Upload transcribe.ipynb

transcribe.ipynb

@@ -1 +1,683 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"machine_shape":"hm","mount_file_id":"15JwXGAHSNDvOfhIDFaj8h2bt5FijajiD","authorship_tag":"ABX9TyOIpJiulD+u85Vw+4eh9A8m"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":[],"metadata":{"id":"Pj64tkijY4tT","executionInfo":{"status":"ok","timestamp":1756685653771,"user_tz":240,"elapsed":5,"user":{"displayName":"Beshir Ibrahim","userId":"16736839346810179639"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["%%capture\n","# Core libraries\n","!pip install torch torchaudio transformers pydub numpy pyctcdecode\n","# If you need mp3 input support\n","!sudo apt-get update -qq\n","!sudo apt-get install -y ffmpeg\n","# For KenLM ARPA/bin support\n","!pip install https://github.com/kpu/kenlm/archive/master.zip"],"metadata":{"id":"d6IIQn8_hEAy","executionInfo":{"status":"ok","timestamp":1756686608615,"user_tz":240,"elapsed":56491,"user":{"displayName":"Beshir Ibrahim","userId":"16736839346810179639"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["MODEL_PATH     = \"/content/drive/MyDrive/artifacts/models/hf/hf_tgt/tigre-asr-Wav2Vec2Bert\"              # model and processor path\n","PROCESSOR_PATH = MODEL_PATH\n","AUDIO_FILE     = MODEL_PATH+\"/sample.wav\"\n","OUTPUT_TXT     = None                                   # e.g., \"/path/to/out.txt\" or None to just print\n","# KenLM + lexicon (optional but recommended for beam search)\n","KENLM_ARPA     = MODEL_PATH+\"/lm.arpa\"                  # set to None to decode WITHOUT LM\n","LEXICON_TXT    = MODEL_PATH+\"/lexicon.txt\"              # used to load unigrams; set to None if not available"],"metadata":{"id":"B81FMlsQlSOh","executionInfo":{"status":"ok","timestamp":1756686366477,"user_tz":240,"elapsed":13,"user":{"displayName":"Beshir Ibrahim","userId":"16736839346810179639"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["import warnings\n","import logging\n","\n","# Silence all Python warnings\n","warnings.filterwarnings(\"ignore\")\n","# Silence pyctcdecode logger\n","logging.getLogger(\"pyctcdecode\").setLevel(logging.ERROR)\n","# Silence torchaudio warnings (optionally all)\n","logging.getLogger(\"torchaudio\").setLevel(logging.ERROR)"],"metadata":{"id":"Y90co7BOmK9n","executionInfo":{"status":"ok","timestamp":1756685692591,"user_tz":240,"elapsed":6,"user":{"displayName":"Beshir Ibrahim","userId":"16736839346810179639"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["# Audio / chunking\n","TARGET_SR      = 16000\n","CHUNK_SEC      = 5        # chunk length in seconds\n","OVERLAP_SEC    = 0        # overlap between chunks in seconds (0 for minimal code)\n","# Beam search params\n","BEAM_WIDTH     = 150\n","LM_ALPHA       = 0.5\n","LM_BETA        = 1.0"],"metadata":{"id":"7DOmsFxbnzwK","executionInfo":{"status":"ok","timestamp":1756685693508,"user_tz":240,"elapsed":5,"user":{"displayName":"Beshir Ibrahim","userId":"16736839346810179639"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["import os\n","import torch\n","import numpy as np\n","import torchaudio\n","from typing import List, Optional\n","\n","# Use pydub for robust mp3 handling\n","from pydub import AudioSegment\n","\n","from transformers import Wav2Vec2BertForCTC, Wav2Vec2BertProcessor\n","\n","# Optional LM decoding\n","try:\n","    from pyctcdecode import build_ctcdecoder\n","    _HAS_PYCTC = True\n","except Exception:\n","    _HAS_PYCTC = False\n","\n","# Pick device\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","\n","def _load_audio(path: str, target_sr: int = 16000) -> torch.Tensor:\n","    \"\"\"Load WAV or MP3 to mono float32 tensor [1, T] at target_sr.