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Merge pull request #71 from McCloudS/webui

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Added configuration webui
This commit is contained in:
McCloudS
2024-03-24 16:05:45 -06:00
committed by GitHub
parent 865485e0d2 70abe2c3dd
commit 681d21896c
1 changed files with 189 additions and 36 deletions
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225
subgen.py
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@@ -15,7 +15,7 @@ import io
import random
from typing import BinaryIO, Union, Any
from fastapi import FastAPI, File, UploadFile, Query, Header, Body, Form, Request
from fastapi.responses import StreamingResponse, RedirectResponse
from fastapi.responses import StreamingResponse, RedirectResponse, HTMLResponse
import numpy as np
import stable_whisper
from stable_whisper import Segment
@@ -29,41 +29,76 @@ def convert_to_bool(in_bool):
# Convert the input to string and lower case, then check against true values
return str(in_bool).lower() in ('true', 'on', '1', 'y', 'yes')
# Replace your getenv calls with appropriate default values here
plextoken = os.getenv('PLEXTOKEN', 'token here')
plexserver = os.getenv('PLEXSERVER', 'http://192.168.1.111:32400')
jellyfintoken = os.getenv('JELLYFINTOKEN', 'token here')
jellyfinserver = os.getenv('JELLYFINSERVER', 'http://192.168.1.111:8096')
whisper_model = os.getenv('WHISPER_MODEL', 'medium')
whisper_threads = int(os.getenv('WHISPER_THREADS', 4))
concurrent_transcriptions = int(os.getenv('CONCURRENT_TRANSCRIPTIONS', 2))
transcribe_device = os.getenv('TRANSCRIBE_DEVICE', 'cpu')
procaddedmedia = convert_to_bool(os.getenv('PROCADDEDMEDIA', True))
procmediaonplay = convert_to_bool(os.getenv('PROCMEDIAONPLAY', True))
namesublang = os.getenv('NAMESUBLANG', 'aa')
skipifinternalsublang = os.getenv('SKIPIFINTERNALSUBLANG', 'eng')
webhookport = int(os.getenv('WEBHOOKPORT', 9000))
word_level_highlight = convert_to_bool(os.getenv('WORD_LEVEL_HIGHLIGHT', False))
debug = convert_to_bool(os.getenv('DEBUG', True))
use_path_mapping = convert_to_bool(os.getenv('USE_PATH_MAPPING', False))
path_mapping_from = os.getenv('PATH_MAPPING_FROM', r'/tv')
path_mapping_to = os.getenv('PATH_MAPPING_TO', r'/Volumes/TV')
model_location = os.getenv('MODEL_PATH', './models')
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', '')
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))
reload_script_on_change = convert_to_bool(os.getenv('RELOAD_SCRIPT_ON_CHANGE', False))
model_prompt = os.getenv('USE_MODEL_PROMPT', 'False')
custom_model_prompt = os.getenv('CUSTOM_MODEL_PROMPT', '')
lrc_for_audio_files = convert_to_bool(os.getenv('LRC_FOR_AUDIO_FILES', True))
custom_regroup = os.getenv('CUSTOM_REGROUP', 'cm_sl=84_sl=42++++++1')
# Function to read environment variables from a file and return them as a dictionary
def get_env_variables_from_file(filename):
env_vars = {}
try:
with open(filename, 'r') as file:
for line in file:
if line.strip() and not line.startswith('#'):
key, value = line.strip().split('=', 1)
env_vars[key.strip()] = value.strip()
except FileNotFoundError:
print(f"File {filename} not found. Using default values.")
return env_vars
def set_env_variables_from_file(filename):
try:
with open(filename, 'r') as file:
for line in file:
if line.strip() and not line.startswith('#'):
key, value = line.strip().split('=', 1)
os.environ[key.strip()] = value.strip().strip('\"').strip("'")
except FileNotFoundError:
print(f"File {filename} not found. Environment variables not set.")
