{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":23717,"offset":23707,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"Th\u00f4ng b\u00e1o b\u1ea1n \u0111ang th\u1ea5y l\u00e0 m\u1ed9t **warning** t\u1eeb th\u01b0 vi\u1ec7n Transformers li\u00ean quan \u0111\u1ebfn vi\u1ec7c kh\u1edfi t\u1ea1o tokenizer, kh\u00f4ng ph\u1ea3i l\u1ed7i nghi\u00eam tr\u1ecdng, server c\u1ee7a b\u1ea1n v\u1eabn \u0111ang ho\u1ea1t \u0111\u1ed9ng b\u00ecnh th\u01b0\u1eddng.\n\n---\n\n### Gi\u1ea3i th\u00edch warning (c\u1ea3nh b\u00e1o)\n\n```\nThe tokenizer class you load from this checkpoint is not the same type...\n```\n\n- B\u1ea1n \u0111ang load tokenizer t\u1eeb checkpoint c\u00f3 tokenizer class l\u00e0 `T5Tokenizer`.\n- Nh\u01b0ng b\u1ea1n g\u1ecdi h\u00e0m kh\u1edfi t\u1ea1o c\u1ee7a `MT5Tokenizer`.\n- Transformers \u0111ang d\u00f9ng c\u01a1 ch\u1ebf \"legacy\" \u0111\u1ec3 gi\u1eef t\u01b0\u01a1ng th\u00edch v\u1edbi m\u00e3 c\u0169.\n- N\u1ebfu mu\u1ed1n d\u00f9ng h\u00e0nh vi m\u1edbi (new behavior), b\u1ea1n c\u00f3 th\u1ec3 set `legacy=False` khi g\u1ecdi `MT5Tokenizer.from_pretrained(...)`.  \n  C\u00f2n kh\u00f4ng, b\u1ea1n c\u00f3 th\u1ec3 b\u1ecf qua c\u1ea3nh b\u00e1o n\u00e0y v\u00ec n\u00f3 kh\u00f4ng \u1ea3nh h\u01b0\u1edfng nghi\u00eam tr\u1ecdng.\n\n---\n\n### C\u00e1ch x\u1eed l\u00fd\n\nN\u1ebfu b\u1ea1n mu\u1ed1n g\u1ee1 c\u1ea3nh b\u00e1o n\u00e0y, s\u1eeda \u0111o\u1ea1n:\n\n```python\ntokenizer_chat = MT5Tokenizer.from_pretrained(model_chat_name, use_fast=False)\n```\n\nth\u00e0nh (b\u1ed5 sung `legacy=False`):\n\n```python\ntokenizer_chat = MT5Tokenizer.from_pretrained(model_chat_name, use_fast=False, legacy=False)\n```\n\n**L\u01b0u \u00fd:**  \nB\u1ea1n n\u00ean tham kh\u1ea3o k\u1ef9 pull request sau (v\u1edbi nh\u1eefng thay \u0111\u1ed5i v\u1ec1 tokenizer legacy):  \nhttps://github.com/huggingface/transformers/pull/24565\n\n---\n\n### T\u1ed5ng k\u1ebft\n\n- \u0110\u00e2y ch\u1ec9 l\u00e0 c\u1ea3nh b\u00e1o, kh\u00f4ng g\u00e2y l\u1ed7i \u1ee9ng d\u1ee5ng.\n- N\u1ebfu d\u1ecbch v\u1ee5 c\u1ee7a b\u1ea1n ch\u1ea1y \u1ed5n, c\u00f3 th\u1ec3 **b\u1ecf qua** m\u00e0 kh\u00f4ng c\u1ea7n thay \u0111\u1ed5i.\n- N\u1ebfu mu\u1ed1n h\u1ebft c\u1ea3nh b\u00e1o, \u00e1p d\u1ee5ng `legacy=False` nh\u01b0 tr\u00ean, nh\u01b0ng n\u00ean test k\u1ef9.\n\n---\n\nN\u1ebfu b\u1ea1n c\u1ea7n h\u1ed7 tr\u1ee3 ph\u1ea7n kh\u00e1c ho\u1eb7c g\u1eb7p l\u1ed7i th\u1ef1c s\u1ef1 khi ch\u1ea1y app, c\u1ee9 g\u1eedi th\u00f4ng tin m\u00ecnh 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":2489,"n_turns_in_conversation":27,"n_words":382,"source_row_offset":23707,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"5c1f66cab0523b697792f5b8","turn_index":19,"user_text":"INFO:__main__:Loading ASR model...\nINFO:__main__:Loading chatbot model...\nThe tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \nThe tokenizer class you load from this checkpoint is 'T5Tokenizer'. \nThe class this function is called from is 'MT5Tokenizer'.\nYou are using the default legacy behaviour of the <class 'transformers.models.mt5.tokenization_mt5.MT5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\nINFO:     Started server process [20020]\nINFO:     Waiting for application startup.\nINFO:     Application startup complete.\nINFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)"},{"assistant_initiates":false,"asst_text":"L\u1ed7i **`Method Not Allowed`** (HTTP 405) ngh\u0129a l\u00e0 b\u1ea1n \u0111\u00e3 g\u1ecdi \u0111\u1ebfn \u0111\u00fang URL nh\u01b0ng s\u1eed d\u1ee5ng sai ph\u01b0\u01a1ng th\u1ee9c HTTP. V\u00ed d\u1ee5, endpoint c\u1ee7a b\u1ea1n ch\u1ec9 \u0111\u1ecbnh nh\u1eadn **POST**, nh\u01b0ng b\u1ea1n l\u1ea1i g\u1eedi request b\u1eb1ng **GET** ho\u1eb7c ph\u01b0\u01a1ng th\u1ee9c kh\u00e1c.\n\n---\n\n## Nguy\u00ean nh\u00e2n ph\u1ed5 bi\u1ebfn\n\n- API b\u1ea1n \u0111\u1ecbnh ngh\u0129a l\u00e0 `@app.post(\"/chat\")`, ngh\u0129a l\u00e0 ch\u1ec9 nh\u1eadn ph\u01b0\u01a1ng th\u1ee9c POST.\n- B\u1ea1n g\u1eedi request GET, PUT, DELETE... \u0111\u1ebfn `/chat` \u0111\u1ec1u s\u1ebd b\u1ecb tr\u1ea3 v\u1ec1 l\u1ed7i 405.