\"\"\"\n","    ext = os.path.splitext(path)[1].lower()\n","    if ext == \".mp3\":\n","        audio = AudioSegment.from_file(path, format=\"mp3\")\n","        audio = audio.set_channels(1).set_frame_rate(target_sr)\n","        samples = np.array(audio.get_array_of_samples()).astype(np.float32)\n","        # pydub gives int PCM range; normalize if needed (assume 16-bit)\n","        if samples.dtype != np.float32:\n","            samples = samples.astype(np.float32)\n","        # If sample_width==2 (16-bit), divide by 32768\n","        if audio.sample_width == 2:\n","            samples /= 32768.0\n","        return torch.from_numpy(samples).unsqueeze(0)\n","    else:\n","        wav, sr = torchaudio.load(path)\n","        if wav.shape[0] > 1:\n","            wav = wav.mean(dim=0, keepdim=True)  # stereo -> mono\n","        if sr != target_sr:\n","            wav = torchaudio.transforms.Resample(sr, target_sr)(wav)\n","        # ensure float32 in [-1,1]\n","        if wav.dtype != torch.float32:\n","            wav = wav.to(torch.float32)\n","        return wav\n","\n","def _chunks(wave: torch.Tensor, sr: int, chunk_sec: int, overlap_sec: int):\n","    \"\"\"Yield possibly-overlapping chunks [1, T_chunk].\"\"\"\n","    chunk = int(chunk_sec * sr)\n","    step  = max(1, chunk - int(overlap_sec * sr))\n","    T     = wave.size(-1)\n","    for start in range(0, T, step):\n","        end = min(start + chunk, T)\n","        yield wave[:, start:end]\n","        if end >= T:\n","            break\n","\n","def _load_unigrams(lexicon_path: Optional[str]) -> List[str]:\n","    \"\"\"Read first token per line from lexicon into a unigram list.\"\"\"\n","    if not lexicon_path or not os.path.exists(lexicon_path):\n","        return []\n","    words = set()\n","    with open(lexicon_path, \"r\", encoding=\"utf-8\") as f:\n","        for line in f:\n","            w = line.strip().split()\n","            if w:\n","                words.add(w[0])\n","    return sorted(words)\n","\n","def _build_decoder(model, processor):\n","    \"\"\"Build a pyctcdecode decoder from model vocab + KenLM (if configured).\"\"\"\n","    # Build vocab (id -> token)\n","    vocab_size = model.lm_head.out_features\n","    labels = []\n","    for i in range(vocab_size):\n","        tok = processor.tokenizer.convert_ids_to_tokens([i])[0]\n","        # remove common BPE markers\n","        tok = tok.lstrip(\"Ġ\").lstrip(\"▁\")\n","        labels.append(tok)\n","\n","    # No LM? Use labels only; with LM? also pass unigrams + alpha/beta\n","    if not _HAS_PYCTC:\n","        return None\n","\n","    if KENLM_ARPA and os.path.exists(KENLM_ARPA):\n","        unigrams = _load_unigrams(LEXICON_TXT)\n","        return build_ctcdecoder(\n","            labels=labels,\n","            kenlm_model_path=KENLM_ARPA,\n","            unigrams=unigrams if unigrams else None,\n","            alpha=LM_ALPHA,\n","            beta=LM_BETA\n","        )\n","    else:\n","        # Fallback to lexicon-less decoder (greedy-ish beam without LM)\n","        return build_ctcdecoder(labels=labels)\n","\n","def _postprocess(text: str) -> str:\n","    \"\"\"Light cleanup: strip special markers, collapse dup words, ensure end punctuation.\"\"\"\n","    text = text.replace(\"<|\", \"\").replace(\"|>\", \"\").replace(\"<>\", \"\").strip()\n","    words, cleaned = text.split(), []\n","    for w in words:\n","        if not cleaned or cleaned[-1] != w:\n","            cleaned.append(w)\n","    out = \" \".join(cleaned).strip()\n","    if out and out[-1] not in \".!?\":\n","        out += \".\"\n","    return out\n","\n","def transcribe_one_file() -> str:\n","    # Load model + processor\n","    model = Wav2Vec2BertForCTC.from_pretrained(MODEL_PATH).to(device).eval()\n","    processor = Wav2Vec2BertProcessor.from_pretrained(PROCESSOR_PATH)\n","\n","    # Optional decoder\n","    decoder = _build_decoder(model, processor)\n","\n","    # Load audio\n","    wav = _load_audio(AUDIO_FILE, TARGET_SR)\n","\n","    # Transcribe by chunks\n","    pieces = []\n","    for chunk in _chunks(wav, TARGET_SR, CHUNK_SEC, OVERLAP_SEC):\n","        # processor for Wav2Vec2Bert expects raw audio -> input_features\n","        inputs = processor(chunk.squeeze().numpy(), sampling_rate=TARGET_SR, return_tensors=\"pt\").to(device)\n","        with torch.no_grad():\n","            logits = model(input_features=inputs.input_features).logits  # [1, T, V]\n","        logp = logits[0].cpu().numpy()\n","\n","        if decoder is not None:\n","            hypo = decoder.decode(logp, beam_width=BEAM_WIDTH)\n","        else:\n","            # Greedy fallback if pyctcdecode not available\n","            