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Patch: fix "language_code" in /detect-language endpoint

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Prevents Bazarr from throttling whisperai (subgen) provider with LanguageReverseError
- "detected_language" -> full language name, e.g. "english" or "nynorsk" (unchanged)
- "language_code" -> two letter language code (changed)
This commit is contained in:
benjroy
2024-05-15 10:40:53 -07:00
parent 4e4e1c6689
commit 0480cb3888
1 changed files with 11 additions and 4 deletions
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15
subgen.py
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@@ -28,6 +28,10 @@ from watchdog.observers.polling import PollingObserver as Observer
from watchdog.events import FileSystemEventHandler from watchdog.events import FileSystemEventHandler
import faster_whisper import faster_whisper
def get_key_by_value(d, value):
reverse_dict = {v: k for k, v in d.items()}
return reverse_dict.get(value)
def convert_to_bool(in_bool): def convert_to_bool(in_bool):
# Convert the input to string and lower case, then check against true values # Convert the input to string and lower case, then check against true values
return str(in_bool).lower() in ('true', 'on', '1', 'y', 'yes') return str(in_bool).lower() in ('true', 'on', '1', 'y', 'yes')
@@ -466,7 +470,8 @@ def detect_language(
audio_file: UploadFile = File(...), audio_file: UploadFile = File(...),
#encode: bool = Query(default=True, description="Encode audio first through ffmpeg") # This is always false from Bazarr #encode: bool = Query(default=True, description="Encode audio first through ffmpeg") # This is always false from Bazarr
): ):
detected_lang_code = "" # Initialize with an empty string detected_language = "" # Initialize with an empty string
language_code = "" # Initialize with an empty string
if int(detect_language_length) != 30: if int(detect_language_length) != 30:
logging.info(f"Detect language is set to detect on the first {detect_language_length} seconds of the audio.") logging.info(f"Detect language is set to detect on the first {detect_language_length} seconds of the audio.")
try: try:
@@ -477,8 +482,10 @@ def detect_language(
task_queue.put(task_id) task_queue.put(task_id)
audio_data = np.frombuffer(audio_file.file.read(), np.int16).flatten().astype(np.float32) / 32768.0 audio_data = np.frombuffer(audio_file.file.read(), np.int16).flatten().astype(np.float32) / 32768.0
detected_lang_code = model.transcribe_stable(whisper.pad_or_trim(audio_data, int(detect_language_length) * 16000), input_sr=16000).language detected_language = model.transcribe_stable(whisper.pad_or_trim(audio_data, int(detect_language_length) * 16000), input_sr=16000).language
# reverse lookup of language -> code, ex: "english" -> "en", "nynorsk" -> "nn", ...
language_code = get_key_by_value(whisper_languages, detected_language)
except Exception as e: except Exception as e:
logging.info(f"Error processing or transcribing Bazarr {audio_file.filename}: {e}") logging.info(f"Error processing or transcribing Bazarr {audio_file.filename}: {e}")
@@ -486,7 +493,7 @@ def detect_language(
task_queue.task_done() task_queue.task_done()
delete_model() delete_model()
return {"detected_language": whisper_languages.get(detected_lang_code, detected_lang_code) , "language_code": detected_lang_code} return {"detected_language": detected_language, "language_code": language_code}
def start_model(): def start_model():
global model global model