{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":23707,"offset":23697,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"D\u01b0\u1edbi \u0111\u00e2y l\u00e0 v\u00ed d\u1ee5 code \u0111\u1ea7y \u0111\u1ee7, s\u1eeda l\u1ea1i \u0111o\u1ea1n x\u1eed l\u00fd TTS v\u1edbi `edge-tts` theo d\u1ea1ng mp3 v\u00e0 chuy\u1ec3n sang wav b\u1eb1ng `pydub`, \u0111\u1ed3ng th\u1eddi x\u1eed l\u00fd l\u01b0u file t\u1ea1m an to\u00e0n, tr\u00e1nh l\u1ed7i format kh\u00f4ng h\u1ee3p l\u1ec7.\n\n```python\nimport asyncio\nfrom fastapi import FastAPI, UploadFile, File, HTTPException\nfrom fastapi.responses import FileResponse\nimport torch\nimport io\nimport os\nimport uuid\nimport aiofiles\nimport edge_tts\nfrom pydub import AudioSegment\n\nfrom transformers import (\n    WhisperProcessor,\n    WhisperForConditionalGeneration,\n    MT5Tokenizer,\n    AutoModelForSeq2SeqLM\n)\n\nfrom inference_wav2lip import run_wav2lip\nfrom concurrent.futures import ThreadPoolExecutor\n\nUPLOAD_DIR = \"uploads\"\nOUTPUT_DIR = \"outputs\"\nos.makedirs(UPLOAD_DIR, exist_ok=True)\nos.makedirs(OUTPUT_DIR, exist_ok=True)\n\napp = FastAPI()\n\n# Load Whisper ASR model + processor\nmodel_asr_name = \"hoangdeeptry/whisper-vietnamese-3\"\nprocessor_asr = WhisperProcessor.from_pretrained(model_asr_name)\nmodel_asr = WhisperForConditionalGeneration.from_pretrained(model_asr_name)\nmodel_asr.eval()\n\n# Load chatbot Vietnamese mT5\nmodel_chat_name = \"google/mt5-base\"\ntokenizer_chat = MT5Tokenizer.from_pretrained(model_chat_name, use_fast=False)\nmodel_chat = AutoModelForSeq2SeqLM.from_pretrained(model_chat_name)\nmodel_chat.eval()\n\nexecutor = ThreadPoolExecutor(max_workers=2)\n\n\nasync def speech2text(audio_bytes: bytes) -> str:\n    def _speech2text_sync(audio_bytes_sync):\n        import soundfile as sf\n        import torchaudio\n        audio_input, sample_rate = sf.read(io.BytesIO(audio_bytes_sync))\n        if sample_rate != 16000:\n            waveform, sr = torchaudio.load(io.BytesIO(audio_bytes_sync))\n            resampler = torchaudio.transforms.Resample(sr, 16000)\n            audio_input = resampler(waveform).squeeze().numpy()\n        inputs = processor_asr(audio_input, sampling_rate=16000, return_tensors=\"pt\")\n        with torch.no_grad():\n            predicted_ids = model_asr.generate(inputs.input_features)\n        transcription = processor_asr.batch_decode(predicted_ids, skip_special_tokens=True)[0]\n        return transcription\n\n    loop = asyncio.get_event_loop()\n    return await loop.run_in_executor(executor, _speech2text_sync, audio_bytes)\n\n\nasync def chat_llm(text: str) -> str:\n    def _chat_llm_sync(text_sync):\n        inputs = tokenizer_chat.encode(text_sync, return_tensors=\"pt\", max_length=512, truncation=True)\n        outputs = model_chat.generate(inputs, max_length=128, num_beams=5)\n        reply = tokenizer_chat.decode(outputs[0], skip_special_tokens=True)\n        return reply\n\n    loop = asyncio.get_event_loop()\n    return await loop.run_in_executor(executor, _chat_llm_sync, text)\n\n\nasync def text2speech(text: str) -> io.BytesIO:\n    communicate = edge_tts.Communicate(text, voice=\"vi-VN-Nam-AiNeural\")\n    buffer = io.BytesIO()\n    async for chunk in communicate.stream():\n        buffer.write(chunk)\n    buffer.seek(0)\n    return buffer\n\n\n@app.post(\"/chat\")\nasync def chat_api(face: UploadFile = File(...), audio: UploadFile = File(...)):\n    unique_id = str(uuid.uuid4())\n    face_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_{face.filename}\")\n    audio_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_{audio.filename}\")\n    output_video_path = os.path.join(OUTPUT_DIR, f\"{unique_id}_out.mp4\")\n\n    tts_mp3_path = None\n    tts_wav_path = None\n\n    try:\n        async with aiofiles.open(face_path, 'wb') as f_face:\n            await f_face.write(await face.read())\n\n        async with aiofiles.open(audio_path, 'wb') as f_audio:\n            audio_bytes = await audio.read()\n            await f_audio.write(audio_bytes)\n\n        # ASR\n        transcription = await speech2text(audio_bytes)\n        if not transcription.strip():\n            raise HTTPException(status_code=400, detail=\"No speech detected\")\n\n        # Chatbot\n        reply_text = await chat_llm(transcription)\n\n        # TTS (xu\u1ea5t ra mp3)\n        tts_audio = await text2speech(reply_text)\n        tts_mp3_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_tts.mp3\")\n        with open(tts_mp3_path, \"wb\") as f_tts:\n            f_tts.write(tts_audio.getbuffer())\n\n        # Convert mp3 sang wav \u0111\u1ec3 wav2lip x\u1eed l\u00fd\n        tts_wav_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_tts.wav\")\n        audio = AudioSegment.from_file(tts_mp3_path, format=\"mp3\")\n        audio.export(tts_wav_path, format=\"wav\")\n\n        # Wav2Lip lip-sync ch\u1ea1y trong executor tr\u00e1nh block event loop\n        loop = asyncio.get_event_loop()\n        await loop.run_in_executor(executor, run_wav2lip, face_path, tts_wav_path, output_video_path)\n\n        return FileResponse(output_video_path, media_type=\"video/mp4\")\n\n    except Exception as e:\n        print(\"Error:\", e)\n        raise HTTPException(status_code=500, detail=str(e))\n\n    finally:\n        for fpath in [face_path, audio_path]:\n            if os.path.exists(fpath):\n                os.remove(fpath)\n        for fpath in [tts_mp3_path, tts_wav_path]:\n            if fpath and os.path.exists(fpath):\n                os.remove(fpath)\n```\n\n---\n\n**L\u01b0u \u00fd:**\n\n- B\u1ea1n c\u1ea7n c\u00e0i th\u00eam th\u01b0 vi\u1ec7n `pydub` v\u00e0 `ffmpeg` (ffmpeg c\u1ea7n c\u00e0i ngo\u00e0i h\u1ec7 th\u1ed1ng):\n\n```bash\npip install pydub\n```\n\n- C\u00e0i ffmpeg:\n\n  - Windows: t\u1ea3i ffmpeg binary v\u00e0 th\u00eam v\u00e0o PATH\n  - Linux/macOS: c\u00e0i qua package manager (e.g., `apt install ffmpeg` ho\u1eb7c `brew install ffmpeg`)\n\n- M\u00f4 h\u00ecnh v\u00e0 `inference_wav2lip` b\u1ea1n ph\u1ea3i \u0111\u1ea3m b\u1ea3o s\u1eb5n s\u00e0ng.\n\n---\n\nN\u1ebfu b\u1ea1n c\u1ea7n m\u00ecnh h\u1ed7 tr\u1ee3 ch\u1ec9nh debug ho\u1eb7c t\u00edch h\u1ee3p th\u00eam, c\u1ee9 h\u1ecfi nh\u00e9!","behavior":"degenerate_repetition","conversation_hash":"8330353f2b73067201e62db73b0e3570","conversation_redacted":false,"conversation_toxic":false,"country":"Vietnam","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Vietnamese","model":"gpt-4.1-mini-2025-04-14","n_chars":5450,"n_turns_in_conversation":27,"n_words":491,"source_row_offset":23697,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"a8994320a794e9b0ab6eb879","turn_index":9,"user_text":"\u0111\u1ea7y \u0111\u1ee7 code "},{"assistant_initiates":false,"asst_text":"D\u01b0\u1edbi \u0111\u00e2y l\u00e0 \u0111o\u1ea1n code **\u0111\u1ea7y \u0111\u1ee7, s\u1eeda l\u1ed7i m\u1edf audio \u0111\u1ecbnh d\u1ea1ng kh\u00f4ng \u0111\u00fang**, t\u00edch h\u1ee3p to\u00e0n b\u1ed9 ph\u1ea7n b\u1ea1n c\u1ea7n t\u1eeb import \u0111\u1ebfn \u0111\u1ecbnh ngh\u0129a FastAPI, c\u00e1c h\u00e0m ch\u00ednh, v\u00e0 endpoint `/chat`. M\u00ecnh \u0111\u00e3 ch\u1ec9nh \u0111\u1ec3:\n\n- TTS d\u00f9ng `edge-tts` t\u1ea1o file mp3.