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@@ -1,33 +1,33 @@
-# ベースイメージを指定
-FROM python:3.11-slim
-
-RUN apt-get update -y && apt-get upgrade -y && apt-get install -y ffmpeg
-RUN apt-get update -y && apt-get upgrade -y && apt-get install --reinstall -y wget
-RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb && dpkg -i cuda-keyring_1.0-1_all.deb && apt-get update -y && apt-get upgrade -y && apt-get install -y libcudnn8 libcudnn8-dev
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -m -u 1000 user
-
-# Switch to the "user" user
-USER user
-
-# Set home to the user"s home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-# 作業ディレクトリを設定
-WORKDIR $HOME/app
-
-# 必要なパッケージをインストール
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-# アプリケーションのソースコードをコピー
-COPY --chown=user . $HOME/app
-
-# .cacheディレクトリを作成
-RUN mkdir -p .cache
-
-# サーバーを起動するコマンドを指定
-CMD ["python", "app.py", "--host", "0.0.0.0", "--port", "7860", "--lan", "en", "--model", "AtPeak/whisper-medium-finance-faster-float16"]
-# CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+# ベースイメージを指定
+FROM python:3.11-slim
+
+RUN apt-get update -y && apt-get upgrade -y && apt-get install -y ffmpeg
+RUN apt-get update -y && apt-get upgrade -y && apt-get install --reinstall -y wget
+RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb && dpkg -i cuda-keyring_1.0-1_all.deb && apt-get update -y && apt-get upgrade -y && apt-get install -y libcudnn8 libcudnn8-dev
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user"s home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# 作業ディレクトリを設定
+WORKDIR $HOME/app
+
+# 必要なパッケージをインストール
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# アプリケーションのソースコードをコピー
+COPY --chown=user . $HOME/app
+
+# .cacheディレクトリを作成
+RUN mkdir -p .cache
+
+# サーバーを起動するコマンドを指定
+CMD ["python", "app.py", "--host", "0.0.0.0", "--port", "7860", "--lan", "en", "--model", "tiny", "--min-chunk-size", "1.5", "--generate-audio", "True"]
+# CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -1,397 +1,427 @@
-import os
-import io
-import requests
-import argparse
-import asyncio
-import numpy as np
-import ffmpeg
-from time import time
-
-from fastapi import FastAPI, WebSocket, WebSocketDisconnect
-from fastapi.responses import HTMLResponse
-from fastapi.middleware.cors import CORSMiddleware
-
-from src.whisper_streaming.whisper_online import backend_factory, online_factory, add_shared_args
-
-import logging
-import logging.config
-from transformers import pipeline
-from huggingface_hub import login
-
-HUGGING_FACE_TOKEN = os.environ['HUGGING_FACE_TOKEN']
-login(HUGGING_FACE_TOKEN)
-
-os.environ['HF_HOME'] = './.cache'
-
-MODEL_NAME = 'Helsinki-NLP/opus-tatoeba-en-ja'
-TRANSLATOR = pipeline('translation', model=MODEL_NAME, device='cuda')
-TRANSLATOR('Warming up!')
