Update subgen.py

Added ASR webhook for Bazarr

subgen/subgen.py

@@ -8,8 +8,10 @@
 import queue
 import logging
 import gc
+import io
 from array import array
-from typing import Union, Any
+from typing import BinaryIO, Union, Any
+import random
 
 # List of packages to install
 packages_to_install = [
@@ -20,22 +22,18 @@
     'faster-whisper',
     'uvicorn',
     'python-multipart',
+    'whisper',
     # Add more packages as needed
 ]
 
-for package in packages_to_install:
-    print(f"Installing {package}...")
-    try:
-        subprocess.run(['pip3', 'install', package], check=True)
-        print(f"{package} has been successfully installed.")
-    except subprocess.CalledProcessError as e:
-        print(f"Failed to install {package}: {e}")
-
 from fastapi import FastAPI, File, UploadFile, Query, Header, Body, Form, Request
-from fastapi.responses import StreamingResponse, RedirectResponse    
+from fastapi.responses import StreamingResponse, RedirectResponse
+import numpy as np
 import stable_whisper
 import requests
 import av
+import ffmpeg
+import whisper
 
 def convert_to_bool(in_bool):
     if isinstance(in_bool, bool):
@@ -169,6 +167,86 @@ def receive_emby_webhook(
 
     return ""
 
+@app.post("/asr")
+async def asr(
+        task: Union[str, None] = Query(default="transcribe", enum=["transcribe", "translate"]),
+        language: 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:
+        print(f"Transcribing file from Bazarr/ASR webhook")
+        start_time = time.time()
+        start_model()
+
+        #give the 'process' a random name so mutliple Bazaar transcribes can operate at the same time.
+        random_name = random.choices("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890", k=6)
+        files_to_transcribe.insert(0, f"Bazarr-detect-langauge-{random_name}")
+        result = model.transcribe_stable(np.frombuffer(audio_file.file.read(), np.int16).flatten().astype(np.float32) / 32768.0, task=task, input_sr=16000)
+        elapsed_time = time.time() - start_time
+        minutes, seconds = divmod(int(elapsed_time), 60)
+        print(f"Bazarr transcription is completed, it took {minutes} minutes and {seconds} seconds to complete.")
+    except Exception as e:
+        print(f"Error processing or transcribing Bazarr {audio_file.filename}: {e}")
+    files_to_transcribe.remove(f"Bazarr-detect-langauge-{random_name}")
+    delete_model()
+    return StreamingResponse(
+        iter(result.to_srt_vtt(filepath = None, word_level=word_level_highlight)),
+        media_type="text/plain",
+        headers={
+            'Source': 'Transcribed using stable-ts, faster-whisper from Subgen!',
+        })
+
+@app.post("/detect-language")
+async def detect_language(
+        audio_file: UploadFile = File(...),
+        #encode: bool = Query(default=True, description="Encode audio first through ffmpeg") # This is always false from Bazarr
+):    
+    start_model()
+
+    #give the 'process' a random name so mutliple Bazaar transcribes can operate at the same time.
+    random_name = random.choices("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890", k=6)
+    files_to_transcribe.insert(0, f"Bazarr-detect-langauge-{random_name}")
+    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
+
+    files_to_transcribe.remove(f"Bazarr-detect-langauge-{random_name}")
+    delete_model()
+    return {"detected_language": get_lang_pair(whisper_languages, detected_lang_code), "language_code": detected_lang_code}
+
+def start_model():
+    global model
+    if model is None:
+        logging.debug("Model was purged, need to re-create")
+        model = stable_whisper.load_faster_whisper(whisper_model, download_root=model_location, device=transcribe_device, cpu_threads=whisper_threads, num_workers=concurrent_transcriptions)
+
+def delete_model():
+    if len(files_to_transcribe) == 0:
+        global model
+        logging.debug("Queue is empty, clearing/releasing VRAM")
+        del model
+        gc.collect()
+
+def get_lang_pair(whisper_languages, key):
+  """Returns the other side of the pair in the Whisper languages dictionary.
+
+  Args:
+    whisper_languages: A dictionary of Whisper languages.
+    key: The key to look up in the dictionary.
+
+  Returns:
+    The other side of the pair in the Whisper languages dictionary, or None if the
+    key is not found in the dictionary.
+  """
+
+  other_side = whisper_languages.get(key)
+  if other_side is None:
+    return key
+  else:
+    return whisper_languages[other_side]
+
 def gen_subtitles(file_path: str, transcribe_or_translate_str: str, front=True) -> None:
     """Generates subtitles for a video file.
 