\n- Convert mp3 sang wav b\u1eb1ng `pydub` \u0111\u1ec3 `run_wav2lip` s\u1eed d\u1ee5ng.\n- Gi\u1eef an to\u00e0n x\u00f3a c\u00e1c file t\u1ea1m.\n- Ch\u1ea1y Wav2Lip trong `executor` tr\u00e1nh block async event loop.\n\n---\n\n```python\nimport asyncio\nfrom fastapi import FastAPI, UploadFile, File, HTTPException\nfrom fastapi.responses import FileResponse\nimport torch\nimport io\nimport os\nimport uuid\nimport aiofiles\nimport edge_tts\nfrom pydub import AudioSegment\n\nfrom transformers import (\n    WhisperProcessor,\n    WhisperForConditionalGeneration,\n    MT5Tokenizer,\n    AutoModelForSeq2SeqLM\n)\n\nfrom inference_wav2lip import run_wav2lip\nfrom concurrent.futures import ThreadPoolExecutor\n\n# T\u1ea1o th\u01b0 m\u1ee5c l\u01b0u t\u1ea1m n\u1ebfu ch\u01b0a c\u00f3\nUPLOAD_DIR = \"uploads\"\nOUTPUT_DIR = \"outputs\"\nos.makedirs(UPLOAD_DIR, exist_ok=True)\nos.makedirs(OUTPUT_DIR, exist_ok=True)\n\napp = FastAPI()\n\n# Load Whisper ASR model + processor (l\u01b0u \u00fd: c\u00f3 th\u1ec3 c\u1ea7n CUDA n\u1ebfu m\u00e1y b\u1ea1n c\u00f3 GPU)\nmodel_asr_name = \"hoangdeeptry/whisper-vietnamese-3\"\nprocessor_asr = WhisperProcessor.from_pretrained(model_asr_name)\nmodel_asr = WhisperForConditionalGeneration.from_pretrained(model_asr_name)\nmodel_asr.eval()\n\n# Load chatbot Vietnamese mT5\nmodel_chat_name = \"google/mt5-base\"\ntokenizer_chat = MT5Tokenizer.from_pretrained(model_chat_name, use_fast=False)\nmodel_chat = AutoModelForSeq2SeqLM.from_pretrained(model_chat_name)\nmodel_chat.eval()\n\nexecutor = ThreadPoolExecutor(max_workers=2)\n\n\nasync def speech2text(audio_bytes: bytes) -> str:\n    def _speech2text_sync(audio_bytes_sync):\n        import soundfile as sf\n        import torchaudio\n\n        audio_input, sample_rate = sf.read(io.BytesIO(audio_bytes_sync))\n        if sample_rate != 16000:\n            waveform, sr = torchaudio.load(io.BytesIO(audio_bytes_sync))\n            resampler = torchaudio.transforms.Resample(sr, 16000)\n            audio_input = resampler(waveform).squeeze().numpy()\n\n        inputs = processor_asr(audio_input, sampling_rate=16000, return_tensors=\"pt\")\n        with torch.no_grad():\n            predicted_ids = model_asr.generate(inputs.input_features)\n        transcription = processor_asr.batch_decode(predicted_ids, skip_special_tokens=True)[0]\n        return transcription\n\n    loop = asyncio.get_event_loop()\n    return await loop.run_in_executor(executor, _speech2text_sync, audio_bytes)\n\n\nasync def chat_llm(text: str) -> str:\n    def _chat_llm_sync(text_sync):\n        inputs = tokenizer_chat.encode(text_sync, return_tensors=\"pt\", max_length=512, truncation=True)\n        outputs = model_chat.generate(inputs, max_length=128, num_beams=5)\n        reply = tokenizer_chat.decode(outputs[0], skip_special_tokens=True)\n        return reply\n\n    loop = asyncio.get_event_loop()\n    return await loop.run_in_executor(executor, _chat_llm_sync, text)\n\n\nasync def text2speech(text: str) -> io.BytesIO:\n    communicate = edge_tts.Communicate(text, voice=\"vi-VN-Nam-AiNeural\")\n    buffer = io.BytesIO()\n    async for chunk in communicate.stream():\n        buffer.write(chunk)\n    buffer.seek(0)\n    return buffer\n\n\n@app.post(\"/chat\")\nasync def chat_api(face: UploadFile = File(...), audio: UploadFile = File(...)):\n    unique_id = str(uuid.uuid4())\n    face_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_{face.filename}\")\n    audio_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_{audio.filename}\")\n    output_video_path = os.path.join(OUTPUT_DIR, f\"{unique_id}_out.mp4\")\n\n    tts_mp3_path = None\n    tts_wav_path = None\n\n    try:\n        # L\u01b0u file m\u1eb7t\n        async with aiofiles.open(face_path, 'wb') as f_face:\n            await f_face.write(await face.read())\n\n        # L\u01b0u file audio upload\n        async with aiofiles.open(audio_path, 'wb') as f_audio:\n            audio_bytes = await audio.read()\n            await f_audio.write(audio_bytes)\n\n        # Ch\u1ea1y ASR chuy\u1ec3n gi\u1ecdng n\u00f3i sang text\n        transcription = await speech2text(audio_bytes)\n        if not transcription.strip():\n            raise HTTPException(status_code=400, detail=\"No speech detected\")\n\n        # Chatbot tr\u1ea3 l\u1eddi\n        reply_text = await chat_llm(transcription)\n\n        # TTS t\u1ea1o audio mp3\n        tts_audio = await text2speech(reply_text)\n        tts_mp3_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_tts.mp3\")\n        with open(tts_mp3_path, \"wb\") as f_tts:\n            f_tts.write(tts_audio.getbuffer())\n\n        # Convert mp3 sang wav \u0111\u1ec3 Wav2Lip x\u1eed l\u00fd\n        tts_wav_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_tts.wav\")\n        audio = AudioSegment.from_file(tts_mp3_path, format=\"mp3\")\n        audio.export(tts_wav_path, format=\"wav\")\n\n        # Ch\u1ea1y Wav2Lip lip-sync ch\u1ea1y trong executor tr\u00e1nh block event loop\n        loop = asyncio.get_event_loop()\n        await loop.run_in_executor(executor, run_wav2lip, face_path, tts_wav_path, output_video_path)\n\n        # Tr\u1ea3 v\u1ec1 file video mp4\n        return FileResponse(output_video_path, media_type=\"video/mp4\")\n\n    except Exception as e:\n        print(\"Error:\", e)\n        raise HTTPException(status_code=500, detail=str(e))\n\n    finally:\n        # Xo\u00e1 file t\u1ea1m n\u1ebfu t\u1ed3n t\u1ea1i\n        for fpath in [face_path, audio_path]:\n            if os.path.exists(fpath):\n                os.remove(fpath)\n        for fpath in [tts_mp3_path, tts_wav_path]:\n            if fpath and os.path.exists(fpath):\n                os.remove(fpath)\n\n\n# (N\u1ebfu b\u1ea1n c\u1ea7n ph\u1ee5c v\u1ee5 static files ho\u1eb7c giao di\u1ec7n web, mount static files \u1edf \u0111\u00e2y)\n# from fastapi.staticfiles import StaticFiles\n# app.mount(\"/\", StaticFiles(directory=\"static\", html=True), name=\"static\")\n\n# Ch\u1ea1y server\nif __name__ == \"__main__\":\n    import uvicorn\n    uvicorn.run(app, host=\"0.0.0.0\", port=8000, log_level=\"info\")\n```\n\n---\n\n## M\u1ed9t s\u1ed1 l\u01b0u \u00fd quan tr\u1ecdng:\n\n- C\u00e0i th\u00eam th\u01b0 vi\u1ec7n `pydub` n\u1ebfu ch\u01b0a c\u00f3:\n\n```bash\npip install pydub\n```\n\n- C\u00e0i `ffmpeg` \u0111\u1ec3 `pydub` ho\u1ea1t \u0111\u1ed9ng (theo h\u01b0\u1edbng d\u1eabn ph\u00f9 h\u1ee3p OS c\u1ee7a b\u1ea1n).\n\n- Ki\u1ec3m tra \u0111\u1ea3m b\u1ea3o `inference_wav2lip.py` \u0111\u01b0\u1ee3c c\u1ea5u h\u00ecnh \u0111\u00fang v\u00e0 h\u00e0m `run_wav2lip` ho\u1ea1t \u0111\u1ed9ng \u1ed5n.\n\n- C\u00e1c m\u00f4 h\u00ecnh Huggingface n\u1ebfu ch\u01b0a download s\u1ebd t\u1ef1 t\u1ea3i khi ch\u1ea1y, c\u1ea7n c\u00f3 internet.