/
streaming_server.py
executable file
·857 lines (722 loc) · 27 KB
/
streaming_server.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
#!/usr/bin/env python3
# Copyright 2022-2023 Xiaomi Corp.
#
"""
A server for streaming ASR recognition. By streaming it means the audio samples
are coming in real-time. You don't need to wait until all audio samples are
captured before sending them for recognition.
It supports multiple clients sending at the same time.
Usage:
./streaming_server.py --help
Example:
(1) Without a certificate
python3 ./python-api-examples/streaming_server.py \
--encoder ./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.onnx \
--decoder ./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx \
--joiner ./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.onnx \
--tokens ./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt
(2) With a certificate
(a) Generate a certificate first:
cd python-api-examples/web
./generate-certificate.py
cd ../..
(b) Start the server
python3 ./python-api-examples/streaming_server.py \
--encoder ./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.onnx \
--decoder ./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx \
--joiner ./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.onnx \
--tokens ./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt \
--certificate ./python-api-examples/web/cert.pem
Please refer to
https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-transducer/index.html
https://k2-fsa.github.io/sherpa/onnx/pretrained_models/wenet/index.html
to download pre-trained models.
The model in the above help messages is from
https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-transducer/zipformer-transducer-models.html#csukuangfj-sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20-bilingual-chinese-english
To use a WeNet streaming Conformer CTC model, please use
python3 ./python-api-examples/streaming_server.py \
--tokens=./sherpa-onnx-zh-wenet-wenetspeech/tokens.txt \
--wenet-ctc=./sherpa-onnx-zh-wenet-wenetspeech/model-streaming.onnx
"""
import argparse
import asyncio
import http
import json
import logging
import socket
import ssl
from concurrent.futures import ThreadPoolExecutor
from datetime import datetime
from pathlib import Path
from typing import List, Optional, Tuple
import numpy as np
import sherpa_onnx
import websockets
from http_server import HttpServer
def setup_logger(
log_filename: str,
log_level: str = "info",
use_console: bool = True,
) -> None:
"""Setup log level.
Args:
log_filename:
The filename to save the log.
log_level:
The log level to use, e.g., "debug", "info", "warning", "error",
"critical"
use_console:
True to also print logs to console.
"""
now = datetime.now()
date_time = now.strftime("%Y-%m-%d-%H-%M-%S")
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
log_filename = f"{log_filename}-{date_time}.txt"
Path(log_filename).parent.mkdir(parents=True, exist_ok=True)
level = logging.ERROR
if log_level == "debug":
level = logging.DEBUG
elif log_level == "info":
level = logging.INFO
elif log_level == "warning":
level = logging.WARNING
elif log_level == "critical":
level = logging.CRITICAL
logging.basicConfig(
filename=log_filename,
format=formatter,
level=level,
filemode="w",
)
if use_console:
console = logging.StreamHandler()
console.setLevel(level)
console.setFormatter(logging.Formatter(formatter))
logging.getLogger("").addHandler(console)
def add_model_args(parser: argparse.ArgumentParser):
parser.add_argument(
"--encoder",
type=str,
help="Path to the transducer encoder model",
)
parser.add_argument(
"--decoder",
type=str,
help="Path to the transducer decoder model.",
)
parser.add_argument(
"--joiner",
type=str,
help="Path to the transducer joiner model.",
)
parser.add_argument(
"--zipformer2-ctc",
type=str,
help="Path to the model file from zipformer2 ctc",
)
parser.add_argument(
"--wenet-ctc",
type=str,
help="Path to the model.onnx from WeNet",
)
parser.add_argument(
"--paraformer-encoder",
type=str,
help="Path to the paraformer encoder model",
)
parser.add_argument(
"--paraformer-decoder",
type=str,
help="Path to the transducer decoder model.",
)
parser.add_argument(
"--tokens",
type=str,
required=True,
help="Path to tokens.txt",
)
parser.add_argument(
"--sample-rate",
type=int,
default=16000,
help="Sample rate of the data used to train the model. "
"Caution: If your input sound files have a different sampling rate, "
"we will do resampling inside",
)
parser.add_argument(
"--feat-dim",
type=int,
default=80,
help="Feature dimension of the model",
)
parser.add_argument(
"--provider",
type=str,
default="cpu",
help="Valid values: cpu, cuda, coreml",
)
def add_decoding_args(parser: argparse.ArgumentParser):
parser.add_argument(
"--decoding-method",
type=str,
default="greedy_search",
help="""Decoding method to use. Current supported methods are:
- greedy_search
- modified_beam_search
""",
)
add_modified_beam_search_args(parser)
def add_hotwords_args(parser: argparse.ArgumentParser):
parser.add_argument(
"--hotwords-file",
type=str,
default="",
help="""
The file containing hotwords, one words/phrases per line, and for each
phrase the bpe/cjkchar are separated by a space. For example:
▁HE LL O ▁WORLD
你 好 世 界
""",
)
parser.add_argument(
"--hotwords-score",
type=float,
default=1.5,
help="""
The hotword score of each token for biasing word/phrase. Used only if
--hotwords-file is given.