if transcribe_device == "gpu":
transcribe_device = "cuda"
def update_env_variables():
global plextoken, plexserver, jellyfintoken, jellyfinserver, whisper_model, whisper_threads
global concurrent_transcriptions, transcribe_device, procaddedmedia, procmediaonplay
global namesublang, skipifinternalsublang, webhookport, word_level_highlight, debug
global use_path_mapping, path_mapping_from, path_mapping_to, model_location, monitor
global transcribe_folders, transcribe_or_translate, force_detected_language_to
global clear_vram_on_complete, compute_type, append, reload_script_on_change
global model_prompt, custom_model_prompt, lrc_for_audio_files, custom_regroup
plextoken = os.getenv('PLEXTOKEN', 'token here')
plexserver = os.getenv('PLEXSERVER', 'http://192.168.1.111:32400')
jellyfintoken = os.getenv('JELLYFINTOKEN', 'token here')
jellyfinserver = os.getenv('JELLYFINSERVER', 'http://192.168.1.111:8096')
whisper_model = os.getenv('WHISPER_MODEL', 'medium')
whisper_threads = int(os.getenv('WHISPER_THREADS', 4))
concurrent_transcriptions = int(os.getenv('CONCURRENT_TRANSCRIPTIONS', 2))
transcribe_device = os.getenv('TRANSCRIBE_DEVICE', 'cpu')
procaddedmedia = convert_to_bool(os.getenv('PROCADDEDMEDIA', True))
procmediaonplay = convert_to_bool(os.getenv('PROCMEDIAONPLAY', True))
namesublang = os.getenv('NAMESUBLANG', 'aa')
skipifinternalsublang = os.getenv('SKIPIFINTERNALSUBLANG', 'eng')
webhookport = int(os.getenv('WEBHOOKPORT', 9000))
word_level_highlight = convert_to_bool(os.getenv('WORD_LEVEL_HIGHLIGHT', False))
debug = convert_to_bool(os.getenv('DEBUG', True))
use_path_mapping = convert_to_bool(os.getenv('USE_PATH_MAPPING', False))
path_mapping_from = os.getenv('PATH_MAPPING_FROM', r'/tv')
path_mapping_to = os.getenv('PATH_MAPPING_TO', r'/Volumes/TV')
model_location = os.getenv('MODEL_PATH', './models')
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', '')
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))
reload_script_on_change = convert_to_bool(os.getenv('RELOAD_SCRIPT_ON_CHANGE', False))
model_prompt = os.getenv('USE_MODEL_PROMPT', 'False')
custom_model_prompt = os.getenv('CUSTOM_MODEL_PROMPT', '')
lrc_for_audio_files = convert_to_bool(os.getenv('LRC_FOR_AUDIO_FILES', True))
custom_regroup = os.getenv('CUSTOM_REGROUP', 'cm_sl=84_sl=42++++++1')
if transcribe_device == "gpu":
transcribe_device = "cuda"
set_env_variables_from_file('subgen.env')
update_env_variables()
if monitor:
from watchdog.observers.polling import PollingObserver as Observer
@@ -164,10 +199,93 @@ def appendLine(result):
@app.get("/emby")
@app.get("/detect-language")
@app.get("/tautulli")
@app.get("/")
def handle_get_request(request: Request):
return {"You accessed this request incorrectly via a GET request. See https://github.com/McCloudS/subgen for proper configuration"}
# Function to generate HTML form with values filled from the environment file
@app.get("/", response_class=HTMLResponse)
def form_get():
# Read the environment variables from the file
env_values = get_env_variables_from_file('subgen.env')
html_content = "<html><head><title>Subgen settings!</title></head><body>"
html_content += '<img src="https://raw.githubusercontent.com/McCloudS/subgen/main/icon.png" alt="Header Image" style="display: block; margin-left: auto; margin-right: auto; width: 10%;">'
html_content += "<html><body><form action=\"/submit\" method=\"post\">"
for var_name, var_info in env_variables.items():
# Use the value from the environment file if it exists, otherwise use the default
value = env_values.get(var_name, str(var_info['default']))
html_content += f"<br><div><strong>{var_name}</strong>: {var_info['description']} (<strong>default: {var_info['default']}</strong>)<br>"
if var_name == "TRANSCRIBE_OR_TRANSLATE":
# Add a dropdown for TRANSCRIBE_OR_TRANSLATE with options 'Transcribe' and 'Translate'
selected_value = value if value in ['transcribe', 'translate'] else var_info['default']
html_content += f"<select name=\"{var_name}\">"
html_content += f"<option value=\"transcribe\"{' selected' if selected_value == 'transcribe' else ''}>Transcribe</option>"
html_content += f"<option value=\"translate\"{' selected' if selected_value == 'translate' else ''}>Translate</option>"
html_content += "</select><br>"
elif isinstance(var_info['default'], bool):
# Convert the value to a boolean
value_bool = value.lower() == 'true'