ids = logp.argmax(axis=-1)\n","            tokens = processor.tokenizer.convert_ids_to_tokens(ids.tolist())\n","            hypo = \"\".join(tokens)\n","\n","        if hypo.strip():\n","            pieces.append(hypo.strip())\n","\n","        # cleanup per chunk\n","        del inputs, logits, logp\n","\n","    text = _postprocess(\" \".join(pieces))\n","    return text\n","\n","if __name__ == \"__main__\":\n","    out = transcribe_one_file()\n","    if OUTPUT_TXT:\n","        os.makedirs(os.path.dirname(OUTPUT_TXT), exist_ok=True)\n","        with open(OUTPUT_TXT, \"w\", encoding=\"utf-8\") as f:\n","            f.write(out + \"\\n\")\n","    print(out)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W1rQvavueaBI","executionInfo":{"status":"ok","timestamp":1756686391018,"user_tz":240,"elapsed":15969,"user":{"displayName":"Beshir Ibrahim","userId":"16736839346810179639"}},"outputId":"8c5a1a6a-dd57-4b82-f891-0f4e45945a93"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["ሕርጊጎ ምነ ምን ዘበን አትራክ እንዴ አንበተት እብ መረባቤዐ ግሩም ለትሐሌ መዲነት ተ.\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"qs6x1lHOlthS"},"execution_count":null,"outputs":[]}]}
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+        "id": "Pj64tkijY4tT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%%capture\n",
+        "# Core libraries\n",
+        "!pip install torch torchaudio transformers pydub numpy pyctcdecode\n",
+        "# If you need mp3 input support\n",
+        "!sudo apt-get update -qq\n",
+        "!sudo apt-get install -y ffmpeg\n",
+        "# For KenLM ARPA/bin support\n",
+        "!pip install https://github.com/kpu/kenlm/archive/master.zip"
+      ],
+      "metadata": {
+        "id": "d6IIQn8_hEAy"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "local_dir      = \"/content/tigre-asr-Wav2Vec2Bert\"  # or any local path\n",
+        "OUTPUT_TXT     = None                                   # e.g., \"/path/to/out.txt\" or None to just print"
+      ],
+      "metadata": {
+        "id": "5j0nwQYG2B4T"
+      },
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import snapshot_download\n",
+        "\n",
+        "MODEL_PATH     = \"BeitTigreAI/tigre-asr-Wav2Vec2Bert\"\n",
+        "PROCESSOR_PATH = MODEL_PATH\n",
+        "\n",
+        "snapshot_download(\n",
+        "    repo_id                 = MODEL_PATH,\n",
+        "    repo_type               = \"model\",\n",
+        "    local_dir               = local_dir,\n",
+        "    local_dir_use_symlinks  = False  # copies files fully\n",
+        ")\n",
+        "\n",
+        "AUDIO_FILE = f\"{local_dir}/sample.wav\"\n",
+        "KENLM_ARPA = f\"{local_dir}/lm.arpa\"     # if uploaded\n",
+        "LEXICON_TXT = f\"{local_dir}/lexicon.txt\""
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "ce0bb5a384f24c0e8a1d1b2620d34f6b",
+            "33cc7addcccd495f820391aeb5d08dd0",
+            "ca564eec03874e6487ab7559580bba74",
+            "143a309bafb64dca81327f331ace379e",
+            "f6db87a6251145f08348dfe0fd503ac1",
+            "77ad67ab59704d4abff46bbde1fdf8cc",
+            "8807473008224ebb92932a247d95ad6b",
+            "4ac03a80fc1a45fc80a9bbc1e746ab21",
+            "fd67dc9a87864db6998a5d0de5b7cc59",
+            "50586c1a6dc340e5aba4e8dd6a1e2fcb",
+            "a51b63ac35054c1786e6b9d032fdc2ca"
+          ]
+        },
+        "id": "WNhgqJET08VF",
+        "outputId": "559f767c-d269-4908-ca5e-1bffb8c39395"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Fetching 12 files:   0%|          | 0/12 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ce0bb5a384f24c0e8a1d1b2620d34f6b"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import warnings\n",
+        "import logging\n",
+        "\n",
+        "# Silence all Python warnings\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "# Silence pyctcdecode logger\n",
+        "logging.getLogger(\"pyctcdecode\").setLevel(logging.ERROR)\n",
+        "# Silence torchaudio warnings (optionally all)\n",
+        "logging.getLogger(\"torchaudio\").setLevel(logging.ERROR)"
+      ],
+      "metadata": {
+        "id": "Y90co7BOmK9n"
+      },
