fix: 🐛 修复服务端 python ASREngine 无法使用conformer_talcs模型 (#3230)

* fix: 🐛 fix python ASREngine not pass codeswitch

* docs: 📝 Update Docs

* 修改模型判断方式
pull/3252/head
guanyc 1 year ago committed by GitHub
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@ -34,6 +34,8 @@ Currently the engine type supports two forms: python and inference (Paddle Infer
paddlespeech_server start --config_file ./conf/application.yaml
```
> **Note:** For mixed Chinese and English speech recognition, please use the `./conf/conformer_talcs_application.yaml` configuration file
Usage:
```bash
@ -85,6 +87,7 @@ Here are sample files for this ASR client demo that can be downloaded:
```bash
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/ch_zh_mix.wav
```
**Note:** The response time will be slightly longer when using the client for the first time
@ -92,8 +95,11 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
If `127.0.0.1` is not accessible, you need to use the actual service IP address.
```
```bash
paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
# Chinese and English mixed speech recognition, using `./conf/conformer_talcs_application.yaml` config file
paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./ch_zh_mix.wav
```
Usage:

@ -37,6 +37,8 @@
paddlespeech_server start --config_file ./conf/application.yaml
```
> **注意:** 中英文混合语音识别请使用 `./conf/conformer_talcs_application.yaml` 配置文件
使用方法:
```bash
@ -79,6 +81,8 @@
[2022-02-23 14:57:56] [INFO] [server.py:204] Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)
```
### 4. ASR 客户端使用方法
ASR 客户端的输入是一个 WAV 文件(`.wav`),并且采样率必须与模型的采样率相同。
@ -87,6 +91,7 @@ ASR 客户端的输入是一个 WAV 文件(`.wav`),并且采样率必须
```bash
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/ch_zh_mix.wav
```
**注意:** 初次使用客户端时响应时间会略长
@ -94,8 +99,11 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
`127.0.0.1` 不能访问,则需要使用实际服务 IP 地址
```
```bash
paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
# 中英文混合语音识别 , 请使用 `./conf/conformer_talcs_application.yaml` 配置文件
paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./ch_zh_mix.wav
```
使用帮助:

@ -0,0 +1,163 @@
# This is the parameter configuration file for PaddleSpeech Offline Serving.
#################################################################################
# SERVER SETTING #
#################################################################################
host: 0.0.0.0
port: 8090
# The task format in the engin_list is: <speech task>_<engine type>
# task choices = ['asr_python', 'asr_inference', 'tts_python', 'tts_inference', 'cls_python', 'cls_inference', 'text_python', 'vector_python']
protocol: 'http'
engine_list: ['asr_python', 'tts_python', 'cls_python', 'text_python', 'vector_python']
#################################################################################
# ENGINE CONFIG #
#################################################################################
################################### ASR #########################################
################### speech task: asr; engine_type: python #######################
asr_python:
model: 'conformer_talcs'
lang: 'zh_en'
sample_rate: 16000
cfg_path: # [optional]
ckpt_path: # [optional]
decode_method: 'attention_rescoring'
force_yes: True
codeswitch: True
device: # set 'gpu:id' or 'cpu'
################### speech task: asr; engine_type: inference #######################
asr_inference:
# model_type choices=['deepspeech2offline_aishell']
model_type: 'deepspeech2offline_aishell'
am_model: # the pdmodel file of am static model [optional]
am_params: # the pdiparams file of am static model [optional]
lang: 'zh'
sample_rate: 16000
cfg_path:
decode_method:
force_yes: True
am_predictor_conf:
device: # set 'gpu:id' or 'cpu'
switch_ir_optim: True
glog_info: False # True -> print glog
summary: True # False -> do not show predictor config
################################### TTS #########################################