# Determine the selected value based on the current value or the default value
selected_value = value_bool if value in ['True', 'False'] else var_info['default']
html_content += f"<select name=\"{var_name}\">"
html_content += f"<option value=\"True\"{' selected' if selected_value else ''}>True</option>"
html_content += f"<option value=\"False\"{' selected' if not selected_value else ''}>False</option>"
html_content += "</select><br>"
else:
html_content += f"<input type=\"text\" name=\"{var_name}\" value=\"{env_values.get(var_name, '') if var_name in env_values else ''}\" placeholder=\"{var_info['default']}\"/></div>"
html_content += "<br><input type=\"submit\" value=\"Save\"/></form></body></html>"
return html_content
@app.post("/submit")
async def form_post(request: Request):
env_path = 'subgen.env'
form_data = await request.form()
# Read the existing content of the file
try:
with open(f"{env_path}", "r") as file:
lines = file.readlines()
except FileNotFoundError:
lines = []
# Create a dictionary of existing variables
existing_vars = {}
for line in lines:
if "=" in line:
var, val = line.split("=", 1)
existing_vars[var.strip()] = val.strip()
# Update the file with new values from the form
with open(f"{env_path}", "w") as file:
for key, value in form_data.items():
# Normalize the key to uppercase
key = key.upper()
# Convert the value to the correct type (boolean or string)
if isinstance(env_variables[key]['default'], bool):
value = value.strip().lower() == 'true'
else:
value = value.strip()
# Write to file only if the value is different from the default
if env_variables[key]["default"] != value and value:
# Check if the variable already exists and if the value is different
if key in existing_vars and existing_vars[key] != str(value):
# Update the existing variable with the new value
existing_vars[key] = str(value)
elif key not in existing_vars:
# Add the new variable to the dictionary
existing_vars[key] = str(value)
elif key in existing_vars:
# Remove the entry from the existing variables if the value is empty
del existing_vars[key]
del os.environ[key]
# Write the updated variables to the file
for var, val in existing_vars.items():
file.write(f"{var}={val}\n")
update_env_variables()
return(f"Configuration saved to {env_path}, reloading your subgen with your new values!")
@app.get("/status")
def status():
in_docker = os.path.exists('/.dockerenv')
@@ -879,8 +997,43 @@ greetings_translations = {
"su": "Wilujeng, hatur nuhun ka lékturing abdi.",
}
env_variables = {
"TRANSCRIBE_DEVICE": {"description": "Can transcribe via gpu (Cuda only) or cpu. Takes option of 'cpu', 'gpu', 'cuda'.", "default": "cpu", "value": ""},
"WHISPER_MODEL": {"description": "Can be: 'tiny', 'tiny.en', 'base', 'base.en', 'small', 'small.en', 'medium', 'medium.en', 'large-v1','large-v2', 'large-v3', 'large', 'distil-large-v2', 'distil-medium.en', 'distil-small.en'", "default": "medium", "value": ""},
"CONCURRENT_TRANSCRIPTIONS": {"description": "Number of files it will transcribe in parallel", "default": "2", "value": ""},
"WHISPER_THREADS": {"description": "Number of threads to use during computation", "default": "4", "value": ""},
"MODEL_PATH": {"description": "This is where the WHISPER_MODEL will be stored. This defaults to placing it where you execute the script in the folder 'models'", "default": "./models", "value": ""},
"PROCADDEDMEDIA": {"description": "Will gen subtitles for all media added regardless of existing external/embedded subtitles (based off of SKIPIFINTERNALSUBLANG)", "default": True, "value": ""},
"PROCMEDIAONPLAY": {"description": "Will gen subtitles for all played media regardless of existing external/embedded subtitles (based off of SKIPIFINTERNALSUBLANG)", "default": True, "value": ""},
"NAMESUBLANG": {"description": "Allows you to pick what it will name the subtitle. Instead of using EN, I'm using AA, so it doesn't mix with existing external EN subs, and AA will populate higher on the list in Plex.", "default": "aa", "value": ""},
"SKIPIFINTERNALSUBLANG": {"description": "Will not generate a subtitle if the file has an internal sub matching the 3 letter code of this variable", "default": "eng", "value": ""},
"WORD_LEVEL_HIGHLIGHT": {"description": "Highlights each word as it's spoken in the subtitle.", "default": False, "value": ""},