\n\n---\n\nN\u1ebfu b\u1ea1n mu\u1ed1n m\u00ecnh h\u1ed7 tr\u1ee3 ph\u1ea7n x\u1eed l\u00fd Wav2Lip ho\u1eb7c ph\u1ea7n frontend k\u00e8m theo, c\u1ee9 g\u1eedi n\u00f3i nh\u00e9!","behavior":"degenerate_repetition","conversation_hash":"8330353f2b73067201e62db73b0e3570","conversation_redacted":false,"conversation_toxic":false,"country":"Vietnam","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Vietnamese","model":"gpt-4.1-mini-2025-04-14","n_chars":6289,"n_turns_in_conversation":27,"n_words":635,"source_row_offset":23698,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"b8074da33cf2df9bbb354c46","turn_index":10,"user_text":"c\u00f2n thi\u1ebfu b\u1ea1n \u01a1i, hi\u1ec3n th\u1ecb \u0111\u1ea7y \u0111\u1ee7 code"},{"assistant_initiates":false,"asst_text":"L\u1ed7i `\"No module named 'torchaudio'\"` x\u1ea3y ra khi Python kh\u00f4ng t\u00ecm th\u1ea5y th\u01b0 vi\u1ec7n `torchaudio` trong m\u00f4i tr\u01b0\u1eddng hi\u1ec7n t\u1ea1i.\n\n---\n\n## C\u00e1ch kh\u1eafc ph\u1ee5c:\n\n1. **C\u00e0i \u0111\u1eb7t torchaudio**\n\nT\u00f9y theo phi\u00ean b\u1ea3n PyTorch v\u00e0 m\u00f4i tr\u01b0\u1eddng b\u1ea1n \u0111ang d\u00f9ng, b\u1ea1n c\u00e0i `torchaudio` t\u01b0\u01a1ng \u1ee9ng.\n\nV\u00ed d\u1ee5:\n\n```bash\npip install torchaudio\n```\n\n---\n\n2. **C\u00e0i \u0111\u00fang phi\u00ean b\u1ea3n `torchaudio` ph\u00f9 h\u1ee3p v\u1edbi PyTorch**\n\nTruy c\u1eadp trang ch\u1ee7 [PyTorch](https://pytorch.org/) v\u00e0 xem h\u01b0\u1edbng d\u1eabn c\u00e0i \u0111\u1eb7t cho h\u1ec7 \u0111i\u1ec1u h\u00e0nh, CUDA hay CPU.\n\nV\u00ed d\u1ee5 c\u00e0i cho CPU tr\u00ean Windows:\n\n```bash\npip install torch torchaudio torchvision cpuonly -f https://download.pytorch.org/whl/torch_stable.html\n```\n\n---\n\n3. **Ki\u1ec3m tra phi\u00ean b\u1ea3n PyTorch**\n\nN\u1ebfu b\u1ea1n c\u00f3 PyTorch r\u1ed3i, nh\u1edb c\u00e0i `torchaudio` c\u00f9ng phi\u00ean b\u1ea3n.\n\n---\n\n4. **S\u1eed d\u1ee5ng m\u00f4i tr\u01b0\u1eddng \u1ea3o**\n\nB\u1ea1n n\u00ean t\u1ea1o m\u00f4i tr\u01b0\u1eddng \u1ea3o \u0111\u1ec3 \u0111\u1ea3m b\u1ea3o kh\u00f4ng xung \u0111\u1ed9t:\n\n```bash\npython -m venv venv\nsource venv/bin/activate   # unix/mac\nvenv\\Scripts\\activate      # windows\n\npip install torch torchaudio fastapi uvicorn ...\n```\n\n---\n\n## T\u1ed5ng k\u1ebft:\n\n- C\u00e0i `torchaudio` b\u1eb1ng l\u1ec7nh: `pip install torchaudio`\n- Ho\u1eb7c theo h\u01b0\u1edbng d\u1eabn ch\u00ednh th\u1ee9c c\u1ee7a PyTorch\n- N\u1ebfu v\u1eabn l\u1ed7i, b\u1ea1n c\u00f3 th\u1ec3 g\u1eedi th\u00f4ng tin OS, python version, pytorch version \u0111\u1ec3 \u0111\u01b0\u1ee3c h\u01b0\u1edbng d\u1eabn chi ti\u1ebft h\u01a1n.\n\n---\n\nN\u1ebfu c\u1ea7n m\u00ecnh h\u1ed7 tr\u1ee3 c\u00e0i \u0111\u1eb7t ho\u1eb7c h\u01b0\u1edbng d\u1eabn th\u00eam, c\u1ee9 h\u1ecfi nh\u00e9!","behavior":"degenerate_repetition","conversation_hash":"8330353f2b73067201e62db73b0e3570","conversation_redacted":false,"conversation_toxic":false,"country":"Vietnam","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Vietnamese","model":"gpt-4.1-mini-2025-04-14","n_chars":1299,"n_turns_in_conversation":27,"n_words":217,"source_row_offset":23699,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"0d3eb0ce6bae1920e04ad90b","turn_index":11,"user_text":"No module named 'torchaudio'"},{"assistant_initiates":false,"asst_text":"L\u1ed7i `{\"detail\":\"Not Found\"}` trong FastAPI th\u01b0\u1eddng x\u1ea3y ra khi b\u1ea1n g\u1ecdi m\u1ed9t endpoint **kh\u00f4ng t\u1ed3n t\u1ea1i** ho\u1eb7c URL kh\u00f4ng \u0111\u00fang.\n\n---\n\n### Nguy\u00ean nh\u00e2n ph\u1ed5 bi\u1ebfn\n\n- \u0110\u01b0\u1eddng d\u1eabn b\u1ea1n g\u1ecdi tr\u00ean client (Postman, browser, frontend) kh\u00f4ng kh\u1edbp v\u1edbi endpoint b\u1ea1n khai b\u00e1o (`@app.post(\"/chat\")`).\n- Ph\u01b0\u01a1ng th\u1ee9c HTTP sai (v\u00ed d\u1ee5 b\u1ea1n g\u1eedi GET m\u00e0 endpoint ch\u1ec9 nh\u1eadn POST).\n- B\u1ea1n g\u1ecdi sai host ho\u1eb7c port (nh\u01b0 `http://localhost:8000/` m\u00e0 app kh\u00f4ng c\u00f3 route t\u01b0\u01a1ng \u1ee9ng).\n- C\u00f3 th\u1ec3 app ch\u01b0a ch\u1ea1y ho\u1eb7c ch\u1ea1y \u1edf port kh\u00e1c.\n\n---\n\n### Ki\u1ec3m tra v\u00e0 c\u00e1ch kh\u1eafc ph\u1ee5c\n\n1. **Ki\u1ec3m tra URL v\u00e0 ph\u01b0\u01a1ng th\u1ee9c**\n\n- Khi b\u1ea1n ch\u1ea1y server v\u1edbi l\u1ec7nh:\n\n  ```bash\n  uvicorn main:app --reload\n  ```\n\n  Th\u00ec server m\u1eb7c \u0111\u1ecbnh \u1edf `http://127.0.0.1:8000`.\n\n- Endpoint b\u1ea1n c\u00f3:\n\n  ```python\n  @app.post(\"/chat\")\n  async def chat_api(...):\n      ...\n  ```\n\n- B\u1ea1n ph\u1ea3i g\u1eedi **POST request** t\u1edbi: `http://127.0.0.1:8000/chat`\n\n2. **Ki\u1ec3m tra ki\u1ec3u request**\n\n- N\u1ebfu b\u1ea1n g\u1eedi request b\u1eb1ng browser (g\u00f5 url tr\u1ef1c ti\u1ebfp), n\u00f3 s\u1ebd l\u00e0 GET n\u00ean b\u1ecb tr\u1ea3 v\u1ec1 404 Not Found.\n- B\u1ea1n c\u1ea7n d\u00f9ng Postman ho\u1eb7c curl v\u1edbi method POST, v\u00ed d\u1ee5:\n\n  ```bash\n  curl -X POST \"http://127.0.0.1:8000/chat\" -F \"face=@path/to/face.jpg\" -F \"audio=@path/to/audio.wav\"\n  ```\n\n3. **Ki\u1ec3m tra log uvicorn**\n\n- Khi request t\u1edbi, log uvicorn s\u1ebd hi\u1ec3n th\u1ecb \u0111\u01b0\u1eddng d\u1eabn \u0111\u01b0\u1ee3c g\u1ecdi v\u00e0 l\u1ed7i n\u1ebfu c\u00f3.\n- N\u1ebfu kh\u00f4ng c\u00f3 log g\u00ec, c\u00f3 th\u1ec3 app ch\u01b0a ch\u1ea1y ho\u1eb7c sai host/port.\n\n4. **Ki\u1ec3m tra mount static files**\n\nN\u1ebfu b\u1ea1n c\u00f3 \u0111o\u1ea1n:\n\n```python\napp.mount(\"/\", StaticFiles(directory=\"static\", html=True), name=\"static\")\n```\n\nth\u00ec GET `\"/\"` s\u1ebd tr\u1ea3 file static (index.html), m\u00e0 c\u00e1c route kh\u00e1c v\u1eabn ch\u1ea1y \u0111\u01b0\u1ee3c. Nh\u01b0ng n\u1ebfu b\u1ea1n g\u1ecdi \u0111\u00fang POST `/chat` th\u00ec v\u1eabn kh\u00f4ng b\u1ecb \u1ea3nh h\u01b0\u1edfng.\n\n---\n\n### V\u00ed d\u1ee5 test \u0111\u01a1n gi\u1ea3n sau khi ch\u1ea1y server:\n\n**Test b\u1eb1ng curl (gi\u1ea3 \u0111\u1ecbnh b\u1ea1n c\u00f3 file face.jpg, audio.wav):**\n\n```bash\ncurl -X POST \"http://127.0.0.1:8000/chat\" -F \"face=@face.jpg\" -F \"audio=@audio.wav\"\n```\n\n**N\u1ebfu mu\u1ed1n test nhanh b\u1eb1ng Swagger UI:**\n\n- M\u1edf tr\u00ecnh duy\u1ec7t \u0111\u1ebfn \u0111\u01b0\u1eddng d\u1eabn:\n\n  ```\n  http://127.0.0.1:8000/docs\n  ```\n\n- \u0110\u00e2y l\u00e0 trang giao di\u1ec7n API t\u01b0\u01a1ng t\u00e1c do FastAPI cung c\u1ea5p, b\u1ea1n c\u00f3 th\u1ec3 test POST `/chat` d\u1ec5 d\u00e0ng.\n\n---\n\n### T\u00f3m l\u1ea1i\n\n- G\u1eedi request \u0111\u00fang **ph\u01b0\u01a1ng th\u1ee9c** (POST).\n- G\u1eedi request \u0111\u00fang **\u0111\u01b0\u1eddng d\u1eabn** (`/chat`).\n- \u0110\u1ea3m b\u1ea3o app \u0111ang ch\u1ea1y \u0111\u00fang host-port.\n- D\u00f9ng Swagger UI \u0111\u1ec3 test.