\n- G\u1eedi request b\u1eb1ng tr\u00ecnh duy\u1ec7t tr\u1ef1c ti\u1ebfp (nh\u1ea5n enter URL) s\u1ebd g\u1eedi GET, n\u00ean b\u1ea1n s\u1ebd g\u1eb7p l\u1ed7i n\u00e0y cho endpoint ch\u1ec9 nh\u1eadn POST.\n\n---\n\n## C\u00e1ch kh\u1eafc ph\u1ee5c\n\n1. **G\u1eedi request v\u1edbi \u0111\u00fang ph\u01b0\u01a1ng th\u1ee9c POST**\n\n- N\u1ebfu b\u1ea1n d\u00f9ng `curl`, v\u00ed d\u1ee5:\n\n  ```bash\n  curl -X POST http://localhost:8000/chat -F \"face=@path/to/face.jpg\" -F \"audio=@path/to/audio.wav\"\n  ```\n\n- N\u1ebfu d\u00f9ng Postman ho\u1eb7c c\u00f4ng c\u1ee5 API, ch\u1ecdn ph\u01b0\u01a1ng th\u1ee9c **POST** r\u1ed3i g\u1eedi request.\n\n2. **Ki\u1ec3m tra \u0111o\u1ea1n frontend g\u1eedi request**\n\n- \u0110\u1ea3m b\u1ea3o b\u1ea1n g\u1eedi POST, kh\u00f4ng ph\u1ea3i GET.\n\n---\n\n## Ki\u1ec3m tra nhanh b\u1eb1ng Swagger UI\n\n- M\u1edf tr\u00ecnh duy\u1ec7t tr\u00ean `http://localhost:8000/docs`\n\n- T\u00ecm v\u00e0 th\u1eed g\u1ecdi th\u1eed `/chat` v\u1edbi method POST.\n\n---\n\n## N\u1ebfu mu\u1ed1n h\u1ed7 tr\u1ee3 th\u00eam\n\nB\u1ea1n c\u00f3 th\u1ec3 g\u1eedi:\n\n- C\u00e1ch b\u1ea1n g\u1ecdi API (c\u00f4ng c\u1ee5 d\u00f9ng, \u0111o\u1ea1n code frontend, l\u1ec7nh curl...)\n\n- \u0110\u1ecba ch\u1ec9 URL v\u00e0 method b\u1ea1n d\u00f9ng.\n\nM\u00ecnh s\u1ebd h\u1ed7 tr\u1ee3 h\u01b0\u1edbng d\u1eabn 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":1258,"n_turns_in_conversation":27,"n_words":231,"source_row_offset":23708,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"6b8b2c5f32d0509078d5a792","turn_index":20,"user_text":"Method Not Allowed"},{"assistant_initiates":false,"asst_text":"D\u01b0\u1edbi \u0111\u00e2y l\u00e0 \u0111o\u1ea1n code FastAPI \u0111\u1ea7y \u0111\u1ee7 chu\u1ea9n, \u0111\u1ea3m b\u1ea3o endpoint `/chat` ch\u1ec9 nh\u1eadn POST, b\u1ea1n c\u00f3 th\u1ec3 test d\u1ec5 d\u00e0ng b\u1eb1ng Postman ho\u1eb7c curl v\u1edbi method POST.\n\nM\u00ecnh c\u0169ng b\u1ed5 sung logging c\u01a1 b\u1ea3n v\u00e0 c\u1ea5u tr\u00fac file t\u1ea1m \u0111\u01b0\u1ee3c x\u00f3a s\u1ea1ch tr\u00e1nh r\u00e1c, \u0111\u1ed3ng th\u1eddi ch\u00fa th\u00edch r\u00f5 t\u1eebng ph\u1ea7n.\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\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# Thi\u1ebft l\u1eadp logging\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n# Thi\u1ebft l\u1eadp path ffmpeg cho pydub n\u1ebfu c\u1ea7n\nAudioSegment.converter = which(\"ffmpeg\") or \"ffmpeg.exe\"\n\n# Th\u01b0 m\u1ee5c l\u01b0u tr\u1eef t\u1ec7p\nUPLOAD_DIR = \"uploads\"\nOUTPUT_DIR = \"outputs\"\nos.makedirs(UPLOAD_DIR, exist_ok=True)\nos.makedirs(OUTPUT_DIR, exist_ok=True)\n\n# Kh\u1edfi t\u1ea1o FastAPI app\napp = FastAPI()\n\n# Load c\u00e1c model m\u1ed9t l\u1ea7n l\u00fac kh\u1edfi \u0111\u1ed9ng server\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 \u0111\u1ec3 ch\u1ea1y c\u00e1c h\u00e0m blocking kh\u00f4ng block event loop\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\")  # Ch\u1ec9 nh\u1eadn POST\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            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_seg = AudioSegment.from_file(tts_mp3_path, format=\"mp3\")\n        audio_seg.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\"Returning output video at {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=f\"Server error: {str(e)}\")\n\n    finally:\n        # X\u00f3a file t\u1ea1m\n        for p in [face_path, audio_path, tts_mp3_path, tts_wav_path, output_video_path]:\n            if p and os.path.exists(p):\n                os.remove(p)\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## H\u01b0\u1edbng d\u1eabn test\n\n- G\u1eedi **POST** request \u0111\u1ebfn `http://localhost:8000/chat`.\n- Body d\u1ea1ng `form-data` g\u1ed3m 2 tr\u01b0\u1eddng:\n  - `face`: file \u1ea3nh (jpg, png,...)\n  - `audio`: file audio (wav, mp3,...)\n\n---\n\nN\u1ebfu b\u1ea1n g\u1ecdi API b\u1eb1ng tr\u00ecnh duy\u1ec7t (nh\u1eadp URL):  \n- K\u1ebft qu\u1ea3 s\u1ebd l\u00e0 l\u1ed7i **Method Not Allowed**, v\u00ec tr\u00ecnh duy\u1ec7t g\u1eedi GET, trong khi server ch\u1ec9 nh\u1eadn POST v\u1edbi `/chat`.