+      "execution_count": 23,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Audio / chunking\n",
+        "TARGET_SR      = 16000\n",
+        "CHUNK_SEC      = 5        # chunk length in seconds\n",
+        "OVERLAP_SEC    = 0        # overlap between chunks in seconds (0 for minimal code)\n",
+        "# Beam search params\n",
+        "BEAM_WIDTH     = 150\n",
+        "LM_ALPHA       = 0.5\n",
+        "LM_BETA        = 1.0"
+      ],
+      "metadata": {
+        "id": "7DOmsFxbnzwK"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import torch\n",
+        "import numpy as np\n",
+        "import torchaudio\n",
+        "from typing import List, Optional\n",
+        "\n",
+        "# Use pydub for robust mp3 handling\n",
+        "from pydub import AudioSegment\n",
+        "\n",
+        "from transformers import Wav2Vec2BertForCTC, Wav2Vec2BertProcessor\n",
+        "\n",
+        "# Optional LM decoding\n",
+        "try:\n",
+        "    from pyctcdecode import build_ctcdecoder\n",
+        "    _HAS_PYCTC = True\n",
+        "except Exception:\n",
+        "    _HAS_PYCTC = False\n",
+        "\n",
+        "# Pick device\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "\n",
+        "def _load_audio(path: str, target_sr: int = 16000) -> torch.Tensor:\n",
+        "    \"\"\"Load WAV or MP3 to mono float32 tensor [1, T] at target_sr.\"\"\"\n",
+        "    ext = os.path.splitext(path)[1].lower()\n",
+        "    if ext == \".mp3\":\n",
+        "        audio = AudioSegment.from_file(path, format=\"mp3\")\n",
+        "        audio = audio.set_channels(1).set_frame_rate(target_sr)\n",
+        "        samples = np.array(audio.get_array_of_samples()).astype(np.float32)\n",
+        "        # pydub gives int PCM range; normalize if needed (assume 16-bit)\n",
+        "        if samples.dtype != np.float32:\n",
+        "            samples = samples.astype(np.float32)\n",
+        "        # If sample_width==2 (16-bit), divide by 32768\n",
+        "        if audio.sample_width == 2:\n",
+        "            samples /= 32768.0\n",
+        "        return torch.from_numpy(samples).unsqueeze(0)\n",
+        "    else:\n",
+        "        wav, sr = torchaudio.load(path)\n",
+        "        if wav.shape[0] > 1:\n",
+        "            wav = wav.mean(dim=0, keepdim=True)  # stereo -> mono\n",
+        "        if sr != target_sr:\n",
+        "            wav = torchaudio.transforms.Resample(sr, target_sr)(wav)\n",
+        "        # ensure float32 in [-1,1]\n",
+        "        if wav.dtype != torch.float32:\n",
+        "            wav = wav.to(torch.float32)\n",
+        "        return wav\n",
+        "\n",
+        "def _chunks(wave: torch.Tensor, sr: int, chunk_sec: int, overlap_sec: int):\n",
+        "    \"\"\"Yield possibly-overlapping chunks [1, T_chunk].\"\"\"\n",
+        "    chunk = int(chunk_sec * sr)\n",
+        "    step  = max(1, chunk - int(overlap_sec * sr))\n",
+        "    T     = wave.size(-1)\n",
+        "    for start in range(0, T, step):\n",
+        "        end = min(start + chunk, T)\n",
+        "        yield wave[:, start:end]\n",
+        "        if end >= T:\n",
+        "            break\n",
+        "\n",
+        "def _load_unigrams(lexicon_path: Optional[str]) -> List[str]:\n",
+        "    \"\"\"Read first token per line from lexicon into a unigram list.\"\"\"\n",
+        "    if not lexicon_path or not os.path.exists(lexicon_path):\n",
+        "        return []\n",
+        "    words = set()\n",
+        "    with open(lexicon_path, \"r\", encoding=\"utf-8\") as f:\n",
+        "        for line in f:\n",
+        "            w = line.strip().split()\n",
+        "            if w:\n",
+        "                words.add(w[0])\n",
+        "    return sorted(words)\n",
+        "\n",
+        "def _build_decoder(model, processor):\n",
+        "    \"\"\"Build a pyctcdecode decoder from model vocab + KenLM (if configured).\"\"\"\n",
+        "    # Build vocab (id -> token)\n",
+        "    vocab_size = model.lm_head.out_features\n",
+        "    labels = []\n",
+        "    for i in range(vocab_size):\n",
