fixed config.json

.ipynb_checkpoints/ASR_Inference-checkpoint.ipynb

@@ -1,960 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-03-17T11:10:25.794375Z",
-     "start_time": "2021-03-17T11:10:24.301013Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/earendil/anaconda3/envs/cuda110/lib/python3.8/site-packages/torchaudio/backend/utils.py:53: UserWarning: \"sox\" backend is being deprecated. The default backend will be changed to \"sox_io\" backend in 0.8.0 and \"sox\" backend will be removed in 0.9.0. Please migrate to \"sox_io\" backend. Please refer to https://github.com/pytorch/audio/issues/903 for the detail.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "from transformers import Wav2Vec2ForCTC\n",
-    "from transformers import Wav2Vec2Processor\n",
-    "from datasets import load_dataset, load_metric\n",
-    "import re\n",
-    "import torchaudio\n",
-    "import librosa\n",
-    "import numpy as np\n",
-    "from datasets import load_dataset, load_metric\n",
-    "import torch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-03-17T11:10:29.608803Z",
-     "start_time": "2021-03-17T11:10:29.599700Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�]'\n",
-    "\n",
-    "def remove_special_characters(batch):\n",
-    "    batch[\"text\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower() + \" \"\n",
-    "    return batch\n",
-    "\n",
-    "def speech_file_to_array_fn(batch):\n",
-    "    speech_array, sampling_rate = torchaudio.load(batch[\"path\"])\n",
-    "    batch[\"speech\"] = speech_array[0].numpy()\n",
-    "    batch[\"sampling_rate\"] = sampling_rate\n",
-    "    batch[\"target_text\"] = batch[\"text\"]\n",
-    "    return batch\n",
-    "\n",
-    "def resample(batch):\n",
-    "    batch[\"speech\"] = librosa.resample(np.asarray(batch[\"speech\"]), 48_000, 16_000)\n",
-    "    batch[\"sampling_rate\"] = 16_000\n",
-    "    return batch\n",
-    "\n",
-    "def prepare_dataset(batch):\n",
-    "    # check that all files have the correct sampling rate\n",
-    "    assert (\n",
-    "        len(set(batch[\"sampling_rate\"])) == 1\n",
-    "    ), f\"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.\"\n",
-    "\n",
-    "    batch[\"input_values\"] = processor(batch[\"speech\"], sampling_rate=batch[\"sampling_rate\"][0]).input_values\n",
-    "    \n",
-    "    with processor.as_target_processor():\n",
-    "        batch[\"labels\"] = processor(batch[\"target_text\"]).input_ids\n",
-    "    return batch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-03-17T11:11:02.120225Z",
-     "start_time": "2021-03-17T11:10:56.182488Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Special tokens have been added in the vocabulary, make sure the associated word embedding are fine-tuned or trained.\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = Wav2Vec2ForCTC.from_pretrained(\".\").to(\"cuda\")\n",
-    "processor = Wav2Vec2Processor.from_pretrained(\".\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-03-17T11:12:18.847005Z",
-     "start_time": "2021-03-17T11:12:14.919077Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using custom data configuration el-afd0a157f05ee080\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading and preparing dataset common_voice/el (download: 363.89 MiB, generated: 4.75 MiB, post-processed: Unknown size, total: 368.64 MiB) to /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f...\n"
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-     "text": [
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Dataset common_voice downloaded and prepared to /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f. Subsequent calls will reuse this data.\n"
-     ]
-    }
-   ],
-   "source": [
-    "common_voice_test = load_dataset(\"common_voice\", \"el\", data_dir=\"cv-corpus-6.1-2020-12-11\", split=\"test\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
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-     "start_time": "2021-03-17T11:12:18.857252Z"
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-   "outputs": [],
-   "source": [
-    "common_voice_test = common_voice_test.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])"
-   ]
-  },
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-     "text": [
-      "\n"
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-    "common_voice_test = common_voice_test.map(remove_special_characters, remove_columns=[\"sentence\"])"
-   ]
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-   "execution_count": 9,
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-    "common_voice_test = common_voice_test.map(speech_file_to_array_fn, remove_columns=common_voice_test.column_names)"
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-      "/home/earendil/anaconda3/envs/cuda110/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
-      "  return array(a, dtype, copy=False, order=order)\n"
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-    "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names, batch_size=8, num_proc=8, batched=True)"
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-     "name": "stdout",
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-     "text": [
-      "\r"
-     ]
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-     "metadata": {},
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-    {
-     "name": "stdout",
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-     "text": [
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r"
-     ]
-    },
-    {
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-     "metadata": {},
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r"
-     ]
-    },
-    {
-     "data": {
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Dataset common_voice downloaded and prepared to /home/earendil/.cache/huggingface/datasets/common_voice/el-ac779bf2c9f7c09b/6.1.0/0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f. Subsequent calls will reuse this data.\n"
-     ]
-    }
-   ],
-   "source": [
-    "common_voice_test_transcription = load_dataset(\"common_voice\", \"el\", data_dir=\"./cv-corpus-6.1-2020-12-11\", split=\"test\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-03-14T19:33:39.856174Z",
-     "start_time": "2021-03-14T19:33:14.402825Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Change this value to try inference on different CommonVoice extracts\n",
-    "example = 678\n",
-    "\n",
-    "input_dict = processor(common_voice_test[\"input_values\"][example], return_tensors=\"pt\", sampling_rate=16_000, padding=True)\n",
-    "\n",
-    "logits = model(input_dict.input_values.to(\"cuda\")).logits\n",
-    "\n",
-    "pred_ids = torch.argmax(logits, dim=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-03-14T19:33:39.887236Z",
-     "start_time": "2021-03-14T19:33:39.881958Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prediction:\n",
-      "πού θέλεις να πάμε ρώτησε φοβισμένα ο βασιλιάς\n",
-      "\n",
-      "Reference:\n",
-      "πού θέλεις να πάμε; ρώτησε φοβισμένα ο βασιλιάς.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Prediction:\")\n",
-    "print(processor.decode(pred_ids[0]))\n",
-    "# πού θέλεις να πάμε ρώτησε φοβισμένα ο βασιλιάς\n",
-    "\n",
-    "print(\"\\nReference:\")\n",
-    "print(common_voice_test_transcription[\"sentence\"][example].lower())\n",
-    "# πού θέλεις να πάμε; ρώτησε φοβισμένα ο βασιλιάς."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-03-17T11:15:35.637739Z",
-     "start_time": "2021-03-17T11:14:14.689842Z"
-    }
-   },
-   "outputs": [
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-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "def map_to_result(batch):\n",
-    "  model.to(\"cuda\")\n",
-    "  input_values = processor(\n",
-    "      batch[\"input_values\"], \n",
-    "      sampling_rate=16_000, \n",
-    "      return_tensors=\"pt\"\n",
-    "  ).input_values.to(\"cuda\")\n",
-    "\n",
-    "  with torch.no_grad():\n",
-    "    logits = model(input_values).logits\n",
-    "\n",
-    "  pred_ids = torch.argmax(logits, dim=-1)\n",
-    "  batch[\"pred_str\"] = processor.batch_decode(pred_ids)[0]\n",
-    "\n",
-    "  return batch\n",
-    "\n",
-    "results = common_voice_test.map(map_to_result)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-03-17T11:17:11.951524Z",
-     "start_time": "2021-03-17T11:17:08.856552Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test WER: 0.396\n"
-     ]
-    }
-   ],
-   "source": [
-    "def compute_metrics(pred):\n",
-    "    pred_logits = pred.predictions\n",
-    "    pred_ids = np.argmax(pred_logits, axis=-1)\n",
-    "\n",
-    "    pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n",
-    "\n",
-    "    pred_str = processor.batch_decode(pred_ids)\n",
-    "    # we do not want to group tokens when computing the metrics\n",
-    "    label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n",
-    "\n",
-    "    wer = wer_metric.compute(predictions=pred_str, references=label_str)\n",
-    "\n",
-    "    return {\"wer\": wer}\n",
-    "\n",
-    "wer_metric = load_metric(\"wer\")\n",
-    "\n",
-    "print(\"Test WER: {:.3f}\".format(wer_metric.compute(predictions=results[\"pred_str\"], references= [item.lower() for item in common_voice_test_transcription['sentence']])))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
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README.md