@@ -177,7 +255,6 @@ def gen_subtitles(file_path: str, transcribe_or_translate_str: str, front=True)
         transcription_or_translation: The type of transcription or translation to perform.
         front: Whether to add the file to the front of the transcription queue.
     """
-    global model
 
     try:
         if not is_video_file(file_path):
@@ -202,9 +279,7 @@ def gen_subtitles(file_path: str, transcribe_or_translate_str: str, front=True)
             print(f"{len(files_to_transcribe)} files in the queue for transcription")
             print(f"Transcribing file: {os.path.basename(file_path)}")
             start_time = time.time()
-            if model is None:
-                logging.debug("Model was purged, need to re-create")
-                model = stable_whisper.load_faster_whisper(whisper_model, download_root=model_location, device=transcribe_device, cpu_threads=whisper_threads, num_workers=concurrent_transcriptions)
+            start_model()
 
             result = model.transcribe_stable(file_path, task=transcribe_or_translate_str)
             result.to_srt_vtt(file_path.rsplit('.', 1)[0] + subextension, word_level=word_level_highlight)
@@ -216,15 +291,9 @@ def gen_subtitles(file_path: str, transcribe_or_translate_str: str, front=True)
             print(f"File {os.path.basename(file_path)} is already in the transcription list. Skipping.")
 
     except Exception as e:
-        print(f"Error processing or transcribing {file_path}: {e}")
+        print(f"Error processing or transcribing {video_file_path}: {e}")
     finally:
-        if len(files_to_transcribe) == 0:
-            logging.debug("Queue is empty, clearing/releasing VRAM")
-            try:
-                del model
-            except Exception as e:
-                None
-            gc.collect()
+        delete_model()
 
 def has_subtitle_language(video_file, target_language):
     try:
@@ -346,6 +415,108 @@ def transcribe_existing():
     transcribe_folders = transcribe_folders.split(",")
     transcribe_existing()
 
+whisper_languages = {
+    "en": "english",
+    "zh": "chinese",
+    "de": "german",
+    "es": "spanish",
+    "ru": "russian",
+    "ko": "korean",
+    "fr": "french",
+    "ja": "japanese",
+    "pt": "portuguese",
+    "tr": "turkish",
+    "pl": "polish",
+    "ca": "catalan",
+    "nl": "dutch",
+    "ar": "arabic",
+    "sv": "swedish",
+    "it": "italian",
+    "id": "indonesian",
+    "hi": "hindi",
+    "fi": "finnish",
+    "vi": "vietnamese",
+    "he": "hebrew",
+    "uk": "ukrainian",
+    "el": "greek",
+    "ms": "malay",
+    "cs": "czech",
+    "ro": "romanian",
+    "da": "danish",
+    "hu": "hungarian",
+    "ta": "tamil",
+    "no": "norwegian",
+    "th": "thai",
+    "ur": "urdu",
+    "hr": "croatian",
+    "bg": "bulgarian",
+    "lt": "lithuanian",
+    "la": "latin",
+    "mi": "maori",
+    "ml": "malayalam",
+    "cy": "welsh",
+    "sk": "slovak",
+    "te": "telugu",
+    "fa": "persian",
+    "lv": "latvian",
+    "bn": "bengali",
+    "sr": "serbian",
+    "az": "azerbaijani",
+    "sl": "slovenian",
+    "kn": "kannada",
+    "et": "estonian",
+    "mk": "macedonian",
+    "br": "breton",
+    "eu": "basque",
+    "is": "icelandic",
+    "hy": "armenian",
+    "ne": "nepali",
+    "mn": "mongolian",
+    "bs": "bosnian",
+    "kk": "kazakh",
+    "sq": "albanian",
+    "sw": "swahili",
+    "gl": "galician",
+    "mr": "marathi",
+    "pa": "punjabi",
+    "si": "sinhala",
+    "km": "khmer",
+    "sn": "shona",
+    "yo": "yoruba",
+    "so": "somali",
+    "af": "afrikaans",
+    "oc": "occitan",
+    "ka": "georgian",
+    "be": "belarusian",
+    "tg": "tajik",
+    "sd": "sindhi",
+    "gu": "gujarati",
+    "am": "amharic",
+    "yi": "yiddish",
+    "lo": "lao",
+    "uz": "uzbek",
+    "fo": "faroese",
+    "ht": "haitian creole",
+    "ps": "pashto",
+    "tk": "turkmen",
+    "nn": "nynorsk",
+    "mt": "maltese",
+    "sa": "sanskrit",
+    "lb": "luxembourgish",
+    "my": "myanmar",
+    "bo": "tibetan",
+    "tl": "tagalog",
+    "mg": "malagasy",
+    "as": "assamese",
+    "tt": "tatar",
+    "haw": "hawaiian",
+    "ln": "lingala",
+    "ha": "hausa",
+    "ba": "bashkir",
+    "jw": "javanese",
+    "su": "sundanese",
+}
+
 print("Starting webhook!")
 if __name__ == "__main__":
     import uvicorn