-
-DEEPL_API_KEY = os.environ['DEEPL_API_KEY']
-
-SOURCE_LANG = 'EN'
-TARGET_LANG = 'JA'
-
-def translator_wrapper(source_text, mode='deepl'):
-    if mode == 'deepl':
-        params = {
-                    'auth_key' : DEEPL_API_KEY,
-                    'text' : source_text,
-                    'source_lang' : SOURCE_LANG, # 翻訳対象の言語
-                    "target_lang": TARGET_LANG  # 翻訳後の言語
-                }
-
-        # リクエストを投げる
-        try:
-            request = requests.post("https://api-free.deepl.com/v2/translate", data=params, timeout=5) # URIは有償版, 無償版で異なるため要注意
-            result = request.json()['translations'][0]['text']
-        except requests.exceptions.Timeout:
-            result = "(timed out)"
-        return result
-
-    elif mode == 'marianmt':
-        return TRANSLATOR(source_text)[0]['translation_text']
-
-    elif mode == 'google':
-        import  requests
-
-        # https://www.eyoucms.com/news/ziliao/other/29445.html
-        language_type = ""
-        target = 'ja-jp'
-        url = "https://translation.googleapis.com/language/translate/v2"
-        data = {
-            'key':"AIzaSyCX0-Wdxl_rgvcZzklNjnqJ1W9YiKjcHUs", #  認証の設定:APIキー
-            'source': language_type,
-            'target': target,
-            'q': source_text,
-            'format': "text"
-        }
-        #headers = {'X-HTTP-Method-Override': 'GET'}
-        #response = requests.post(url, data=data, headers=headers)
-        response = requests.post(url, data)
-        # print(response.json())
-        print(response)
-        res = response.json()
-        print(res["data"]["translations"][0]["translatedText"])
-        result = res["data"]["translations"][0]["translatedText"]
-        print(result)
-        return result
-
-
-def setup_logging():
-    logging_config = {
-        'version': 1,
-        'disable_existing_loggers': False,
-        'formatters': {
-            'standard': {
-                'format': '%(asctime)s %(levelname)s [%(name)s]: %(message)s',
-            },
-        },
-        'handlers': {
-            'console': {
-                'level': 'INFO',
-                'class': 'logging.StreamHandler',
-                'formatter': 'standard',
-            },
-        },
-        'root': {
-            'handlers': ['console'],
-            'level': 'DEBUG',
-        },
-        'loggers': {
-            'uvicorn': {
-                'handlers': ['console'],
-                'level': 'INFO',
-                'propagate': False,
-            },
-            'uvicorn.error': {
-                'level': 'INFO',
-            },
-            'uvicorn.access': {
-                'level': 'INFO',
-            },
-            'src.whisper_streaming.online_asr': {  # Add your specific module here
-                'handlers': ['console'],
-                'level': 'DEBUG',
-                'propagate': False,
-            },
-            'src.whisper_streaming.whisper_streaming': {  # Add your specific module here
-                'handlers': ['console'],
-                'level': 'DEBUG',
-                'propagate': False,
-            },
-        },
-    }
-
-    logging.config.dictConfig(logging_config)
-
-setup_logging()
-logger = logging.getLogger(__name__)
-
-
-
-
-
-
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-parser = argparse.ArgumentParser(description="Whisper FastAPI Online Server")
-parser.add_argument(
-    "--host",
-    type=str,
-    default="localhost",
-    help="The host address to bind the server to.",
-)
-parser.add_argument(
-    "--port", type=int, default=8000, help="The port number to bind the server to."
-)
-parser.add_argument(
-    "--warmup-file",
-    type=str,
-    dest="warmup_file",
-    help="The path to a speech audio wav file to warm up Whisper so that the very first chunk processing is fast. It can be e.g. https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav .",
-)
-
-parser.add_argument(
-    "--diarization",
-    type=bool,
-    default=False,
-    help="Whether to enable speaker diarization.",
-)
-
-
-add_shared_args(parser)
-args = parser.parse_args()
-# args.model = 'medium'
-
-asr, tokenizer = backend_factory(args)
-
-if args.diarization:
-    from src.diarization.diarization_online import DiartDiarization
-
-
-# Load demo HTML for the root endpoint
-with open("src/web/live_transcription.html", "r", encoding="utf-8") as f:
-    html = f.read()
-
-
-@app.get("/")
-async def get():
-    return HTMLResponse(html)
-
-
-SAMPLE_RATE = 16000
-CHANNELS = 1
-SAMPLES_PER_SEC = SAMPLE_RATE * int(args.min_chunk_size)
-BYTES_PER_SAMPLE = 2  # s16le = 2 bytes per sample
-BYTES_PER_SEC = SAMPLES_PER_SEC * BYTES_PER_SAMPLE
-
-
-async def start_ffmpeg_decoder():
-    """
-    Start an FFmpeg process in async streaming mode that reads WebM from stdin
-    and outputs raw s16le PCM on stdout. Returns the process object.