"PLEXSERVER": {"description": "This needs to be set to your local plex server address/port", "default": "http://plex:32400", "value": ""},
"PLEXTOKEN": {"description": "This needs to be set to your plex token", "default": "token here", "value": ""},
"JELLYFINSERVER": {"description": "Set to your Jellyfin server address/port", "default": "http://jellyfin:8096", "value": ""},
"JELLYFINTOKEN": {"description": "Generate a token inside the Jellyfin interface", "default": "token here", "value": ""},
"WEBHOOKPORT": {"description": "Change this if you need a different port for your webhook", "default": "9000", "value": ""},
"USE_PATH_MAPPING": {"description": "Similar to sonarr and radarr path mapping, this will attempt to replace paths on file systems that don't have identical paths. Currently only support for one path replacement.", "default": False, "value": ""},
"PATH_MAPPING_FROM": {"description": "This is the path of my media relative to my Plex server", "default": "/tv", "value": ""},
"PATH_MAPPING_TO": {"description": "This is the path of that same folder relative to my Mac Mini that will run the script", "default": "/Volumes/TV", "value": ""},
"TRANSCRIBE_FOLDERS": {"description": "Takes a pipe '|' separated list and iterates through and adds those files to be queued for subtitle generation if they don't have internal subtitles", "default": "", "value": ""},
"TRANSCRIBE_OR_TRANSLATE": {"description": "Takes either 'transcribe' or 'translate'. Transcribe will transcribe the audio in the same language as the input. Translate will transcribe and translate into English.", "default": "transcribe", "value": ""},
"COMPUTE_TYPE": {"description": "Set compute-type using the following information: https://github.com/OpenNMT/CTranslate2/blob/master/docs/quantization.md", "default": "auto", "value": ""},
"DEBUG": {"description": "Provides some debug data that can be helpful to troubleshoot path mapping and other issues. If set to true, any modifications to the script will auto-reload it (if it isn't actively transcoding). Useful to make small tweaks without re-downloading the whole file.", "default": True, "value": ""},
"FORCE_DETECTED_LANGUAGE_TO": {"description": "This is to force the model to a language instead of the detected one, takes a 2 letter language code.", "default": "", "value": ""},
"CLEAR_VRAM_ON_COMPLETE": {"description": "This will delete the model and do garbage collection when queue is empty. Good if you need to use the VRAM for something else.", "default": True, "value": ""},
"UPDATE": {"description": "Will pull latest subgen.py from the repository if True. False will use the original subgen.py built into the Docker image. Standalone users can use this with launcher.py to get updates.","default": False,"value": ""},
"APPEND": {"description": "Will add the following at the end of a subtitle: 'Transcribed by whisperAI with faster-whisper ({whisper_model}) on {datetime.now()}'","default": False,"value": ""},
"MONITOR": {"description": "Will monitor TRANSCRIBE_FOLDERS for real-time changes to see if we need to generate subtitles","default": False,"value": ""},
"USE_MODEL_PROMPT": {"description": "When set to True, will use the default prompt stored in greetings_translations 'Hello, welcome to my lecture.' to try and force the use of punctuation in transcriptions that don't.","default": False,"value": ""},
"CUSTOM_MODEL_PROMPT": {"description": "If USE_MODEL_PROMPT is True, you can override the default prompt (See: [prompt engineering in whisper](https://medium.com/axinc-ai/prompt-engineering-in-whisper-6bb18003562d%29) for great examples).","default": "","value": ""},
"LRC_FOR_AUDIO_FILES": {"description": "Will generate LRC (instead of SRT) files for filetypes: '.mp3', '.flac', '.wav', '.alac', '.ape', '.ogg', '.wma', '.m4a', '.m4b', '.aac', '.aiff'","default": True,"value": ""},
"CUSTOM_REGROUP": {"description": "Attempts to regroup some of the segments to make a cleaner looking subtitle. See #68 for discussion. Set to blank if you want to use Stable-TS default regroups algorithm of cm_sp=,* /，_sg=.5_mg=.3+3_sp=.* /。/?/？","default": "cm_sl=84_sl=42++++++1","value": ""}
}
if __name__ == "__main__":
import uvicorn
update_env_variables()
logging.info(f"Subgen v{subgen_version}")
logging.info("Starting Subgen with listening webhooks!")
logging.info(f"Transcriptions are limited to running {str(concurrent_transcriptions)} at a time")