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Update subgen.py

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This commit is contained in:
McCloudS
2024-03-21 09:39:19 -06:00
committed by GitHub
parent 2ccf2ea329
commit 94a83a4914
1 changed files with 5 additions and 23 deletions
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subgen.py
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@@ -53,8 +53,6 @@ monitor = convert_to_bool(os.getenv('MONITOR', False))
transcribe_folders = os.getenv('TRANSCRIBE_FOLDERS', '')
transcribe_or_translate = os.getenv('TRANSCRIBE_OR_TRANSLATE', 'transcribe')
force_detected_language_to = os.getenv('FORCE_DETECTED_LANGUAGE_TO', '')
hf_transformers = convert_to_bool(os.getenv('HF_TRANSFORMERS', False))
hf_batch_size = int(os.getenv('HF_BATCH_SIZE', 24))
clear_vram_on_complete = convert_to_bool(os.getenv('CLEAR_VRAM_ON_COMPLETE', True))
compute_type = os.getenv('COMPUTE_TYPE', 'auto')
append = convert_to_bool(os.getenv('APPEND', False))
@@ -334,10 +332,7 @@ def asr(
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(audio_data, task=task, input_sr=16000, language=language, batch_size=hf_batch_size, progress_callback=progress)
else:
result = model.transcribe_stable(audio_data, task=task, input_sr=16000, language=language, progress_callback=progress)
result = model.transcribe_stable(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)
@@ -370,10 +365,7 @@ def detect_language(
random_name = random.choices("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890", k=6)
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(audio_data), input_sr=16000, batch_size=hf_batch_size).language
else:
detected_lang_code = model.transcribe_stable(whisper.pad_or_trim(audio_data), input_sr=16000).language
detected_lang_code = model.transcribe_stable(whisper.pad_or_trim(audio_data), input_sr=16000).language
except Exception as e:
logging.info(f"Error processing or transcribing Bazarr {audio_file.filename}: {e}")
@@ -389,11 +381,7 @@ def start_model():
global model
if model is None:
logging.debug("Model was purged, need to re-create")
if(hf_transformers):
logging.debug("Using Hugging Face Transformers, whisper_threads, concurrent_transcriptions, and model_location variables are ignored!")
model = stable_whisper.load_hf_whisper(whisper_model, device=transcribe_device)
else:
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)
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 and len(files_to_transcribe) == 0:
@@ -444,10 +432,7 @@ def gen_subtitles(file_path: str, transcribe_or_translate: str, front=True, forc
if force_detected_language_to:
forceLanguage = force_detected_language_to
logging.info(f"Forcing language to {forceLanguage}")
if(hf_transformers):
result = model.transcribe(file_path, language=forceLanguage, batch_size=hf_batch_size, task=transcribe_or_translate, progress_callback=progress)
else:
result = model.transcribe_stable(file_path, language=forceLanguage, task=transcribe_or_translate, progress_callback=progress)
result = model.transcribe_stable(file_path, language=forceLanguage, task=transcribe_or_translate, progress_callback=progress)
appendLine(result)
result.to_srt_vtt(get_file_name_without_extension(file_path) + subextension, word_level=word_level_highlight)
elapsed_time = time.time() - start_time
@@ -772,10 +757,7 @@ if __name__ == "__main__":
logging.info(f"Transcriptions are limited to running {str(concurrent_transcriptions)} at a time")
logging.info(f"Running {str(whisper_threads)} threads per transcription")
logging.info(f"Using {transcribe_device} to encode")
if hf_transformers:
logging.info(f"Using Hugging Face Transformers")
else:
logging.info(f"Using faster-whisper")
logging.info(f"Using faster-whisper")
if transcribe_folders:
transcribe_existing(transcribe_folders)
uvicorn.run("subgen:app", host="0.0.0.0", port=int(webhookport), reload=reload_script_on_change, use_colors=True)