-    """
-    process = (
-        ffmpeg.input("pipe:0", format="webm")
-        .output(
-            "pipe:1",
-            format="s16le",
-            acodec="pcm_s16le",
-            ac=CHANNELS,
-            ar=str(SAMPLE_RATE),
-        )
-        .run_async(pipe_stdin=True, pipe_stdout=True, pipe_stderr=True)
-    )
-    return process
-
-
-
-@app.websocket("/asr")
-async def websocket_endpoint(websocket: WebSocket):
-    await websocket.accept()
-    print("WebSocket connection opened.")
-
-    ffmpeg_process = await start_ffmpeg_decoder()
-    pcm_buffer = bytearray()
-    print("Loading online.")
-    online = online_factory(args, asr, tokenizer)
-    print("Online loaded.")
-
-    if args.diarization:
-        diarization = DiartDiarization(SAMPLE_RATE)
-
-    # Continuously read decoded PCM from ffmpeg stdout in a background task
-    async def ffmpeg_stdout_reader():
-        nonlocal pcm_buffer
-        loop = asyncio.get_event_loop()
-        full_transcription = ""
-        beg = time()
-        
-        chunk_history = []  # Will store dicts: {beg, end, text, speaker}
-
-        buffers = [{'speaker': '0', 'text': '', 'translation': None}]
-        buffer_line = ''
-        
-        while True:
-            print('in while')
-            try:
-                print('try in while')
-                elapsed_time = int(time() - beg)
-                beg = time()
-                print('before await loop.run_in_executor()')
-                chunk = await loop.run_in_executor(None, ffmpeg_process.stdout.read, 32000 * elapsed_time)
-
-                print('before if not chunk')
-                if not chunk:  # The first chunk will be almost empty, FFmpeg is still starting up
-                    chunk = await loop.run_in_executor(None, ffmpeg_process.stdout.read, 4096)
-                    if not chunk:  # FFmpeg might have closed
-                        print("FFmpeg stdout closed.")
-                        break
-
-                pcm_buffer.extend(chunk)
-
-                print('before if len(pcm_buffer)')
-                if len(pcm_buffer) >= BYTES_PER_SEC:
-                    print('in if len(pcm_buffer)')
-                    # Convert int16 -> float32
-                    pcm_array = (np.frombuffer(pcm_buffer, dtype=np.int16).astype(np.float32) / 32768.0)
-                    pcm_buffer = bytearray()  # Initialize the PCM buffer
-                    online.insert_audio_chunk(pcm_array)
-                    beg_trans, end_trans, trans = online.process_iter()
-                    
-                    if trans:
-                        chunk_history.append({
-                        "beg": beg_trans,
-                        "end": end_trans,
-                        "text": trans,
-                        "speaker": "0"
-                        })
-                    full_transcription += trans
-                    
-                    # ----------------
-                    # Process buffer
-                    # ----------------
-                    if args.vac:
-                        # We need to access the underlying online object to get the buffer
-                        buffer = online.online.concatenate_tsw(online.online.transcript_buffer.buffer)[2]
-                    else:
-                        buffer = online.concatenate_tsw(online.transcript_buffer.buffer)[2]
-
-                    if buffer in full_transcription:  # With VAC, the buffer is not updated until the next chunk is processed
-                        buffer = ""
-
-                    buffer_line += buffer
-                                        
-                    punctuations = (',', '.', '?', '!', 'and', 'or', 'but', 'however')
-                    if any(punctuation in buffer_line for punctuation in punctuations):
-                        last_punctuation_index = max((buffer_line.rfind(p) + len(p) + 1) for p in punctuations if p in buffer_line)
-                        extracted_text = buffer_line[:last_punctuation_index]
-                        buffer_line = buffer_line[last_punctuation_index:]
-                        buffers.append({'speaker': '0', 'text': extracted_text, 'translation': None})
-
-                    # Translation loop
-                    print('buffers for loop')
