn(std::vector<std::vector<float>> &vad_feats);
void Forward(
const std::vector<std::vector<float>> &chunk_feats,

2
funasr/runtime/onnxruntime/src/funasr-onnx-offline-punc.cpp
View File

@ -36,7 +36,7 @@ int main(int argc, char *argv[])
TCLAP::CmdLine cmd("funasr-onnx-offline-punc", ' ', "1.0");
TCLAP::ValueArg<std::string> model_dir("", MODEL_DIR, "the punc model path, which contains model.onnx, punc.yaml", true, "", "string");
TCLAP::ValueArg<std::string> quantize("", QUANTIZE, "false (Default), load the model of model.onnx in model_dir. If set true, load the model of model_quant.onnx in model_dir", false, "false", "string");
TCLAP::ValueArg<std::string> txt_path("", TXT_PATH, "txt file path, one sentence per line", false, "", "string");
TCLAP::ValueArg<std::string> txt_path("", TXT_PATH, "txt file path, one sentence per line", true, "", "string");
cmd.add(model_dir);
cmd.add(quantize);

32
funasr/runtime/onnxruntime/src/funasr-onnx-offline-rtf.cpp
View File

@ -39,7 +39,7 @@ void runReg(FUNASR_HANDLE asr_handle, vector<string> wav_list,
// warm up
for (size_t i = 0; i < 1; i++)
{
FUNASR_RESULT result=FunASRRecogFile(asr_handle, wav_list[0].c_str(), RASR_NONE, NULL);
FUNASR_RESULT result=FunASRInfer(asr_handle, wav_list[0].c_str(), RASR_NONE, NULL, 16000);
}
while (true) {
@ -50,7 +50,7 @@ void runReg(FUNASR_HANDLE asr_handle, vector<string> wav_list,
}
gettimeofday(&start, NULL);
FUNASR_RESULT result=FunASRRecogFile(asr_handle, wav_list[i].c_str(), RASR_NONE, NULL);
FUNASR_RESULT result=FunASRInfer(asr_handle, wav_list[i].c_str(), RASR_NONE, NULL, 16000);
gettimeofday(&end, NULL);
seconds = (end.tv_sec - start.tv_sec);
@ -77,6 +77,15 @@ void runReg(FUNASR_HANDLE asr_handle, vector<string> wav_list,
}
}
bool is_target_file(const std::string& filename, const std::string target) {
std::size_t pos = filename.find_last_of(".");
if (pos == std::string::npos) {
return false;
}
std::string extension = filename.substr(pos + 1);
return (extension == target);
}
void GetValue(TCLAP::ValueArg<std::string>& value_arg, string key, std::map<std::string, std::string>& model_path)
{
if (value_arg.isSet()){
@ -94,19 +103,19 @@ int main(int argc, char *argv[])
TCLAP::ValueArg<std::string> model_dir("", MODEL_DIR, "the model path, which contains model.onnx, config.yaml, am.mvn", true, "", "string");
TCLAP::ValueArg<std::string> quantize("", QUANTIZE, "false (Default), load the model of model.onnx in model_dir. If set true, load the model of model_quant.onnx in model_dir", false, "false", "string");
TCLAP::ValueArg<std::string> wav_scp("", WAV_SCP, "wave scp path", true, "", "string");
TCLAP::ValueArg<std::string> wav_path("", WAV_PATH, "the input could be: wav_path, e.g.: asr_example.wav; pcm_path, e.g.: asr_example.pcm; wav.scp, kaldi style wav list (wav_id \t wav_path)", true, "", "string");
TCLAP::ValueArg<std::int32_t> thread_num("", THREAD_NUM, "multi-thread num for rtf", true, 0, "int32_t");
cmd.add(model_dir);
cmd.add(quantize);
cmd.add(wav_scp);
cmd.add(wav_path);
cmd.add(thread_num);
cmd.parse(argc, argv);
std::map<std::string, std::string> model_path;
GetValue(model_dir, MODEL_DIR, model_path);
GetValue(quantize, QUANTIZE, model_path);
GetValue(wav_scp, WAV_SCP, model_path);
GetValue(wav_path, WAV_PATH, model_path);
struct timeval start, end;
gettimeofday(&start, NULL);
@ -125,10 +134,14 @@ int main(int argc, char *argv[])
// read wav_scp
vector<string> wav_list;
if(model_path.find(WAV_SCP)!=model_path.end()){
ifstream in(model_path.at(WAV_SCP));
string wav_path_ = model_path.at(WAV_PATH);
if(is_target_file(wav_path_, "wav") || is_target_file(wav_path_, "pcm")){
wav_list.emplace_back(wav_path_);
}
else if(is_target_file(wav_path_, "scp")){
ifstream in(wav_path_);
if (!in.is_open()) {
LOG(ERROR) << "Failed to open file: " << model_path.at(WAV_SCP);
LOG(ERROR) << "Failed to open file: " << model_path.at(WAV_SCP) ;
return 0;
}
string line;
@ -140,6 +153,9 @@ int main(int argc, char *argv[])
wav_list.emplace_back(column2);
}
in.close();
}else{
LOG(ERROR)<<"Please check the wav extension!";
exit(-1);
}
// 多线程测试