+        "        tok = processor.tokenizer.convert_ids_to_tokens([i])[0]\n",
+        "        # remove common BPE markers\n",
+        "        tok = tok.lstrip(\"Ġ\").lstrip(\"▁\")\n",
+        "        labels.append(tok)\n",
+        "\n",
+        "    # No LM? Use labels only; with LM? also pass unigrams + alpha/beta\n",
+        "    if not _HAS_PYCTC:\n",
+        "        return None\n",
+        "\n",
+        "    if KENLM_ARPA and os.path.exists(KENLM_ARPA):\n",
+        "        unigrams = _load_unigrams(LEXICON_TXT)\n",
+        "        return build_ctcdecoder(\n",
+        "            labels=labels,\n",
+        "            kenlm_model_path=KENLM_ARPA,\n",
+        "            unigrams=unigrams if unigrams else None,\n",
+        "            alpha=LM_ALPHA,\n",
+        "            beta=LM_BETA\n",
+        "        )\n",
+        "    else:\n",
+        "        # Fallback to lexicon-less decoder (greedy-ish beam without LM)\n",
+        "        return build_ctcdecoder(labels=labels)\n",
+        "\n",
+        "def _postprocess(text: str) -> str:\n",
+        "    \"\"\"Light cleanup: strip special markers, collapse dup words, ensure end punctuation.\"\"\"\n",
+        "    text = text.replace(\"<|\", \"\").replace(\"|>\", \"\").replace(\"<>\", \"\").strip()\n",
+        "    words, cleaned = text.split(), []\n",
+        "    for w in words:\n",
+        "        if not cleaned or cleaned[-1] != w:\n",
+        "            cleaned.append(w)\n",
+        "    out = \" \".join(cleaned).strip()\n",
+        "    if out and out[-1] not in \".!?\":\n",
+        "        out += \".\"\n",
+        "    return out\n",
+        "\n",
+        "def transcribe_one_file() -> str:\n",
+        "    # Load model + processor\n",
+        "    model = Wav2Vec2BertForCTC.from_pretrained(MODEL_PATH).to(device).eval()\n",
+        "    processor = Wav2Vec2BertProcessor.from_pretrained(PROCESSOR_PATH)\n",
+        "\n",
+        "    # Optional decoder\n",
+        "    decoder = _build_decoder(model, processor)\n",
+        "\n",
+        "    # Load audio\n",
+        "    wav = _load_audio(AUDIO_FILE, TARGET_SR)\n",
+        "\n",
+        "    # Transcribe by chunks\n",
+        "    pieces = []\n",
+        "    for chunk in _chunks(wav, TARGET_SR, CHUNK_SEC, OVERLAP_SEC):\n",
+        "        # processor for Wav2Vec2Bert expects raw audio -> input_features\n",
+        "        inputs = processor(chunk.squeeze().numpy(), sampling_rate=TARGET_SR, return_tensors=\"pt\").to(device)\n",
+        "        with torch.no_grad():\n",
+        "            logits = model(input_features=inputs.input_features).logits  # [1, T, V]\n",
+        "        logp = logits[0].cpu().numpy()\n",
+        "\n",
+        "        if decoder is not None:\n",
+        "            hypo = decoder.decode(logp, beam_width=BEAM_WIDTH)\n",
+        "        else:\n",
+        "            # Greedy fallback if pyctcdecode not available\n",
+        "            ids = logp.argmax(axis=-1)\n",
+        "            tokens = processor.tokenizer.convert_ids_to_tokens(ids.tolist())\n",
+        "            hypo = \"\".join(tokens)\n",
+        "\n",
+        "        if hypo.strip():\n",
+        "            pieces.append(hypo.strip())\n",
+        "\n",
+        "        # cleanup per chunk\n",
+        "        del inputs, logits, logp\n",
+        "\n",
+        "    text = _postprocess(\" \".join(pieces))\n",
+        "    return text\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    out = transcribe_one_file()\n",
+        "    if OUTPUT_TXT:\n",
+        "        os.makedirs(os.path.dirname(OUTPUT_TXT), exist_ok=True)\n",
+        "        with open(OUTPUT_TXT, \"w\", encoding=\"utf-8\") as f:\n",
+        "            f.write(out + \"\\n\")\n",
+        "    print(out)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "W1rQvavueaBI",
+        "outputId": "76136358-df59-4f98-bfcc-8e01dc51ed6d"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "ሕርጊጎ ምነ ምን ዘበን አትራክ እንዴ አንበተት እብ መረባቤዐ ግሩም ለትሐሌ መዲነት ተ.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "qs6x1lHOlthS"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}