@@ -22,6 +22,9 @@ model-index:
        - name: Test WER
          type: wer
          value: 10.497628
+        - name: Test CER
+         type: cer
+         value: 2.875260
 ---
 
 # Greek (el) version of the XLSR-Wav2Vec2 automatic speech recognition (ASR) model
@@ -204,6 +207,7 @@ Instructions and code to replicate the process are provided in the Fine_Tune_XLS
 | ----------- | ----------- |
 | Training Loss | 0.0545 |
 | Validation Loss | 0.1661 |
+| CER on CommonVoice Test (%) *| 2.8753 |
 | WER on CommonVoice Test (%) *| 10.4976 |
 * Reference transcripts were lower-cased and striped of punctuation and special characters.
 

config.json

@@ -36,7 +36,7 @@
     2
   ],
   "ctc_loss_reduction": "mean",
-  "ctc_zero_infinity": false,
+  "ctc_zero_infinity": true,
   "do_stable_layer_norm": true,
   "eos_token_id": 2,
   "feat_extract_activation": "gelu",
@@ -70,7 +70,7 @@
   "num_conv_pos_embeddings": 128,
   "num_feat_extract_layers": 7,
   "num_hidden_layers": 24,
-  "pad_token_id": 52,
+  "pad_token_id": 54,
   "transformers_version": "4.4.0.dev0",
-  "vocab_size": 53
+  "vocab_size": 55
 }