\n\nN\u1ebfu b\u1ea1n v\u1eabn g\u1eb7p l\u1ed7i, b\u1ea1n c\u00f3 th\u1ec3 g\u1eedi \u0111o\u1ea1n l\u1ec7nh b\u1ea1n g\u1ecdi API ho\u1eb7c c\u00f4ng c\u1ee5 b\u1ea1n d\u00f9ng (Postman/curl), m\u00ecnh gi\u00fap ki\u1ec3m tra chi ti\u1ebft!","behavior":"degenerate_repetition","conversation_hash":"8330353f2b73067201e62db73b0e3570","conversation_redacted":false,"conversation_toxic":false,"country":"Vietnam","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Vietnamese","model":"gpt-4.1-mini-2025-04-14","n_chars":2361,"n_turns_in_conversation":27,"n_words":396,"source_row_offset":23700,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"ed28ad19f977627cc5753d50","turn_index":12,"user_text":"{\"detail\":\"Not Found\"}"},{"assistant_initiates":false,"asst_text":"L\u1ed7i 404 `{\"detail\":\"Not Found\"}` b\u1ea1n g\u1eb7p khi g\u1ecdi API th\u01b0\u1eddng do y\u00eau c\u1ea7u kh\u00f4ng kh\u1edbp v\u1edbi b\u1ea5t k\u1ef3 route n\u00e0o trong \u1ee9ng d\u1ee5ng FastAPI c\u1ee7a b\u1ea1n.\n\n\u0110\u1ec3 gi\u00fap b\u1ea1n kh\u1eafc ph\u1ee5c tri\u1ec7t \u0111\u1ec3, m\u00ecnh g\u1ee3i \u00fd c\u00e1c b\u01b0\u1edbc th\u1ef1c hi\u1ec7n v\u00e0 ki\u1ec3m tra k\u1ef9 c\u00e0ng nh\u01b0 sau:\n\n---\n\n## 1. Ki\u1ec3m tra server \u0111\u00e3 ch\u1ea1y \u0111\u00fang v\u00e0 \u0111\u00fang port ch\u01b0a\n\n- Ch\u1ea1y server b\u1eb1ng l\u1ec7nh (gi\u1ea3 s\u1eed file b\u1ea1n \u0111\u1eb7t t\u00ean `main.py`):\n\n```bash\nuvicorn main:app --reload\n```\n\nL\u01b0u \u00fd n\u1ebfu file c\u1ee7a b\u1ea1n t\u00ean kh\u00e1c (v\u00ed d\u1ee5 `app.py`), thay `main:app` th\u00e0nh `app:app`.\n\n- M\u1eb7c \u0111\u1ecbnh server ch\u1ea1y tr\u00ean \u0111\u1ecba ch\u1ec9 `http://127.0.0.1:8000`.\n\n---\n\n## 2. Ki\u1ec3m tra route `/chat` \u0111\u00e3 \u0111\u01b0\u1ee3c khai b\u00e1o \u0111\u00fang ch\u01b0a\n\nTrong code b\u1ea1n g\u1eedi, route c\u1ee7a b\u1ea1n khai b\u00e1o l\u00e0:\n\n```python\n@app.post(\"/chat\")\nasync def chat_api(...):\n    ...\n```\n\n\u0110i\u1ec1u n\u00e0y ngh\u0129a l\u00e0 b\u1ea1n ph\u1ea3i g\u1ecdi **POST** \u0111\u1ebfn endpoint `http://127.0.0.1:8000/chat`.\n\n---\n\n## 3. G\u1ecdi API \u0111\u00fang c\u00e1ch\n\n- **Kh\u00f4ng th\u1ec3 truy c\u1eadp `/chat` b\u1eb1ng tr\u00ecnh duy\u1ec7t** v\u00ec b\u1ea1n d\u00f9ng POST, tr\u00ecnh duy\u1ec7t m\u1eb7c \u0111\u1ecbnh l\u00e0 GET s\u1ebd tr\u1ea3 404.\n\n- B\u1ea1n c\u00f3 th\u1ec3 test b\u1eb1ng c\u00f4ng c\u1ee5 nh\u01b0 **cURL**, **Postman**, ho\u1eb7c d\u00f9ng Swagger UI c\u1ee7a FastAPI:\n\n### 3.1 D\u00f9ng cURL (\u0111i\u1ec1n \u0111\u00fang file c\u1ee7a b\u1ea1n):\n\n```bash\ncurl -X POST \"http://127.0.0.1:8000/chat\" -F \"face=@/path/to/face.jpg\" -F \"audio=@/path/to/audio.wav\"\n```\n\n### 3.2 D\u00f9ng Postman\n\n- M\u1edf Postman, t\u1ea1o request POST \u0111\u1ebfn `http://127.0.0.1:8000/chat`.\n\n- \u1ede tab \"Body\", ch\u1ecdn form-data.\n\n- Th\u00eam key `face` ki\u1ec3u File, ch\u1ecdn file \u1ea3nh.\n\n- Th\u00eam key `audio` ki\u1ec3u File, ch\u1ecdn file \u00e2m thanh.\n\n- G\u1eedi request.\n\n### 3.3 D\u00f9ng Swagger UI\n\n- M\u1edf tr\u00ecnh duy\u1ec7t t\u1edbi:\n\n```\nhttp://127.0.0.1:8000/docs\n```\n\n- T\u1ea1i \u0111\u00e2y b\u1ea1n s\u1ebd th\u1ea5y danh s\u00e1ch API, ch\u1ecdn `/chat`, nh\u1ea5n \"Try it out\", t\u1ea3i file face v\u00e0 audio l\u00ean r\u1ed3i g\u1eedi.\n\n---\n\n## 4. Ki\u1ec3m tra xem c\u00f3 mount static files ghi \u0111\u00e8 kh\u00f4ng\n\nN\u1ebfu b\u1ea1n c\u00f3 \u0111o\u1ea1n:\n\n```python\nfrom fastapi.staticfiles import StaticFiles\napp.mount(\"/\", StaticFiles(directory=\"static\", html=True), name=\"static\")\n```\n\nTh\u00ec khi b\u1ea1n truy c\u1eadp URL `/`, server s\u1ebd ph\u1ee5c v\u1ee5 th\u01b0 m\u1ee5c static, nh\u01b0ng c\u00e1c API kh\u00e1c v\u1eabn ph\u1ea3i ho\u1ea1t \u0111\u1ed9ng b\u00ecnh th\u01b0\u1eddng.\n\n**L\u01b0u \u00fd**: \u0110\u1ea3m b\u1ea3o kh\u00f4ng c\u00f3 route `/chat` n\u00e0o b\u1ecb ghi \u0111\u00e8 ho\u1eb7c \u0111\u1eb7t sai v\u1ecb tr\u00ed khai b\u00e1o.\n\n---\n\n## 5. Ki\u1ec3m tra location b\u1ea1n g\u1ecdi API\n\nN\u1ebfu b\u1ea1n g\u1ecdi API t\u1eeb frontend ho\u1eb7c m\u00f4i tr\u01b0\u1eddng kh\u00e1c, h\u00e3y ch\u1eafc ch\u1eafn baseURL v\u00e0 port \u0111\u00fang.\n\n---\n\n## 6. V\u00ed d\u1ee5 code FastAPI t\u1ed1i gi\u1ea3n \u0111\u1ec3 ki\u1ec3m tra\n\nB\u1ea1n th\u1eed t\u1ea1o file `test_main.py` v\u1edbi n\u1ed9i dung:\n\n```python\nfrom fastapi import FastAPI\n\napp = FastAPI()\n\n@app.post(\"/chat\")\nasync def chat_api():\n    return {\"message\": \"Hello from /chat\"}\n```\n\nCh\u1ea1y l\u1ec7nh:\n\n```bash\nuvicorn test_main:app --reload\n```\n\nG\u1ecdi th\u1eed:\n\n```bash\ncurl -X POST http://127.0.0.1:8000/chat\n```\n\nN\u1ebfu v\u1eabn ra 404 ngh\u0129a l\u00e0 c\u00f3 v\u1ea5n \u0111\u1ec1 m\u00f4i tr\u01b0\u1eddng ho\u1eb7c b\u1ea1n g\u1ecdi sai host/port.\n\n---\n\n## 7. M\u1ed9t s\u1ed1 l\u01b0u \u00fd nh\u1ecf kh\u00e1c\n\n- N\u1ebfu ch\u1ea1y app trong Docker, ki\u1ec3m tra port mapping.\n\n- N\u1ebfu g\u1ecdi t\u1eeb m\u00e1y kh\u00e1c ho\u1eb7c trong m\u1ea1ng kh\u00e1c, ki\u1ec3m tra firewall, host.\n\n- Ki\u1ec3m tra nhanh log uvicorn xem request c\u00f3 t\u1edbi server kh\u00f4ng?\n\n---\n\nN\u1ebfu b\u1ea1n \u0111\u00e3 th\u1eed t\u1ea5t c\u1ea3 tr\u00ean m\u00e0 v\u1eabn l\u1ed7i, vui l\u00f2ng g\u1eedi th\u00eam:\n\n- L\u1ec7nh b\u1ea1n d\u00f9ng ch\u1ea1y uvicorn.\n\n- L\u1ec7nh ho\u1eb7c c\u00e1ch b\u1ea1n g\u1ecdi API.\n\n- C\u1ea5u tr\u00fac file (t\u00ean file code, v\u1ecb tr\u00ed).\n\n- Log khi b\u1eadt `uvicorn --log-level debug`.\n\nM\u00ecnh s\u1ebd h\u1ed7 tr\u1ee3 ph\u00e2n t\u00edch chi ti\u1ebft h\u01a1n!","behavior":"degenerate_repetition","conversation_hash":"8330353f2b73067201e62db73b0e3570","conversation_redacted":false,"conversation_toxic":false,"country":"Vietnam","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Vietnamese","model":"gpt-4.1-mini-2025-04-14","n_chars":3069,"n_turns_in_conversation":27,"n_words":548,"source_row_offset":23701,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"bfa02f77ef948e593c6810ae","turn_index":13,"user_text":"v\u1eabn l\u1ed7i"},{"assistant_initiates":false,"asst_text":"L\u1ed7i `{\"detail\":\"Not Found\"}` ngh\u0129a l\u00e0 server FastAPI c\u1ee7a b\u1ea1n nh\u1eadn \u0111\u01b0\u1ee3c request t\u1edbi m\u1ed9t \u0111\u01b0\u1eddng d\u1eabn (URL) m\u00e0 kh\u00f4ng t\u00ecm th\u1ea5y route t\u01b0\u01a1ng \u1ee9ng. N\u1ebfu b\u1ea1n \u0111\u00e3 ch\u1eafc ch\u1eafn g\u1ecdi \u0111\u01b0\u1eddng `/chat` v\u1edbi ph\u01b0\u01a1ng th\u1ee9c POST theo \u0111\u00fang code b\u1ea1n \u0111\u01b0a, nh\u01b0ng v\u1eabn nh\u1eadn l\u1ed7i n\u00e0y, th\u00ec c\u00f3 th\u1ec3 do m\u1ed9t s\u1ed1 nguy\u00ean nh\u00e2n ph\u1ed5 bi\u1ebfn sau \u0111\u00e2y:\n\n---\n\n### 1. Ki\u1ec3m tra l\u1ea1i t\u00ean file v\u00e0 tham s\u1ed1 ch\u1ea1y Uvicorn\n\n- Gi\u1ea3 s\u1eed file code c\u1ee7a b\u1ea1n n\u1eb1m trong file t\u00ean `main.py` v\u00e0 bi\u1ebfn FastAPI t\u00ean `app` th\u00ec l\u1ec7nh ch\u1ea1y FastAPI ph\u1ea3i l\u00e0:\n\n```bash\nuvicorn main:app --reload\n```\n\nN\u1ebfu t\u00ean file ho\u1eb7c bi\u1ebfn FastAPI kh\u00e1c, v\u00ed d\u1ee5 `app.py` v\u00e0 bi\u1ebfn l\u00e0 `app`, th\u00ec:\n\n```bash\nuvicorn app:app --reload\n```\n\nN\u1ebfu sai t\u00ean file ho\u1eb7c bi\u1ebfn th\u00ec Uvicorn kh\u00f4ng load \u0111\u00fang app n\u00ean khi g\u1ecdi API s\u1ebd ra 404.