dasemu/Transcriptarr
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Code cleaning

Browse Source
This commit is contained in:
McCloudS
2024-03-14 00:09:59 -06:00
committed by GitHub
parent a7e286aa38
commit f2a874b65d
1 changed files with 19 additions and 19 deletions
Download Patch File Download Diff File Expand all files Collapse all files

38
subgen/subgen.py
View File

@@ -260,7 +260,7 @@ def receive_jellyfin_webhook(
fullpath = get_jellyfin_file_name(ItemId, jellyfinserver, jellyfintoken)
logging.debug(f"Path of file: {fullpath}")
gen_subtitles(path_mapping(fullpath), transcribe_or_translate, True)
titles(path_mapping(fullpath), transcribe_or_translate, True)
try:
refresh_jellyfin_metadata(ItemId, jellyfinserver, jellyfintoken)
logging.info(f"Metadata for item {ItemId} refreshed successfully.")
@@ -287,7 +287,7 @@ def receive_emby_webhook(
if((event == "library.new" and procaddedmedia) or (event == "playback.start" and procmediaonplay)):
logging.debug("Path of file: " + fullpath)
gen_subtitles(path_mapping(fullpath), transcribe_or_translate, True)
titles(path_mapping(fullpath), transcribe_or_translate, True)
else:
return {"This doesn't appear to be a properly configured Emby webhook, please review the instructions again!"}
@@ -315,16 +315,16 @@ def asr(
try:
logging.info(f"Transcribing file from Bazarr/ASR webhook")
result = None
#give the 'process' a random name so mutliple Bazaar transcribes can operate at the same time.
random_name = random.choices("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890", k=6)
random_name = ''.join(random.choices(string.ascii_letters + string.digits, k=6))
start_time = time.time()
start_model()
files_to_transcribe.insert(0, f"Bazarr-asr-{random_name}")
audio_data = np.frombuffer(audio_file.file.read(), np.int16).flatten().astype(np.float32) / 32768.0
if(hf_transformers):
result = model.transcribe(np.frombuffer(audio_file.file.read(), np.int16).flatten().astype(np.float32) / 32768.0, task=task, input_sr=16000, language=language, batch_size=hf_batch_size, progress_callback=progress)
result = model.transcribe(np.frombuffer(audio_data, task=task, input_sr=16000, language=language, batch_size=hf_batch_size, progress_callback=progress)
else:
result = model.transcribe_stable(np.frombuffer(audio_file.file.read(), np.int16).flatten().astype(np.float32) / 32768.0, task=task, input_sr=16000, language=language, progress_callback=progress)
result = model.transcribe_stable(np.frombuffer(audio_data, task=task, input_sr=16000, language=language, progress_callback=progress)
appendLine(result)
elapsed_time = time.time() - start_time
minutes, seconds = divmod(int(elapsed_time), 60)
@@ -353,15 +353,15 @@ def detect_language(
):
detected_lang_code = "" # Initialize with an empty string
try:
#give the 'process' a random name so mutliple Bazaar transcribes can operate at the same time.
random_name = random.choices("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890", k=6)
random_name = ''.join(random.choices(string.ascii_letters + string.digits, k=6))
start_model()
files_to_transcribe.insert(0, f"Bazarr-detect-language-{random_name}")
audio_data = np.frombuffer(audio_file.file.read(), np.int16).flatten().astype(np.float32) / 32768.0
if(hf_transformers):
detected_lang_code = model.transcribe(whisper.pad_or_trim(np.frombuffer(audio_file.file.read(), np.int16).flatten().astype(np.float32) / 32768.0), input_sr=16000, batch_size=hf_batch_size).language
detected_lang_code = model.transcribe(whisper.pad_or_trim(audio_data, input_sr=16000, batch_size=hf_batch_size).language
else:
detected_lang_code = model.transcribe_stable(whisper.pad_or_trim(np.frombuffer(audio_file.file.read(), np.int16).flatten().astype(np.float32) / 32768.0), input_sr=16000).language
detected_lang_code = model.transcribe_stable(whisper.pad_or_trim(np.frombuffer(audio_data, input_sr=16000).language
except Exception as e:
logging.info(f"Error processing or transcribing Bazarr {audio_file.filename}: {e}")
@@ -384,20 +384,20 @@ def start_model():
model = stable_whisper.load_faster_whisper(whisper_model, download_root=model_location, device=transcribe_device, cpu_threads=whisper_threads, num_workers=concurrent_transcriptions, compute_type=compute_type)
def delete_model():
if clear_vram_on_complete:
if len(files_to_transcribe) == 0:
global model
logging.debug("Queue is empty, clearing/releasing VRAM")
model = None
gc.collect()
if clear_vram_on_complete and len(files_to_transcribe) == 0:
global model
logging.debug("Queue is empty, clearing/releasing VRAM")
model = None
gc.collect()
def gen_subtitles(file_path: str, transcribe_or_translate: str, front=True, forceLanguage=None) -> None:
"""Generates subtitles for a video file.
Args:
file_path: The path to the video file.
transcription_or_translation: The type of transcription or translation to perform.
front: Whether to add the file to the front of the transcription queue.
file_path: str - The path to the video file.
transcribe_or_translate: str - The type of transcription or translation to perform.
front: bool - Whether to add the file to the front of the transcription queue. Default is True.
forceLanguage: str - The language to force for transcription or translation. Default is None.
"""
try: