speech_recognition/FunASR
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Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into main

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雾聪 2023-06-02 22:08:10 +08:00
commit eee6af2ece
12 changed files with 74 additions and 27 deletions
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1
README.md
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@ -13,7 +13,6 @@
| [**Highlights**](#highlights)
| [**Installation**](#installation)
| [**Docs**](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
| [**Tutorial_CN**](https://github.com/alibaba-damo-academy/FunASR/wiki#funasr%E7%94%A8%E6%88%B7%E6%89%8B%E5%86%8C)
| [**Papers**](https://github.com/alibaba-damo-academy/FunASR#citations)
| [**Runtime**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
| [**Model Zoo**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md)

21
egs_modelscope/asr/TEMPLATE/README.md
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@ -20,11 +20,13 @@ rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyu
print(rec_result)
```
#### [Paraformer-online Model](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary)
##### Streaming Decoding
```python
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
model_revision='v1.0.6',
model_revision='v1.0.4',
update_model='v1.0.4',
mode='paraformer_streaming'
)
import soundfile
@ -42,6 +44,23 @@ speech_chunk = speech[chunk_stride:chunk_stride+chunk_stride]
rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict)
print(rec_result)
```
##### Fake Streaming Decoding
```python
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
model_revision='v1.0.6',
update_model='v1.0.6',
mode="paraformer_fake_streaming"
)
audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav'
rec_result = inference_pipeline(audio_in=audio_in)
print(rec_result)
```
Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/241)
#### [UniASR Model](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary)

1
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo.py
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@ -5,6 +5,7 @@ inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
model_revision='v1.0.6',
update_model='v1.0.6',
mode="paraformer_fake_streaming"
)
audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav'

3
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo_online.py
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@ -14,7 +14,8 @@ os.environ["MODELSCOPE_CACHE"] = "./"
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
model_revision='v1.0.6',
model_revision='v1.0.4',
update_model='v1.0.4',
mode="paraformer_streaming"
)

1
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/demo.py
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@ -5,6 +5,7 @@ inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online',
model_revision='v1.0.6',
update_model='v1.0.6',
mode="paraformer_fake_streaming"
)
audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav'

3
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/demo_online.py
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@ -14,7 +14,8 @@ os.environ["MODELSCOPE_CACHE"] = "./"
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online',
model_revision='v1.0.6',
model_revision='v1.0.4',
update_model='v1.0.4',
mode="paraformer_streaming"
)

2
egs_modelscope/tp/TEMPLATE/README.md
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@ -11,7 +11,7 @@ from modelscope.utils.constant import Tasks
inference_pipeline = pipeline(
task=Tasks.speech_timestamp,
model='damo/speech_timestamp_prediction-v1-16k-offline',
output_dir=None)
model_revision='v1.1.0')
rec_result = inference_pipeline(
audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_timestamps.wav',

16
fun_text_processing/inverse_text_normalization/id/id_unit_test.tsv
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@ -1,10 +1,10 @@
dua ribu dua puluh dua 2022
tiga ribu 300
tiga ribu 3000
sembilan ribu sembilan ratus sembilan puluh sembilan 9999
seribu satu 100001
ribu 100
seribu satu 1001
ribu 1000
seribu 1000
seribu dua ratus delapan puluh sembilan 10289
seribu dua ratus delapan puluh sembilan 1289
ribu dua ratus delapan puluh sembilan 1289
nol satu dua tiga empat lima enam tujuh delapan sembilan 01 2345-6789
empat belas 14
@ -22,8 +22,8 @@ satu miliar 1 miliar
seratus dua puluh tiga 123
ratus dua puluh tiga 123
dua puluh empat maret 24 maret
ribu tujuh puluh enam 10076
seribu tujuh puluh enam 100076
ribu tujuh puluh enam rupiah 10076 rupiah
ribu tujuh puluh enam 1076
seribu tujuh puluh enam 1076
ribu tujuh puluh enam rupiah 1076 rupiah
tujuh puluh enam 76
ditambah enam dua dua satu enam lima tiga sembilan nol enam nol lima +62 21 6539-0605
ditambah enam dua dua satu enam lima tiga sembilan nol enam nol lima +62 21 6539-0605

1 dua ribu dua puluh dua 2022
2 tiga ribu 300 3000
3 sembilan ribu sembilan ratus sembilan puluh sembilan 9999
4 seribu satu 100001 1001
5 ribu 100 1000
6 seribu 1000
7 seribu dua ratus delapan puluh sembilan 10289 1289
8 ribu dua ratus delapan puluh sembilan 1289
9 nol satu dua tiga empat lima enam tujuh delapan sembilan 01 2345-6789
10 empat belas 14
22 seratus dua puluh tiga 123
23 ratus dua puluh tiga 123
24 dua puluh empat maret 24 maret
25 ribu tujuh puluh enam 10076 1076
26 seribu tujuh puluh enam 100076 1076
27 ribu tujuh puluh enam rupiah 10076 rupiah 1076 rupiah