\n\n---\n\n### 2. X\u00e1c minh URL ch\u00ednh x\u00e1c b\u1ea1n g\u1ecdi\n\n- API `/chat` trong code b\u1ea1n khai b\u00e1o nh\u1eadn ph\u01b0\u01a1ng th\u1ee9c POST, **n\u00ean b\u1ea1n kh\u00f4ng th\u1ec3 truy c\u1eadp b\u1eb1ng GET (v\u00ed d\u1ee5 g\u00f5 URL tr\u1ef1c ti\u1ebfp tr\u00ean tr\u00ecnh duy\u1ec7t).**\n\n- B\u1ea1n ph\u1ea3i g\u1ecdi:\n\n  - URL: `http://localhost:8000/chat` (ho\u1eb7c `127.0.0.1:8000/chat`)\n\n  - Ph\u01b0\u01a1ng th\u1ee9c: POST\n\n  - Body: form-data v\u1edbi 2 file l\u00e0 `face` v\u00e0 `audio` (theo code b\u1ea1n \u0111\u01b0a)\n\nV\u00ed d\u1ee5 g\u1ecdi b\u1eb1ng curl:\n\n```bash\ncurl -X POST http://localhost:8000/chat -F \"face=@path/to/face.jpg\" -F \"audio=@path/to/audio.wav\"\n```\n\n---\n\n### 3. Ki\u1ec3m tra xem app c\u00f3 mount th\u01b0 m\u1ee5c static root `/` kh\u00f4ng\n\nN\u1ebfu b\u1ea1n c\u00f3 \u0111o\u1ea1n:\n\n```python\napp.mount(\"/\", StaticFiles(directory=\"static\", html=True), name=\"static\")\n```\n\nth\u00ec th\u01b0 m\u1ee5c `static` s\u1ebd ph\u1ee5c v\u1ee5 t\u1ea5t c\u1ea3 request **GET** t\u1edbi `/` v\u00e0 c\u00e1c \u0111\u01b0\u1eddng d\u1eabn con. Nh\u01b0ng `/chat` l\u00e0 POST n\u00ean kh\u00f4ng b\u1ecb \u1ea3nh h\u01b0\u1edfng.\n\nTuy nhi\u00ean, n\u1ebfu b\u1ea1n kh\u00f4ng c\u00f3 th\u01b0 m\u1ee5c `static` ho\u1eb7c mount sai, c\u00f3 th\u1ec3 b\u1ecb l\u1ed7i ho\u1eb7c \u1ea3nh h\u01b0\u1edfng.\n\nB\u1ea1n c\u00f3 th\u1ec3 th\u1eed comment d\u00f2ng mount static \u0111\u1ec3 test l\u1ea1i nhanh.\n\n---\n\n### 4. Test app b\u1eb1ng t\u00e0i li\u1ec7u t\u01b0\u01a1ng t\u00e1c c\u1ee7a FastAPI\n\nTruy c\u1eadp:\n\n```\nhttp://localhost:8000/docs\n```\n\nTrang n\u00e0y hi\u1ec3n th\u1ecb t\u1ea5t c\u1ea3 c\u00e1c endpoint FastAPI \u0111ang c\u00f3. N\u1ebfu b\u1ea1n kh\u00f4ng th\u1ea5y `/chat` xu\u1ea5t hi\u1ec7n \u1edf \u0111\u00e2y ngh\u0129a l\u00e0 app c\u1ee7a b\u1ea1n kh\u00f4ng load \u0111\u00fang ho\u1eb7c route kh\u00f4ng \u0111\u01b0\u1ee3c \u0111\u0103ng k\u00fd.\n\n---\n\n### 5. C\u00e1ch ki\u1ec3m tra nhanh\n\nT\u1ea1o file m\u1edbi `test_main.py` v\u1edbi n\u1ed9i dung t\u1ed1i gi\u1ea3n:\n\n```python\nfrom fastapi import FastAPI\n\napp = FastAPI()\n\n@app.post(\"/chat\")\nasync def test_chat():\n    return {\"msg\": \"Chat endpoint is working\"}\n```\n\nCh\u1ea1y b\u1eb1ng:\n\n```bash\nuvicorn test_main:app --reload\n```\n\nG\u1eedi POST request \u0111\u1ebfn `http://localhost:8000/chat`.\n\nN\u1ebfu ch\u1ea1y \u0111\u01b0\u1ee3c th\u00ec kh\u00f4ng c\u00f3 l\u1ed7i 404.\n\n---\n\n### 6. L\u1eddi khuy\u00ean b\u1ed5 sung\n\n- Ki\u1ec3m tra log c\u1ee7a Uvicorn khi ch\u1ea1y server v\u00e0 khi b\u1ea1n g\u1eedi request: n\u1ebfu request kh\u00f4ng hi\u1ec3n th\u1ecb trong log, c\u00f3 th\u1ec3 request kh\u00f4ng t\u1edbi \u0111\u00fang server.\n\n- N\u1ebfu d\u00f9ng Docker ho\u1eb7c m\u00f4i tr\u01b0\u1eddng \u1ea3o, ki\u1ec3m tra c\u1ea5u h\u00ecnh m\u1ea1ng, port mapping...\n\n---\n\nN\u1ebfu v\u1eabn g\u1eb7p kh\u00f3 kh\u0103n, b\u1ea1n vui l\u00f2ng g\u1eedi:\n\n- File t\u00ean g\u00ec? (vd: main.py, app.py)\n\n- L\u1ec7nh b\u1ea1n ch\u1ea1y uvicorn.\n\n- C\u00e1ch b\u1ea1n g\u1ecdi API (curl, postman, frontend...).\n\n- Log output khi ch\u1ea1y server v\u00e0 g\u1ecdi API.\n\nM\u00ecnh s\u1ebd h\u1ed7 tr\u1ee3 b\u1ea1n nhanh h\u01a1n!","behavior":"degenerate_repetition","conversation_hash":"8330353f2b73067201e62db73b0e3570","conversation_redacted":false,"conversation_toxic":false,"country":"Vietnam","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Vietnamese","model":"gpt-4.1-mini-2025-04-14","n_chars":2837,"n_turns_in_conversation":27,"n_words":506,"source_row_offset":23702,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"8009db5e39318fe309108a60","turn_index":14,"user_text":"{\"detail\":\"Not Found\"}"},{"assistant_initiates":false,"asst_text":"C\u1ea3nh b\u00e1o:\n\n```\nRuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work\n```\n\nngh\u0129a l\u00e0 th\u01b0 vi\u1ec7n **pydub** kh\u00f4ng t\u00ecm th\u1ea5y ph\u1ea7n m\u1ec1m **ffmpeg** ho\u1eb7c **avconv** tr\u00ean m\u00e1y b\u1ea1n \u0111\u1ec3 x\u1eed l\u00fd chuy\u1ec3n \u0111\u1ed5i \u00e2m thanh. Vi\u1ec7c n\u00e0y c\u00f3 th\u1ec3 l\u00e0m c\u00e1c ch\u1ee9c n\u0103ng nh\u01b0 chuy\u1ec3n mp3 sang wav b\u1ecb l\u1ed7i ho\u1eb7c kh\u00f4ng ho\u1ea1t \u0111\u1ed9ng \u0111\u00fang.\n\n---\n\n## C\u00e1ch kh\u1eafc ph\u1ee5c\n\n### 1. C\u00e0i \u0111\u1eb7t ffmpeg\n\n- **Windows:**\n\n  - Truy c\u1eadp trang t\u1ea3i ch\u00ednh th\u1ee9c: https://ffmpeg.org/download.html\n\n  - Ho\u1eb7c t\u1ea3i b\u1ea3n build s\u1eb5n t\u1eeb:  \n    https://www.gyan.dev/ffmpeg/builds/  \n    https://github.com/BtbN/FFmpeg-Builds/releases\n\n  - T\u1ea3i package Static build (ex: `ffmpeg-release-essentials.zip`), gi\u1ea3i n\u00e9n ra th\u01b0 m\u1ee5c (vd: `C:\\ffmpeg`).\n\n  - Th\u00eam \u0111\u01b0\u1eddng d\u1eabn \u0111\u1ebfn th\u01b0 m\u1ee5c `bin` trong th\u01b0 m\u1ee5c gi\u1ea3i n\u00e9n v\u00e0o bi\u1ebfn m\u00f4i tr\u01b0\u1eddng **PATH**:\n\n    V\u00ed d\u1ee5 th\u00eam `C:\\ffmpeg\\bin` v\u00e0o PATH.\n\n  - Kh\u1edfi \u0111\u1ed9ng l\u1ea1i terminal/IDE \u0111\u1ec3 c\u1eadp nh\u1eadt bi\u1ebfn PATH.\n\n- **Linux (Ubuntu/Debian):**\n\n```bash\nsudo apt update\nsudo apt install ffmpeg\n```\n\n- **macOS (d\u00f9ng Homebrew):**\n\n```bash\nbrew install ffmpeg\n```\n\n---\n\n### 2. Ki\u1ec3m tra c\u00e0i \u0111\u1eb7t\n\nSau khi c\u00e0i \u0111\u1eb7t xong, m\u1edf terminal/g\u00f5:\n\n```bash\nffmpeg -version\n```\n\nN\u1ebfu hi\u1ec3n th\u1ecb th\u00f4ng tin phi\u00ean b\u1ea3n, ngh\u0129a l\u00e0 \u0111\u00e3 c\u00e0i th\u00e0nh c\u00f4ng.\n\n---\n\n### 3. Ch\u1ea1y l\u1ea1i \u1ee9ng d\u1ee5ng\n\nSau khi c\u00e0i \u0111\u1eb7t ffmpeg v\u00e0 \u0111\u1ea3m b\u1ea3o `ffmpeg` c\u00f3 trong PATH, pydub s\u1ebd d\u00f9ng \u0111\u01b0\u1ee3c \u0111\u1ec3 chuy\u1ec3n \u0111\u1ed5i t\u1eadp tin \u00e2m thanh.