inference.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2bdeda95",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCTC, Wav2Vec2Processor\n",
    "from datasets import load_dataset, load_metric, Audio\n",
    "import numpy as np\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8f840be9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoModelForCTC.from_pretrained(\".\").to('cuda')\n",
    "processor = Wav2Vec2Processor.from_pretrained(\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "46339a6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = AutoModelForCTC.from_pretrained(\"vitouphy/xls-r-300m-km\").to('cuda')\n",
    "# processor = Wav2Vec2Processor.from_pretrained(\"vitouphy/xls-r-300m-km\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2c28d4f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default-fbad308ab5a03eb2\n",
      "Reusing dataset csv (/workspace/.cache/huggingface/datasets/csv/default-fbad308ab5a03eb2/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)\n"
     ]
    }
   ],
   "source": [
    "common_voice_test = load_dataset('csv', data_files='km_kh_male/line_index_test.csv', split = 'train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f14c1cfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "common_voice_test  = (common_voice_test\n",
    "                      .remove_columns([\"Unnamed: 0\", \"drop\"])\n",
    "                      .rename_column('text', 'sentence'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b60360b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "common_voice_test  = common_voice_test.cast_column(\"path\", Audio(sampling_rate=16_000)).rename_column('path', 'audio')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "64758ba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'audio': {'path': '/workspace/xls-r-300m-km/km_kh_male/wavs/khm_1443_3799144408.wav',\n",
       "  'array': array([-1.0600963e-06,  1.2359066e-06, -1.4001107e-06, ...,\n",
       "         -3.1423504e-05,  4.4914182e-06,  0.0000000e+00], dtype=float32),\n",
       "  'sampling_rate': 16000},\n",
       " 'sentence': 'ស៊ី ដាច់ ម៉ូតូ នៅ ពេល ដែល ប្រើ ឱ្យ ឌុប សម្ភារៈ គ្រឿង សង្ហារឹម យក ទៅ ឱ្យ ម៉ូយ នៅ ម្ដុំ វត្ដ សំរោងអណ្ដែត'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_voice_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "93cd7415",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataset(batch):\n",
    "    audio = batch[\"audio\"]\n",
    "    \n",
    "    # batched output is \"un-batched\"\n",
    "    batch[\"input_values\"] = processor(np.array(audio[\"array\"]), sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n",
    "    batch[\"input_length\"] = len(batch[\"input_values\"])\n",
    "    \n",
    "    with processor.as_target_processor():\n",
    "        batch[\"labels\"] = processor(batch[\"sentence\"]).input_ids\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "04751885",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/csv/default-fbad308ab5a03eb2/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-abf3b661c395248b.arrow\n"
     ]
    }
   ],
   "source": [
    "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e55d9cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4f637d1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "It is strongly recommended to pass the ``sampling_rate`` argument to this function. Failing to do so can result in silent errors that might be hard to debug.\n"
     ]
    }
   ],
   "source": [
    "input_dict = processor(common_voice_test[i][\"input_values\"], return_tensors=\"pt\", padding=True)\n",
    "logits = model(input_dict.input_values.to(\"cuda\")).logits\n",
    "pred_ids = torch.argmax(logits, dim=-1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "85334ad6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:\n",
      "ក្រុង ប៉ោយប៉ែត នឹង ក្វាះ ទឹក ស្អាត ប្រើ ចាប់ ពី សប្តាហ ក្រោយ ទៅ\n",
      "\n",
      "Reference:\n",
      "ក្រុង ប៉ោយប៉ែត នឹង ខ្វះ ទឹក ស្អាត ប្រើ ចាប់ ពី សប្តាហ៍ ក្រោយ ទៅ\n"
     ]
    }
   ],
   "source": [
    "print(\"Prediction:\")\n",
    "pred_ids = pred_ids[pred_ids != processor.tokenizer.pad_token_id]\n",
    "print(processor.decode(pred_ids))\n",
    "\n",
    "print(\"\\nReference:\")\n",
    "print(processor.decode(common_voice_test['labels'][i]))\n",
    "# print(common_voice_test_transcription[0][\"sentence\"].lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be1c8d79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f7eaba0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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