""",
)
def add_modified_beam_search_args(parser: argparse.ArgumentParser):
parser.add_argument(
"--num-active-paths",
type=int,
default=4,
help="""Used only when --decoding-method is modified_beam_search.
It specifies number of active paths to keep during decoding.
""",
)
def add_blank_penalty_args(parser: argparse.ArgumentParser):
parser.add_argument(
"--blank-penalty",
type=float,
default=0.0,
help="""
The penalty applied on blank symbol during decoding.
Note: It is a positive value that would be applied to logits like
this `logits[:, 0] -= blank_penalty` (suppose logits.shape is
[batch_size, vocab] and blank id is 0).
""",
)
def add_endpointing_args(parser: argparse.ArgumentParser):
parser.add_argument(
"--use-endpoint",
type=int,
default=1,
help="1 to enable endpoiting. 0 to disable it",
)
parser.add_argument(
"--rule1-min-trailing-silence",
type=float,
default=2.4,
help="""This endpointing rule1 requires duration of trailing silence
in seconds) to be >= this value""",
)
parser.add_argument(
"--rule2-min-trailing-silence",
type=float,
default=1.2,
help="""This endpointing rule2 requires duration of trailing silence in
seconds) to be >= this value.""",
)
parser.add_argument(
"--rule3-min-utterance-length",
type=float,
default=20,
help="""This endpointing rule3 requires utterance-length (in seconds)
to be >= this value.""",
)
def get_args():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
add_model_args(parser)
add_decoding_args(parser)
add_endpointing_args(parser)
add_hotwords_args(parser)
add_blank_penalty_args(parser)
parser.add_argument(
"--port",
type=int,
default=6006,
help="The server will listen on this port",
)
parser.add_argument(
"--nn-pool-size",
type=int,
default=1,
help="Number of threads for NN computation and decoding.",
)
parser.add_argument(
"--max-batch-size",
type=int,
default=3,
help="""Max batch size for computation. Note if there are not enough
requests in the queue, it will wait for max_wait_ms time. After that,
even if there are not enough requests, it still sends the
available requests in the queue for computation.
""",
)
parser.add_argument(
"--max-wait-ms",
type=float,
default=10,
help="""Max time in millisecond to wait to build batches for inference.
If there are not enough requests in the stream queue to build a batch
of max_batch_size, it waits up to this time before fetching available
requests for computation.
""",
)
parser.add_argument(
"--max-message-size",
type=int,
default=(1 << 20),
help="""Max message size in bytes.
The max size per message cannot exceed this limit.
""",
)
parser.add_argument(
"--max-queue-size",
type=int,
default=32,
help="Max number of messages in the queue for each connection.",
)
parser.add_argument(
"--max-active-connections",
type=int,
default=200,
help="""Maximum number of active connections. The server will refuse
to accept new connections once the current number of active connections
equals to this limit.
""",
)
parser.add_argument(
"--num-threads",
type=int,
default=2,
help="Number of threads to run the neural network model",
)
parser.add_argument(
"--certificate",
type=str,
help="""Path to the X.509 certificate. You need it only if you want to
use a secure websocket connection, i.e., use wss:// instead of ws://.
You can use ./web/generate-certificate.py
to generate the certificate `cert.pem`.
Note ./web/generate-certificate.py will generate three files but you
only need to pass the generated cert.pem to this option.