31
funasr/runtime/onnxruntime/src/funasr-onnx-offline-vad.cpp
View File

@ -21,6 +21,15 @@
using namespace std;
bool is_target_file(const std::string& filename, const std::string target) {
std::size_t pos = filename.find_last_of(".");
if (pos == std::string::npos) {
return false;
}
std::string extension = filename.substr(pos + 1);
return (extension == target);
}
void GetValue(TCLAP::ValueArg<std::string>& value_arg, string key, std::map<std::string, std::string>& model_path)
{
if (value_arg.isSet()){
@ -58,20 +67,17 @@ int main(int argc, char *argv[])
TCLAP::ValueArg<std::string> model_dir("", MODEL_DIR, "the vad model path, which contains model.onnx, vad.yaml, vad.mvn", true, "", "string");
TCLAP::ValueArg<std::string> quantize("", QUANTIZE, "false (Default), load the model of model.onnx in model_dir. If set true, load the model of model_quant.onnx in model_dir", false, "false", "string");
TCLAP::ValueArg<std::string> wav_path("", WAV_PATH, "wave file path", false, "", "string");
TCLAP::ValueArg<std::string> wav_scp("", WAV_SCP, "wave scp path", false, "", "string");
TCLAP::ValueArg<std::string> wav_path("", WAV_PATH, "the input could be: wav_path, e.g.: asr_example.wav; pcm_path, e.g.: asr_example.pcm; wav.scp, kaldi style wav list (wav_id \t wav_path)", true, "", "string");
cmd.add(model_dir);
cmd.add(quantize);
cmd.add(wav_path);
cmd.add(wav_scp);
cmd.parse(argc, argv);
std::map<std::string, std::string> model_path;
GetValue(model_dir, MODEL_DIR, model_path);
GetValue(quantize, QUANTIZE, model_path);
GetValue(wav_path, WAV_PATH, model_path);
GetValue(wav_scp, WAV_SCP, model_path);
struct timeval start, end;
gettimeofday(&start, NULL);
@ -89,14 +95,14 @@ int main(int argc, char *argv[])
long modle_init_micros = ((seconds * 1000000) + end.tv_usec) - (start.tv_usec);
LOG(INFO) << "Model initialization takes " << (double)modle_init_micros / 1000000 << " s";
// read wav_path and wav_scp
// read wav_path
vector<string> wav_list;
if(model_path.find(WAV_PATH)!=model_path.end()){
wav_list.emplace_back(model_path.at(WAV_PATH));
string wav_path_ = model_path.at(WAV_PATH);
if(is_target_file(wav_path_, "wav") || is_target_file(wav_path_, "pcm")){
wav_list.emplace_back(wav_path_);
}
if(model_path.find(WAV_SCP)!=model_path.end()){
ifstream in(model_path.at(WAV_SCP));
else if(is_target_file(wav_path_, "scp")){
ifstream in(wav_path_);
if (!in.is_open()) {
LOG(ERROR) << "Failed to open file: " << model_path.at(WAV_SCP) ;
return 0;
@ -110,13 +116,16 @@ int main(int argc, char *argv[])
wav_list.emplace_back(column2);
}
in.close();
}else{
LOG(ERROR)<<"Please check the wav extension!";
exit(-1);
}
float snippet_time = 0.0f;
long taking_micros = 0;
for(auto& wav_file : wav_list){
gettimeofday(&start, NULL);
FUNASR_RESULT result=FsmnVadWavFile(vad_hanlde, wav_file.c_str(), RASR_NONE, NULL);
FUNASR_RESULT result=FsmnVadInfer(vad_hanlde, wav_file.c_str(), FSMN_VAD_OFFLINE, NULL, 16000);
gettimeofday(&end, NULL);
seconds = (end.tv_sec - start.tv_sec);
taking_micros += ((seconds * 1000000) + end.tv_usec) - (start.tv_usec);