-                    for i, buffer in enumerate(buffers):
-                        print(i, buffer)
-                        if buffer['translation'] is not None:
-                            continue
-                        if buffer['text'] == '':
-                            continue
-
-                        transcription = buffer['text']
-                        buffers[i]['translation'] = translator_wrapper(transcription, mode='google')
-                        buffers[i]['text'] += ('|' + buffers[i]['translation'])
-
-                    # ----------------
-                    # Process lines
-                    # ----------------
-                    print('Process lines')
-                    lines = [{"speaker": "0", "text": ""}]
-                    
-                    if args.diarization:
-                        await diarization.diarize(pcm_array)
-                        # diarization.assign_speakers_to_chunks(chunk_history)
-                        chunk_history = diarization.assign_speakers_to_chunks(chunk_history)
-
-                    for ch in chunk_history:
-                        if args.diarization and ch["speaker"] and ch["speaker"][-1] != lines[-1]["speaker"]:
-                            lines.append({"speaker": ch["speaker"], "text": ch['text']})
-
-                        else:
-                            lines.append({"speaker": ch["speaker"], "text": ch['text']})
-
-                    for i, line in enumerate(lines):
-                        if line['text'].strip() == '':
-                            continue
-                        # translation = translator(line['text'])[0]['translation_text']
-                        # translation = translation.replace(' ', '')
-                        # lines[i]['text'] = line['text'] + translation
-                        lines[i]['text'] = line['text']
-
-                    # translation = translator(buffer)[0]['translation_text']
-                    # translation = translation.replace(' ', '')
-                    # buffer += translation
-
-                    print('Before making response')
-                    response = {"lines": buffers, "buffer": ''}
-                    await websocket.send_json(response)
-                    
-            except Exception as e:
-                print(f"Exception in ffmpeg_stdout_reader: {e}")
-                break
-
-        print("Exiting ffmpeg_stdout_reader...")
-
-    stdout_reader_task = asyncio.create_task(ffmpeg_stdout_reader())
-
-    try:
-        while True:
-            # Receive incoming WebM audio chunks from the client
-            message = await websocket.receive_bytes()
-            # Pass them to ffmpeg via stdin
-            ffmpeg_process.stdin.write(message)
-            ffmpeg_process.stdin.flush()
-
-    except WebSocketDisconnect:
-        print("WebSocket connection closed.")
-    except Exception as e:
-        print(f"Error in websocket loop: {e}")
-    finally:
-        # Clean up ffmpeg and the reader task
-        try:
-            ffmpeg_process.stdin.close()
-        except:
-            pass
-        stdout_reader_task.cancel()
-
-        try:
-            ffmpeg_process.stdout.close()
-        except:
-            pass
-
-        ffmpeg_process.wait()
-        del online
-        
-        if args.diarization:
-            # Stop Diart
-            diarization.close()
-
-
-
-
-if __name__ == "__main__":
-    import uvicorn
-
-    uvicorn.run(
-        "app:app", host=args.host, port=args.port, reload=True,
-        log_level="info"
-    )
+import os
+import io
+import requests
+import argparse
+import asyncio
+import numpy as np
+import ffmpeg
+from time import time
+
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+from fastapi.responses import HTMLResponse
+from fastapi.middleware.cors import CORSMiddleware
+
+from src.whisper_streaming.whisper_online import backend_factory, online_factory, add_shared_args
+
+import logging
+import logging.config
+from transformers import pipeline
+from huggingface_hub import login
+
+HUGGING_FACE_TOKEN = os.environ['HUGGING_FACE_TOKEN']
+login(HUGGING_FACE_TOKEN)
+
+# os.environ['HF_HOME'] = './.cache'
+
+MODEL_NAME = 'Helsinki-NLP/opus-tatoeba-en-ja'
+TRANSLATOR = pipeline('translation', model=MODEL_NAME, device='cuda')
+TRANSLATOR('Warming up!')
