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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Install Requirements and Depedencies, Also login to huggingface through command line"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "vK6B3ubS21Ky",
+ "outputId": "1fc66279-80cf-4dac-95b8-8791cdbc00d3"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting datasets==1.18.3\n",
+ " Downloading datasets-1.18.3-py3-none-any.whl (311 kB)\n",
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+ "\u001b[?25hCollecting huggingface-hub<1.0.0,>=0.1.0\n",
+ " Downloading huggingface_hub-0.4.0-py3-none-any.whl (67 kB)\n",
+ "\u001b[K |████████████████████████████████| 67 kB 5.1 MB/s \n",
+ "\u001b[?25hCollecting aiohttp\n",
+ " Downloading aiohttp-3.8.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
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+ "Collecting xxhash\n",
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+ "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets==1.18.3) (2.10)\n",
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+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets==1.18.3) (2021.10.8)\n",
+ "Collecting aiosignal>=1.1.2\n",
+ " Downloading aiosignal-1.2.0-py3-none-any.whl (8.2 kB)\n",
+ "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets==1.18.3) (2.0.11)\n",
+ "Collecting asynctest==0.13.0\n",
+ " Downloading asynctest-0.13.0-py3-none-any.whl (26 kB)\n",
+ "Collecting frozenlist>=1.1.1\n",
+ " Downloading frozenlist-1.3.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (144 kB)\n",
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+ "\u001b[?25hCollecting yarl<2.0,>=1.0\n",
+ " Downloading yarl-1.7.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (271 kB)\n",
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+ "Installing collected packages: multidict, frozenlist, yarl, asynctest, async-timeout, aiosignal, fsspec, aiohttp, xxhash, huggingface-hub, datasets\n",
+ "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-1.18.3 frozenlist-1.3.0 fsspec-2022.1.0 huggingface-hub-0.4.0 multidict-6.0.2 xxhash-2.0.2 yarl-1.7.2\n",
+ "Collecting transformers\n",
+ " Downloading transformers-4.16.2-py3-none-any.whl (3.5 MB)\n",
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+ "Collecting sacremoses\n",
+ " Downloading sacremoses-0.0.47-py2.py3-none-any.whl (895 kB)\n",
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+ "Collecting pyyaml>=5.1\n",
+ " Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
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+ "Collecting tokenizers!=0.11.3,>=0.10.1\n",
+ " Downloading tokenizers-0.11.4-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.8 MB)\n",
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+ "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
+ "Installing collected packages: pyyaml, tokenizers, sacremoses, transformers\n",
+ " Attempting uninstall: pyyaml\n",
+ " Found existing installation: PyYAML 3.13\n",
+ " Uninstalling PyYAML-3.13:\n",
+ " Successfully uninstalled PyYAML-3.13\n",
+ "Successfully installed pyyaml-6.0 sacremoses-0.0.47 tokenizers-0.11.4 transformers-4.16.2\n",
+ "Collecting pyctcdecode\n",
+ " Downloading pyctcdecode-0.3.0-py2.py3-none-any.whl (43 kB)\n",
+ "\u001b[K |████████████████████████████████| 43 kB 1.2 MB/s \n",
+ "\u001b[?25hCollecting pygtrie<3.0,>=2.1\n",
+ " Downloading pygtrie-2.4.2.tar.gz (35 kB)\n",
+ "Collecting hypothesis<7,>=6.14\n",
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+ "Building wheels for collected packages: pygtrie\n",
+ " Building wheel for pygtrie (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for pygtrie: filename=pygtrie-2.4.2-py3-none-any.whl size=19063 sha256=8df2d93eca581ee990684593e9aea4702e3fc1600c3b12431977e7f7f2a72d2c\n",
+ " Stored in directory: /root/.cache/pip/wheels/d3/f8/ba/1d828b1603ea422686eb694253a43cb3a5901ea4696c1e0603\n",
+ "Successfully built pygtrie\n",
+ "Installing collected packages: pygtrie, hypothesis, pyctcdecode\n",