\n\n---\n\nN\u1ebfu c\u00f2n th\u1eafc m\u1eafc hay l\u1ed7i g\u00ec, c\u1ee9 b\u00e1o m\u00ecnh 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":6509,"n_turns_in_conversation":27,"n_words":594,"source_row_offset":23709,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"6e5887e0d8faddf69fffeadc","turn_index":21,"user_text":"b\u1ea1n s\u1eeda code \u0111\u1ea7y \u0111\u1ee7 gi\u00fap"},{"assistant_initiates":false,"asst_text":"L\u1ed7i `{\"detail\":\"Not Found\"}` khi b\u1ea1n g\u1ecdi API FastAPI th\u01b0\u1eddng do:\n\n- G\u1ecdi sai URL\n- G\u1ecdi \u0111\u00fang URL nh\u01b0ng s\u1eed d\u1ee5ng sai HTTP method\n- Server ch\u01b0a load \u0111\u01b0\u1ee3c \u0111\u00fang app/endpoint\n- C\u00f3 mount static file ghi \u0111\u00e8 ho\u1eb7c sai c\u1ea5u h\u00ecnh route\n\n---\n\n## C\u00e1c ki\u1ec3m tra v\u00e0 h\u01b0\u1edbng d\u1eabn kh\u1eafc ph\u1ee5c\n\n### 1. \u0110\u1ea3m b\u1ea3o g\u1ecdi \u0111\u00fang URL endpoint `/chat` v\u00e0 method l\u00e0 POST\n\nV\u00ed d\u1ee5 g\u1ecdi `POST` t\u1edbi:\n\n```\nhttp://localhost:8000/chat\n```\n\nKh\u00f4ng ph\u1ea3i GET, PUT, DELETE...\n\nB\u1ea1n kh\u00f4ng th\u1ec3 d\u00f9ng tr\u00ecnh duy\u1ec7t truy c\u1eadp \u0111\u01b0\u1eddng d\u1eabn n\u00e0y v\u00ec tr\u00ecnh duy\u1ec7t m\u1eb7c \u0111\u1ecbnh g\u1eedi GET s\u1ebd nh\u1eadn `404 Not Found`.\n\n---\n\n### 2. Ki\u1ec3m tra l\u1ec7nh ch\u1ea1y server\n\n- N\u1ebfu file FastAPI t\u00ean l\u00e0 `main.py` v\u00e0 app l\u00e0 `app`, h\u00e3y ch\u1ea1y:\n\n```bash\nuvicorn main:app --reload\n```\n\n- N\u1ebfu file b\u1ea1n t\u00ean kh\u00e1c, v\u00ed d\u1ee5 `app.py`, b\u1ea1n ch\u1ea1y:\n\n```bash\nuvicorn app:app --reload\n```\n\nSai t\u00ean file ho\u1eb7c bi\u1ebfn app s\u1ebd d\u1eabn t\u1edbi kh\u00f4ng load \u0111\u01b0\u1ee3c endpoint.\n\n---\n\n### 3. Test API b\u1eb1ng c\u00f4ng c\u1ee5 Postman ho\u1eb7c curl\n\n#### D\u00f9ng 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#### D\u00f9ng fastapi swagger interactive:\n\nM\u1edf tr\u00ecnh duy\u1ec7t truy c\u1eadp:\n\n```\nhttp://localhost:8000/docs\n```\n\nB\u1ea1n s\u1ebd th\u1ea5y danh s\u00e1ch API, th\u1eed g\u1ecdi th\u1eed `/chat` b\u1eb1ng giao di\u1ec7n n\u00e0y.\n\n---\n\n### 4. Ki\u1ec3m tra xem c\u00f3 mount static files kh\u00f4ng g\u00e2y \u1ea3nh h\u01b0\u1edfng\n\nN\u1ebfu b\u1ea1n c\u00f3 d\u00f2ng code:\n\n```python\nfrom fastapi.staticfiles import StaticFiles\n\napp.mount(\"/\", StaticFiles(directory=\"static\", html=True), name=\"static\")\n```\n\nTh\u00ec m\u1ecdi request GET \u0111\u1ebfn `/` v\u00e0 \u0111\u01b0\u1eddng d\u1eabn con s\u1ebd ph\u1ee5c v\u1ee5 file t\u0129nh, nh\u01b0ng request POST t\u1edbi `/chat` v\u1eabn ph\u1ea3i \u0111\u01b0\u1ee3c x\u1eed l\u00fd b\u00ecnh th\u01b0\u1eddng.\n\nB\u1ea1n c\u00f3 th\u1ec3 th\u1eed comment d\u00f2ng n\u00e0y t\u1ea1m th\u1eddi \u0111\u1ec3 ki\u1ec3m tra.\n\n---\n\n### 5. T\u1ea1o file test nh\u1ecf \u0111\u1ec3 x\u00e1c minh\n\nT\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():\n    return {\"result\": \"success\"}\n```\n\nCh\u1ea1y:\n\n```bash\nuvicorn test_main:app --reload\n```\n\nG\u1eedi POST t\u1edbi `/chat` n\u1ebfu v\u1eabn l\u1ed7i Not Found th\u00ec ch\u1eafc ch\u1eafn m\u00f4i tr\u01b0\u1eddng ho\u1eb7c c\u00e1ch ch\u1ea1y server, g\u1ecdi api c\u00f3 v\u1ea5n \u0111\u1ec1.\n\n---\n\n### 6. M\u1ed9t s\u1ed1 l\u01b0u \u00fd kh\u00e1c\n\n- Ki\u1ec3m tra log khi kh\u1edfi ch\u1ea1y uvicorn c\u00f3 l\u1ed7i g\u00ec kh\u00f4ng.\n- Ki\u1ec3m tra port server ch\u1ea1y c\u00f3 \u0111\u00fang kh\u00f4ng.\n- N\u1ebfu d\u00f9ng Docker hay m\u00f4i tr\u01b0\u1eddng \u1ea3o, ki\u1ec3m tra c\u1ea5u h\u00ecnh m\u1ea1ng.\n\n---\n\nN\u1ebfu b\u1ea1n v\u1eabn ch\u01b0a gi\u1ea3i quy\u1ebft \u0111\u01b0\u1ee3c, vui l\u00f2ng g\u1eedi:\n\n- T\u00ean file FastAPI c\u1ee7a b\u1ea1n.\n- L\u1ec7nh b\u1ea1n d\u00f9ng \u0111\u1ec3 ch\u1ea1y uvicorn.\n- C\u00e1ch b\u1ea1n g\u1ecdi API (v\u00ed d\u1ee5 curl, postman, code frontend).\n- To\u00e0n b\u1ed9 \u0111o\u1ea1n code FastAPI (\u0111\u1ec3 m\u00ecnh ki\u1ec3m tra).\n- Log ch\u1ea1y server v\u00e0 log khi g\u1ecdi y\u00eau c\u1ea7u.