+
+def translator_wrapper(source_text, translation_target_lang, mode):
+    if mode == 'deepl':
+        params = {
+                    'auth_key' : os.environ['DEEPL_API_KEY'],
+                    'text' : source_text,
+                    'source_lang' : 'EN', # 翻訳対象の言語
+                    "target_lang": 'JA',  # 翻訳後の言語
+                }
+
+        # リクエストを投げる
+        try:
+            request = requests.post("https://api-free.deepl.com/v2/translate", data=params, timeout=5) # URIは有償版, 無償版で異なるため要注意
+            result = request.json()['translations'][0]['text']
+        except requests.exceptions.Timeout:
+            result = "(timed out)"
+        return result
+
+    elif mode == 'marianmt':
+        return TRANSLATOR(source_text)[0]['translation_text']
+
+    elif mode == 'google':
+        import  requests
+
+        # https://www.eyoucms.com/news/ziliao/other/29445.html
+        language_type = ""
+        url = "https://translation.googleapis.com/language/translate/v2"
+        data = {
+            'key':"AIzaSyCX0-Wdxl_rgvcZzklNjnqJ1W9YiKjcHUs", #  認証の設定:APIキー
+            'source': language_type,
+            'target': translation_target_lang,
+            'q': source_text,
+            'format': "text"
+        }
+        #headers = {'X-HTTP-Method-Override': 'GET'}
+        #response = requests.post(url, data=data, headers=headers)
+        response = requests.post(url, data)
+        # print(response.json())
+        print(response)
+        res = response.json()
+        print(res["data"]["translations"][0]["translatedText"])
+        result = res["data"]["translations"][0]["translatedText"]
+        print(result)
+        return result
+
+
+def setup_logging():
+    logging_config = {
+        'version': 1,
+        'disable_existing_loggers': False,
+        'formatters': {
+            'standard': {
+                'format': '%(asctime)s %(levelname)s [%(name)s]: %(message)s',
+            },
+        },
+        'handlers': {
+            'console': {
+                'level': 'INFO',
+                'class': 'logging.StreamHandler',
+                'formatter': 'standard',
+            },
+        },
+        'root': {
+            'handlers': ['console'],
+            'level': 'DEBUG',
+        },
+        'loggers': {
+            'uvicorn': {
+                'handlers': ['console'],
+                'level': 'INFO',
+                'propagate': False,
+            },
+            'uvicorn.error': {
+                'level': 'INFO',
+            },
+            'uvicorn.access': {
+                'level': 'INFO',
+            },
+            'src.whisper_streaming.online_asr': {  # Add your specific module here
+                'handlers': ['console'],
+                'level': 'DEBUG',
+                'propagate': False,
+            },
+            'src.whisper_streaming.whisper_streaming': {  # Add your specific module here
+                'handlers': ['console'],
+                'level': 'DEBUG',
+                'propagate': False,
+            },
+        },
+    }
+
+    logging.config.dictConfig(logging_config)
+
+setup_logging()
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+parser = argparse.ArgumentParser(description="Whisper FastAPI Online Server")
+parser.add_argument(
+    "--host",
+    type=str,
+    default="localhost",
+    help="The host address to bind the server to.",
+)
+parser.add_argument(
+    "--port", type=int, default=8000, help="The port number to bind the server to."
+)
+parser.add_argument(
+    "--warmup-file",
+    type=str,
+    dest="warmup_file",
+    help="The path to a speech audio wav file to warm up Whisper so that the very first chunk processing is fast. It can be e.g. https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav .",
+)
+parser.add_argument(
+    "--diarization",
+    type=bool,
+    default=False,
+    help="Whether to enable speaker diarization.",
+)
+parser.add_argument(
+    "--generate-audio",
+    type=bool,
+    default=False,
+    help="Whether to generate translation audio.",
+)
+
+
+add_shared_args(parser)
+args = parser.parse_args()
+# args.model = 'medium'
+
+if args.lan == 'ja':
+    translation_target_lang = 'en'
+elif args.lan == 'en':
+    translation_target_lang = 'ja'
+
+asr, tokenizer = backend_factory(args)
+
+if args.diarization:
+    from src.diarization.diarization_online import DiartDiarization
+
+
+# Load demo HTML for the root endpoint
+with open("src/web/live_transcription.html", "r", encoding="utf-8") as f:
+    html = f.read()
+
+
+@app.get("/")
+async def get():
+    return HTMLResponse(html)
+
+
+SAMPLE_RATE = 16000
+CHANNELS = 1
+SAMPLES_PER_SEC = int(SAMPLE_RATE * args.min_chunk_size)
+BYTES_PER_SAMPLE = 2  # s16le = 2 bytes per sample
+BYTES_PER_SEC = SAMPLES_PER_SEC * BYTES_PER_SAMPLE
+print('SAMPLE_RATE', SAMPLE_RATE)
+print('CHANNELS', CHANNELS)
+print('SAMPLES_PER_SEC', SAMPLES_PER_SEC)
+print('BYTES_PER_SAMPLE', BYTES_PER_SAMPLE)
+print('BYTES_PER_SEC', BYTES_PER_SEC)
+
+
+def generate_audio(japanese_text, speed=1.0):
+    api_url = "https://j6im8slpwcevr7g0.us-east-1.aws.endpoints.huggingface.cloud"
+    headers = {
+        "Accept" : "application/json",
+        "Authorization": f"Bearer {HUGGING_FACE_TOKEN}",
+        "Content-Type": "application/json" 
+    }
+
+    payload = {
+        "inputs": japanese_text,
+        "speed": speed,
+    }
+
+    response = requests.post(api_url, headers=headers, json=payload).json()
+    if 'error' in response:
+        print(response)
+        return ''
+    return response
+
+
+async def start_ffmpeg_decoder():
+    """
+    Start an FFmpeg process in async streaming mode that reads WebM from stdin
+    and outputs raw s16le PCM on stdout. Returns the process object.
