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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "5205c0d3-2272-4a43-9345-9553af479fe6",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7afc85b57ea24d31a2fdcc2b1f5c9ace",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from huggingface_hub import notebook_login\n",
"notebook_login()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "38bdf299-f60d-43ea-9230-df1be861e406",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using custom data configuration sharpcoder--bjorn_training-8c32a3534606a113\n",
"Reusing dataset parquet (/home/sharpcoder/.cache/huggingface/datasets/sharpcoder___parquet/sharpcoder--bjorn_training-8c32a3534606a113/0.0.0/7328ef7ee03eaf3f86ae40594d46a1cec86161704e02dd19f232d81eee72ade8)\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "18cae671f8fd4f9baac804c91fee03bf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from datasets import load_dataset, load_metric\n",
"ds = load_dataset(\"sharpcoder/bjorn_training\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "75b32151-eb53-4476-8c1f-7e6da72e173e",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0611b2fa6cf740d6925d03cf3ba525a2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def extract_all_chars(batch):\n",
" all_text = \" \".join(batch[\"text\"])\n",
" vocab = list(set(all_text))\n",
" return {\"vocab\": [vocab], \"all_text\": [all_text]}\n",
"\n",
"vocabs = ds.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=ds.column_names[\"train\"])\n",
"vocab_list = list(set(vocabs[\"train\"][\"vocab\"][0]))\n",
"vocab_dict = {v: k for k, v in enumerate(vocab_list)}\n",
"vocab_dict[\"|\"] = vocab_dict[\" \"]\n",
"del vocab_dict[\" \"]\n",
"vocab_dict[\"[UNK]\"] = len(vocab_dict)\n",
"vocab_dict[\"[PAD]\"] = len(vocab_dict)\n",
"len(vocab_dict)\n",
"import json\n",
"with open('vocab.json', 'w') as vocab_file:\n",
" json.dump(vocab_dict, vocab_file)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "d214872e-d4b1-4aa7-be07-8a1591961968",
"metadata": {},
"outputs": [],
"source": [
"from transformers import Wav2Vec2CTCTokenizer\n",
"from transformers import Wav2Vec2FeatureExtractor\n",
"from transformers import Wav2Vec2Processor\n",
"\n",
"tokenizer = Wav2Vec2CTCTokenizer(\"./vocab.json\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\" \")\n",
"feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=False)\n",
"processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "e906c45f-6971-43c3-ad0a-b13363100bdf",
"metadata": {},
"outputs": [],
"source": [
"def prepare_dataset(batch):\n",
" audio = batch[\"audio\"]\n",
"\n",
" # batched output is \"un-batched\" to ensure mapping is correct\n",
" batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sample_rate\"]).input_values[0]\n",
" batch[\"input_length\"] = len(batch[\"input_values\"])\n",
" \n",
" with processor.as_target_processor():\n",
" batch[\"labels\"] = processor(batch[\"text\"]).input_ids\n",
" return batch"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "8c083db6-eab5-4f25-9a08-eab50d2d30ac",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"num_proc must be <= 1. Reducing num_proc to 1 for dataset of size 1.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ae21f7b6a50241e4ab4dd2b5c7c5689c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?ex/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds_prepared = ds.map(prepare_dataset, remove_columns=ds.column_names[\"train\"], num_proc=4)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "50c9a6ad-9e79-4a1c-a5ce-6e1f73a96e4d",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"\n",
"from dataclasses import dataclass, field\n",
"from typing import Any, Dict, List, Optional, Union\n",
"\n",
"@dataclass\n",
"class DataCollatorCTCWithPadding:\n",
" \"\"\"\n",
" Data collator that will dynamically pad the inputs received.\n",
" Args:\n",
" processor (:class:`~transformers.Wav2Vec2Processor`)\n",
" The processor used for proccessing the data.\n",
" padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n",
" Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n",
" among:\n",
" * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n",
" sequence if provided).\n",
" * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n",
" maximum acceptable input length for the model if that argument is not provided.\n",
" * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n",
" different lengths).\n",
" \"\"\"\n",
"\n",
" processor: Wav2Vec2Processor\n",
" padding: Union[bool, str] = True\n",
"\n",
" def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
" # split inputs and labels since they have to be of different lenghts and need\n",
" # different padding methods\n",
" input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n",
" label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
"\n",
" batch = self.processor.pad(\n",
" input_features,\n",
" padding=self.padding,\n",
" return_tensors=\"pt\",\n",
" )\n",
" with self.processor.as_target_processor():\n",
" labels_batch = self.processor.pad(\n",
" label_features,\n",
" padding=self.padding,\n",
" return_tensors=\"pt\",\n",
" )\n",
"\n",
" # replace padding with -100 to ignore loss correctly\n",
" labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
"\n",
" batch[\"labels\"] = labels\n",
"\n",
" return batch\n",
" \n",
"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}"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "1025ffdf-cb83-4895-89ab-a98bc3fab642",
"metadata": {},
"outputs": [],
"source": [
"data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)\n",
"wer_metric = load_metric(\"wer\")"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "71351cf4-6d00-40ae-89cc-cedb87073625",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"loading configuration file https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json from cache at /home/sharpcoder/.cache/huggingface/transformers/cbb3014bb9f03ead9b94f4a791ff8e777465307670e85079d35e28cbc5d88727.0e2d739358c9b58747bd19db5f9f4320dacabbeb1e6282f5cc1069c5c55a82d2\n",
"Model config Wav2Vec2Config {\n",
" \"_name_or_path\": \"facebook/wav2vec2-base-960h\",\n",
" \"activation_dropout\": 0.1,\n",
" \"apply_spec_augment\": true,\n",
" \"architectures\": [\n",
" \"Wav2Vec2ForCTC\"\n",
" ],\n",
" \"attention_dropout\": 0.1,\n",
" \"bos_token_id\": 1,\n",
" \"classifier_proj_size\": 256,\n",
" \"codevector_dim\": 256,\n",
" \"contrastive_logits_temperature\": 0.1,\n",
" \"conv_bias\": false,\n",
" \"conv_dim\": [\n",
" 512,\n",
" 512,\n",
" 512,\n",
" 512,\n",
" 512,\n",
" 512,\n",
" 512\n",
" ],\n",
" \"conv_kernel\": [\n",
" 10,\n",
" 3,\n",
" 3,\n",
" 3,\n",
" 3,\n",
" 2,\n",
" 2\n",
" ],\n",
" \"conv_stride\": [\n",
" 5,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2,\n",
" 2\n",
" ],\n",
" \"ctc_loss_reduction\": \"mean\",\n",
" \"ctc_zero_infinity\": false,\n",
" \"diversity_loss_weight\": 0.1,\n",
" \"do_stable_layer_norm\": false,\n",
" \"eos_token_id\": 2,\n",
" \"feat_extract_activation\": \"gelu\",\n",
" \"feat_extract_dropout\": 0.0,\n",
" \"feat_extract_norm\": \"group\",\n",
" \"feat_proj_dropout\": 0.1,\n",
" \"feat_quantizer_dropout\": 0.0,\n",
" \"final_dropout\": 0.1,\n",
" \"gradient_checkpointing\": false,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout\": 0.1,\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"layer_norm_eps\": 1e-05,\n",
" \"layerdrop\": 0.1,\n",
" \"mask_feature_length\": 10,\n",
" \"mask_feature_prob\": 0.0,\n",
" \"mask_time_length\": 10,\n",
" \"mask_time_prob\": 0.05,\n",
" \"model_type\": \"wav2vec2\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_codevector_groups\": 2,\n",
" \"num_codevectors_per_group\": 320,\n",
" \"num_conv_pos_embedding_groups\": 16,\n",
" \"num_conv_pos_embeddings\": 128,\n",
" \"num_feat_extract_layers\": 7,\n",
" \"num_hidden_layers\": 12,\n",
" \"num_negatives\": 100,\n",
" \"pad_token_id\": 19,\n",
" \"proj_codevector_dim\": 256,\n",
" \"transformers_version\": \"4.11.3\",\n",
" \"use_weighted_layer_sum\": false,\n",
" \"vocab_size\": 32\n",
"}\n",
"\n",
"loading weights file https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/pytorch_model.bin from cache at /home/sharpcoder/.cache/huggingface/transformers/4cb133d3cf3e58e8a4e088b1fc826611a3bcf3d98b20a0bb49ce8cd5362411b7.beeaccfa4baf44ba6123c23938d8a17f48344361a5e7041782e537dfd78a2037\n",
"All model checkpoint weights were used when initializing Wav2Vec2ForCTC.\n",
"\n",
"Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base-960h and are newly initialized: ['wav2vec2.masked_spec_embed']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"from transformers import Wav2Vec2ForCTC\n",
"\n",
"model = Wav2Vec2ForCTC.from_pretrained(\n",
" #\"facebook/wav2vec2-base\",\n",
" \"facebook/wav2vec2-base-960h\",\n",
" ctc_loss_reduction=\"mean\", \n",
" pad_token_id=processor.tokenizer.pad_token_id,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "208eac7d-9fdd-4c82-b46f-25c1a1f246ee",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"PyTorch: setting up devices\n",
"The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
]
}
],
"source": [
"from transformers import TrainingArguments\n",
"from transformers import Trainer\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./\",\n",
" group_by_length=True,\n",
" per_device_train_batch_size=8,\n",
" evaluation_strategy=\"steps\",\n",
" num_train_epochs=30,\n",
" fp16=False,\n",
" gradient_checkpointing=True,\n",
" save_steps=500,\n",
" eval_steps=500,\n",
" logging_steps=500,\n",
" learning_rate=1e-4,\n",
" weight_decay=0.005,\n",
" warmup_steps=1000,\n",
" save_total_limit=2,\n",
")\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" data_collator=data_collator,\n",
" args=training_args,\n",
" compute_metrics=compute_metrics,\n",
" train_dataset=ds_prepared[\"train\"],\n",
" eval_dataset=ds_prepared[\"train\"],\n",
" tokenizer=processor.feature_extractor,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "d58f6b8c-441c-4fa9-a308-e687948875e1",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
"***** Running training *****\n",
" Num examples = 1\n",
" Num Epochs = 3\n",
" Instantaneous batch size per device = 8\n",
" Total train batch size (w. parallel, distributed & accumulation) = 8\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 3\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='3' max='3' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [3/3 00:02, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table><p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n"
]
},
{
"data": {
"text/plain": [
"TrainOutput(global_step=3, training_loss=15.702210744222006, metrics={'train_runtime': 3.157, 'train_samples_per_second': 0.95, 'train_steps_per_second': 0.95, 'total_flos': 94374986431680.0, 'train_loss': 15.702210744222006, 'epoch': 3.0})"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "333d43cf-add3-4d78-bbca-b44c638519fe",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Saving model checkpoint to ./\n",
"Configuration saved in ./config.json\n",
"Model weights saved in ./pytorch_model.bin\n",
"Configuration saved in ./preprocessor_config.json\n"
]
},
{
"ename": "AttributeError",
"evalue": "'Trainer' object has no attribute 'repo'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [47]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_to_hub\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhub_model_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msharpcoder/wav2vec2_bjorn\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/trainer.py:2677\u001b[0m, in \u001b[0;36mTrainer.push_to_hub\u001b[0;34m(self, commit_message, blocking, **kwargs)\u001b[0m\n\u001b[1;32m 2674\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_world_process_zero():\n\u001b[1;32m 2675\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m-> 2677\u001b[0m git_head_commit_url \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrepo\u001b[49m\u001b[38;5;241m.\u001b[39mpush_to_hub(commit_message\u001b[38;5;241m=\u001b[39mcommit_message, blocking\u001b[38;5;241m=\u001b[39mblocking)\n\u001b[1;32m 2678\u001b[0m \u001b[38;5;66;03m# push separately the model card to be independant from the rest of the model\u001b[39;00m\n\u001b[1;32m 2679\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mshould_save:\n",
"\u001b[0;31mAttributeError\u001b[0m: 'Trainer' object has no attribute 'repo'"
]
}
],
"source": [
"trainer.push_to_hub(hub_model_id=\"sharpcoder/wav2vec2_bjorn\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a5cb9a88-2443-4bd9-85ac-12bf80a9e325",
"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": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|