+ "Successfully installed hypothesis-6.36.1 pyctcdecode-0.3.0 pygtrie-2.4.2\n",
+ "Collecting jiwer\n",
+ " Downloading jiwer-2.3.0-py3-none-any.whl (15 kB)\n",
+ "Collecting python-Levenshtein==0.12.2\n",
+ " Downloading python-Levenshtein-0.12.2.tar.gz (50 kB)\n",
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+ "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from python-Levenshtein==0.12.2->jiwer) (57.4.0)\n",
+ "Building wheels for collected packages: python-Levenshtein\n",
+ " Building wheel for python-Levenshtein (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for python-Levenshtein: filename=python_Levenshtein-0.12.2-cp37-cp37m-linux_x86_64.whl size=149862 sha256=9f2040039739ea899365d0e09ec9144de3fa92b4964a8c65af9b5f775f2379a5\n",
+ " Stored in directory: /root/.cache/pip/wheels/05/5f/ca/7c4367734892581bb5ff896f15027a932c551080b2abd3e00d\n",
+ "Successfully built python-Levenshtein\n",
+ "Installing collected packages: python-Levenshtein, jiwer\n",
+ "Successfully installed jiwer-2.3.0 python-Levenshtein-0.12.2\n",
+ "Collecting https://github.com/kpu/kenlm/archive/master.zip\n",
+ " Downloading https://github.com/kpu/kenlm/archive/master.zip (541 kB)\n",
+ "\u001b[K |████████████████████████████████| 541 kB 3.0 MB/s \n",
+ "\u001b[?25hBuilding wheels for collected packages: kenlm\n",
+ " Building wheel for kenlm (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for kenlm: filename=kenlm-0.0.0-cp37-cp37m-linux_x86_64.whl size=2336711 sha256=8be5703f0c372f4d8b2a3d9bdb5e5c1511bd7167b8ae6c34ec4eccb3eba52d00\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-b2yxhs9a/wheels/3d/aa/02/7b4a2eab5d7a2a9391bd9680dbad6270808a147bc3b7047e4e\n",
+ "Successfully built kenlm\n",
+ "Installing collected packages: kenlm\n",
+ "Successfully installed kenlm-0.0.0\n",
+ "\n",
+ " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
+ " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
+ " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
+ " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
+ " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
+ "\n",
+ " To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/token.\n",
+ " (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C.\n",
+ " \n",
+ "Token: \n",
+ "Login successful\n",
+ "Your token has been saved to /root/.huggingface/token\n",
+ "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
+ "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
+ "\n",
+ "git config --global credential.helper store\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install datasets==1.18.3\n",
+ "!pip install transformers\n",
+ "!pip install pyctcdecode\n",
+ "!pip install jiwer\n",
+ "!pip install https://github.com/kpu/kenlm/archive/master.zip\n",
+ "!huggingface-cli login"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Preprocessing Data, initialize loss function and evaluation function that edited from based on [This Github Repo](https://github.com/harveenchadha/bol/blob/main/demos/robus/evaluate_model_hf_with_lm.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 81,
+ "referenced_widgets": [
+ "cd9b21068c814aed8fa9c1653f077ce6",
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+ "b8124a9bb56b41819242a091136059a8",
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+ "eae4a425b4bc4564ac4ecf7c15421d77",
+ "2e9a8d5a111645ae94fdeddb7dac1697",
+ "ffb6ee6c418741de85927bb630b02484",
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+ "def774a3f0d64d45bf9dc275b932bf1c",
+ "a12aed326d6e43a790c3bcaa0a0da700",
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+ "838163d1875c46b48659b029a9d1e742",
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+ "f0dba9aed06149399de6e00f291b361a",
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+ ]
+ },
+ "id": "wUoMGUOH24Sv",
+ "outputId": "e6c82489-de68-4f31-dd90-dd1a6742eade"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "cd9b21068c814aed8fa9c1653f077ce6",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/1.90k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2e9a8d5a111645ae94fdeddb7dac1697",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/2.16k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "import torchaudio\n",
+ "from datasets import load_dataset, load_metric, Audio\n",
+ "from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM\n",
+ "import re\n",
+ "wer = load_metric(\"wer\")\n",
+ "cer = load_metric(\"cer\")\n",
+ "\n",
+ "\n",
+ "chars_to_ignore_regex = '[\\é\\!\\,\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\'\\’\\—\\–\\·]'\n",
+ "\n",
+ "def load_data(dataset_id, language, split='test'):\n",
+ " test_dataset = load_dataset(dataset_id, language, split=split, use_auth_token=True)\n",
+ " test_dataset = test_dataset.cast_column(\"audio\", Audio(sampling_rate=16_000))\n",
+ " return test_dataset\n",
+ "\n",
+ "def speech_file_to_array_fn(batch):\n",
+ " batch[\"sentence\"] = re.sub(chars_to_ignore_regex, \"\", batch[\"sentence\"]).lower() + \" \"\n",
+ " batch[\"sentence\"] = re.sub('!', '', batch[\"sentence\"]).lower() + \" \"\n",
+ " batch[\"sentence\"] = batch[\"sentence\"].replace('\\\"',\"\").replace(\"&\",\"\").replace(\"'\",\"\").replace(\"(\",\"\").lower() + \" \"\n",
+ " batch[\"sentence\"] = batch[\"sentence\"].replace('[',\"\").replace(\"]\",\"\").replace(\"\\\\\",\"\").replace(\"«\",\"\").replace(\"»\",\"\").replace(\")\",\"\").lower() + \" \"\n",
+ " batch[\"sentence\"] = batch[\"sentence\"].replace(\" \",\" \").replace(\" \",\" \").replace(\" \",\" \").lower() + \" \"\n",
+ " \n",
+ " batch[\"speech\"] = batch[\"audio\"][\"array\"]\n",
+ " return batch\n",
+ "\n",
+ "\n",
+ "def evaluate_with_lm(batch):\n",
+ " inputs = processor(batch[\"speech\"], sampling_rate=16_000, return_tensors=\"pt\", padding=True)\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " logits = model(**inputs.to('cuda')).logits\n",
+ " int_result = processor.batch_decode(logits.cpu().numpy())\n",
+ "\n",
+ " batch[\"pred_strings\"] = int_result.text\n",
+ "\n",
+ " del int_result\n",
+ " torch.cuda.empty_cache()\n",
+ "\n",
+ " return batch\n",
+ "\n",
+ "def evaluate(batch):\n",
+ "\n",
+ " inputs = processor(batch[\"speech\"], sampling_rate=16_000, return_tensors=\"pt\", padding=True)\n",
+ " with torch.no_grad():\n",
+ " logits = model(inputs.input_values.to('cuda')).logits\n",
+ "\n",
+ " pred_ids = torch.argmax(logits, dim=-1)\n",
+ " batch[\"pred_strings\"] = processor.batch_decode(pred_ids, skip_special_tokens=True)\n",
+ " return batch\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Evaluate using language model\n",
+ "Results:
\n",
+ "WER: 0.086074 (8.6074)
\n",
+ "CER: 0.024147 (2.4147)"
+ ]
+ },
+ {
+ "cell_type": "code",
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+ "text": [
+ "Downloading and preparing dataset common_voice/ia to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/ia/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8...\n"
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+ "0 examples [00:00, ? examples/s]"
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+ "output_type": "stream",
+ "text": [
+ "Dataset common_voice downloaded and prepared to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/ia/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8. Subsequent calls will reuse this data.\n"
+ ]
+ },
+ {
+ "data": {
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+ "model_id": "e36a6d5d7a9247119b3d0d6b1d3ab3cf",
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+ "version_minor": 0
+ },
+ "text/plain": [
+ "0ex [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {},
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+ },
+ {
+ "data": {
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+ " 0%| | 0/427 [00:00, ?ba/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WER: 0.086074\n",
+ "CER: 0.024147\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "import torchaudio\n",
+ "from datasets import load_dataset, load_metric, Audio\n",
+ "from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM\n",
+ "import re\n",
