def test_set_level(self, mock_get_logger, mock_stream_handler):
logger = MagicMock(spec=std_logging.Logger)
stream_handler = MagicMock(spec=std_logging.Handler)
mock_get_logger.return_value = logger
mock_stream_handler.return_value = stream_handler
driver = logging.make_driver()
request = Subject()
sink = logging.Sink(request=request)
driver.call(sink)
request.on_next(logging.SetLevel(logger='foo', level='DEBUG'))
mock_get_logger.assert_called_with('foo')
logger.setLevel.assert_called_with(std_logging.DEBUG)
mock_stream_handler.assert_called_with()
stream_handler.setLevel.assert_called_with(std_logging.DEBUG)
def deepspeech_server(sources):
argv = sources.argv.argv
stt = sources.httpd.route
stt_response = sources.deepspeech.text.share()
ds_logs = sources.deepspeech.log
config_data = sources.file.response
http_ds_error, route_ds_error = make_error_router()
args = argparse.argparse(
argv=argv.skip(1).subscribe_on(aio_scheduler),
parser=Observable.just(
argparse.Parser(description="deepspeech server")),
arguments=Observable.from_([
argparse.ArgumentDef(name='--config',
help="Path of the server configuration file")
]))
config_file = (args.filter(lambda i: i.key == 'config').map(
lambda i: file.Read(id='config', path=i.value)))
config = parse_config(config_data).subscribe_on(aio_scheduler)
logs_config = (config.flat_map(lambda i: Observable.from_(i.log.level).map(
lambda i: logging.SetLevel(logger=i.logger, level=i.level)).concat(
Observable.just(logging.SetLevelDone()))))
logs = Observable.merge(logs_config, ds_logs)
log_ready = sources.logging.response.take(1)
ds_stt = (stt.flat_map(lambda i: i.request).map(
lambda i: deepspeech.SpeechToText(data=i.data, context=i.context)))
ds_arg = (
# config is hot, the combine operator allows to keep its last value
# until logging is initialized
log_ready.combine_latest(
config, lambda _, i: i).map(lambda i: deepspeech.Initialize(
model=i.deepspeech.model,
alphabet=i.deepspeech.alphabet,
lm=i.deepspeech.lm,
trie=i.deepspeech.trie,
features=deepspeech.FeaturesParameters(
n_features=i.deepspeech.features.n_features,
n_context=i.deepspeech.features.n_context,
beam_width=i.deepspeech.features.beam_width,
lm_alpha=i.deepspeech.features.lm_alpha,
lm_beta=i.deepspeech.features.lm_beta,
) if i.deepspeech.features is not None else None)))
ds = ds_stt.merge(ds_arg)
http_init = (config.flat_map(lambda i: Observable.from_([
httpd.Initialize(request_max_size=i.server.http.request_max_size),
httpd.AddRoute(
methods=['POST'],
path='/stt',
id='stt',
),
httpd.StartServer(host=i.server.http.host, port=i.server.http.port),
])))
http_response = (stt_response.let(lambda x: route_ds_error(
x,
error_map=lambda e: httpd.Response(
data="Speech to text error".encode('utf-8'),
context=e.args[0].context,
status=500))).map(lambda i: httpd.Response(
data=i.text.encode('utf-8'),
context=i.context,
)))
http = Observable.merge(http_init, http_response, http_ds_error)
return DeepspeechSink(file=file.Sink(request=config_file),
logging=logging.Sink(request=logs),
deepspeech=deepspeech.Sink(speech=ds),
httpd=httpd.Sink(control=http))
def deepspeech_server(aio_scheduler, sources):
argv = sources.argv.argv
stt = sources.httpd.route
stt_response = sources.deepspeech.text
ds_logs = sources.deepspeech.log
http_ds_error, route_ds_error = make_error_router()
args = parse_arguments(argv)
read_request, read_response = args.pipe(
ops.map(lambda i: file.Read(id='config', path=i.value)),
file.read(sources.file.response),
)
read_request = read_request.pipe(
ops.subscribe_on(aio_scheduler),
)
config = parse_config(read_response)
logs_config = config.pipe(
ops.flat_map(lambda i: rx.from_(i.log.level, scheduler=ImmediateScheduler())),
ops.map(lambda i: logging.SetLevel(logger=i.logger, level=i.level)),
)
logs = rx.merge(logs_config, ds_logs)
ds_stt = stt.pipe(
ops.flat_map(lambda i: i.request),
ops.map(lambda i: deepspeech.SpeechToText(data=i.data, context=i.context)),
)
# config is hot, the combine operator allows to keep its last value
# until logging is initialized
ds_arg = config.pipe(
ops.map(lambda i: deepspeech.Initialize(
model=i.deepspeech.model,
scorer=deepspeech.Scorer(
scorer=getattr(i.deepspeech, 'scorer', None),
lm_alpha=getattr(i.deepspeech, 'lm_alpha', None),
lm_beta=getattr(i.deepspeech, 'lm_beta', None),
),
beam_width=getattr(i.deepspeech, 'beam_width', None),
)),
)
ds = rx.merge(ds_stt, ds_arg)
http_init = config.pipe(
ops.flat_map(lambda i: rx.from_([
httpd.Initialize(request_max_size=i.server.http.request_max_size),
httpd.AddRoute(
methods=['POST'],
path='/stt',
id='stt',
headers=MultiDict([('Content-Type', 'text/plain')]),
),
httpd.StartServer(
host=i.server.http.host,
port=i.server.http.port),
])),
)
http_response = stt_response.pipe(
route_ds_error(
error_map=lambda e: httpd.Response(
data="Speech to text error".encode('utf-8'),
context=e.args[0].context,
status=500
)),
ops.map(lambda i: httpd.Response(
data=i.text.encode('utf-8'),
context=i.context,
)),
)
http = rx.merge(http_init, http_response, http_ds_error)
return DeepspeechSink(
file=file.Sink(request=read_request),
logging=logging.Sink(request=logs),
deepspeech=deepspeech.Sink(speech=ds),
httpd=httpd.Sink(control=http)
)