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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "521e21ab",
"metadata": {},
"outputs": [],
"source": [
"# This notebook is currently designed for a GPU using fp16. Hyperparameters however are barely tuned."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1732f970",
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"import torch\n",
"from pathlib import Path"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f55f4047",
"metadata": {},
"outputs": [],
"source": [
"EXPERIMENT_NAME = '00'\n",
"DATA_PATH = Path('../data/common_voice/de')\n",
"\n",
"model_dir = Path('decoder_only/de') / EXPERIMENT_NAME\n",
"log_dir = model_dir / 'logs'\n",
"log_dir.mkdir(exist_ok=True, parents=True)\n",
"\n",
"config = {\n",
" 'use_train_frac': 1.0, # When using all samples the wav2vec-outputs take up ~275GB disk space!!(~360,000 samples)\n",
" 'use_val_frac': 0.2,\n",
" 'encoder_id': 'jonatasgrosman/wav2vec2-large-xlsr-53-german',\n",
" 'decoder_id': 'dbmdz/german-gpt2',\n",
" 'decoder_pad_token': '_',\n",
" 'decoder_bos_token': '~',\n",
" 'num_beams': 1,\n",
" 'batch_size': 16,\n",
" 'weight_decay': 0.,\n",
" 'accumulate_grad': 2,\n",
" 'max_epochs': 10,\n",
" 'max_len': 36 # len(max(tokenizer(common_voice['validation']['sentence'] + common_voice['test']['sentence']).input_ids, key=len))\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "eb3de6a4",
"metadata": {},
"source": [
"# Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b176328e",
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import notebook_login\n",
"from datasets import load_dataset\n",
"from datasets.features import Audio\n",
"from transformers import Wav2Vec2Model, Wav2Vec2FeatureExtractor"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "54e70696",
"metadata": {},
"outputs": [],
"source": [
"notebook_login()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f0d22752",
"metadata": {},
"outputs": [],
"source": [
"def extract_features_to_files(model, feature_extractor, dataset_split, batch_size, output_path):\n",
" output_path = Path(output_path)\n",
" output_path.mkdir(parents=True, exist_ok=True)\n",
"\n",
" model.eval().cuda()\n",
" for i in range(0, len(dataset_split), batch_size):\n",
" batch = dataset_split[i:i+batch_size]\n",
" sent_batch = batch['sentence']\n",
" audio_batch = batch['audio']\n",
" for i, eg in enumerate(audio_batch):\n",
" # Remove the longest examples, should be only three and these may lead to OOM- or Index-Errors.\n",
" if len(eg['array']) > 300_000:\n",
" print('Too Long.')\n",
" audio_batch.pop(i)\n",
" sent_batch.pop(i)\n",
" features = feature_extractor([eg['array'] for eg in audio_batch],\n",
" sampling_rate=16_000,\n",
" return_tensors='pt',\n",
" padding='longest')\n",
"\n",
" with torch.no_grad():\n",
" out = model(features.input_values.cuda(), attention_mask=features.attention_mask.cuda())\n",
"\n",
" assert len(sent_batch) == len(audio_batch) == len(out.last_hidden_state)\n",
" for sent, audio, hs in zip(sent_batch, audio_batch, out.last_hidden_state.bfloat16().cpu()):\n",
" file_name = audio['path'].split('/')[-1]\n",
" torch.save(\n",
" # .clone() is necessary: https://github.com/pytorch/pytorch/issues/1995\n",
" {'sentence': sent, 'wave2vec_features': hs.clone()},\n",
" output_path / file_name\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "06324b6f",
"metadata": {},
"outputs": [],
"source": [
"if not DATA_PATH.exists():\n",
" \n",
" common_voice = load_dataset('mozilla-foundation/common_voice_7_0', 'de', use_auth_token=True)\n",
" \n",
" random.seed(419)\n",
" train_inds = list(range(len(common_voice['train'])))\n",