\n\n---\n\n### 4. L\u01b0u \u00fd\n\nN\u1ebfu b\u1ea1n v\u1eabn g\u1eb7p l\u1ed7i d\u00f9 \u0111\u00e3 c\u00e0i, b\u1ea1n c\u00f3 th\u1ec3 ch\u1ec9 \u0111\u1ecbnh tr\u1ef1c ti\u1ebfp \u0111\u01b0\u1eddng d\u1eabn ffmpeg trong code nh\u01b0 sau:\n\n```python\nfrom pydub import AudioSegment\nfrom pydub.utils import which\n\nAudioSegment.converter = which(\"ffmpeg\") or \"C:/ffmpeg/bin/ffmpeg.exe\"  # ch\u00fa \u00fd d\u00f9ng \u0111\u01b0\u1eddng d\u1eabn \u0111\u00fang v\u1edbi m\u00e1y b\u1ea1n\n```\n\n---\n\nN\u1ebfu c\u1ea7n h\u01b0\u1edbng d\u1eabn chi ti\u1ebft c\u00e0i ffmpeg tr\u00ean h\u1ec7 \u0111i\u1ec1u h\u00e0nh c\u1ee7a b\u1ea1n, b\u1ea1n c\u1ee9 h\u1ecfi nh\u00e9!","behavior":"degenerate_repetition","conversation_hash":"8330353f2b73067201e62db73b0e3570","conversation_redacted":false,"conversation_toxic":false,"country":"Vietnam","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Vietnamese","model":"gpt-4.1-mini-2025-04-14","n_chars":1997,"n_turns_in_conversation":27,"n_words":322,"source_row_offset":23703,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"4089eb217bbc46abe830f2f5","turn_index":15,"user_text":"c\u1ea3nh b\u00e1o:  D:\\Project_Pycharm\\digital_human_demo\\.venv\\lib\\site-packages\\pydub\\utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work\n  warn(\"Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work\", RuntimeWarning)"},{"assistant_initiates":false,"asst_text":"L\u1ed7i **Internal Server Error (500)** l\u00e0 l\u1ed7i chung do server tr\u1ea3 v\u1ec1 khi x\u1ea3y ra ngo\u1ea1i l\u1ec7 (exception) kh\u00f4ng \u0111\u01b0\u1ee3c x\u1eed l\u00fd. \u0110\u1ec3 x\u00e1c \u0111\u1ecbnh v\u00e0 s\u1eeda l\u1ed7i n\u00e0y trong FastAPI, b\u1ea1n c\u1ea7n xem chi ti\u1ebft th\u00f4ng b\u00e1o l\u1ed7i (stacktrace) \u0111\u1ec3 bi\u1ebft nguy\u00ean nh\u00e2n th\u1eadt s\u1ef1.\n\nD\u01b0\u1edbi \u0111\u00e2y l\u00e0 c\u00e1c b\u01b0\u1edbc v\u00e0 g\u1ee3i \u00fd gi\u00fap b\u1ea1n debug:\n\n---\n\n## 1. B\u1eadt log chi ti\u1ebft cho uvicorn khi ch\u1ea1y server\n\nThay v\u00ec ch\u1ea1y:\n\n```bash\nuvicorn main:app --reload\n```\n\nb\u1ea1n ch\u1ea1y th\u00eam ch\u1ebf \u0111\u1ed9 verbose log:\n\n```bash\nuvicorn main:app --reload --log-level debug\n```\n\n\u0110i\u1ec1u n\u00e0y gi\u00fap b\u1ea1n th\u1ea5y chi ti\u1ebft l\u1ed7i trong console khi server g\u1eb7p s\u1ef1 c\u1ed1.\n\n---\n\n## 2. Th\u00eam x\u1eed l\u00fd l\u1ed7i b\u1eaft bu\u1ed9c hi\u1ec3n th\u1ecb l\u1ed7i ra JSON\n\nB\u1ea1n c\u00f3 th\u1ec3 th\u00eam middleware ho\u1eb7c exception handler \u0111\u1ec3 b\u1eaft l\u1ed7i to\u00e0n c\u1ee5c, v\u00ed d\u1ee5:\n\n```python\nfrom fastapi import Request\nfrom fastapi.responses import JSONResponse\n\n@app.exception_handler(Exception)\nasync def global_exception_handler(request: Request, exc: Exception):\n    import traceback\n    tb = ''.join(traceback.format_exception(type(exc), exc, exc.__traceback__))\n    print(f\"Exception on path {request.url.path}:\\n{tb}\")\n    return JSONResponse(\n        status_code=500,\n        content={\"detail\": \"Internal Server Error\", \"error\": str(exc)}\n    )\n```\n\n\u0110i\u1ec1u n\u00e0y gi\u00fap b\u1ea1n xem r\u00f5 h\u01a1n l\u1ed7i trong response JSON.\n\n---\n\n## 3. Debug t\u1eebng b\u01b0\u1edbc trong endpoint `/chat`\n\nB\u1ea1n c\u00f3 th\u1ec3 th\u00eam nhi\u1ec1u `print()` ho\u1eb7c import logging \u0111\u1ec3 in ra th\u00f4ng tin m\u1ed7i b\u01b0\u1edbc trong h\u00e0m `/chat`, v\u00ed d\u1ee5:\n\n```python\nprint(\"L\u01b0u file m\u1eb7t...\")\nprint(\"L\u01b0u file audio...\")\nprint(f\"Nh\u1eadn file audio, size={len(audio_bytes)} bytes\")\nprint(f\"Ph\u1ea7n chuy\u1ec3n speech2text...\")\nprint(f\"Transcript: {transcription}\")\nprint(f\"Ph\u1ea7n chat bot tr\u1ea3 l\u1eddi...\")\nprint(f\"Reply: {reply_text}\")\nprint(f\"Ph\u1ea7n text2speech...\")\nprint(f\"Ph\u1ea7n l\u01b0u file TTS...\")\nprint(f\"Ch\u1ea1y Wav2Lip...\")\nprint(f\"Tr\u1ea3 video...\")\n```\n\nQuan s\u00e1t v\u00e0 xem b\u01b0\u1edbc n\u00e0o g\u00e2y l\u1ed7i.\n\n---\n\n## 4. L\u01b0u \u00fd m\u1ed9t s\u1ed1 l\u1ed7i ph\u1ed5 bi\u1ebfn\n\n- File audio kh\u00f4ng h\u1ee3p l\u1ec7, \u0111\u1ecdc audio b\u1eb1ng `soundfile` ho\u1eb7c `torchaudio` l\u1ed7i.\n\n- Thi\u1ebfu th\u01b0 vi\u1ec7n c\u1ea7n thi\u1ebft (c\u00e1c dependencies).\n\n- `edge-tts` kh\u00f4ng t\u1ea1o ra d\u1eef li\u1ec7u \u00e2m thanh \u0111\u00fang.\n\n- H\u00e0m `run_wav2lip` ph\u00e1t sinh l\u1ed7i (v\u00ed d\u1ee5 file \u0111\u1ea7u v\u00e0o kh\u00f4ng h\u1ee3p l\u1ec7).\n\n- Thi\u1ebfu `ffmpeg` ho\u1eb7c `pydub` kh\u00f4ng ch\u1ea1y \u0111\u01b0\u1ee3c v\u00ec kh\u00f4ng t\u00ecm th\u1ea5y ffmpeg.\n\n---\n\n## 5. Ki\u1ec3m th\u1eed ri\u00eang t\u1eebng ph\u1ea7n\n\n- T\u1ea1o c\u00e1c h\u00e0m test nh\u1ecf, v\u00ed d\u1ee5 ch\u1ec9 ch\u1ea1y:\n\n```python\ntranscription = await speech2text(audio_bytes)\nprint(\"Transcription:\", transcription)\n```\n\n- Ki\u1ec3m tra `chat_llm`.\n\n- Ch\u1ea1y `text2speech` v\u00e0 l\u01b0u file t\u1ea1m \u0111\u1ec3 ki\u1ec3m tra.\n\n- Test `run_wav2lip` tr\u00ean c\u00e1c file m\u1eabu c\u1ed1 \u0111\u1ecbnh.\n\n---\n\n## 6. N\u1ebfu b\u1ea1n th\u1ea5y l\u1ed7i `\"No module\"` ho\u1eb7c l\u1ed7i import, c\u00e0i th\u00eam module b\u1ecb thi\u1ebfu (v\u00ed d\u1ee5 `torchaudio`, `pydub`, `edge-tts`,...) b\u1eb1ng pip.\n\n---\n\n### N\u1ebfu b\u1ea1n c\u00f3 th\u1ec3, vui l\u00f2ng g\u1eedi:\n\n- Log chi ti\u1ebft l\u1ed7i (stacktrace) b\u1ea1n nh\u1eadn \u0111\u01b0\u1ee3c t\u1eeb console khi 500 xu\u1ea5t hi\u1ec7n.\n\n- M\u1ed9t s\u1ed1 \u0111o\u1ea1n code b\u1ea1n nghi ng\u1edd g\u00e2y l\u1ed7i.\n\nM\u00ecnh s\u1ebd h\u1ed7 tr\u1ee3 b\u1ea1n ph\u00e2n t\u00edch c\u1ee5 th\u1ec3 h\u01a1n!","behavior":"degenerate_repetition","conversation_hash":"8330353f2b73067201e62db73b0e3570","conversation_redacted":false,"conversation_toxic":false,"country":"Vietnam","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Vietnamese","model":"gpt-4.1-mini-2025-04-14","n_chars":2827,"n_turns_in_conversation":27,"n_words":447,"source_row_offset":23704,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"226558836fb2a4d62301f93a","turn_index":16,"user_text":"L\u1ed7i server: Internal Server Erro"},{"assistant_initiates":false,"asst_text":"B\u1ea1n vui l\u00f2ng g\u1eedi \u0111o\u1ea1n code c\u1ea7n t\u1ed1i \u01b0u nh\u00e9, m\u00ecnh s\u1ebd gi\u00fap b\u1ea1n!","behavior":"degenerate_repetition","conversation_hash":"8330353f2b73067201e62db73b0e3570","conversation_redacted":false,"conversation_toxic":false,"country":"Vietnam","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Vietnamese","model":"gpt-4.1-mini-2025-04-14","n_chars":81,"n_turns_in_conversation":27,"n_words":19,"source_row_offset":23705,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"9ca6b8f5698f123642d27d83","turn_index":17,"user_text":"t\u1ed1i \u01b0u \u0111o\u1ea1n code sau"},{"assistant_initiates":false,"asst_text":"D\u01b0\u1edbi \u0111\u00e2y l\u00e0 phi\u00ean b\u1ea3n t\u1ed1i \u01b0u v\u00e0 s\u1ea1ch h\u01a1n c\u1ee7a code b\u1ea1n, v\u1edbi c\u00e1c \u0111i\u1ec3m c\u1ea3i ti\u1ebfn:\n\n- **T\u00e1ch log r\u00f5 trong API** (d\u00f9ng logging thay v\u00ec print, tr\u00e1nh g\u1ecdi print ngo\u00e0i h\u00e0m `__main__`)\n- **X\u00f3a to\u00e0n b\u1ed9 file t\u1ea1m (face, audio, tts mp3, wav, output video) trong `finally`** tr\u00e1nh s\u00f3t file\n- **D\u00f9ng async/await v\u1edbi ThreadPoolExecutor \u0111\u00fang c\u00e1ch**\n- **Th\u00eam ki\u1ec3m tra v\u00e0 x\u1eed l\u00fd l\u1ed7i chi ti\u1ebft, tr\u00e1nh l\u1ed7i unbound bi\u1ebfn**\n- **C\u1ea3i thi\u1ec7n import, setup ffmpeg cho pydub**\n\n---\n\n```python\nimport asyncio\nimport logging\nimport os\nimport io\nimport uuid\n\nfrom fastapi import FastAPI, UploadFile, File, HTTPException\nfrom fastapi.responses import FileResponse\nfrom fastapi.staticfiles import StaticFiles\n\nimport torch\nimport aiofiles\nimport edge_tts\nfrom pydub import AudioSegment\nfrom pydub.utils import which\n\nfrom transformers import (\n    WhisperProcessor,\n    WhisperForConditionalGeneration,\n    MT5Tokenizer,\n    AutoModelForSeq2SeqLM\n)\n\nfrom inference_wav2lip import run_wav2lip\nfrom concurrent.futures import ThreadPoolExecutor\n\n# Setup logging\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n# Setup ffmpeg path for pydub\nAudioSegment.converter = which(\"ffmpeg\") or \"ffmpeg.exe\"\n\n# Directories\nUPLOAD_DIR = \"uploads\"\nOUTPUT_DIR = \"outputs\"\nos.makedirs(UPLOAD_DIR, exist_ok=True)\nos.makedirs(OUTPUT_DIR, exist_ok=True)\n\n# FastAPI app\napp = FastAPI()\n\n# Mount static files if needed\napp.mount(\"/\", StaticFiles(directory=\"static\", html=True), name=\"static\")\n\n# Load models once\nlogger.info(\"Loading ASR model...