\n\nM\u00ecnh s\u1ebd h\u1ed7 tr\u1ee3 ti\u1ebfp 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":2468,"n_turns_in_conversation":27,"n_words":442,"source_row_offset":23710,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"c75885d09acec1828ddf9294","turn_index":22,"user_text":"{\"detail\":\"Not Found\"}"},{"assistant_initiates":false,"asst_text":"L\u1ed7i **Internal Server Error (500)** khi b\u1ea1n b\u1ea5m d\u1eebng thu \u00e2m (v\u00e0 g\u1eedi d\u1eef li\u1ec7u \u00e2m thanh l\u00ean server) th\u01b0\u1eddng l\u00e0 do c\u00f3 l\u1ed7i trong qu\u00e1 tr\u00ecnh x\u1eed l\u00fd request \u1edf ph\u00eda server. \u0110\u1ec3 x\u1eed l\u00fd v\u00e0 kh\u1eafc ph\u1ee5c l\u1ed7i n\u00e0y, b\u1ea1n c\u1ea7n l\u00e0m m\u1ed9t s\u1ed1 b\u01b0\u1edbc nh\u01b0 sau:\n\n---\n\n## 1. Xem log server chi ti\u1ebft\n\n- M\u1edf terminal ch\u1ea1y server, n\u00e2ng m\u1ee9c log l\u00ean DEBUG ho\u1eb7c INFO v\u00e0 xem traceback l\u1ed7i chi ti\u1ebft khi b\u1ea5m n\u00fat d\u1eebng thu \u00e2m g\u1eedi request.\n\n- V\u00ed d\u1ee5 ch\u1ea1y uvicorn:\n\n```bash\nuvicorn main:app --reload --log-level debug\n```\n\nKhi s\u1ef1 ki\u1ec7n l\u1ed7i x\u1ea3y ra, b\u1ea1n s\u1ebd th\u1ea5y chi ti\u1ebft l\u1ed7i (th\u00f4ng b\u00e1o exception v\u00e0 traceback) gi\u00fap x\u00e1c \u0111\u1ecbnh nguy\u00ean nh\u00e2n ch\u00ednh x\u00e1c.\n\n---\n\n## 2. Ki\u1ec3m tra:\n\n### a) D\u1eef li\u1ec7u \u00e2m thanh g\u1eedi c\u00f3 \u0111\u00fang kh\u00f4ng?\n\n- Ki\u1ec3m tra ph\u1ea7n frontend g\u1eedi request, \u0111\u1eb7c bi\u1ec7t ph\u1ea7n audio g\u1eedi l\u00ean: \u0111\u1ecbnh d\u1ea1ng \u00e2m thanh, c\u00f3 d\u1eef li\u1ec7u kh\u00f4ng, header `Content-Type` chu\u1ea9n.\n\n- N\u1ebfu g\u1eedi file r\u1ed7ng ho\u1eb7c \u0111\u1ecbnh d\u1ea1ng kh\u00f4ng \u0111\u00fang, ph\u00eda server \u0111\u1ecdc \u00e2m thanh c\u00f3 th\u1ec3 l\u1ed7i.\n\n### b) Ph\u1ea7n \u0111\u1ecdc audio tr\u00ean server c\u00f3 l\u1ed7i?\n\n- \u0110o\u1ea1n code b\u1ea1n d\u00f9ng `soundfile` v\u00e0 `torchaudio` \u0111\u1ecdc audio t\u1eeb bytes r\u1ea5t nh\u1ea1y c\u1ea3m v\u1edbi \u0111\u1ecbnh d\u1ea1ng v\u00e0 codec c\u1ee7a file.\n\n- C\u00f3 th\u1ec3 file b\u1ea1n g\u1eedi kh\u00f4ng chu\u1ea9n WAV ho\u1eb7c sample rate kh\u00e1c 16kHz m\u00e0 \u0111o\u1ea1n code x\u1eed l\u00fd ch\u01b0a chu\u1ea9n.\n\n- B\u1ea1n n\u00ean th\u00eam x\u1eed l\u00fd ngo\u1ea1i l\u1ec7 (try/except) trong h\u00e0m `speech2text()` khi \u0111\u1ecdc file \u00e2m thanh, in l\u1ed7i ra \u0111\u1ec3 d\u1ec5 debug.\n\n---\n\n## 3. V\u00ed d\u1ee5 c\u00e1ch b\u1ed5 sung x\u1eed l\u00fd ngo\u1ea1i l\u1ec7 trong speech2text\n\n```python\nasync def speech2text(audio_bytes: bytes) -> str:\n    def _speech2text_sync(audio_bytes_sync):\n        import soundfile as sf\n        import torchaudio\n        try:\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        except Exception as e:\n            print(\"Error in speech2text processing:\", e)\n            raise\n\n    loop = asyncio.get_event_loop()\n    return await loop.run_in_executor(executor, _speech2text_sync, audio_bytes)\n```\n\n---\n\n## 4. Ki\u1ec3m tra xem client c\u00f3 g\u1eedi file audio \u0111\u00fang?\n\n- N\u1ebfu b\u1ea1n d\u00f9ng JavaScript l\u1ea5y \u00e2m thanh t\u1eeb mic, c\u00f3 th\u1ec3 b\u1ea1n g\u1eedi nh\u1ea7m d\u1ea1ng Blob kh\u00f4ng \u0111\u00fang MIME type ho\u1eb7c ch\u01b0a base64 chuy\u1ec3n v\u1ec1 \u0111\u00fang nh\u1ecb ph\u00e2n.\n\n- \u0110\u1ea3m b\u1ea3o g\u1eedi file d\u01b0\u1edbi d\u1ea1ng `FormData` upload \u0111\u00fang:\n\n```js\nconst formData = new FormData();\nformData.append(\"face\", faceFile);\nformData.append(\"audio\", audioFile);\nfetch(\"/chat\", { method: \"POST\", body: formData });\n```\n\n---\n\n## 5. Xem l\u1ea1i ph\u1ea7n `aiofiles` ghi file tr\u00ean server\n\n- \u0110\u1ea3m b\u1ea3o file \u0111\u01b0\u1ee3c ghi \u0111\u1ea7y \u0111\u1ee7, kh\u00f4ng b\u1ecb l\u1ed7i m\u1ea5t k\u1ebft n\u1ed1i gi\u1eefa ch\u1eebng.\n\n---\n\n## 6. T\u00f3m t\u1eaft\n\n- M\u1edf log server \u1edf ch\u1ebf \u0111\u1ed9 debug xem l\u1ed7i chi ti\u1ebft t\u1ea1i server.\n- Ki\u1ec3m tra k\u1ef9 d\u1eef li\u1ec7u audio g\u1eedi l\u00ean server.\n- Th\u00eam try/except quanh \u0111o\u1ea1n \u0111\u1ecdc \u00e2m thanh v\u00e0 in l\u1ed7i.\n- Test t\u1eebng b\u01b0\u1edbc: g\u1eedi file \u00e2m thanh sample chu\u1ea9n xem server x\u1eed l\u00fd \u1ed5n kh\u00f4ng.\n- Ch\u1ea1y th\u1eed c\u00e1i demo endpoint `/chat` v\u1edbi file audio \u0111\u01a1n gi\u1ea3n xem c\u00f3 OK kh\u00f4ng.\n\n---\n\nN\u1ebfu b\u1ea1n gi\u1eef nguy\u00ean \u0111o\u1ea1n code server hi\u1ec7n t\u1ea1i, vui l\u00f2ng g\u1eedi log l\u1ed7i chi ti\u1ebft khi x\u1ea3y ra Internal Server Error \u0111\u1ec3 m\u00ecnh gi\u00fap ph\u00e2n t\u00edch k\u1ef9 h\u01a1n 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":3471,"n_turns_in_conversation":27,"n_words":548,"source_row_offset":23711,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"4f002c5f9d863c7b8778b78b","turn_index":23,"user_text":"khi thu \u00e2m. click n\u00fat d\u1eebng thu \u00e2m th\u00ec b\u00e0o l\u1ed7i Internal Server Error"},{"assistant_initiates":false,"asst_text":"D\u01b0\u1edbi \u0111\u00e2y l\u00e0 phi\u00ean b\u1ea3n code **\u0111\u1ea7y \u0111\u1ee7, t\u1ed1i \u01b0u v\u00e0 b\u1ed5 sung x\u1eed l\u00fd ngo\u1ea1i l\u1ec7 chi ti\u1ebft trong ph\u1ea7n \u0111\u1ecdc file \u00e2m thanh** \u0111\u1ec3 tr\u00e1nh l\u1ed7i Internal Server Error khi x\u1eed l\u00fd file \u00e2m thanh kh\u00f4ng h\u1ee3p l\u1ec7 ho\u1eb7c c\u00f3 v\u1ea5n \u0111\u1ec1.\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\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\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\nAudioSegment.converter = which(\"ffmpeg\") or \"ffmpeg.exe\"\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\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\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        try:\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        except Exception as e:\n            logger.error(f\"Error processing speech2text: {e}\", exc_info=True)\n            raise\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        logger.info(\"Saving face image...\")\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 speech to text...\")\n        transcription = await speech2text(audio_bytes)\n        if not transcription.strip():\n            raise HTTPException(status_code=400, detail=\"No speech detected\")\n\n        logger.info(f\"Transcription result: {transcription}\")\n\n        logger.info(\"Generating chatbot reply...\")\n        reply_text = await chat_llm(transcription)\n        logger.info(f\"Chatbot reply: {reply_text}\")\n\n        logger.info(\"Generating TTS audio...\")\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_seg = AudioSegment.from_file(tts_mp3_path, format=\"mp3\")\n        audio_seg.export(tts_wav_path, format=\"wav\")\n\n        logger.info(\"Running Wav2Lip...\")\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\"Returning 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\"Internal server error: {e}\", exc_info=True)\n        raise HTTPException(status_code=500, detail=str(e))\n\n    finally:\n        # X\u00f3a s\u1ea1ch file t\u1ea1m sau khi x\u1eed l\u00fd\n        for f in [face_path, audio_path, tts_mp3_path, tts_wav_path, output_video_path]:\n            if f and os.path.exists(f):\n                try:\n                    os.remove(f)\n                except Exception as ex:\n                    logger.warning(f\"Failed to delete temp file {f}: {ex}\")\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## L\u01b0u \u00fd\n\n- Ph\u1ea7n kh\u00f3a `speech2text` \u0111\u00e3 c\u00f3 try-except \u0111\u1ec3 b\u1eaft l\u1ed7i \u0111\u1ecdc file \u00e2m thanh.\n- Ph\u1ea7n log s\u1ebd gi\u00fap b\u1ea1n d\u1ec5 d\u00e0ng th\u1ea5y l\u1ed7i chi ti\u1ebft \u1edf server console.\n- H\u00e3y ch\u1eafc ch\u1eafn frontend g\u1eedi file audio h\u1ee3p l\u1ec7 (\u0111\u1ecbnh d\u1ea1ng wav/mp3).\n- B\u1ea1n c\u00f3 th\u1ec3 ki\u1ec3m tra request g\u1eedi l\u00ean c\u00f3 d\u1eef li\u1ec7u \u0111\u00fang b\u1eb1ng c\u00e1ch debug frontend ho\u1eb7c m\u1ea1ng.