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Cleaned up conditional logging for optional video_file

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This commit is contained in:
McCloudS
2024-11-17 18:25:44 -07:00
committed by GitHub
parent 5df2187197
commit c6c684cdc8
1 changed files with 30 additions and 20 deletions
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50
subgen.py
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@@ -328,17 +328,18 @@ def batch(
@app.post("//asr")
@app.post("/asr")
async def asr(
task: Union[str, None] = Query(default="transcribe", enum=["transcribe", "translate"]),
language: Union[str, None] = Query(default=None),
video_file: Union[str, None] = Query(default="Name not supplied"),
initial_prompt: Union[str, None] = Query(default=None), #not used by Bazarr
audio_file: UploadFile = File(...),
encode: bool = Query(default=True, description="Encode audio first through ffmpeg"), #not used by Bazarr/always False
output: Union[str, None] = Query(default="srt", enum=["txt", "vtt", "srt", "tsv", "json"]),
word_timestamps: bool = Query(default=False, description="Word level timestamps") #not used by Bazarr
task: Union[str, None] = Query(default="transcribe", enum=["transcribe", "translate"]),
language: Union[str, None] = Query(default=None),
video_file: Union[str, None] = Query(default=None),
initial_prompt: Union[str, None] = Query(default=None), # Not used by Bazarr
audio_file: UploadFile = File(...),
encode: bool = Query(default=True, description="Encode audio first through ffmpeg"), # Not used by Bazarr/always False
output: Union[str, None] = Query(default="srt", enum=["txt", "vtt", "srt", "tsv", "json"]),
word_timestamps: bool = Query(default=False, description="Word-level timestamps"), # Not used by Bazarr
):
try:
logging.info(f"Transcribing file '{video_file}' from Bazarr/ASR webhook")
logging.info(f"Transcribing file '{video_file}' from Bazarr/ASR webhook" if video_file else "Transcribing file from Bazarr/ASR webhook")
result = None
random_name = ''.join(random.choices("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890", k=6))
@@ -348,47 +349,56 @@ async def asr(
start_time = time.time()
start_model()
task_id = { 'path': f"Bazarr-asr-{random_name}" }
task_id = {'path': f"Bazarr-asr-{random_name}"}
task_queue.put(task_id)
args = {}
args['progress_callback'] = progress
if not encode:
args['audio'] = np.frombuffer(audio_file.file.read(), np.int16).flatten().astype(np.float32) / 32768.0
args['input_sr'] = 16000
else:
args['audio'] = audio_file.file.read()
if model_prompt:
args['initial_prompt'] = greetings_translations.get(language, '') or custom_model_prompt
if custom_regroup:
args['regroup'] = custom_regroup
args.update(kwargs)
result = model.transcribe_stable(task=task, language=language, **args)
appendLine(result)
elapsed_time = time.time() - start_time
minutes, seconds = divmod(int(elapsed_time), 60)
logging.info(f"Transcription of '{video_file}' from Bazarr complete, it took {minutes} minutes and {seconds} seconds to complete.")
logging.info(
f"Transcription of '{video_file}' from Bazarr complete, it took {minutes} minutes and {seconds} seconds to complete." if video_file
else f"Transcription complete, it took {minutes} minutes and {seconds} seconds to complete.")
except Exception as e:
logging.info(f"Error processing or transcribing Bazarr file: {video_file} -- Exception: {e}")
logging.error(
f"Error processing or transcribing Bazarr file: {video_file} -- Exception: {e}" if video_file
else f"Error processing or transcribing Bazarr file Exception: {e}"
)
finally:
await audio_file.close()
task_queue.task_done()
delete_model()
if result:
return StreamingResponse(
iter(result.to_srt_vtt(filepath = None, word_level=word_level_highlight)),
iter(result.to_srt_vtt(filepath=None, word_level=word_level_highlight)),
media_type="text/plain",
headers={
'Source': 'Transcribed using stable-ts from Subgen!',
})
}
)
else:
return
@app.post("//detect-language")
@app.post("/detect-language")
async def detect_language(