+    """
+    process = (
+        ffmpeg
+        .input("pipe:0", format="webm")
+        .output(
+            "pipe:1",
+            format="s16le",
+            acodec="pcm_s16le",
+            ac=CHANNELS,
+            ar=str(SAMPLE_RATE),
+            # fflags='nobuffer',
+        )
+        .global_args('-loglevel', 'quiet')
+        .run_async(pipe_stdin=True, pipe_stdout=True, pipe_stderr=False, quiet=True)
+    )
+    return process
+
+import queue
+import threading
+
+@app.websocket("/asr")
+async def websocket_endpoint(websocket: WebSocket):
+    await websocket.accept()
+    print("WebSocket connection opened.")
+
+    ffmpeg_process = await start_ffmpeg_decoder()
+    pcm_buffer = bytearray()
+    print("Loading online.")
+    online = online_factory(args, asr, tokenizer)
+    print("Online loaded.")
+
+    if args.diarization:
+        diarization = DiartDiarization(SAMPLE_RATE)
+
+    # Continuously read decoded PCM from ffmpeg stdout in a background task
+    async def ffmpeg_stdout_reader():
+        nonlocal pcm_buffer
+        loop = asyncio.get_event_loop()
+        full_transcription = ""
+        beg = time()
+        
+        chunk_history = []  # Will store dicts: {beg, end, text, speaker}
+
+        buffers = [{'speaker': '0', 'text': '', 'translation': None, 'audio_url': None}]
+        buffer_line = ''
+        
+        # Create a queue to hold the chunks
+        chunk_queue = queue.Queue()
+
+        # Function to read from ffmpeg stdout in a separate thread
+        def read_ffmpeg_stdout():
+            while True:
+                try:
+                    chunk = ffmpeg_process.stdout.read(BYTES_PER_SEC)
+                    if not chunk:
+                        break
+                    chunk_queue.put(chunk)
+                except Exception as e:
+                    print(f"Exception in read_ffmpeg_stdout: {e}")
+                    break
+
+        # Start the thread
+        threading.Thread(target=read_ffmpeg_stdout, daemon=True).start()
+
+        while True:
+            try:
+                # Get the chunk from the queue
+                chunk = await loop.run_in_executor(None, chunk_queue.get)
+                if not chunk:
+                    print("FFmpeg stdout closed.")
+                    break
+
+                pcm_buffer.extend(chunk)
+                print('len(pcm_buffer): ', len(pcm_buffer))
+                print('BYTES_PER_SEC: ', BYTES_PER_SEC)
+
+                if len(pcm_buffer) >= BYTES_PER_SEC:
+                    # Convert int16 -> float32
+                    pcm_array = (np.frombuffer(pcm_buffer, dtype=np.int16).astype(np.float32) / 32768.0)
+                    pcm_buffer = bytearray()  # Initialize the PCM buffer
+                    online.insert_audio_chunk(pcm_array)
+                    beg_trans, end_trans, trans = online.process_iter()
+                    
+                    if trans:
+                        chunk_history.append({
+                        "beg": beg_trans,
+                        "end": end_trans,
+                        "text": trans,
+                        "speaker": "0"
+                        })
+                    full_transcription += trans
+                    
+                    # ----------------
+                    # Process buffer
+                    # ----------------
+                    if args.vac:
+                        # We need to access the underlying online object to get the buffer
+                        buffer_text = online.online.concatenate_tsw(online.online.transcript_buffer.buffer)[2]
+                    else:
+                        buffer_text = online.concatenate_tsw(online.transcript_buffer.buffer)[2]
+
+                    if buffer_text in full_transcription:  # With VAC, the buffer is not updated until the next chunk is processed
+                        buffer_text = ""
+
+                    buffer_line += buffer_text
+                                        
+                    punctuations = (',', '.', '?', '!', 'and', 'or', 'but', 'however')
+                    if not any(punctuation in buffer_line for punctuation in punctuations):
+                        continue
+
+                    last_punctuation_index = max((buffer_line.rfind(p) + len(p) + 1) for p in punctuations if p in buffer_line)
+                    extracted_text = buffer_line[:last_punctuation_index]
+                    buffer_line = buffer_line[last_punctuation_index:]
+                    buffer = {'speaker': '0', 'text': extracted_text, 'translation': None}
+
+                    translation = translator_wrapper(buffer['text'], translation_target_lang, mode='google')
+
+                    buffer['translation'] = translation
+                    buffer['text'] += ('|' + translation)
+                    buffer['audio_url'] = generate_audio(translation, speed=2.5) if args.generate_audio else ''
+                    buffers.append(buffer)
+
+                    # ----------------
+                    # Process lines
+                    # ----------------
+                    '''
+                    print('Process lines')
+                    lines = [{"speaker": "0", "text": ""}]
+                    
+                    if args.diarization:
+                        await diarization.diarize(pcm_array)
+                        # diarization.assign_speakers_to_chunks(chunk_history)
+                        chunk_history = diarization.assign_speakers_to_chunks(chunk_history)
+
+                    for ch in chunk_history:
+                        if args.diarization and ch["speaker"] and ch["speaker"][-1] != lines[-1]["speaker"]:
+                            lines.append({"speaker": ch["speaker"], "text": ch['text']})
+
+                        else:
+                            lines.append({"speaker": ch["speaker"], "text": ch['text']})
+
+                    for i, line in enumerate(lines):
+                        if line['text'].strip() == '':
+                            continue
+                        # translation = translator(line['text'])[0]['translation_text']
+                        # translation = translation.replace(' ', '')
+                        # lines[i]['text'] = line['text'] + translation
+                        lines[i]['text'] = line['text']
+                    '''
+
+                    print('Before making response')
+                    response = {'line': buffer, 'buffer': ''}
+                    print(response)
+                    await websocket.send_json(response)
+
+            except Exception as e:
+                print(f"Exception in ffmpeg_stdout_reader: {e}")
+                break
+
+        print("Exiting ffmpeg_stdout_reader...")
+
+    stdout_reader_task = asyncio.create_task(ffmpeg_stdout_reader())
+
+    try:
+        while True:
+            # Receive incoming WebM audio chunks from the client
+            message = await websocket.receive_bytes()
+            # Pass them to ffmpeg via stdin
+            ffmpeg_process.stdin.write(message)
+            ffmpeg_process.stdin.flush()
+
+    except WebSocketDisconnect:
+        print("WebSocket connection closed.")
+    except Exception as e:
+        print(f"Error in websocket loop: {e}")
+    finally:
+        # Clean up ffmpeg and the reader task
+        try:
+            ffmpeg_process.stdin.close()
+        except:
+            pass
+        stdout_reader_task.cancel()
+
+        try:
+            ffmpeg_process.stdout.close()
+        except:
+            pass
+
+        ffmpeg_process.wait()
+        del online
+        
+        if args.diarization:
+            # Stop Diart
+            diarization.close()
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(
+        "app:app", host=args.host, port=args.port, reload=True,
+        log_level="info"
+    )