\n\n---\n\nN\u1ebfu c\u1ea7n h\u1ed7 tr\u1ee3 th\u00eam v\u1ec1 frontend ho\u1eb7c ph\u1ea7n kh\u00e1c 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":6317,"n_turns_in_conversation":27,"n_words":549,"source_row_offset":23712,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"6bba5344f12c2487712df561","turn_index":24,"user_text":"b\u1ea1n s\u1eeda code \u0111\u1ea7y \u0111\u1ee7 "},{"assistant_initiates":false,"asst_text":"L\u1ed7i n\u00e0y:\n\n```\nsoundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x...>: Format not recognised.\n```\n\nngh\u0129a l\u00e0 `soundfile` (th\u01b0\u1eddng trong \u0111o\u1ea1n code b\u1ea1n d\u00f9ng l\u00e0 `sf.read()`) kh\u00f4ng th\u1ec3 nh\u1eadn di\u1ec7n \u0111\u1ecbnh d\u1ea1ng file \u00e2m thanh b\u1ea1n truy\u1ec1n v\u00e0o d\u01b0\u1edbi d\u1ea1ng `BytesIO`.\n\n---\n\n## Nguy\u00ean nh\u00e2n ph\u1ed5 bi\u1ebfn:\n\n1. **D\u1eef li\u1ec7u b\u1ea1n truy\u1ec1n v\u00e0o `sf.read()` kh\u00f4ng ph\u1ea3i l\u00e0 m\u1ed9t file audio h\u1ee3p l\u1ec7 (v\u00ed d\u1ee5 d\u1eef li\u1ec7u r\u1ed7ng, ho\u1eb7c \u0111\u1ecbnh d\u1ea1ng mp3 trong khi `soundfile` ch\u1ec9 h\u1ed7 tr\u1ee3 m\u1ed9t s\u1ed1 \u0111\u1ecbnh d\u1ea1ng nh\u01b0 WAV, FLAC,...).**\n\n2. **D\u1eef li\u1ec7u l\u00e0 mp3 nh\u01b0ng `soundfile` kh\u00f4ng h\u1ed7 tr\u1ee3 mp3, ph\u1ea3i d\u00f9ng `torchaudio` \u0111\u1ec3 \u0111\u1ecdc ho\u1eb7c chuy\u1ec3n \u0111\u1ed5i tr\u01b0\u1edbc.**\n\n---\n\n## C\u00e1ch x\u1eed l\u00fd\n\n### 1. Ki\u1ec3m tra \u0111\u1ecbnh d\u1ea1ng audio b\u1ea1n nh\u1eadn \u0111\u01b0\u1ee3c \u1edf server\n\n- Ph\u1ed5 bi\u1ebfn, client g\u1eedi file \u00e2m thanh d\u1ea1ng MP3, trong khi b\u1ea1n g\u1ecdi `soundfile.read()`, `soundfile` **kh\u00f4ng h\u1ed7 tr\u1ee3 mp3** n\u00ean s\u1ebd b\u00e1o l\u1ed7i.\n\n- Gi\u1ea3i ph\u00e1p l\u00e0 \u01b0u ti\u00ean d\u00f9ng `torchaudio.load()` thay v\u00ec `soundfile.read()` trong tr\u01b0\u1eddng h\u1ee3p file kh\u00f4ng ph\u1ea3i WAV.\n\n- Ho\u1eb7c ki\u1ec3m tra \u0111\u1ecbnh d\u1ea1ng file (extension ho\u1eb7c mime) \u0111\u1ec3 quy\u1ebft \u0111\u1ecbnh c\u00e1ch \u0111\u1ecdc.\n\n---\n\n### 2. V\u00ed d\u1ee5 s\u1eeda h\u00e0m `speech2text`\n\n```python\ndef _speech2text_sync(audio_bytes_sync):\n    import soundfile as sf\n    import torchaudio\n    import io\n    \n    # Th\u1eed \u0111\u1ecdc b\u1eb1ng soundfile (h\u1ed7 tr\u1ee3 wav/flac)\n    try:\n        audio_input, sample_rate = sf.read(io.BytesIO(audio_bytes_sync))\n        # N\u1ebfu \u0111\u1ecdc th\u00e0nh c\u00f4ng m\u00e0 sample_rate kh\u00f4ng ph\u1ea3i 16kHz th\u00ec resample\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    except RuntimeError:\n        # N\u1ebfu soundfile kh\u00f4ng \u0111\u1ecdc \u0111\u01b0\u1ee3c (v\u00ed d\u1ee5 file mp3) -> \u0111\u1ecdc b\u1eb1ng torchaudio\n        waveform, sr = torchaudio.load(io.BytesIO(audio_bytes_sync))\n        if sr != 16000:\n            resampler = torchaudio.transforms.Resample(sr, 16000)\n            waveform = resampler(waveform)\n        audio_input = 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\n---\n\n### 3. L\u01b0u \u00fd\n\n- `torchaudio.load()` h\u1ed7 tr\u1ee3 nhi\u1ec1u \u0111\u1ecbnh d\u1ea1ng h\u01a1n, bao g\u1ed3m mp3, wav, flac,...\n\n- Nh\u01b0ng `torchaudio.load()` tr\u1ea3 v\u1ec1 tensor, c\u1ea7n convert sang `numpy` nh\u01b0 v\u00ed d\u1ee5 tr\u00ean.\n\n---\n\n## T\u00f3m l\u1ea1i\n\nThay th\u1ebf \u0111o\u1ea1n \u0111\u1ecdc audio c\u1ee7a b\u1ea1n b\u1eb1ng:\n\n- Th\u1eed \u0111\u1ecdc b\u1eb1ng `soundfile.read()`.\n\n- N\u1ebfu l\u1ed7i, fallback \u0111\u1ecdc b\u1eb1ng `torchaudio.load()`.\n\n\u0110i\u1ec1u n\u00e0y s\u1ebd x\u1eed l\u00fd \u0111\u01b0\u1ee3c file \u0111\u1ecbnh d\u1ea1ng mp3 ho\u1eb7c wav b\u1ea5t k\u1ec3.\n\n---\n\nN\u1ebfu c\u1ea7n, b\u1ea1n c\u00f3 th\u1ec3 g\u1eedi nguy\u00ean \u0111o\u1ea1n h\u00e0m b\u1ecb l\u1ed7i, m\u00ecnh gi\u00fap m\u00ecnh s\u1eeda 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":2904,"n_turns_in_conversation":27,"n_words":392,"source_row_offset":23713,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"a7e6173c02f801d010f52fba","turn_index":25,"user_text":"aise LibsndfileError(err, prefix=\"Error opening {0!r}: \".format(self.name))\nsoundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x000001A2FAF18130>: Format not recognised."