add some files

eval.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+import argparse
+import re
+from typing import Dict
+
+import torch
+from datasets import Audio, Dataset, load_dataset, load_metric
+
+from transformers import AutoFeatureExtractor, pipeline
+
+
+def log_results(result: Dataset, args: Dict[str, str]):
+    """DO NOT CHANGE. This function computes and logs the result metrics."""
+
+    log_outputs = args.log_outputs
+    dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])
+
+    # load metric
+    wer = load_metric("wer")
+    cer = load_metric("cer")
+
+    # compute metrics
+    wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
+    cer_result = cer.compute(references=result["target"], predictions=result["prediction"])
+
+    # print & log results
+    result_str = f"WER: {wer_result}\n" f"CER: {cer_result}"
+    print(result_str)
+
+    with open(f"{dataset_id}_eval_results.txt", "w") as f:
+        f.write(result_str)
+
+    # log all results in text file. Possibly interesting for analysis
+    if log_outputs is not None:
+        pred_file = f"log_{dataset_id}_predictions.txt"
+        target_file = f"log_{dataset_id}_targets.txt"
+
+        with open(pred_file, "w") as p, open(target_file, "w") as t:
+
+            # mapping function to write output
+            def write_to_file(batch, i):
+                p.write(f"{i}" + "\n")
+                p.write(batch["prediction"] + "\n")
+                t.write(f"{i}" + "\n")
+                t.write(batch["target"] + "\n")
+
+            result.map(write_to_file, with_indices=True)
+
+
+def normalize_text(text: str) -> str:
+    """DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
+
+    chars_to_ignore_regex = '[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
+
+    text = re.sub(chars_to_ignore_regex, "", text.lower())
+
+    # In addition, we can normalize the target text, e.g. removing new lines characters etc...
+    # note that order is important here!
+    token_sequences_to_ignore = ["\n\n", "\n", "   ", "  "]
+
+    for t in token_sequences_to_ignore:
+        text = " ".join(text.split(t))
+
+    return text
+
+
+def main(args):
+    # load dataset
+    dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
+
+    # for testing: only process the first two examples as a test
+    # dataset = dataset.select(range(10))
+
+    # load processor
+    feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
+    sampling_rate = feature_extractor.sampling_rate
+
+    # resample audio
+    dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
+
+    # load eval pipeline
+    if args.device is None:
+        args.device = 0 if torch.cuda.is_available() else -1
+    asr = pipeline("automatic-speech-recognition", model=args.model_id, device=args.device)
+
+    # map function to decode audio
+    def map_to_pred(batch):
+        prediction = asr(
+            batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s
+        )
+
+        batch["prediction"] = prediction["text"]
+        batch["target"] = normalize_text(batch["sentence"])
+        return batch
+
+    # run inference on all examples
+    result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
+
+    # compute and log_results
+    # do not change function below
+    log_results(result, args)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
+    )
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        required=True,
+        help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
+    )
+    parser.add_argument(
+        "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
+    )
+    parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
+    parser.add_argument(
+        "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
+    )
+    parser.add_argument(
+        "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
+    )
+    parser.add_argument(
+        "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
+    )
+    parser.add_argument(
+        "--device",
+        type=int,
+        default=None,
+        help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
+    )
+    args = parser.parse_args()
+
+    main(args)

eval.sh

@@ -0,0 +1,6 @@
+./eval.py \
+--model_id ./ \
+--dataset "mozilla-foundation/common_voice_8_0" \
+--config ja \
+--split test \
+--log_outputs

inference.ipynb

@@ -0,0 +1,479 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3eace62e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModelForCTC, Wav2Vec2Processor\n",
+    "from datasets import load_dataset, load_metric, Audio\n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "47d5c062",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# model = AutoModelForCTC.from_pretrained(\".\").to('cuda')\n",
+    "processor = Wav2Vec2Processor.from_pretrained(\".\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "1ffed05d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using custom data configuration default-f6158d05a859ae5c\n",
+      "Reusing dataset csv (/workspace/.cache/huggingface/datasets/csv/default-f6158d05a859ae5c/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": 48,
+   "id": "bb365941",
+   "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": null,
+   "id": "34979efb",
+   "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": null,
+   "id": "66ac6b14",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e135b397",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/common_voice/tr/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd)\n"
+     ]
+    }
+   ],
+   "source": [