""",
)
parser.add_argument(
"--doc-root",
type=str,
default="./python-api-examples/web",
help="Path to the web root",
)
return parser.parse_args()
def create_recognizer(args) -> sherpa_onnx.OnlineRecognizer:
if args.encoder:
recognizer = sherpa_onnx.OnlineRecognizer.from_transducer(
tokens=args.tokens,
encoder=args.encoder,
decoder=args.decoder,
joiner=args.joiner,
num_threads=args.num_threads,
sample_rate=args.sample_rate,
feature_dim=args.feat_dim,
decoding_method=args.decoding_method,
max_active_paths=args.num_active_paths,
hotwords_score=args.hotwords_score,
hotwords_file=args.hotwords_file,
blank_penalty=args.blank_penalty,
enable_endpoint_detection=args.use_endpoint != 0,
rule1_min_trailing_silence=args.rule1_min_trailing_silence,
rule2_min_trailing_silence=args.rule2_min_trailing_silence,
rule3_min_utterance_length=args.rule3_min_utterance_length,
provider=args.provider,
)
elif args.paraformer_encoder:
recognizer = sherpa_onnx.OnlineRecognizer.from_paraformer(
tokens=args.tokens,
encoder=args.paraformer_encoder,
decoder=args.paraformer_decoder,
num_threads=args.num_threads,
sample_rate=args.sample_rate,
feature_dim=args.feat_dim,
decoding_method=args.decoding_method,
enable_endpoint_detection=args.use_endpoint != 0,
rule1_min_trailing_silence=args.rule1_min_trailing_silence,
rule2_min_trailing_silence=args.rule2_min_trailing_silence,
rule3_min_utterance_length=args.rule3_min_utterance_length,
provider=args.provider,
)
elif args.zipformer2_ctc:
recognizer = sherpa_onnx.OnlineRecognizer.from_zipformer2_ctc(
tokens=args.tokens,
model=args.zipformer2_ctc,
num_threads=args.num_threads,
sample_rate=args.sample_rate,
feature_dim=args.feat_dim,
decoding_method=args.decoding_method,
enable_endpoint_detection=args.use_endpoint != 0,
rule1_min_trailing_silence=args.rule1_min_trailing_silence,
rule2_min_trailing_silence=args.rule2_min_trailing_silence,
rule3_min_utterance_length=args.rule3_min_utterance_length,
provider=args.provider,
)
elif args.wenet_ctc:
recognizer = sherpa_onnx.OnlineRecognizer.from_wenet_ctc(
tokens=args.tokens,
model=args.wenet_ctc,
num_threads=args.num_threads,
sample_rate=args.sample_rate,
feature_dim=args.feat_dim,
decoding_method=args.decoding_method,
enable_endpoint_detection=args.use_endpoint != 0,
rule1_min_trailing_silence=args.rule1_min_trailing_silence,
rule2_min_trailing_silence=args.rule2_min_trailing_silence,
rule3_min_utterance_length=args.rule3_min_utterance_length,
provider=args.provider,
)
else:
raise ValueError("Please provide a model")
return recognizer
def format_timestamps(timestamps: List[float]) -> List[str]:
return ["{:.3f}".format(t) for t in timestamps]
class StreamingServer(object):
def __init__(
self,
recognizer: sherpa_onnx.OnlineRecognizer,
nn_pool_size: int,
max_wait_ms: float,
max_batch_size: int,
max_message_size: int,
max_queue_size: int,
max_active_connections: int,
doc_root: str,
certificate: Optional[str] = None,
):
"""
Args:
recognizer:
An instance of online recognizer.
nn_pool_size:
Number of threads for the thread pool that is responsible for
neural network computation and decoding.
max_wait_ms:
Max wait time in milliseconds in order to build a batch of
`batch_size`.
max_batch_size:
Max batch size for inference.
max_message_size:
Max size in bytes per message.
max_queue_size:
Max number of messages in the queue for each connection.
max_active_connections:
Max number of active connections. Once number of active client
equals to this limit, the server refuses to accept new connections.
beam_search_params:
Dictionary containing all the parameters for beam search.
online_endpoint_config:
Config for endpointing.
doc_root:
Path to the directory where files like index.html for the HTTP
server locate.
certificate:
Optional. If not None, it will use secure websocket.
You can use ./web/generate-certificate.py to generate
it (the default generated filename is `cert.pem`).
"""
self.recognizer = recognizer
self.certificate = certificate
self.http_server = HttpServer(doc_root)
self.nn_pool_size = nn_pool_size
self.nn_pool = ThreadPoolExecutor(
max_workers=nn_pool_size,
thread_name_prefix="nn",
)
self.stream_queue = asyncio.Queue()
self.max_wait_ms = max_wait_ms
self.max_batch_size = max_batch_size
self.max_message_size = max_message_size
self.max_queue_size = max_queue_size
self.max_active_connections = max_active_connections
self.current_active_connections = 0
self.sample_rate = int(recognizer.config.feat_config.sampling_rate)
async def stream_consumer_task(self):
"""This function extracts streams from the queue, batches them up, sends
them to the neural network model for computation and decoding.