},{"assistant_initiates":false,"asst_text":"L\u1ed7i\n\n```\nAttributeError: 'NoneType' object has no attribute 'strip'\n```\n\nxu\u1ea5t hi\u1ec7n t\u1ea1i d\u00f2ng:\n\n```python\nif not transcription.strip():\n```\n\nc\u00f3 ngh\u0129a bi\u1ebfn `transcription` = `None`, n\u00ean b\u1ea1n kh\u00f4ng th\u1ec3 g\u1ecdi `.strip()` \u0111\u01b0\u1ee3c.\n\n---\n\n## Nguy\u00ean nh\u00e2n c\u00f3 th\u1ec3\n\n- H\u00e0m `speech2text` tr\u1ea3 v\u1ec1 `None` thay v\u00ec string khi g\u1eb7p l\u1ed7i ho\u1eb7c kh\u00f4ng \u0111\u1ecdc \u0111\u01b0\u1ee3c \u00e2m thanh.\n- Trong h\u00e0m `speech2text`, c\u00f3 th\u1ec3 \u0111o\u1ea1n code ch\u1ea1y b\u1ecb fail nh\u01b0ng kh\u00f4ng raise l\u1ed7i m\u00e0 tr\u1ea3 v\u1ec1 gi\u00e1 tr\u1ecb `None`.\n\n---\n\n## C\u00e1ch x\u1eed l\u00fd \u0111\u01a1n gi\u1ea3n\n\n### 1. Th\u00eam ki\u1ec3m tra trong `speech2text` \u0111\u1ec3 lu\u00f4n tr\u1ea3 v\u1ec1 string (\u00edt nh\u1ea5t l\u00e0 empty string) ho\u1eb7c raise l\u1ed7i khi kh\u00f4ng nh\u1eadn \u0111\u01b0\u1ee3c transcription.\n\n### 2. Trong `chat_api`, ki\u1ec3m tra `transcription is None` tr\u01b0\u1edbc khi d\u00f9ng `.strip()`.\n\n---\n\n## V\u00ed d\u1ee5 s\u1eeda `speech2text`:\n\n```python\nasync def speech2text(audio_bytes: bytes) -> str:\n    def _speech2text_sync(audio_bytes_sync):\n        import soundfile as sf\n        import torchaudio\n        import io\n        \n        try:\n            # Th\u1eed \u0111\u1ecdc b\u1eb1ng soundfile\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        except RuntimeError:\n            # Fallback \u0111\u1ecdc b\u1eb1ng torchaudio cho mp3 ho\u1eb7c l\u1ed7i \u0111\u1ecdc\n            waveform, sr = torchaudio.load(io.BytesIO(audio_bytes_sync))\n            if sr != 16000:\n                resampler = torchaudio.transforms.Resample(sr, 16000)\n                waveform = resampler(waveform)\n            audio_input = 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)\n        return transcription[0] if transcription else \"\"\n\n    loop = asyncio.get_event_loop()\n    result = await loop.run_in_executor(executor, _speech2text_sync, audio_bytes_sync)\n    if result is None:\n        return \"\"\n    return result\n```\n\n### S\u1eeda ki\u1ec3m tra trong `chat_api`:\n\n```python\ntranscription = await speech2text(audio_bytes)\nif not transcription or not transcription.strip():\n    raise HTTPException(status_code=400, detail=\"No speech detected\")\n```\n\n---\n\n## K\u1ebft lu\u1eadn\n\nB\u1ea1n c\u1ea7n ch\u1eafc ch\u1eafn `speech2text` tr\u1ea3 v\u1ec1 m\u1ed9t string, kh\u00f4ng bao gi\u1edd tr\u1ea3 `None`. N\u1ebfu kh\u00f4ng \u0111\u1ecdc \u0111\u01b0\u1ee3c \u00e2m thanh, tr\u1ea3 v\u1ec1 chu\u1ed7i r\u1ed7ng ho\u1eb7c raise l\u1ed7i.\n\n---\n\nN\u1ebfu b\u1ea1n c\u1ea7n, m\u00ecnh c\u00f3 th\u1ec3 h\u1ed7 tr\u1ee3 b\u1ea1n s\u1eeda \u0111o\u1ea1n code c\u1ee5 th\u1ec3 lu\u00f4n 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":2806,"n_turns_in_conversation":27,"n_words":325,"source_row_offset":23714,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:26 GMT","turn_id":"6583b11877506439957e54a9","turn_index":26,"user_text":"File \"D:\\Project_Pycharm\\digital_human_demo\\app.py\", line 122, in chat_api\n    if not transcription.strip():\nAttributeError: 'NoneType' object has no attribute 'strip'"},{"assistant_initiates":false,"asst_text":"\u0645\u0631\u062d\u0628\u064b\u0627! \u0625\u0630\u0627 \u0643\u0646\u062a \u062a\u0631\u064a\u062f \u0623\u0643\u0648\u0627\u062f \u0644\u062a\u062d\u0648\u064a\u0644 \u0627\u0644\u0635\u0648\u0631 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0628\u0631\u0645\u062c\u064a\u0627\u062a \u062a\u0639\u062a\u0645\u062f \u0639\u0644\u0649 \u0627\u0644\u0630\u0643\u0627\u0621 \u0627\u0644\u0627\u0635\u0637\u0646\u0627\u0639\u064a \u0645\u062b\u0644 \u0645\u0643\u062a\u0628\u0627\u062a \u0628\u0627\u064a\u062b\u0648\u0646 \u0645\u062b\u0644\u0627\u064b\u060c \u064a\u0645\u0643\u0646\u0646\u064a \u0645\u0633\u0627\u0639\u062f\u062a\u0643 \u0628\u0630\u0644\u0643.