\")\nmodel_asr_name = \"hoangdeeptry/whisper-vietnamese-3\"\nprocessor_asr = WhisperProcessor.from_pretrained(model_asr_name)\nmodel_asr = WhisperForConditionalGeneration.from_pretrained(model_asr_name)\nmodel_asr.eval()\n\nlogger.info(\"Loading chatbot model...\")\nmodel_chat_name = \"google/mt5-base\"\ntokenizer_chat = MT5Tokenizer.from_pretrained(model_chat_name, use_fast=False)\nmodel_chat = AutoModelForSeq2SeqLM.from_pretrained(model_chat_name)\nmodel_chat.eval()\n\n# ThreadPool for blocking calls\nexecutor = ThreadPoolExecutor(max_workers=2)\n\n\nasync def speech2text(audio_bytes: bytes) -> str:\n    def _speech2text_sync(audio_bytes_sync):\n        import soundfile as sf\n        import torchaudio\n\n        audio_input, sample_rate = sf.read(io.BytesIO(audio_bytes_sync))\n        if sample_rate != 16000:\n            waveform, sr = torchaudio.load(io.BytesIO(audio_bytes_sync))\n            resampler = torchaudio.transforms.Resample(sr, 16000)\n            audio_input = resampler(waveform).squeeze().numpy()\n\n        inputs = processor_asr(audio_input, sampling_rate=16000, return_tensors=\"pt\")\n        with torch.no_grad():\n            predicted_ids = model_asr.generate(inputs.input_features)\n        transcription = processor_asr.batch_decode(predicted_ids, skip_special_tokens=True)[0]\n        return transcription\n\n    loop = asyncio.get_event_loop()\n    transcription = await loop.run_in_executor(executor, _speech2text_sync, audio_bytes)\n    logger.info(f\"ASR transcription: {transcription}\")\n    return transcription\n\n\nasync def chat_llm(text: str) -> str:\n    def _chat_llm_sync(text_sync):\n        inputs = tokenizer_chat.encode(text_sync, return_tensors=\"pt\", max_length=512, truncation=True)\n        outputs = model_chat.generate(inputs, max_length=128, num_beams=5)\n        reply = tokenizer_chat.decode(outputs[0], skip_special_tokens=True)\n        return reply\n\n    loop = asyncio.get_event_loop()\n    reply = await loop.run_in_executor(executor, _chat_llm_sync, text)\n    logger.info(f\"Chatbot reply: {reply}\")\n    return reply\n\n\nasync def text2speech(text: str) -> io.BytesIO:\n    communicate = edge_tts.Communicate(text, voice=\"vi-VN-Nam-AiNeural\")\n    buffer = io.BytesIO()\n    async for chunk in communicate.stream():\n        buffer.write(chunk)\n    buffer.seek(0)\n    logger.info(f\"TTS audio generated, size={buffer.getbuffer().nbytes} bytes\")\n    return buffer\n\n\n@app.post(\"/chat\")\nasync def chat_api(face: UploadFile = File(...), audio: UploadFile = File(...)):\n    unique_id = str(uuid.uuid4())\n    face_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_{face.filename}\")\n    audio_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_{audio.filename}\")\n    output_video_path = os.path.join(OUTPUT_DIR, f\"{unique_id}_out.mp4\")\n\n    tts_mp3_path = None\n    tts_wav_path = None\n\n    try:\n        logger.info(\"Saving face file...\")\n        async with aiofiles.open(face_path, \"wb\") as f_face:\n            await f_face.write(await face.read())\n\n        logger.info(\"Saving audio file...\")\n        async with aiofiles.open(audio_path, \"wb\") as f_audio:\n            audio_bytes = await audio.read()\n            await f_audio.write(audio_bytes)\n\n        logger.info(\"Running ASR...\")\n        transcription = await speech2text(audio_bytes)\n        if not transcription.strip():\n            logger.warning(\"No speech detected\")\n            raise HTTPException(status_code=400, detail=\"No speech detected\")\n\n        logger.info(\"Running chatbot LLM...\")\n        reply_text = await chat_llm(transcription)\n\n        logger.info(\"Running TTS...\")\n        tts_audio = await text2speech(reply_text)\n        tts_mp3_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_tts.mp3\")\n        with open(tts_mp3_path, \"wb\") as f_tts:\n            f_tts.write(tts_audio.getbuffer())\n\n        logger.info(\"Converting TTS mp3 to wav...\")\n        tts_wav_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_tts.wav\")\n        audio = AudioSegment.from_file(tts_mp3_path, format=\"mp3\")\n        audio.export(tts_wav_path, format=\"wav\")\n\n        logger.info(\"Running Wav2Lip lip-sync...\")\n        loop = asyncio.get_event_loop()\n        await loop.run_in_executor(executor, run_wav2lip, face_path, tts_wav_path, output_video_path)\n\n        logger.info(f\"Return video file: {output_video_path}\")\n        return FileResponse(output_video_path, media_type=\"video/mp4\")\n\n    except Exception as e:\n        logger.error(f\"Error in /chat endpoint: {e}\", exc_info=True)\n        raise HTTPException(status_code=500, detail=str(e))\n\n    finally:\n        # X\u00f3a file t\u1ea1m, ki\u1ec3m tra t\u1ed3n t\u1ea1i tr\u01b0\u1edbc khi x\u00f3a\n        logger.info(\"Cleaning up temporary files...\")\n        for path in [face_path, audio_path, tts_mp3_path, tts_wav_path]:\n            if path and os.path.exists(path):\n                os.remove(path)\n        if os.path.exists(output_video_path):\n            os.remove(output_video_path)\n\n\nif __name__ == \"__main__\":\n    import uvicorn\n\n    uvicorn.run(app, host=\"0.0.0.0\", port=8000, log_level=\"info\")\n```\n\n---\n\n### Nh\u1eefng \u0111i\u1ec3m t\u1ed1i \u01b0u ch\u00ednh\n\n- D\u00f9ng Logging chu\u1ea9n (`logging`), d\u1ec5 ki\u1ec3m so\u00e1t, kh\u00f4ng in l\u1ed9n x\u1ed9n ra console\n- \u0110\u1ea3m b\u1ea3o c\u00e1c file t\u1ea1m lu\u00f4n \u0111\u01b0\u1ee3c x\u00f3a s\u1ea1ch\n- Ch\u1ea1y c\u00e1c h\u00e0m blocking trong executor tr\u00e1nh block event loop\n- Kh\u00f4ng in log debug ngo\u00e0i h\u00e0m main m\u00e0 \u0111\u1eb7t trong c\u00e1c function\n- D\u00f9ng `asyncio.get_event_loop()` m\u1ed9t l\u1ea7n trong h\u00e0m async g\u1ecdi executor\n- X\u1eed l\u00fd l\u1ed7i chi ti\u1ebft, log stacktrace r\u00f5 r\u00e0ng gi\u00fap debug d\u1ec5 d\u00e0ng\n\nN\u1ebfu b\u1ea1n c\u1ea7n th\u00eam t\u1ed1i \u01b0u v\u1ec1 m\u1eb7t hi\u1ec7u n\u0103ng ho\u1eb7c cache model, b\u1ea1n c\u00f3 th\u1ec3 m\u1edf r\u1ed9ng th\u00eam