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benchmark.py
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benchmark.py
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import math
import os
import random
from argparse import ArgumentParser
from collections import namedtuple
from concurrent.futures import ProcessPoolExecutor
from typing import *
import editdistance
from dataset import *
from engine import *
from normalizer import Normalizer
WorkerResult = namedtuple('WorkerResult', ['num_errors', 'num_words', 'audio_sec', 'process_sec'])
RESULTS_FOLDER = os.path.join(os.path.dirname(__file__), "results")
def process(
engine: Engines,
engine_params: Dict[str, Any],
dataset: Datasets,
dataset_folder: str,
indices: Sequence[int]) -> WorkerResult:
engine = Engine.create(engine, **engine_params)
dataset = Dataset.create(dataset, folder=dataset_folder)
normalizer = Normalizer()
error_count = 0
word_count = 0
for index in indices:
audio_path, ref_transcript = dataset.get(index)
transcript = engine.transcribe(audio_path)
ref_sentence = ref_transcript.strip('\n ').lower()
ref_words = normalizer.to_american(normalizer.normalize_abbreviations(ref_sentence)).split()
transcribed_sentence = transcript.strip('\n ').lower()
transcribed_words = normalizer.to_american(normalizer.normalize_abbreviations(transcribed_sentence)).split()
error_count += editdistance.eval(ref_words, transcribed_words)
word_count += len(ref_words)
engine.delete()
return WorkerResult(
num_errors=error_count,
num_words=word_count,
audio_sec=engine.audio_sec(),
process_sec=engine.process_sec())
def main():
parser = ArgumentParser()
parser.add_argument('--engine', required=True, choices=[x.value for x in Engines])
parser.add_argument('--dataset', required=True, choices=[x.value for x in Datasets])
parser.add_argument('--dataset-folder', required=True)
parser.add_argument('--aws-profile')
parser.add_argument('--azure-speech-key')
parser.add_argument('--azure-speech-location')
parser.add_argument('--google-application-credentials')
parser.add_argument('--deepspeech-pbmm')
parser.add_argument('--deepspeech-scorer')
parser.add_argument('--picovoice-access-key')
parser.add_argument('--watson-speech-to-text-api-key')
parser.add_argument('--watson-speech-to-text-url')
parser.add_argument('--num-examples', type=int, default=None)
parser.add_argument('--num-workers', type=int, default=os.cpu_count())
args = parser.parse_args()
engine = Engines(args.engine)
dataset_type = Datasets(args.dataset)
dataset_folder = args.dataset_folder
num_examples = args.num_examples
num_workers = args.num_workers
engine_params = dict()
if engine is Engines.AMAZON_TRANSCRIBE:
if args.aws_profile is None:
raise ValueError("`aws-profile` is required")
os.environ['AWS_PROFILE'] = args.aws_profile
elif engine is Engines.AZURE_SPEECH_TO_TEXT:
if args.azure_speech_key is None or args.azure_speech_location is None:
raise ValueError("`azure-speech-key` and `azure-speech-location` are required")
engine_params['azure_speech_key'] = args.azure_speech_key
engine_params['azure_speech_location'] = args.azure_speech_location
elif engine is Engines.GOOGLE_SPEECH_TO_TEXT or engine == Engines.GOOGLE_SPEECH_TO_TEXT_ENHANCED:
if args.google_application_credentials is None:
raise ValueError("`google-application-credentials` is required")
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = args.google_application_credentials
elif engine is Engines.PICOVOICE_CHEETAH:
if args.picovoice_access_key is None:
raise ValueError("`picovoice-access-key` is required")
engine_params['access_key'] = args.picovoice_access_key
elif engine is Engines.PICOVOICE_LEOPARD:
if args.picovoice_access_key is None:
raise ValueError("`picovoice-access-key` is required")
engine_params['access_key'] = args.picovoice_access_key
elif engine is Engines.IBM_WATSON_SPEECH_TO_TEXT:
if args.watson_speech_to_text_api_key is None or args.watson_speech_to_text_url is None:
raise ValueError("`watson-speech-to-text-api-key` and `watson-speech-to-text-url` are required")
engine_params['watson_speech_to_text_api_key'] = args.watson_speech_to_text_api_key
engine_params['watson_speech_to_text_url'] = args.watson_speech_to_text_url
dataset = Dataset.create(dataset_type, folder=dataset_folder)
indices = list(range(dataset.size()))
random.shuffle(indices)
if args.num_examples is not None:
indices = indices[:num_examples]
chunk = math.ceil(len(indices) / num_workers)
futures = []
with ProcessPoolExecutor(num_workers) as executor:
for i in range(num_workers):
future = executor.submit(
process,
engine=engine,
engine_params=engine_params,
dataset=dataset_type,
dataset_folder=dataset_folder,
indices=indices[i * chunk: (i + 1) * chunk]
)
futures.append(future)
res = [x.result() for x in futures]
num_errors = sum(x.num_errors for x in res)
num_words = sum(x.num_words for x in res)
rtf = sum(x.process_sec for x in res) / sum(x.audio_sec for x in res)
word_error_rate = 100 * float(num_errors) / num_words
results_log_path = os.path.join(RESULTS_FOLDER, dataset_type.value, f"{str(engine)}.log")
os.makedirs(os.path.dirname(results_log_path), exist_ok=True)
with open(results_log_path, "w") as f:
f.write(f"WER: {str(word_error_rate)}\n")
f.write(f"RTF: {str(rtf)}\n")
print(f'WER: {word_error_rate:.2f}')
print(f'RTF: {rtf}')
if __name__ == '__main__':
main()