\n\n\u0647\u0644 \u062a\u0642\u0635\u062f:\n- \u062a\u062d\u0648\u064a\u0644 \u062a\u0646\u0633\u064a\u0642 \u0627\u0644\u0635\u0648\u0631 (\u0645\u062b\u0644 \u0645\u0646 PNG \u0625\u0644\u0649 JPG)\u061f\n- \u062a\u0639\u062f\u064a\u0644 \u0627\u0644\u0635\u0648\u0631 (\u0643\u0627\u0644\u0642\u0635\u060c \u0627\u0644\u062a\u063a\u064a\u064a\u0631 \u0641\u064a \u0627\u0644\u0623\u0644\u0648\u0627\u0646\u060c \u0623\u0648 \u0627\u0644\u062a\u0623\u062b\u064a\u0631\u0627\u062a)\u061f\n- \u0623\u0648 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0646\u0645\u0627\u0630\u062c \u0630\u0643\u0627\u0621 \u0627\u0635\u0637\u0646\u0627\u0639\u064a \u0644\u0645\u0639\u0627\u0644\u062c\u0629 \u0627\u0644\u0635\u0648\u0631 (\u0645\u062b\u0644 \u062a\u062d\u0633\u064a\u0646 \u062c\u0648\u062f\u0629 \u0627\u0644\u0635\u0648\u0631\u060c \u062a\u062d\u0648\u064a\u0644 \u0627\u0644\u0635\u0648\u0631 \u0627\u0644\u0641\u0646\u064a\u0629\u060c \u062a\u0648\u0644\u064a\u062f \u0635\u0648\u0631 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0630\u0643\u0627\u0621 \u0627\u0644\u0627\u0635\u0637\u0646\u0627\u0639\u064a...)\u061f\n\n\u0645\u0646 \u0641\u0636\u0644\u0643 \u0648\u0636\u0651\u062d \u0644\u064a \u0646\u0648\u0639 \u0627\u0644\u062a\u062d\u0648\u064a\u0644 \u0627\u0644\u0630\u064a \u062a\u0631\u064a\u062f\u0647 \u0644\u0643\u064a \u0623\u0642\u062f\u0645 \u0644\u0643 \u0627\u0644\u0623\u0643\u0648\u0627\u062f \u0627\u0644\u0645\u0646\u0627\u0633\u0628\u0629.\n\n\u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0644\u062a\u062d\u0648\u064a\u0644 \u0635\u0648\u0631\u0629 \u0645\u0646 PNG \u0625\u0644\u0649 JPG \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0645\u0643\u062a\u0628\u0629 PIL \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646:\n\n```python\nfrom PIL import Image\n\n# \u0641\u062a\u062d \u0627\u0644\u0635\u0648\u0631\u0629\nimg = Image.open('input.png')\n\n# \u062a\u062d\u0648\u064a\u0644 \u0627\u0644\u0635\u0648\u0631\u0629 \u0648\u062d\u0641\u0638\u0647\u0627 \u0628\u0635\u064a\u063a\u0629 JPG\nimg.convert('RGB').save('output.jpg', 'JPEG')\n```\n\n\u0623\u0645\u0627 \u0625\u0630\u0627 \u062a\u0631\u064a\u062f \u0634\u064a\u0626\u064b\u0627 \u0645\u062a\u0642\u062f\u0645\u064b\u0627 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0646\u0645\u0627\u0630\u062c \u0627\u0644\u0630\u0643\u0627\u0621 \u0627\u0644\u0627\u0635\u0637\u0646\u0627\u0639\u064a \u0644\u062a\u062d\u0648\u064a\u0644 \u0627\u0644\u0635\u0648\u0631\u060c \u0645\u062b\u0644 \u062a\u062d\u0648\u064a\u0644 \u0627\u0644\u0635\u0648\u0631 \u0625\u0644\u0649 \u0631\u0633\u0645 \u0623\u0648 \u062a\u0639\u062f\u064a\u0644\u0647\u0627 \u0628\u0634\u0643\u0644 \u0630\u0643\u064a\u060c \u0641\u064a\u0645\u0643\u0646\u0646\u064a \u0625\u0631\u0634\u0627\u062f\u0643 \u0644\u0630\u0644\u0643 \u0623\u064a\u0636\u064b\u0627. \u0641\u0642\u0637 \u0623\u062e\u0628\u0631\u0646\u064a \u0627\u0644\u062a\u0641\u0627\u0635\u064a\u0644!\n\n\u0623\u0646\u062a\u0638\u0631 \u0631\u062f\u0643.","behavior":"degenerate_repetition","conversation_hash":"ad6af2a9d6e2b04611c6251a0563fc25","conversation_redacted":false,"conversation_toxic":false,"country":"Germany","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Arabic","model":"gpt-4.1-mini-2025-04-14","n_chars":891,"n_turns_in_conversation":1,"n_words":145,"source_row_offset":23715,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:41:43 GMT","turn_id":"519b4b2be30cef0a389dda0c","turn_index":0,"user_text":"\u0627\u0631\u064a\u062f \u0627\u0643\u0648\u0627\u062f \u062a\u062d\u0648\u064a\u0644 \u0627\u0644\u0635\u0648\u0631 \u0645\u0646 \u0634\u0627\u062a \u062c\u064a \u0628\u064a \u062a\u064a"},{"assistant_initiates":false,"asst_text":"Hello! I'm doing well, thank you. How can I assist you today?","behavior":"degenerate_repetition","conversation_hash":"1f2041611b8beff6b7602ece1f622dba","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4.1-mini-2025-04-14","n_chars":118,"n_turns_in_conversation":1,"n_words":23,"source_row_offset":23716,"source_shard":"turns-00077.parquet","timestamp":"Thu, 08 May 2025 05:42:02 GMT","turn_id":"63650dd97169c16fe03e45d1","turn_index":0,"user_text":"User: hi, how are you?\nUser: hi, how are you?\nAssistant:"}],"shard":{"index":77,"name":"turns-00077.parquet","rows":41273}}