nh\u00e9!","behavior":"degenerate_repetition","conversation_hash":"8330353f2b73067201e62db73b0e3570","conversation_redacted":false,"conversation_toxic":false,"country":"Vietnam","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Vietnamese","model":"gpt-4.1-mini-2025-04-14","n_chars":12847,"n_turns_in_conversation":27,"n_words":1142,"source_row_offset":23706,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"b71caba02e32ab47c3d972af","turn_index":18,"user_text":"import asyncio\nfrom fastapi import FastAPI, UploadFile, File, HTTPException\nfrom fastapi.responses import FileResponse\nimport torch\nimport io\nimport os\nimport uuid\nimport aiofiles\nimport edge_tts\nfrom pydub import AudioSegment\nfrom pydub.utils import which\n\n\nAudioSegment.converter = which(\"ffmpeg\") or \"ffmpeg.exe\"\n\nfrom transformers import (\n    WhisperProcessor,\n    WhisperForConditionalGeneration,\n    MT5Tokenizer,\n    AutoModelForSeq2SeqLM\n)\n\nfrom inference_wav2lip import run_wav2lip\nfrom concurrent.futures import ThreadPoolExecutor\n\n# T\u1ea1o th\u01b0 m\u1ee5c l\u01b0u t\u1ea1m n\u1ebfu ch\u01b0a c\u00f3\nUPLOAD_DIR = \"uploads\"\nOUTPUT_DIR = \"outputs\"\nos.makedirs(UPLOAD_DIR, exist_ok=True)\nos.makedirs(OUTPUT_DIR, exist_ok=True)\n\napp = FastAPI()\n\n# Load Whisper ASR model + processor (l\u01b0u \u00fd: c\u00f3 th\u1ec3 c\u1ea7n CUDA n\u1ebfu m\u00e1y b\u1ea1n c\u00f3 GPU)\nmodel_asr_name = \"hoangdeeptry/whisper-vietnamese-3\"\nprocessor_asr = WhisperProcessor.from_pretrained(model_asr_name)\nmodel_asr = WhisperForConditionalGeneration.from_pretrained(model_asr_name)\nmodel_asr.eval()\n\n# Load chatbot Vietnamese mT5\nmodel_chat_name = \"google/mt5-base\"\ntokenizer_chat = MT5Tokenizer.from_pretrained(model_chat_name, use_fast=False)\nmodel_chat = AutoModelForSeq2SeqLM.from_pretrained(model_chat_name)\nmodel_chat.eval()\n\nexecutor = ThreadPoolExecutor(max_workers=2)\n\n\nasync def speech2text(audio_bytes: bytes) -> str:\n    def _speech2text_sync(audio_bytes_sync):\n        import soundfile as sf\n        import torchaudio\n\n        audio_input, sample_rate = sf.read(io.BytesIO(audio_bytes_sync))\n        if sample_rate != 16000:\n            waveform, sr = torchaudio.load(io.BytesIO(audio_bytes_sync))\n            resampler = torchaudio.transforms.Resample(sr, 16000)\n            audio_input = resampler(waveform).squeeze().numpy()\n\n        inputs = processor_asr(audio_input, sampling_rate=16000, return_tensors=\"pt\")\n        with torch.no_grad():\n            predicted_ids = model_asr.generate(inputs.input_features)\n        transcription = processor_asr.batch_decode(predicted_ids, skip_special_tokens=True)[0]\n        return transcription\n\n    loop = asyncio.get_event_loop()\n    return await loop.run_in_executor(executor, _speech2text_sync, audio_bytes)\n\n\nasync def chat_llm(text: str) -> str:\n    def _chat_llm_sync(text_sync):\n        inputs = tokenizer_chat.encode(text_sync, return_tensors=\"pt\", max_length=512, truncation=True)\n        outputs = model_chat.generate(inputs, max_length=128, num_beams=5)\n        reply = tokenizer_chat.decode(outputs[0], skip_special_tokens=True)\n        return reply\n\n    loop = asyncio.get_event_loop()\n    return await loop.run_in_executor(executor, _chat_llm_sync, text)\n\n\nasync def text2speech(text: str) -> io.BytesIO:\n    communicate = edge_tts.Communicate(text, voice=\"vi-VN-Nam-AiNeural\")\n    buffer = io.BytesIO()\n    async for chunk in communicate.stream():\n        buffer.write(chunk)\n    buffer.seek(0)\n    return buffer\n\n\n@app.post(\"/chat\")\nasync def chat_api(face: UploadFile = File(...), audio: UploadFile = File(...)):\n    unique_id = str(uuid.uuid4())\n    face_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_{face.filename}\")\n    audio_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_{audio.filename}\")\n    output_video_path = os.path.join(OUTPUT_DIR, f\"{unique_id}_out.mp4\")\n\n    tts_mp3_path = None\n    tts_wav_path = None\n\n    try:\n        # L\u01b0u file m\u1eb7t\n        async with aiofiles.open(face_path, 'wb') as f_face:\n            await f_face.write(await face.read())\n\n        # L\u01b0u file audio upload\n        async with aiofiles.open(audio_path, 'wb') as f_audio:\n            audio_bytes = await audio.read()\n            await f_audio.write(audio_bytes)\n\n        # Ch\u1ea1y ASR chuy\u1ec3n gi\u1ecdng n\u00f3i sang text\n        transcription = await speech2text(audio_bytes)\n        if not transcription.strip():\n            raise HTTPException(status_code=400, detail=\"No speech detected\")\n\n        # Chatbot tr\u1ea3 l\u1eddi\n        reply_text = await chat_llm(transcription)\n\n        # TTS t\u1ea1o audio mp3\n        tts_audio = await text2speech(reply_text)\n        tts_mp3_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_tts.mp3\")\n        with open(tts_mp3_path, \"wb\") as f_tts:\n            f_tts.write(tts_audio.getbuffer())\n\n        # Convert mp3 sang wav \u0111\u1ec3 Wav2Lip x\u1eed l\u00fd\n        tts_wav_path = os.path.join(UPLOAD_DIR, f\"{unique_id}_tts.wav\")\n        audio = AudioSegment.from_file(tts_mp3_path, format=\"mp3\")\n        audio.export(tts_wav_path, format=\"wav\")\n\n        # Ch\u1ea1y Wav2Lip lip-sync ch\u1ea1y trong executor tr\u00e1nh block event loop\n        loop = asyncio.get_event_loop()\n        await loop.run_in_executor(executor, run_wav2lip, face_path, tts_wav_path, output_video_path)\n\n        # Tr\u1ea3 v\u1ec1 file video mp4\n        return FileResponse(output_video_path, media_type=\"video/mp4\")\n\n    except Exception as e:\n        print(\"Error:\", e)\n        raise HTTPException(status_code=500, detail=str(e))\n\n    finally:\n        # Xo\u00e1 file t\u1ea1m n\u1ebfu t\u1ed3n t\u1ea1i\n        for fpath in [face_path,   ]:\n            if os.path.exists(fpath):\n                os.remove(fpath)\n        for fpath in [tts_mp3_path, tts_wav_path]:\n            if fpath and os.path.exists(fpath):\n                os.remove(fpath)\n\n\n# (N\u1ebfu b\u1ea1n c\u1ea7n ph\u1ee5c v\u1ee5 static files ho\u1eb7c giao di\u1ec7n web, mount static files \u1edf \u0111\u00e2y)\nfrom fastapi.staticfiles import StaticFiles\napp.mount(\"/\", StaticFiles(directory=\"static\", html=True), name=\"static\")\n\n# Ch\u1ea1y server\nif __name__ == \"__main__\":\n    import uvicorn\n    uvicorn.run(app, host=\"0.0.0.0\", port=8000, log_level=\"info\")\n    print(\"L\u01b0u file m\u1eb7t...\")\n    print(\"L\u01b0u file audio...\")\n    print(f\"Nh\u1eadn file audio, size={len(audio_bytes)} bytes\")\n    print(f\"Ph\u1ea7n chuy\u1ec3n speech2text...\")\n    print(f\"Transcript: {transcription}\")\n    print(f\"Ph\u1ea7n chat bot tr\u1ea3 l\u1eddi...\")\n    print(f\"Reply: {reply_text}\")\n    print(f\"Ph\u1ea7n text2speech...\")\n    print(f\"Ph\u1ea7n l\u01b0u file TTS...\")\n    print(f\"Ch\u1ea1y Wav2Lip...\")\n    print(f\"Tr\u1ea3 video...\")"}],"shard":{"index":77,"name":"turns-00077.parquet","rows":41273}}