28 tujuh puluh enam 76
29 ditambah enam dua dua satu enam lima tiga sembilan nol enam nol lima +62 21 6539-0605

15
fun_text_processing/inverse_text_normalization/id/taggers/cardinal.py
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@ -26,11 +26,10 @@ class CardinalFst(GraphFst):
graph_teen = pynini.string_file(get_abs_path("data/numbers/teen.tsv"))
graph_hundreds = pynini.string_file(get_abs_path("data/numbers/hundreds.tsv"))
graph_thousand = pynini.string_file(get_abs_path("data/numbers/thousand.tsv"))
graph_cents = pynini.cross("seratus", "100") | pynini.cross("ratus", "100") | pynini.union(graph_hundreds, pynutil.insert("0"))
graph_hundred = pynini.cross("ratus", "") | pynini.cross("seratus", "")
graph_hundred_component = pynini.union(graph_digit + delete_space + graph_hundred, pynutil.insert("00"))
graph_hundred_component = pynini.union(graph_digit + delete_space + graph_hundred, pynutil.insert("0"))
graph_hundred_component += delete_space
graph_hundred_component += pynini.union(
graph_teen | pynutil.insert("00"),
@ -44,8 +43,8 @@ class CardinalFst(GraphFst):
(graph_ties | pynutil.insert("0")) + delete_space + (
graph_digit | pynutil.insert("0")),
)
graph_hundred_component = graph_hundred_component | graph_cents | graph_one_hundred_component
graph_hundred_component = graph_hundred_component | graph_one_hundred_component
graph_hundred_component_at_least_one_none_zero_digit = graph_hundred_component @ (
pynini.closure(DAMO_DIGIT) + (DAMO_DIGIT - "0") + pynini.closure(DAMO_DIGIT)
)
@ -54,14 +53,12 @@ class CardinalFst(GraphFst):
)
graph_thousand = pynini.cross("ribu", "") | pynini.cross("seribu", "")
graph_one_thousand_component = pynini.union(pynini.cross("ribu", "1") | pynini.cross("seribu", "1"))
graph_thousand_cents = pynini.cross("seribu", "10") | pynini.cross("ribu","10") | pynini.union(graph_thousand, pynutil.insert(""))
graph_thousands = pynini.union(
graph_hundred_component_at_least_one_none_zero_digit + delete_space + (pynutil.delete("ribu") | pynutil.delete("seribu")),
pynutil.insert("000", weight=0.1),
)
graph_thousand_component = pynini.union(graph_digit + delete_space + graph_thousand, pynutil.insert("000"))
graph_thousand_component += delete_space
graph_thousands = graph_thousands | graph_thousand_cents | graph_thousand_component | graph_one_thousand_component
graph_thousands = graph_thousands | (pynutil.insert("00") + graph_one_thousand_component)
graph_million = pynini.union(
graph_hundred_component_at_least_one_none_zero_digit + delete_space + (pynutil.delete("juta") | pynutil.delete("sejuta")),

4
funasr/runtime/html5/static/main.js
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@ -145,7 +145,9 @@ function stop() {
isRec = false;
info_div.innerHTML="请等候...";
btnStop.disabled = true;
setTimeout(function(){btnStart.disabled = false;info_div.innerHTML="请点击开始";}, 3000 );
setTimeout(function(){
console.log("call stop ws!");
wsconnecter.wsStop();btnStart.disabled = false;info_div.innerHTML="请点击开始";}, 3000 );
rec.stop(function(blob,duration){
console.log(blob);

7
funasr/runtime/html5/static/wsconnecter.js
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@ -28,7 +28,11 @@ function WebSocketConnectMethod( config ) { //定义socket连接方法类
if ( 'WebSocket' in window ) {
speechSokt = new WebSocket( Uri ); // 定义socket连接对象
speechSokt.onopen = function(e){onOpen(e);}; // 定义响应函数
speechSokt.onclose = function(e){onClose(e);};
speechSokt.onclose = function(e){
console.log("onclose ws!");
speechSokt.close();
onClose(e);
};
speechSokt.onmessage = function(e){onMessage(e);};
speechSokt.onerror = function(e){onError(e);};
return 1;
@ -42,6 +46,7 @@ function WebSocketConnectMethod( config ) { //定义socket连接方法类
// 定义停止与发送函数
this.wsStop = function () {
if(speechSokt != undefined) {
console.log("stop ws!");
speechSokt.close();
}
};

27
funasr/runtime/python/websocket/wss_srv_asr.py
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@ -58,16 +58,36 @@ inference_pipeline_asr_online = pipeline(
model=args.asr_model_online,
ngpu=args.ngpu,
ncpu=args.ncpu,
model_revision='v1.0.6',
model_revision='v1.0.4',
update_model='v1.0.4',
mode='paraformer_streaming')
print("model loaded")
print("model loaded! only support one client at the same time now!!!!")
async def ws_reset(websocket):
print("ws reset now, total num is ",len(websocket_users))
websocket.param_dict_asr_online = {"cache": dict()}
websocket.param_dict_vad = {'in_cache': dict(), "is_final": True}
websocket.param_dict_asr_online["is_final"]=True
audio_in=b''.join(np.zeros(int(16000),dtype=np.int16))
inference_pipeline_vad(audio_in=audio_in, param_dict=websocket.param_dict_vad)
inference_pipeline_asr_online(audio_in=audio_in, param_dict=websocket.param_dict_asr_online)
await websocket.close()
async def clear_websocket():
for websocket in websocket_users:
await ws_reset(websocket)
websocket_users.clear()
async def ws_serve(websocket, path):
frames = []
frames_asr = []
frames_asr_online = []
global websocket_users
await clear_websocket()
websocket_users.add(websocket)
websocket.param_dict_asr = {}
websocket.param_dict_asr_online = {"cache": dict()}
@ -139,7 +159,8 @@ async def ws_serve(websocket, path):
except websockets.ConnectionClosed:
print("ConnectionClosed...", websocket_users)
print("ConnectionClosed...", websocket_users,flush=True)
await ws_reset(websocket)
websocket_users.remove(websocket)
except websockets.InvalidState:
print("InvalidState...")