def runner(self):
""" The runner parses the options selected and then calls
the backend functions from WatsonSTT class
"""
try:
account_details = prompt(self.account_details(), style=custom_style_2)
if account_details['watson_stt_url'] == "None":
print()
print("Attempting to read in url from configuration file")
try:
path = Path('./keys/conf.ini').resolve()
config = ConfigParser()
config.read(path)
self.url = config['URL']['WATSON_STT_URL']
print("Succesfully read URL.")
print()
except:
print("Uh oh! We failed to read the URL from the configuration file.")
raise ValueError("Failure to read URL from conf.ini file")
else:
url = account_details['watson_stt_url']
self.url = url
self._save_url(self.url)
if account_details['watson_stt_api_key'] == "None":
print("Attempting to read in API key from configuration file")
try:
path = Path('./keys/conf.ini').resolve()
config = ConfigParser()
config.read(path)
self.api_key = config['API_KEY']['WATSON_STT_API']
print("Succesfully read in API key.")
print()
except:
print("Uh oh! We failed to read the API Key from the configuration file.")
raise ValueError("Failure to read API key from conf.ini file")
else:
api_key = account_details['watson_stt_api_key']
self.api_key = api_key
self._save_api_key(api_key)
answers = prompt(self.main_questions(), style=custom_style_2)
model_options = answers['custom_models_options']
for model_option in model_options:
if 'Train' in model_option:
# ask train questions
train = prompt(self.train_questions(), style=custom_style_2)
model_name = train['model_name']
model_descr = train['model_description']
oov_file_path = train['oov_file_path']
try:
stt = WatsonSTT(url=self.url)
stt.create_model(name=model_name, descr=model_descr)
stt.add_corpus(oov_file_path)
stt.training()
except Exception as e:
print(e)
if 'Update'in model_option:
update = prompt(self.update_questions(), style=custom_style_2)
model_customization_id = update['customization_id']
oov_file_path = update['oov_file_path']
try:
stt = WatsonSTT(url=self.url, customization_id=model_customization_id)
stt.add_corpus(corpus_path=oov_file_path)
stt.training()
except Exception as e:
print(e)
if 'Evaluate' in model_option:
model_id, evaluate_answers = self.evaluate_questions()
evaluate_models = prompt(evaluate_answers, style=custom_style_2)
path_to_audio_file = evaluate_models['audio_file']
evaluate_models = evaluate_models['models_evaluate']
custom_ids = [model_id[eval_model] for eval_model in evaluate_models]
for index, id in enumerate(custom_ids):
stt = WatsonSTT(url=self.url, customization_id=id)
try:
results = stt.transcribe(path_to_audio_file)
print()
print("*" * 60)
print(f"Transcription Results from {evaluate_models[index]}:")
pprint(results)
print()
print("*" * 60)
print()
except Exception as e:
print("*" * 60)
print()
print(f"Transcribing model {evaluate_models[index]} failed.")
print(e)
print("*" * 60)
print()
if 'See Available Models' in model_option:
models = WatsonSTT.all_model_status(url=self.url, api_key=self.api_key)
pprint(models)
# check if the model can be deleted
# error of the model should be 409
if 'Delete' in model_option:
delete_options = prompt(self.delete(), style=custom_style_2)
delete_options = delete_options['delete_all'].strip().lower()
if delete_options in ('y', 'yes'):
clean_up.clean_up(url=self.url, customization_ids=['all'])
elif delete_options in ('n', 'no'):
models_id, models_delete = self._delete_specific_models()
selected_models = prompt(models_delete, style=custom_style_2)
models_to_delete = selected_models['models_to_delete']
custom_ids_del_models = [models_id[del_model] for del_model in models_to_delete]
# delete the models
clean_up.clean_up(self.url, custom_ids_del_models)
else:
print("Only \'yes\' and \'no\' inputs allowed")
raise KeyboardInterrupt
except KeyboardInterrupt:
print("Action Cancelled")
def main():
"""Entry point of the CLI.
The program will accept either a "--visual" flag, where it then kicks out to the visual CLI.
Otherwise, it will accept name, descr, url, oov_file_path, verbose, delete, eval, or audio_file flags.
Passing certain combination of these flags will trigger different actions such as train, evaluate, or delete.
Args:
--visual: kick of the visual CLI. At this point, the control of the program is handed over to the visual.py file
--url: the url of the instance
--name: name of the model
--descr: the description of the model
--oov_file_path: the filepath of the grammar, vocabulary, or corpus used to train the model
--eval: transcribe a model
--verbose: list out the models
--audio_file: path to the audio file
Returns:
None
"""
# setting up the command line
argparser = argparse.ArgumentParser()
argparser.add_argument(
'--visual',
help="Run CLI in visual mode. All other flags passed are ignored",
action="store_true")
argparser.add_argument('--name', help="Name of the model")
argparser.add_argument('--descr',
help="A short description of the custom model")
argparser.add_argument('--url',
help="This is the URL of the Watson STT model. \
Found on the start page of the Watson STT tooling."
)
argparser.add_argument('--oov_file_path',
help="The path of the out-of-vocabulary \
file (the corpus, words, or grammar)"
)
argparser.add_argument('-v', '--verbose', '--list_models', help="Shows you all \
of the models trained on this account" , \
action="store_true")
argparser.add_argument(
'--delete',
nargs='+',
help="Pass the customization id of the models to delete")
argparser.add_argument(
'--eval',
help="Evaluate the trained model against an audio-file. \
\nPass in the \'customization_id\' of the model or \
pass \'latest\' to train the latest trained model. \
\nThe \'audio_file\' flag must be set as well!"
)
argparser.add_argument('--audio_file',
help="The path of the audio file to transcribe.")
args = argparser.parse_args()
visual = args.visual
name = args.name
descr = args.descr
url = args.url
file_path = args.oov_file_path
verbose = args.verbose
delete = args.delete
evaluate = args.eval
audio_file = args.audio_file
if visual:
VisualSTT().runner()
else:
if url is None:
raise Exception("Must pass URL")
# kick of training
if name and descr and url and file_path:
custom_stt = WatsonSTT(url=url)
custom_stt.create_model(name=name, descr=descr)
custom_stt.add_corpus(file_path)
custom_stt.training()
# just add the corpus
# @TODO: how to create a model and train with an existing corpus?
# @TODO: is this feature even neccesary?
if url and file_path and name is None is file_path is None:
print("Adding corpus...")
# @TODO: training a model with an existing uploaded corpus
custom_stt = WatsonSTT(url=url)
custom_stt.add_corpus(file_path)
print("Finished adding corpus")
# print out the models
if url and verbose:
print("Retrieving Models...")
model_status(url)
if url and evaluate and audio_file:
# pass in customization id
print("Checking audio file...")
path = Path(audio_file)
if not path.exists() and not path.is_file():
raise FileExistsError("Cannot find audio file")
if evaluate == "latest":
models = model_status(url, print=0)
models = models['customizations']
if len(models) == 0:
print(
"You do not have any trained models. Please create and train a model before evaluating."
)
return
# convert the date string into date object
for model in models:
model['created'] = _to_date(model['created'])
models = sorted(models, key=itemgetter('created'), reverse=True)
evaluate = models[0]['customization_id']
print("Transcribing the audio file...")
custom_stt = WatsonSTT(url=url, customization_id=evaluate)
results = custom_stt.transcribe(audio_file)
print("Transcribing finished")
print()
pprint(results)
if url and delete:
clean_up.clean_up(url, delete)