31
funasr/runtime/onnxruntime/src/funasr-onnx-offline.cpp
View File

@ -20,6 +20,15 @@
using namespace std;
bool is_target_file(const std::string& filename, const std::string target) {
std::size_t pos = filename.find_last_of(".");
if (pos == std::string::npos) {
return false;
}
std::string extension = filename.substr(pos + 1);
return (extension == target);
}
void GetValue(TCLAP::ValueArg<std::string>& value_arg, string key, std::map<std::string, std::string>& model_path)
{
if (value_arg.isSet()){
@ -41,8 +50,7 @@ int main(int argc, char** argv)
TCLAP::ValueArg<std::string> punc_dir("", PUNC_DIR, "the punc model path, which contains model.onnx, punc.yaml", false, "", "string");
TCLAP::ValueArg<std::string> punc_quant("", PUNC_QUANT, "false (Default), load the model of model.onnx in punc_dir. If set true, load the model of model_quant.onnx in punc_dir", false, "false", "string");
TCLAP::ValueArg<std::string> wav_path("", WAV_PATH, "wave file path", false, "", "string");
TCLAP::ValueArg<std::string> wav_scp("", WAV_SCP, "wave scp path", false, "", "string");
TCLAP::ValueArg<std::string> wav_path("", WAV_PATH, "the input could be: wav_path, e.g.: asr_example.wav; pcm_path, e.g.: asr_example.pcm; wav.scp, kaldi style wav list (wav_id \t wav_path)", true, "", "string");
cmd.add(model_dir);
cmd.add(quantize);
@ -51,7 +59,6 @@ int main(int argc, char** argv)
cmd.add(punc_dir);
cmd.add(punc_quant);
cmd.add(wav_path);
cmd.add(wav_scp);
cmd.parse(argc, argv);
std::map<std::string, std::string> model_path;
@ -62,7 +69,6 @@ int main(int argc, char** argv)
GetValue(punc_dir, PUNC_DIR, model_path);
GetValue(punc_quant, PUNC_QUANT, model_path);
GetValue(wav_path, WAV_PATH, model_path);
GetValue(wav_scp, WAV_SCP, model_path);
struct timeval start, end;
gettimeofday(&start, NULL);
@ -80,14 +86,14 @@ int main(int argc, char** argv)
long modle_init_micros = ((seconds * 1000000) + end.tv_usec) - (start.tv_usec);
LOG(INFO) << "Model initialization takes " << (double)modle_init_micros / 1000000 << " s";
// read wav_path and wav_scp
// read wav_path
vector<string> wav_list;
if(model_path.find(WAV_PATH)!=model_path.end()){
wav_list.emplace_back(model_path.at(WAV_PATH));
string wav_path_ = model_path.at(WAV_PATH);
if(is_target_file(wav_path_, "wav") || is_target_file(wav_path_, "pcm")){
wav_list.emplace_back(wav_path_);
}
if(model_path.find(WAV_SCP)!=model_path.end()){
ifstream in(model_path.at(WAV_SCP));
else if(is_target_file(wav_path_, "scp")){
ifstream in(wav_path_);
if (!in.is_open()) {
LOG(ERROR) << "Failed to open file: " << model_path.at(WAV_SCP) ;
return 0;
@ -101,13 +107,16 @@ int main(int argc, char** argv)
wav_list.emplace_back(column2);
}
in.close();
}else{
LOG(ERROR)<<"Please check the wav extension!";
exit(-1);
}
float snippet_time = 0.0f;
long taking_micros = 0;
for(auto& wav_file : wav_list){
gettimeofday(&start, NULL);
FUNASR_RESULT result=FunOfflineRecogFile(asr_hanlde, wav_file.c_str(), RASR_NONE, NULL);
FUNASR_RESULT result=FunOfflineInfer(asr_hanlde, wav_file.c_str(), RASR_NONE, NULL, 16000);
gettimeofday(&end, NULL);
seconds = (end.tv_sec - start.tv_sec);
taking_micros += ((seconds * 1000000) + end.tv_usec) - (start.tv_usec);

