def main():
global line_count
print("AutoSub v0.1\n")
parser = argparse.ArgumentParser(description="AutoSub v0.1")
parser.add_argument('--model', required=True,
help='DeepSpeech model file')
parser.add_argument('--scorer',
help='DeepSpeech scorer file')
parser.add_argument('--file', required=True,
help='Input video file')
args = parser.parse_args()
ds_model = args.model
if not ds_model.endswith(".pbmm"):
print("Invalid model file. Exiting\n")
exit(1)
# Load DeepSpeech model
ds = Model(ds_model)
if args.scorer:
ds_scorer = args.scorer
if not ds_scorer.endswith(".scorer"):
print("Invalid scorer file. Running inference using only model file\n")
else:
ds.enableExternalScorer(ds_scorer)
input_file = args.file
print("\nInput file:", input_file)
base_directory = os.getcwd()
output_directory = os.path.join(base_directory, "output")
audio_directory = os.path.join(base_directory, "audio")
video_file_name = input_file.split("/")[-1].split(".")[0]
audio_file_name = os.path.join(audio_directory, video_file_name + ".wav")
srt_file_name = os.path.join(output_directory, video_file_name + ".srt")
# Extract audio from input video file
extract_audio(input_file, audio_file_name)
print("Splitting on silent parts in audio file")
silenceRemoval(audio_file_name)
# Output SRT file
file_handle = open(srt_file_name, "a+")
print("\nRunning inference:")
for file in tqdm(sort_alphanumeric(os.listdir(audio_directory))):
audio_segment_path = os.path.join(audio_directory, file)
# Dont run inference on the original audio file
if audio_segment_path.split("/")[-1] != audio_file_name.split("/")[-1]:
ds_process_audio(ds, audio_segment_path, file_handle)
print("\nSRT file saved to", srt_file_name)
file_handle.close()
def main():
global line_count
print("AutoSub v0.1\n")
parser = argparse.ArgumentParser(description="AutoSub v0.1")
parser.add_argument('--model', required=True,
help='DeepSpeech model file')
parser.add_argument('--scorer',
help='DeepSpeech scorer file')
parser.add_argument('--file', required=True,
help='Input video file')
parser.add_argument('--vtt', dest="vtt", action="store_true",
help='Output a vtt file with cue points for individual words instead of a srt file')
args = parser.parse_args()
ds_model = args.model
if not ds_model.endswith(".pbmm"):
print("Invalid model file. Exiting\n")
exit(1)
# Load DeepSpeech model
ds = Model(ds_model)
if args.scorer:
ds_scorer = args.scorer
if not ds_scorer.endswith(".scorer"):
print("Invalid scorer file. Running inference using only model file\n")
else:
ds.enableExternalScorer(ds_scorer)
input_file = args.file
print("\nInput file:", input_file)
base_directory = os.getcwd()
output_directory = os.path.join(base_directory, "output")
audio_directory = os.path.join(base_directory, "audio")
video_file_name = input_file.split(os.sep)[-1].split(".")[0]
audio_file_name = os.path.join(audio_directory, video_file_name + ".wav")
srt_extension = ".srt" if not args.vtt else ".vtt"
srt_file_name = os.path.join(output_directory, video_file_name + srt_extension)
# Extract audio from input video file
extract_audio(input_file, audio_file_name)
print("Splitting on silent parts in audio file")
silenceRemoval(audio_file_name)
# Output SRT or VTT file
file_handle = open(srt_file_name, "a+")
if args.vtt:
file_handle.write("WEBVTT\n")
file_handle.write("Kind: captions\n\n")
print("\nRunning inference:")
for file in tqdm(sort_alphanumeric(os.listdir(audio_directory))):
audio_segment_path = os.path.join(audio_directory, file)
# Dont run inference on the original audio file
if audio_segment_path.split(os.sep)[-1] != audio_file_name.split(os.sep)[-1]:
ds_process_audio(ds, audio_segment_path, file_handle, args.vtt)
if not args.vtt:
print("\nSRT file saved to", srt_file_name)
else:
print("\nVTT file saved to", srt_file_name)
file_handle.close()
# Clean audio/ directory
shutil.rmtree(audio_directory)
os.mkdir(audio_directory)