+    "common_voice_test = load_dataset(\"common_voice\", \"tr\", split=\"test\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "9dd4cfd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'client_id': 'b8fffa3c4745500cd2c5f40a82b65bf1fb2d4c4f8638209a33fe1886fbfffdbd2f93aa43e0bd2026c4643e08aada408165138f75787cee501c4d735aa555a61c',\n",
+       " 'path': 'common_voice_tr_17343551.mp3',\n",
+       " 'audio': {'path': 'cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_17343551.mp3',\n",
+       "  'array': array([0.        , 0.        , 0.        , ..., 0.00157976, 0.00167614,\n",
+       "         0.00091976], dtype=float32),\n",
+       "  'sampling_rate': 48000},\n",
+       " 'sentence': 'Aşırı derecede kapalı bir ortamımız var.',\n",
+       " 'up_votes': 2,\n",
+       " 'down_votes': 0,\n",
+       " 'age': 'thirties',\n",
+       " 'gender': 'male',\n",
+       " 'accent': 'other',\n",
+       " 'locale': 'tr',\n",
+       " 'segment': \"''\"}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "common_voice_test[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f36c3bcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove unnecceesary attributes\n",
+    "common_voice_test = common_voice_test.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "142cffaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "common_voice_test = common_voice_test.cast_column(\"audio\", Audio(sampling_rate=16_000))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "b1103455",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Dataset({\n",
+       "    features: ['path', 'audio', 'sentence'],\n",
+       "    num_rows: 1647\n",
+       "})"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "common_voice_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "e2f9be66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Pek çoğu da Roman toplumundan geliyor.'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "common_voice_test[0]['sentence']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "94a0e9c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "c2bcce8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'path': 'cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_17341269.mp3',\n",
+       " 'array': array([ 0.000000e+00,  0.000000e+00,  0.000000e+00, ...,  8.288735e-06,\n",
+       "        -1.994405e-03, -7.770515e-03], dtype=float32),\n",
+       " 'sampling_rate': 16000}"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "common_voice_test[0]['audio']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "47d9dd9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [34]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mprocessor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcommon_voice_test\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43maudio\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43marray\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msampling_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m16000\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py:138\u001b[0m, in \u001b[0;36mWav2Vec2Processor.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    132\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;124;03m    When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's\u001b[39;00m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;124;03m    [`~Wav2Vec2FeatureExtractor.__call__`] and returns its output. If used in the context\u001b[39;00m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;124;03m    [`~Wav2Vec2Processor.as_target_processor`] this method forwards all its arguments to PreTrainedTokenizer's\u001b[39;00m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;124;03m    [`~PreTrainedTokenizer.__call__`]. Please refer to the docstring of the above two methods for more information.\u001b[39;00m\n\u001b[1;32m    137\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 138\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcurrent_processor\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/transformers/tokenization_utils_base.py:2417\u001b[0m, in \u001b[0;36mPreTrainedTokenizerBase.__call__\u001b[0;34m(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)\u001b[0m\n\u001b[1;32m   2414\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m   2416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_valid_text_input(text):\n\u001b[0;32m-> 2417\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   2418\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext input must of type `str` (single example), `List[str]` (batch or single pretokenized example) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2419\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor `List[List[str]]` (batch of pretokenized examples).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2420\u001b[0m     )\n\u001b[1;32m   2422\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m text_pair \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_valid_text_input(text_pair):\n\u001b[1;32m   2423\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   2424\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext input must of type `str` (single example), `List[str]` (batch or single pretokenized example) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2425\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor `List[List[str]]` (batch of pretokenized examples).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2426\u001b[0m     )\n",
+      "\u001b[0;31mValueError\u001b[0m: text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples)."