201
funasr/runtime/onnxruntime/src/funasrruntime.cpp
View File

@ -11,9 +11,9 @@ extern "C" {
return mm;
}
_FUNASRAPI FUNASR_HANDLE FsmnVadInit(std::map<std::string, std::string>& model_path, int thread_num)
_FUNASRAPI FUNASR_HANDLE FsmnVadInit(std::map<std::string, std::string>& model_path, int thread_num, FSMN_VAD_MODE mode)
{
funasr::VadModel* mm = funasr::CreateVadModel(model_path, thread_num);
funasr::VadModel* mm = funasr::CreateVadModel(model_path, thread_num, mode);
return mm;
}
@ -30,36 +30,7 @@ extern "C" {
}
// APIs for ASR Infer
_FUNASRAPI FUNASR_RESULT FunASRRecogBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len, FUNASR_MODE mode, QM_CALLBACK fn_callback)
{
funasr::Model* recog_obj = (funasr::Model*)handle;
if (!recog_obj)
return nullptr;
int32_t sampling_rate = -1;
funasr::Audio audio(1);
if (!audio.LoadWav(sz_buf, n_len, &sampling_rate))
return nullptr;
float* buff;
int len;
int flag=0;
funasr::FUNASR_RECOG_RESULT* p_result = new funasr::FUNASR_RECOG_RESULT;
p_result->snippet_time = audio.GetTimeLen();
int n_step = 0;
int n_total = audio.GetQueueSize();
while (audio.Fetch(buff, len, flag) > 0) {
string msg = recog_obj->Forward(buff, len, flag);
p_result->msg += msg;
n_step++;
if (fn_callback)
fn_callback(n_step, n_total);
}
return p_result;
}
_FUNASRAPI FUNASR_RESULT FunASRRecogPCMBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len, int sampling_rate, FUNASR_MODE mode, QM_CALLBACK fn_callback)
_FUNASRAPI FUNASR_RESULT FunASRInferBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len, FUNASR_MODE mode, QM_CALLBACK fn_callback, int sampling_rate)
{
funasr::Model* recog_obj = (funasr::Model*)handle;
if (!recog_obj)
@ -87,23 +58,32 @@ extern "C" {
return p_result;
}
_FUNASRAPI FUNASR_RESULT FunASRRecogPCMFile(FUNASR_HANDLE handle, const char* sz_filename, int sampling_rate, FUNASR_MODE mode, QM_CALLBACK fn_callback)
_FUNASRAPI FUNASR_RESULT FunASRInfer(FUNASR_HANDLE handle, const char* sz_filename, FUNASR_MODE mode, QM_CALLBACK fn_callback, int sampling_rate)
{
funasr::Model* recog_obj = (funasr::Model*)handle;
if (!recog_obj)
return nullptr;
funasr::Audio audio(1);
if (!audio.LoadPcmwav(sz_filename, &sampling_rate))
return nullptr;
if(funasr::is_target_file(sz_filename, "wav")){
int32_t sampling_rate_ = -1;
if(!audio.LoadWav(sz_filename, &sampling_rate_))
return nullptr;
}else if(funasr::is_target_file(sz_filename, "pcm")){
if (!audio.LoadPcmwav(sz_filename, &sampling_rate))
return nullptr;
}else{
LOG(ERROR)<<"Wrong wav extension";
exit(-1);
}
float* buff;
int len;
int flag = 0;
funasr::FUNASR_RECOG_RESULT* p_result = new funasr::FUNASR_RECOG_RESULT;
p_result->snippet_time = audio.GetTimeLen();
int n_step = 0;
int n_total = audio.GetQueueSize();
funasr::FUNASR_RECOG_RESULT* p_result = new funasr::FUNASR_RECOG_RESULT;
p_result->snippet_time = audio.GetTimeLen();
while (audio.Fetch(buff, len, flag) > 0) {
string msg = recog_obj->Forward(buff, len, flag);
p_result->msg += msg;
@ -115,45 +95,15 @@ extern "C" {
return p_result;
}
_FUNASRAPI FUNASR_RESULT FunASRRecogFile(FUNASR_HANDLE handle, const char* sz_wavfile, FUNASR_MODE mode, QM_CALLBACK fn_callback)
{
funasr::Model* recog_obj = (funasr::Model*)handle;
if (!recog_obj)
return nullptr;
int32_t sampling_rate = -1;
funasr::Audio audio(1);
if(!audio.LoadWav(sz_wavfile, &sampling_rate))
return nullptr;
float* buff;
int len;
int flag = 0;
int n_step = 0;
int n_total = audio.GetQueueSize();
funasr::FUNASR_RECOG_RESULT* p_result = new funasr::FUNASR_RECOG_RESULT;
p_result->snippet_time = audio.GetTimeLen();
while (audio.Fetch(buff, len, flag) > 0) {
string msg = recog_obj->Forward(buff, len, flag);
p_result->msg+= msg;
n_step++;
if (fn_callback)
fn_callback(n_step, n_total);
}
return p_result;
}
// APIs for VAD Infer
_FUNASRAPI FUNASR_RESULT FsmnVadWavFile(FUNASR_HANDLE handle, const char* sz_wavfile, FUNASR_MODE mode, QM_CALLBACK fn_callback)
_FUNASRAPI FUNASR_RESULT FsmnVadInferBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len, FSMN_VAD_MODE mode, QM_CALLBACK fn_callback, int sampling_rate)
{
funasr::VadModel* vad_obj = (funasr::VadModel*)handle;
if (!vad_obj)
return nullptr;
