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Bengali Long-Form ASR—V21 \n\n### What HELPED:\n-  Unicode NFC normalization (0.444 → 0.429)\n-  Beam search 2-4 (0.444 → 0.366)\n-  Chunk=20.1s (1st place setting)\n\n### This Version Tests:\n1. **Greedy decoding (beams=1)** vs beam search\n2. **Different stride overlaps**\n3. **Zero post-processing** except Unicode normalization","metadata":{"papermill":{"duration":0.01327,"end_time":"2026-02-19T04:56:41.454149","exception":false,"start_time":"2026-02-19T04:56:41.440879","status":"completed"},"tags":[]}},{"id":"12fbaa74","cell_type":"code","source":"# 1. Setup\nimport os\nimport re\nimport unicodedata\nimport warnings\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport librosa\nimport soundfile as sf\nfrom tqdm.auto import tqdm\nfrom transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor\n\nwarnings.filterwarnings('ignore')\n\nprint(f\"PyTorch: {torch.__version__}\")\nprint(f\"CUDA: {torch.cuda.is_available()}\")\nif torch.cuda.is_available():\n    print(f\"GPU: {torch.cuda.get_device_name(0)}\")","metadata":{"execution":{"iopub.execute_input":"2026-02-19T04:56:41.479802Z","iopub.status.busy":"2026-02-19T04:56:41.479521Z","iopub.status.idle":"2026-02-19T04:57:05.320678Z","shell.execute_reply":"2026-02-19T04:57:05.319821Z"},"papermill":{"duration":23.855716,"end_time":"2026-02-19T04:57:05.322302","exception":false,"start_time":"2026-02-19T04:56:41.466586","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"3d7a0523","cell_type":"code","source":"# 2. Official Competition Normalization (EXACT)\n\nZW = r\"[\\u200B-\\u200D\\uFEFF]\"\n\ndef normalize_bn_text(s: str) -> str:\n    if s is None:\n        return \"\"\n    s = str(s)\n    s = unicodedata.normalize(\"NFC\", s)\n    s = re.sub(ZW, \"\", s)\n    s = s.replace(\"\\u00A0\", \" \")\n    s = \" \".join(s.split())\n    return s\n\n# Test\nprint(\"Test:\", normalize_bn_text(\"আমি\\u200Bভালো  আছি\"))","metadata":{"execution":{"iopub.execute_input":"2026-02-19T04:57:05.347548Z","iopub.status.busy":"2026-02-19T04:57:05.347111Z","iopub.status.idle":"2026-02-19T04:57:05.352232Z","shell.execute_reply":"2026-02-19T04:57:05.351630Z"},"papermill":{"duration":0.01926,"end_time":"2026-02-19T04:57:05.353687","exception":false,"start_time":"2026-02-19T04:57:05.334427","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"1ce959cf","cell_type":"code","source":"# 3. Configuration\n\nMODEL_NAME = \"bengaliAI/tugstugi_bengaliai-asr_whisper-medium\"\n\n# Test both greedy and beam search\nNUM_BEAMS = 1  # Start with greedy (simplest, often best)\n# If score > 0.40, change to 2 or 4\n\nCHUNK_LENGTH_S = 20.1\nSTRIDE_LENGTH_S = (5, 3)\nMAX_NEW_TOKENS = 260\nBATCH_SIZE = 8\n\nTEST_AUDIO_DIR = \"/kaggle/input/competitions/dl-sprint-4-0-bengali-long-form-speech-recognition/transcription/transcription/test/audio\"\n\nOUTPUT_CSV = \"/kaggle/working/submission.csv\"\nTARGET_SR = 16000\n\nprint(\"=\" * 60)\nprint(\"V21 CONFIGURATION\")\nprint(\"=\" * 60)\nprint(f\"Model: {MODEL_NAME}\")\nprint(f\"Beams: {NUM_BEAMS} (greedy=1, beam=2-4)\")\nprint(f\"Chunk: {CHUNK_LENGTH_S}s | Stride: {STRIDE_LENGTH_S}\")\nprint(f\"Post-processing: ONLY Unicode normalization\")\nprint(\"=\" * 60)","metadata":{"execution":{"iopub.execute_input":"2026-02-19T04:57:05.378638Z","iopub.status.busy":"2026-02-19T04:57:05.377954Z","iopub.status.idle":"2026-02-19T04:57:05.383328Z","shell.execute_reply":"2026-02-19T04:57:05.382615Z"},"papermill":{"duration":0.019248,"end_time":"2026-02-19T04:57:05.384733","exception":false,"start_time":"2026-02-19T04:57:05.365485","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"fe563538","cell_type":"code","source":"# 4. Collect Files\n\naudio_files = sorted([\n    f for f in os.listdir(\"/kaggle/input/competitions/dl-sprint-4-0-bengali-long-form-speech-recognition/transcription/transcription/test/audio\"\n)\n    if f.endswith(('.wav', '.mp3'))\n])\n\nprint(f\"Found {len(audio_files)} files\")\nprint(f\"First 3: {audio_files[:3]}\")","metadata":{"execution":{"iopub.execute_input":"2026-02-19T04:57:05.409964Z","iopub.status.busy":"2026-02-19T04:57:05.409743Z","iopub.status.idle":"2026-02-19T04:57:05.421154Z","shell.execute_reply":"2026-02-19T04:57:05.420471Z"},"papermill":{"duration":0.025346,"end_time":"2026-02-19T04:57:05.422557","exception":false,"start_time":"2026-02-19T04:57:05.397211","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"4f8787cf","cell_type":"code","source":"# 5. Load Model\n\ndevice = 0 if torch.cuda.is_available() else \"cpu\"\ndtype = torch.float16 if device == 0 else torch.float32\n\nprint(f\"Loading model...\")\n\nmodel = AutoModelForSpeechSeq2Seq.from_pretrained(\n    MODEL_NAME,\n    torch_dtype=dtype,\n    low_cpu_mem_usage=True,\n)\nmodel.to(\"cuda\" if device == 0 else \"cpu\")\nmodel.eval()\n\nprocessor = AutoProcessor.from_pretrained(MODEL_NAME)\n\nasr_pipe = pipeline(\n    \"automatic-speech-recognition\",\n    model=model,\n    tokenizer=processor.tokenizer,\n    feature_extractor=processor.feature_extractor,\n    device=device,\n    torch_dtype=dtype,\n    chunk_length_s=CHUNK_LENGTH_S,\n    stride_length_s=STRIDE_LENGTH_S,\n    return_timestamps=False,\n)\n\nasr_pipe.model.config.use_cache = False\n\nprint(\"✓ Model loaded\")","metadata":{"execution":{"iopub.execute_input":"2026-02-19T04:57:05.447261Z","iopub.status.busy":"2026-02-19T04:57:05.447049Z","iopub.status.idle":"2026-02-19T04:57:19.108278Z","shell.execute_reply":"2026-02-19T04:57:19.107403Z"},"papermill":{"duration":13.675377,"end_time":"2026-02-19T04:57:19.109811","exception":false,"start_time":"2026-02-19T04:57:05.434434","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"22cee480","cell_type":"code","source":"# 6. Transcription Loop\n\nresults = []\n\nprint(f\"\\n{'='*60}\")\nprint(f\"TRANSCRIBING WITH NUM_BEAMS={NUM_BEAMS}\")\nprint(f\"{'='*60}\\n\")\n\nfor i, fname in enumerate(tqdm(audio_files, desc=\"Transcribing\")):\n    fpath = os.path.join(TEST_AUDIO_DIR, fname)\n    file_id = Path(fname).stem\n    \n    try:\n        # Load audio\n        audio, sr = librosa.load(fpath, sr=TARGET_SR)\n        \n        # Transcribe\n        output = asr_pipe(\n            {\"array\": audio, \"sampling_rate\": sr},\n            batch_size=BATCH_SIZE,\n            generate_kwargs={\n                \"num_beams\": NUM_BEAMS,\n                \"max_new_tokens\": MAX_NEW_TOKENS,\n                \"temperature\": 0.0,\n                \"do_sample\": False,\n            }\n        )\n        \n        # Apply ONLY Unicode normalization\n        text = normalize_bn_text(output[\"text\"])\n        \n        results.append({\n            \"filename\": file_id,\n            \"transcript\": text\n        })\n        \n        # Show first result\n        if i == 0:\n            print(f\"\\nFirst result:\")\n            print(f\"  File: {file_id}\")\n            print(f\"  Length: {len(text)} chars, {len(text.split())} words\")\n            print(f\"  Sample: {text[:150]}...\\n\")\n        \n        # Memory cleanup\n        if torch.cuda.is_available() and (i + 1) % 5 == 0:\n            torch.cuda.empty_cache()\n    \n    except Exception as e:\n        print(f\"\\nERROR with {fname}: {e}\")\n        results.append({\"filename\": file_id, \"transcript\": \"\"})\n\ndf = pd.DataFrame(results)\nprint(f\"\\n✓ Transcription complete: {len(df)} files\")","metadata":{"execution":{"iopub.execute_input":"2026-02-19T04:57:19.138377Z","iopub.status.busy":"2026-02-19T04:57:19.138098Z","iopub.status.idle":"2026-02-19T05:53:02.565912Z","shell.execute_reply":"2026-02-19T05:53:02.565168Z"},"papermill":{"duration":3343.443218,"end_time":"2026-02-19T05:53:02.567274","exception":false,"start_time":"2026-02-19T04:57:19.124056","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"0ece5166","cell_type":"code","source":"# 7. Save Submission\n\n# Final normalization pass\ndf['transcript'] = df['transcript'].apply(normalize_bn_text)\n\n# Save\ndf.to_csv(OUTPUT_CSV, index=False, encoding='utf-8')\n\nprint(f\"\\n{'='*60}\")\nprint(\"SAVED SUBMISSION\")\nprint(f\"{'='*60}\")\nprint(f\"File: {OUTPUT_CSV}\")\nprint(f\"Rows: {len(df)}\")\n\n# Verify\nempty = (df['transcript'] == '').sum()\nif empty > 0:\n    print(f\"\\n⚠ WARNING: {empty} empty transcripts!\")\nelse:\n    print(f\"\\n✓ All transcripts non-empty\")\n\n# Stats\nprint(f\"\\nStats:\")\nprint(f\"  Avg length: {df['transcript'].str.len().mean():.0f} chars\")\nprint(f\"  Avg words: {df['transcript'].str.split().str.len().mean():.0f}\")\n\nprint(f\"\\n{'='*60}\")\nprint(f\"Expected score with beams={NUM_BEAMS}:\")\nif NUM_BEAMS == 1:\n    print(\"  0.35-0.40 WER (greedy is fast but may be less accurate)\")\nelif NUM_BEAMS == 2:\n    print(\"  0.32-0.36 WER (good balance)\")\nelse:\n    print(\"  0.30-0.35 WER (best quality, slower)\")\nprint(f\"{'='*60}\")","metadata":{"execution":{"iopub.execute_input":"2026-02-19T05:53:02.654581Z","iopub.status.busy":"2026-02-19T05:53:02.654213Z","iopub.status.idle":"2026-02-19T05:53:02.773483Z","shell.execute_reply":"2026-02-19T05:53:02.772754Z"},"papermill":{"duration":0.164178,"end_time":"2026-02-19T05:53:02.775035","exception":false,"start_time":"2026-02-19T05:53:02.610857","status":"completed"},"tags":[]},"outputs":[],"execution_count":null}]}