+     ]
+    }
+   ],
+   "source": [
+    "processor(np.array(common_voice_test[0]['audio'][\"array\"]), sampling_rate=16000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "5f0e5342",
+   "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": 28,
+   "id": "6786ed7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b74fe324f3bd4d98b6366c614fec7991",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0ex [00:00, ?ex/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [28]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m common_voice_test \u001b[38;5;241m=\u001b[39m \u001b[43mcommon_voice_test\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprepare_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mremove_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommon_voice_test\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumn_names\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py:2107\u001b[0m, in \u001b[0;36mDataset.map\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\u001b[0m\n\u001b[1;32m   2104\u001b[0m disable_tqdm \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mbool\u001b[39m(logging\u001b[38;5;241m.\u001b[39mget_verbosity() \u001b[38;5;241m==\u001b[39m logging\u001b[38;5;241m.\u001b[39mNOTSET) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m utils\u001b[38;5;241m.\u001b[39mis_progress_bar_enabled()\n\u001b[1;32m   2106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m num_proc \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m-> 2107\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_single\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2108\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2109\u001b[0m \u001b[43m        \u001b[49m\u001b[43mwith_indices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwith_indices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2110\u001b[0m \u001b[43m        \u001b[49m\u001b[43mwith_rank\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwith_rank\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2111\u001b[0m \u001b[43m        \u001b[49m\u001b[43minput_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_columns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2112\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatched\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatched\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2113\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2114\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdrop_last_batch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_last_batch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2115\u001b[0m \u001b[43m        \u001b[49m\u001b[43mremove_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremove_columns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2116\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeep_in_memory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_in_memory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2117\u001b[0m \u001b[43m        \u001b[49m\u001b[43mload_from_cache_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mload_from_cache_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2118\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_file_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_file_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2119\u001b[0m \u001b[43m        \u001b[49m\u001b[43mwriter_batch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwriter_batch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2120\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2121\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdisable_nullable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisable_nullable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2122\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfn_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfn_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2123\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnew_fingerprint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_fingerprint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2124\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdisable_tqdm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisable_tqdm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2125\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdesc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdesc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2126\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2127\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2129\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mformat_cache_file_name\u001b[39m(cache_file_name, rank):\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py:519\u001b[0m, in \u001b[0;36mtransmit_tasks.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    517\u001b[0m     \u001b[38;5;28mself\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    518\u001b[0m \u001b[38;5;66;03m# apply actual function\u001b[39;00m\n\u001b[0;32m--> 519\u001b[0m out: Union[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDatasetDict\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    520\u001b[0m datasets: List[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(out\u001b[38;5;241m.\u001b[39mvalues()) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m [out]\n\u001b[1;32m    521\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dataset \u001b[38;5;129;01min\u001b[39;00m datasets:\n\u001b[1;32m    522\u001b[0m     \u001b[38;5;66;03m# Remove task templates if a column mapping of the template is no longer valid\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py:486\u001b[0m, in \u001b[0;36mtransmit_format.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    479\u001b[0m self_format \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    480\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_type,\n\u001b[1;32m    481\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat_kwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_kwargs,\n\u001b[1;32m    482\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_columns,\n\u001b[1;32m    483\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutput_all_columns\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_all_columns,\n\u001b[1;32m    484\u001b[0m }\n\u001b[1;32m    485\u001b[0m \u001b[38;5;66;03m# apply actual function\u001b[39;00m\n\u001b[0;32m--> 486\u001b[0m out: Union[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDatasetDict\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    487\u001b[0m datasets: List[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(out\u001b[38;5;241m.\u001b[39mvalues()) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m [out]\n\u001b[1;32m    488\u001b[0m \u001b[38;5;66;03m# re-apply format to the output\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/datasets/fingerprint.py:413\u001b[0m, in \u001b[0;36mfingerprint_transform.<locals>._fingerprint.