int32_t sampling_rate = -1;
funasr::Audio audio(1);
if(!audio.LoadWav(sz_wavfile, &sampling_rate))
if (!audio.LoadPcmwav(sz_buf, n_len, &sampling_rate))
return nullptr;
funasr::FUNASR_VAD_RESULT* p_result = new funasr::FUNASR_VAD_RESULT;
@ -166,6 +116,35 @@ extern "C" {
return p_result;
}
_FUNASRAPI FUNASR_RESULT FsmnVadInfer(FUNASR_HANDLE handle, const char* sz_filename, FSMN_VAD_MODE mode, QM_CALLBACK fn_callback, int sampling_rate)
{
funasr::VadModel* vad_obj = (funasr::VadModel*)handle;
if (!vad_obj)
return nullptr;
funasr::Audio audio(1);
if(funasr::is_target_file(sz_filename, "wav")){
int32_t sampling_rate_ = -1;
if(!audio.LoadWav(sz_filename, &sampling_rate_))
return nullptr;
}else if(funasr::is_target_file(sz_filename, "pcm")){
if (!audio.LoadPcmwav(sz_filename, &sampling_rate))
return nullptr;
}else{
LOG(ERROR)<<"Wrong wav extension";
exit(-1);
}
funasr::FUNASR_VAD_RESULT* p_result = new funasr::FUNASR_VAD_RESULT;
p_result->snippet_time = audio.GetTimeLen();
vector<std::vector<int>> vad_segments;
audio.Split(vad_obj, vad_segments);
p_result->segments = new vector<std::vector<int>>(vad_segments);
return p_result;
}
// APIs for PUNC Infer
_FUNASRAPI const std::string CTTransformerInfer(FUNASR_HANDLE handle, const char* sz_sentence, FUNASR_MODE mode, QM_CALLBACK fn_callback)
{
@ -178,43 +157,7 @@ extern "C" {
}
// APIs for Offline-stream Infer
_FUNASRAPI FUNASR_RESULT FunOfflineRecogFile(FUNASR_HANDLE handle, const char* sz_wavfile, FUNASR_MODE mode, QM_CALLBACK fn_callback)
{
funasr::OfflineStream* offline_stream = (funasr::OfflineStream*)handle;
if (!offline_stream)
return nullptr;
int32_t sampling_rate = -1;
funasr::Audio audio(1);
if(!audio.LoadWav(sz_wavfile, &sampling_rate))
return nullptr;
if(offline_stream->UseVad()){
audio.Split(offline_stream);
}
float* buff;
int len;
int flag = 0;
int n_step = 0;
int n_total = audio.GetQueueSize();
funasr::FUNASR_RECOG_RESULT* p_result = new funasr::FUNASR_RECOG_RESULT;
p_result->snippet_time = audio.GetTimeLen();
while (audio.Fetch(buff, len, flag) > 0) {
string msg = (offline_stream->asr_handle)->Forward(buff, len, flag);
p_result->msg+= msg;
n_step++;
if (fn_callback)
fn_callback(n_step, n_total);
}
if(offline_stream->UsePunc()){
string punc_res = (offline_stream->punc_handle)->AddPunc((p_result->msg).c_str());
p_result->msg = punc_res;
}
return p_result;
}
_FUNASRAPI FUNASR_RESULT FunOfflineRecogPCMBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len, int sampling_rate, FUNASR_MODE mode, QM_CALLBACK fn_callback)
_FUNASRAPI FUNASR_RESULT FunOfflineInferBuffer(FUNASR_HANDLE handle, const char* sz_buf, int n_len, FUNASR_MODE mode, QM_CALLBACK fn_callback, int sampling_rate)
{
funasr::OfflineStream* offline_stream = (funasr::OfflineStream*)handle;
if (!offline_stream)
@ -249,6 +192,50 @@ extern "C" {
return p_result;
}
_FUNASRAPI FUNASR_RESULT FunOfflineInfer(FUNASR_HANDLE handle, const char* sz_filename, FUNASR_MODE mode, QM_CALLBACK fn_callback, int sampling_rate)
{
funasr::OfflineStream* offline_stream = (funasr::OfflineStream*)handle;
if (!offline_stream)
return nullptr;
funasr::Audio audio(1);
if(funasr::is_target_file(sz_filename, "wav")){
int32_t sampling_rate_ = -1;
if(!audio.LoadWav(sz_filename, &sampling_rate_))
return nullptr;
}else if(funasr::is_target_file(sz_filename, "pcm")){
if (!audio.LoadPcmwav(sz_filename, &sampling_rate))
return nullptr;
}else{
LOG(ERROR)<<"Wrong wav extension";
exit(-1);
}
if(offline_stream->UseVad()){
audio.Split(offline_stream);
}
float* buff;
int len;
int flag = 0;
int n_step = 0;
int n_total = audio.GetQueueSize();
funasr::FUNASR_RECOG_RESULT* p_result = new funasr::FUNASR_RECOG_RESULT;
p_result->snippet_time = audio.GetTimeLen();
while (audio.Fetch(buff, len, flag) > 0) {
string msg = (offline_stream->asr_handle)->Forward(buff, len, flag);
p_result->msg+= msg;
n_step++;
if (fn_callback)
fn_callback(n_step, n_total);
}
if(offline_stream->UsePunc()){
string punc_res = (offline_stream->punc_handle)->AddPunc((p_result->msg).c_str());
p_result->msg = punc_res;
}
return p_result;
}
_FUNASRAPI const int FunASRGetRetNumber(FUNASR_RESULT result)
{
if (!result)