"""
while True:
if self.stream_queue.empty():
await asyncio.sleep(self.max_wait_ms / 1000)
continue
batch = []
try:
while len(batch) < self.max_batch_size:
item = self.stream_queue.get_nowait()
assert self.recognizer.is_ready(item[0])
batch.append(item)
except asyncio.QueueEmpty:
pass
stream_list = [b[0] for b in batch]
future_list = [b[1] for b in batch]
loop = asyncio.get_running_loop()
await loop.run_in_executor(
self.nn_pool,
self.recognizer.decode_streams,
stream_list,
)
for f in future_list:
self.stream_queue.task_done()
f.set_result(None)
async def compute_and_decode(
self,
stream: sherpa_onnx.OnlineStream,
) -> None:
"""Put the stream into the queue and wait it to be processed by the
consumer task.
Args:
stream:
The stream to be processed. Note: It is changed in-place.
"""
loop = asyncio.get_running_loop()
future = loop.create_future()
await self.stream_queue.put((stream, future))
await future
async def process_request(
self,
path: str,
request_headers: websockets.Headers,
) -> Optional[Tuple[http.HTTPStatus, websockets.Headers, bytes]]:
if "sec-websocket-key" not in request_headers:
# This is a normal HTTP request
if path == "/":
path = "/index.html"
if path in ("/upload.html", "/offline_record.html"):
response = r"""
<!doctype html><html><head>
<title>Speech recognition with next-gen Kaldi</title><body>
<h2>Only /streaming_record.html is available for the streaming server.<h2>
<br/>
<br/>
Go back to <a href="/streaming_record.html">/streaming_record.html</a>
</body></head></html>
"""
found = True
mime_type = "text/html"
else:
found, response, mime_type = self.http_server.process_request(path)
if isinstance(response, str):
response = response.encode("utf-8")
if not found:
status = http.HTTPStatus.NOT_FOUND
else:
status = http.HTTPStatus.OK
header = {"Content-Type": mime_type}
return status, header, response
if self.current_active_connections < self.max_active_connections:
self.current_active_connections += 1
return None
# Refuse new connections
status = http.HTTPStatus.SERVICE_UNAVAILABLE # 503
header = {"Hint": "The server is overloaded. Please retry later."}
response = b"The server is busy. Please retry later."
return status, header, response
async def run(self, port: int):
tasks = []
for i in range(self.nn_pool_size):
tasks.append(asyncio.create_task(self.stream_consumer_task()))
if self.certificate:
logging.info(f"Using certificate: {self.certificate}")
ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)
ssl_context.load_cert_chain(self.certificate)
else:
ssl_context = None
logging.info("No certificate provided")
async with websockets.serve(
self.handle_connection,
host="",
port=port,
max_size=self.max_message_size,
max_queue=self.max_queue_size,
process_request=self.process_request,
ssl=ssl_context,
):
ip_list = ["localhost"]
if ssl_context:
ip_list += ["0.0.0.0", "127.0.0.1"]
ip_list.append(socket.gethostbyname(socket.gethostname()))
proto = "http://" if ssl_context is None else "https://"
s = "Please visit one of the following addresses:\n\n"
for p in ip_list:
s += " " + proto + p + f":{port}" "\n"
if not ssl_context:
s += "\nSince you are not providing a certificate, you cannot "
s += "use your microphone from within the browser using "
s += "public IP addresses. Only localhost can be used."
s += "You also cannot use 0.0.0.0 or 127.0.0.1"
logging.info(s)
await asyncio.Future() # run forever
await asyncio.gather(*tasks) # not reachable
async def handle_connection(
self,
socket: websockets.WebSocketServerProtocol,
):
"""Receive audio samples from the client, process it, and send
decoding result back to the client.
Args:
socket:
The socket for communicating with the client.
"""
try:
await self.handle_connection_impl(socket)
except websockets.exceptions.ConnectionClosedError:
logging.info(f"{socket.remote_address} disconnected")
finally:
# Decrement so that it can accept new connections
self.current_active_connections -= 1
logging.info(
f"Disconnected: {socket.remote_address}. "
f"Number of connections: {self.current_active_connections}/{self.max_active_connections}" # noqa
)
async def handle_connection_impl(
self,
socket: websockets.WebSocketServerProtocol,
):
"""Receive audio samples from the client, process it, and send
decoding result back to the client.