+ "\n",
+ "language=\"ia\"\n",
+ "model_id = 'ayameRushia/wav2vec2-large-xls-r-300m-ia'\n",
+ "dataset_id = \"mozilla-foundation/common_voice_8_0\"\n",
+ "split=\"test\"\n",
+ "\n",
+ "processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_id,use_auth_token=True)\n",
+ "model = Wav2Vec2ForCTC.from_pretrained(model_id,use_auth_token=True)\n",
+ "model.to('cuda')\n",
+ "\n",
+ "test_dataset = load_data(dataset_id, language, split)\n",
+ "test_dataset = test_dataset.map(speech_file_to_array_fn)\n",
+ "\n",
+ "result = test_dataset.map(evaluate_with_lm, batched=True, batch_size=4)\n",
+ "\n",
+ "print(\"WER: {:2f}\".format(wer.compute(predictions=result[\"pred_strings\"], references=result[\"sentence\"])))\n",
+ "print(\"CER: {:2f}\".format(cer.compute(predictions=result[\"pred_strings\"], references=result[\"sentence\"])))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YSml3Ykr_xeJ"
+ },
+ "source": [
+ "Delete previous model and clear the cuda cache"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "1DzK6MbO_c4r",
+ "outputId": "2a5ebca6-caf3-4dd4-ac82-cb379cdc0cce"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sun Feb 6 06:38:45 2022 \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
+ "|-------------------------------+----------------------+----------------------+\n",
+ "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
+ "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
+ "| | | MIG M. |\n",
+ "|===============================+======================+======================|\n",
+ "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n",
+ "| N/A 77C P0 38W / 70W | 2810MiB / 15109MiB | 0% Default |\n",
+ "| | | N/A |\n",
+ "+-------------------------------+----------------------+----------------------+\n",
+ " \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| Processes: |\n",
+ "| GPU GI CI PID Type Process name GPU Memory |\n",
+ "| ID ID Usage |\n",
+ "|=============================================================================|\n",
+ "+-----------------------------------------------------------------------------+\n"
+ ]
+ }
+ ],
+ "source": [
+ "del model\n",
+ "torch.cuda.empty_cache()\n",
+ "!nvidia-smi"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Evaluate without using language model\n",
+ "Results:
\n",
+ "WER: 0.201776 (20.1776)
\n",
+ "CER: 0.047205 (4.7205)
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
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+ "outputId": "46ba6112-248c-4321-c374-52f8d2b7ed5f"
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+ "name": "stderr",
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+ "text": [
+ "Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/ia/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)\n",
+ "Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/ia/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8/cache-f4febe81b8fd7934.arrow\n"
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+ "text/plain": [
+ " 0%| | 0/427 [00:00, ?ba/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WER: 0.201776\n",
+ "CER: 0.047205\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataset_id = \"mozilla-foundation/common_voice_8_0\"\n",
+ "language=\"ia\"\n",
+ "split=\"test\"\n",
+ "model_id = 'ayameRushia/wav2vec2-large-xls-r-300m-ia'\n",
+ "\n",
+ "processor = Wav2Vec2Processor.from_pretrained(model_id,use_auth_token=True)\n",
+ "model = Wav2Vec2ForCTC.from_pretrained(model_id,use_auth_token=True)\n",
+ "model.to(\"cuda\")\n",
+ "\n",
+ "test_dataset = load_data(dataset_id, language, split)\n",
+ "test_dataset = test_dataset.map(speech_file_to_array_fn)\n",
+ "\n",
+ "result = test_dataset.map(evaluate, batched=True, batch_size=4)\n",
+ "\n",
+ "print(\"WER: {:2f}\".format(wer.compute(predictions=result[\"pred_strings\"], references=result[\"sentence\"])))\n",
+ "print(\"CER: {:2f}\".format(cer.compute(predictions=result[\"pred_strings\"], references=result[\"sentence\"])))"
+ ]
+ },
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