################### speech task: tts; engine_type: python #######################
tts_python:
# am (acoustic model) choices=['speedyspeech_csmsc', 'fastspeech2_csmsc',
# 'fastspeech2_ljspeech', 'fastspeech2_aishell3',
# 'fastspeech2_vctk', 'fastspeech2_mix',
# 'tacotron2_csmsc', 'tacotron2_ljspeech']
am: 'fastspeech2_csmsc'
am_config:
am_ckpt:
am_stat:
phones_dict:
tones_dict:
speaker_dict:
# voc (vocoder) choices=['pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3',
# 'pwgan_vctk', 'mb_melgan_csmsc', 'style_melgan_csmsc',
# 'hifigan_csmsc', 'hifigan_ljspeech', 'hifigan_aishell3',
# 'hifigan_vctk', 'wavernn_csmsc']
voc: 'mb_melgan_csmsc'
voc_config:
voc_ckpt:
voc_stat:
# others
lang: 'zh'
device: # set 'gpu:id' or 'cpu'
################### speech task: tts; engine_type: inference #######################
tts_inference:
# am (acoustic model) choices=['speedyspeech_csmsc', 'fastspeech2_csmsc']
am: 'fastspeech2_csmsc'
am_model: # the pdmodel file of your am static model (XX.pdmodel)
am_params: # the pdiparams file of your am static model (XX.pdipparams)
am_sample_rate: 24000
phones_dict:
tones_dict:
speaker_dict:
am_predictor_conf:
device: # set 'gpu:id' or 'cpu'
switch_ir_optim: True
glog_info: False # True -> print glog
summary: True # False -> do not show predictor config
# voc (vocoder) choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc']
voc: 'mb_melgan_csmsc'
voc_model: # the pdmodel file of your vocoder static model (XX.pdmodel)
voc_params: # the pdiparams file of your vocoder static model (XX.pdipparams)
voc_sample_rate: 24000
voc_predictor_conf:
device: # set 'gpu:id' or 'cpu'
switch_ir_optim: True
glog_info: False # True -> print glog
summary: True # False -> do not show predictor config
# others
lang: 'zh'
################################### CLS #########################################
################### speech task: cls; engine_type: python #######################
cls_python:
# model choices=['panns_cnn14', 'panns_cnn10', 'panns_cnn6']
model: 'panns_cnn14'
cfg_path: # [optional] Config of cls task.
ckpt_path: # [optional] Checkpoint file of model.
label_file: # [optional] Label file of cls task.
device: # set 'gpu:id' or 'cpu'
################### speech task: cls; engine_type: inference #######################
cls_inference:
# model_type choices=['panns_cnn14', 'panns_cnn10', 'panns_cnn6']
model_type: 'panns_cnn14'
cfg_path:
model_path: # the pdmodel file of am static model [optional]
params_path: # the pdiparams file of am static model [optional]
label_file: # [optional] Label file of cls task.
predictor_conf:
device: # set 'gpu:id' or 'cpu'
switch_ir_optim: True
glog_info: False # True -> print glog
summary: True # False -> do not show predictor config
################################### Text #########################################
################### text task: punc; engine_type: python #######################
text_python:
task: punc
model_type: 'ernie_linear_p3_wudao'
lang: 'zh'
sample_rate: 16000
cfg_path: # [optional]
ckpt_path: # [optional]
vocab_file: # [optional]
device: # set 'gpu:id' or 'cpu'
################################### Vector ######################################
################### Vector task: spk; engine_type: python #######################
vector_python:
task: spk
model_type: 'ecapatdnn_voxceleb12'
sample_rate: 16000
cfg_path: # [optional]
ckpt_path: # [optional]
device: # set 'gpu:id' or 'cpu'

@ -67,13 +67,19 @@ class ASREngine(BaseEngine):
logger.error(e)
return False
cs = False
if self.config.lang == "zh_en" :
cs=True
self.executor._init_from_path(
model_type=self.config.model,
lang=self.config.lang,
sample_rate=self.config.sample_rate,
cfg_path=self.config.cfg_path,
decode_method=self.config.decode_method,
ckpt_path=self.config.ckpt_path)
ckpt_path=self.config.ckpt_path,
codeswitch=cs )
logger.info("Initialize ASR server engine successfully on device: %s." %
(self.device))

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