Args:
socket:
The socket for communicating with the client.
"""
logging.info(
f"Connected: {socket.remote_address}. "
f"Number of connections: {self.current_active_connections}/{self.max_active_connections}" # noqa
)
stream = self.recognizer.create_stream()
segment = 0
while True:
samples = await self.recv_audio_samples(socket)
if samples is None:
break
# TODO(fangjun): At present, we assume the sampling rate
# of the received audio samples equal to --sample-rate
stream.accept_waveform(sample_rate=self.sample_rate, waveform=samples)
while self.recognizer.is_ready(stream):
await self.compute_and_decode(stream)
result = self.recognizer.get_result(stream)
message = {
"text": result,
"segment": segment,
}
if self.recognizer.is_endpoint(stream):
self.recognizer.reset(stream)
segment += 1
await socket.send(json.dumps(message))
tail_padding = np.zeros(int(self.sample_rate * 0.3)).astype(np.float32)
stream.accept_waveform(sample_rate=self.sample_rate, waveform=tail_padding)
stream.input_finished()
while self.recognizer.is_ready(stream):
await self.compute_and_decode(stream)
result = self.recognizer.get_result(stream)
message = {
"text": result,
"segment": segment,
}
await socket.send(json.dumps(message))
async def recv_audio_samples(
self,
socket: websockets.WebSocketServerProtocol,
) -> Optional[np.ndarray]:
"""Receive a tensor from the client.
Each message contains either a bytes buffer containing audio samples
in 16 kHz or contains "Done" meaning the end of utterance.
Args:
socket:
The socket for communicating with the client.
Returns:
Return a 1-D np.float32 tensor containing the audio samples or
return None.
"""
message = await socket.recv()
if message == "Done":
return None
return np.frombuffer(message, dtype=np.float32)
def check_args(args):
if args.encoder:
assert Path(args.encoder).is_file(), f"{args.encoder} does not exist"
assert Path(args.decoder).is_file(), f"{args.decoder} does not exist"
assert Path(args.joiner).is_file(), f"{args.joiner} does not exist"
assert args.paraformer_encoder is None, args.paraformer_encoder
assert args.paraformer_decoder is None, args.paraformer_decoder
assert args.zipformer2_ctc is None, args.zipformer2_ctc
assert args.wenet_ctc is None, args.wenet_ctc
elif args.paraformer_encoder:
assert Path(
args.paraformer_encoder
).is_file(), f"{args.paraformer_encoder} does not exist"
assert Path(
args.paraformer_decoder
).is_file(), f"{args.paraformer_decoder} does not exist"
elif args.zipformer2_ctc:
assert Path(
args.zipformer2_ctc
).is_file(), f"{args.zipformer2_ctc} does not exist"
elif args.wenet_ctc:
assert Path(args.wenet_ctc).is_file(), f"{args.wenet_ctc} does not exist"
else:
raise ValueError("Please provide a model")
if not Path(args.tokens).is_file():
raise ValueError(f"{args.tokens} does not exist")
if args.decoding_method not in (
"greedy_search",
"modified_beam_search",
):
raise ValueError(f"Unsupported decoding method {args.decoding_method}")
if args.decoding_method == "modified_beam_search":
assert args.num_active_paths > 0, args.num_active_paths
def main():
args = get_args()
logging.info(vars(args))
check_args(args)
recognizer = create_recognizer(args)
port = args.port
nn_pool_size = args.nn_pool_size
max_batch_size = args.max_batch_size
max_wait_ms = args.max_wait_ms
max_message_size = args.max_message_size
max_queue_size = args.max_queue_size
max_active_connections = args.max_active_connections
certificate = args.certificate
doc_root = args.doc_root
if certificate and not Path(certificate).is_file():
raise ValueError(f"{certificate} does not exist")
if not Path(doc_root).is_dir():
raise ValueError(f"Directory {doc_root} does not exist")
server = StreamingServer(
recognizer=recognizer,
nn_pool_size=nn_pool_size,
max_batch_size=max_batch_size,
max_wait_ms=max_wait_ms,
max_message_size=max_message_size,
max_queue_size=max_queue_size,
max_active_connections=max_active_connections,
certificate=certificate,
doc_root=doc_root,
)
asyncio.run(server.run(port))
if __name__ == "__main__":
log_filename = "log/log-streaming-server"
setup_logger(log_filename)
main()