9
funasr/runtime/onnxruntime/src/util.cpp
View File

@ -180,4 +180,13 @@ void Glu(Tensor<float> *din, Tensor<float> *dout)
}
}
bool is_target_file(const std::string& filename, const std::string target) {
std::size_t pos = filename.find_last_of(".");
if (pos == std::string::npos) {
return false;
}
std::string extension = filename.substr(pos + 1);
return (extension == target);
}
} // namespace funasr

1
funasr/runtime/onnxruntime/src/util.h
View File

@ -25,6 +25,7 @@ extern void FindMax(float *din, int len, float &max_val, int &max_idx);
extern void Glu(Tensor<float> *din, Tensor<float> *dout);
string PathAppend(const string &p1, const string &p2);
bool is_target_file(const std::string& filename, const std::string target);
} // namespace funasr
#endif

8
funasr/runtime/onnxruntime/src/vad-model.cpp
View File

@ -1,10 +1,14 @@
#include "precomp.h"
namespace funasr {
VadModel *CreateVadModel(std::map<std::string, std::string>& model_path, int thread_num)
VadModel *CreateVadModel(std::map<std::string, std::string>& model_path, int thread_num, int mode)
{
VadModel *mm;
mm = new FsmnVad();
if(mode == FSMN_VAD_OFFLINE){
mm = new FsmnVad();
}else{
LOG(ERROR)<<"Online fsmn vad not imp!";
}
string vad_model_path;
string vad_cmvn_path;

4
funasr/runtime/websocket/websocketsrv.cpp
View File

@ -25,8 +25,8 @@ void WebSocketServer::do_decoder(const std::vector<char>& buffer,
if (!buffer.empty()) {
// fout.write(buffer.data(), buffer.size());
// feed data to asr engine
FUNASR_RESULT Result = FunOfflineRecogPCMBuffer(
asr_hanlde, buffer.data(), buffer.size(), 16000, RASR_NONE, NULL);
FUNASR_RESULT Result = FunOfflineInferBuffer(
asr_hanlde, buffer.data(), buffer.size(), RASR_NONE, NULL, 16000);
std::string asr_result =
((FUNASR_RECOG_RESULT*)Result)->msg; // get decode result