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    407\u001b[0m             kwargs[fingerprint_name] \u001b[38;5;241m=\u001b[39m update_fingerprint(\n\u001b[1;32m    408\u001b[0m                 \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fingerprint, transform, kwargs_for_fingerprint\n\u001b[1;32m    409\u001b[0m             )\n\u001b[1;32m    411\u001b[0m \u001b[38;5;66;03m# Call actual function\u001b[39;00m\n\u001b[0;32m--> 413\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    415\u001b[0m \u001b[38;5;66;03m# Update fingerprint of in-place transforms + update in-place history of transforms\u001b[39;00m\n\u001b[1;32m    417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:  \u001b[38;5;66;03m# update after calling func so that the fingerprint doesn't change if the function fails\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py:2465\u001b[0m, in \u001b[0;36mDataset._map_single\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)\u001b[0m\n\u001b[1;32m   2463\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m batched:\n\u001b[1;32m   2464\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, example \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(pbar):\n\u001b[0;32m-> 2465\u001b[0m         example \u001b[38;5;241m=\u001b[39m \u001b[43mapply_function_on_filtered_inputs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexample\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moffset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2466\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m update_data:\n\u001b[1;32m   2467\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py:2372\u001b[0m, in \u001b[0;36mDataset._map_single.<locals>.apply_function_on_filtered_inputs\u001b[0;34m(inputs, indices, check_same_num_examples, offset)\u001b[0m\n\u001b[1;32m   2370\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m with_rank:\n\u001b[1;32m   2371\u001b[0m     additional_args \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (rank,)\n\u001b[0;32m-> 2372\u001b[0m processed_inputs \u001b[38;5;241m=\u001b[39m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfn_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43madditional_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfn_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2373\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m update_data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   2374\u001b[0m     \u001b[38;5;66;03m# Check if the function returns updated examples\u001b[39;00m\n\u001b[1;32m   2375\u001b[0m     update_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28misinstance\u001b[39m(processed_inputs, (Mapping, pa\u001b[38;5;241m.\u001b[39mTable))\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py:2067\u001b[0m, in \u001b[0;36mDataset.map.<locals>.decorate.<locals>.decorated\u001b[0;34m(item, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2063\u001b[0m decorated_item \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m   2064\u001b[0m     Example(item, features\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeatures) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m batched \u001b[38;5;28;01melse\u001b[39;00m Batch(item, features\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeatures)\n\u001b[1;32m   2065\u001b[0m )\n\u001b[1;32m   2066\u001b[0m \u001b[38;5;66;03m# Use the LazyDict internally, while mapping the function\u001b[39;00m\n\u001b[0;32m-> 2067\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdecorated_item\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2068\u001b[0m \u001b[38;5;66;03m# Return a standard dict\u001b[39;00m\n\u001b[1;32m   2069\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, LazyDict) \u001b[38;5;28;01melse\u001b[39;00m result\n",
+      "Input \u001b[0;32mIn [27]\u001b[0m, in \u001b[0;36mprepare_dataset\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m      2\u001b[0m audio \u001b[38;5;241m=\u001b[39m batch[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maudio\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# batched output is \"un-batched\"\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m batch[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_values\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mprocessor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43maudio\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43marray\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msampling_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maudio\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msampling_rate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39minput_values[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m      6\u001b[0m batch[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_length\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(batch[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_values\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m processor\u001b[38;5;241m.\u001b[39mas_target_processor():\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py:138\u001b[0m, in \u001b[0;36mWav2Vec2Processor.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    132\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;124;03m    When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's\u001b[39;00m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;124;03m    [`~Wav2Vec2FeatureExtractor.__call__`] and returns its output. If used in the context\u001b[39;00m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;124;03m    [`~Wav2Vec2Processor.as_target_processor`] this method forwards all its arguments to PreTrainedTokenizer's\u001b[39;00m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;124;03m    [`~PreTrainedTokenizer.__call__`]. Please refer to the docstring of the above two methods for more information.\u001b[39;00m\n\u001b[1;32m    137\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 138\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcurrent_processor\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/transformers/tokenization_utils_base.py:2417\u001b[0m, in \u001b[0;36mPreTrainedTokenizerBase.__call__\u001b[0;34m(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)\u001b[0m\n\u001b[1;32m   2414\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m   2416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_valid_text_input(text):\n\u001b[0;32m-> 2417\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   2418\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext input must of type `str` (single example), `List[str]` (batch or single pretokenized example) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2419\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor `List[List[str]]` (batch of pretokenized examples).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2420\u001b[0m     )\n\u001b[1;32m   2422\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m text_pair \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_valid_text_input(text_pair):\n\u001b[1;32m   2423\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   2424\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext input must of type `str` (single example), `List[str]` (batch or single pretokenized example) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2425\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor `List[List[str]]` (batch of pretokenized examples).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2426\u001b[0m     )\n",
+      "\u001b[0;31mValueError\u001b[0m: text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples)."
+     ]
+    }
+   ],
+   "source": [
+    "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "81506c80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'path': 'common_voice_tr_17341269.mp3',\n",
+       " 'audio': {'path': 'cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_17341269.mp3',\n",
+       "  'array': array([ 0.000000e+00,  0.000000e+00,  0.000000e+00, ...,  8.288735e-06,\n",
+       "         -1.994405e-03, -7.770515e-03], dtype=float32),\n",
+       "  'sampling_rate': 16000},\n",
+       " 'sentence': 'Pek çoğu da Roman toplumundan geliyor.'}"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "common_voice_test[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "603ecd46",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "760f0031",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e0355fac",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'input_values'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m input_dict \u001b[38;5;241m=\u001b[39m processor(\u001b[43mcommon_voice_test\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput_values\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m, return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'input_values'"
+     ]
+    }
+   ],
+   "source": [
+    "input_dict = processor(common_voice_test[i][\"input_values\"], return_tensors=\"pt\", padding=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c0b3603c",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'input_values'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [13]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m input_dict \u001b[38;5;241m=\u001b[39m processor(\u001b[43mcommon_voice_test\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput_values\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m, return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      2\u001b[0m logits \u001b[38;5;241m=\u001b[39m model(input_dict\u001b[38;5;241m.\u001b[39minput_values\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m))\u001b[38;5;241m.\u001b[39mlogits\n\u001b[1;32m      3\u001b[0m pred_ids \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39margmax(logits, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'input_values'"
+     ]
+    }
+   ],
+   "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": 15,
+   "id": "23db2fe7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction:\n",
+      "ş\n",
+      "\n",
+      "Reference:\n",
+      "Yine de her iki grup farklı sorunlar çıkarıyor.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Prediction:\")\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": "4da2cb6c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f5325dd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

vocab.json

@@ -1 +1 @@
-{"\u1780": 1, "\u1781": 2, "\u1782": 3, "\u1783": 4, "\u1784": 5, "\u1785": 6, "\u1786": 7, "\u1787": 8, "\u1788": 9, "\u1789": 10, "\u178a": 11, "\u178b": 12, "\u178c": 13, "\u178d": 14, "\u178e": 15, "\u178f": 16, "\u1790": 17, "\u1791": 18, "\u1792": 19, "\u1793": 20, "\u1794": 21, "\u1795": 22, "\u1796": 23, "\u1797": 24, "\u1798": 25, "\u1799": 26, "\u179a": 27, "\u179b": 28, "\u179c": 29, "\u179f": 30, "\u17a0": 31, "\u17a1": 32, "\u17a2": 33, "\u17a5": 34, "\u17a7": 35, "\u17aa": 36, "\u17ab": 37, "\u17ac": 38, "\u17ad": 39, "\u17ae": 40, "\u17af": 41, "\u17b1": 42, "\u17b6": 43, "\u17b7": 44, "\u17b8": 45, "\u17b9": 46, "\u17ba": 47, "\u17bb": 48, "\u17bc": 49, "\u17bd": 50, "\u17be": 51, "\u17bf": 52, "\u17c0": 53, "\u17c1": 54, "\u17c2": 55, "\u17c3": 56, "\u17c4": 57, "\u17c5": 58, "\u17c6": 59, "\u17c7": 60, "\u17c8": 61, "\u17c9": 62, "\u17ca": 63, "\u17cb": 64, "\u17cc": 65, "\u17cd": 66, "\u17ce": 67, "\u17cf": 68, "\u17d0": 69, "\u17d2": 70, "|": 0, "[UNK]": 72, "[PAD]": 73}
+{"A": 1, "B": 2, "C": 3, "D": 4, "E": 5, "F": 6, "G": 7, "H": 8, "I": 9, "J": 10, "K": 11, "L": 12, "M": 13, "N": 14, "O": 15, "P": 16, "Q": 17, "R": 18, "S": 19, "T": 20, "U": 21, "V": 22, "W": 23, "X": 24, "Y": 25, "Z": 26, "a": 27, "b": 28, "c": 29, "d": 30, "e": 31, "f": 32, "g": 33, "h": 34, "i": 35, "j": 36, "k": 37, "l": 38, "m": 39, "n": 40, "o": 41, "p": 42, "r": 43, "s": 44, "t": 45, "u": 46, "v": 47, "w": 48, "x": 49, "y": 50, "z": 51, "\u00c7": 52, "\u00d6": 53, "\u00dc": 54, "\u00e2": 55, "\u00e7": 56, "\u00eb": 57, "\u00ee": 58, "\u00f6": 59, "\u00fc": 60, "\u011f": 61, "\u0130": 62, "\u0131": 63, "\u015e": 64, "\u015f": 65, "|": 0, "[UNK]": 67, "[PAD]": 68}