" random.shuffle(train_inds)\n",
" val_inds = list(range(len(common_voice['validation'])))\n",
" random.shuffle(val_inds)\n",
" \n",
" train_inds = train_inds[:int(config['use_train_frac'] * len(train_inds))]\n",
" train = common_voice['train'].select(train_inds)\n",
" train = train.cast_column('audio', Audio(sampling_rate=16_000))\n",
" \n",
" val_inds = val_inds[:int(config['use_val_frac'] * len(val_inds))]\n",
" val = common_voice['validation'].select(val_inds)\n",
" val = val.cast_column('audio', Audio(sampling_rate=16_000))\n",
" \n",
" # Load Model for feature extraction.\n",
" wave2vec_extractor = Wav2Vec2FeatureExtractor.from_pretrained(config['encoder_id'])\n",
" wave2vec = Wav2Vec2Model.from_pretrained(config['encoder_id'])\n",
" wave2vec.eval().cuda()\n",
" \n",
" extract_features_to_files(wave2vec, wave2vec_extractor, train, batch_size=8, output_path=DATA_PATH / 'train')\n",
" extract_features_to_files(wave2vec, wave2vec_extractor, val, batch_size=8, output_path=DATA_PATH / 'val')\n",
" \n",
" wave2vec.cpu()\n",
" torch.cuda.empty_cache()"
]
},
{
"cell_type": "markdown",
"id": "b2ae2a47",
"metadata": {},
"source": [
"# Training"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "188ef54f",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"from accelerate import Accelerator\n",
"from torch.utils.data import DataLoader\n",
"from torch.optim import AdamW\n",
"from torch.utils.tensorboard import SummaryWriter\n",
"from transformers import AutoTokenizer, Wav2Vec2FeatureExtractor\n",
"from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2BaseModelOutput\n",
"from data_loading import make_collate_fn, S2TDataset\n",
"from wer import calculate_wer # Not what's used in eval.py.\n",
"from model import Wav2VecGPT2Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "41518c81",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(config['decoder_id'])\n",
"tokenizer.add_special_tokens({'pad_token': config['decoder_pad_token'], 'bos_token': config['decoder_bos_token']})\n",
"\n",
"model = Wav2VecGPT2Model.from_encoder_decoder_pretrained(\n",
" config['encoder_id'], config['decoder_id'], max_length=config['max_len'], num_beams=config['num_beams']\n",
")\n",
"\n",
"model.config.decoder_start_token_id = tokenizer.bos_token_id\n",
"model.config.pad_token_id = tokenizer.pad_token_id"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a95ec028",
"metadata": {},
"outputs": [],
"source": [
"collate_fn = make_collate_fn(tokenizer)\n",
"\n",
"train_ds = S2TDataset(DATA_PATH / 'train')\n",
"train_dl = DataLoader(train_ds, batch_size=config['batch_size'], shuffle=True, collate_fn=collate_fn, num_workers=4)\n",
"\n",
"val_ds = S2TDataset(DATA_PATH / 'val')\n",
"val_dl = DataLoader(val_ds, batch_size=config['batch_size'], shuffle=False, collate_fn=collate_fn, num_workers=4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0aaeeced",
"metadata": {},
"outputs": [],
"source": [
"high_lr_modules = ['cross_attn', 'crossattention', 'enc_to_dec_proj', 'encoder_outputs_pos_emb']\n",
"high_lr_params = [p for n, p in model.named_parameters() if any(m in n for m in high_lr_modules)]\n",
"\n",
"optimizer_grouped_parameters = [\n",
" {\n",
" \"params\": high_lr_params,\n",
" \"lr\": 5e-4,\n",
" },\n",
" {\n",
" \"params\": [p for n, p in model.decoder.named_parameters() if not any(m in n for m in high_lr_modules)],\n",
" \"lr\": 1e-6,\n",
" },\n",
"]\n",
"optimizer = AdamW(optimizer_grouped_parameters, weight_decay=0.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf98d090",
"metadata": {},
"outputs": [],
"source": [
"accelerator = Accelerator(fp16=True)\n",
"print(f'Using {accelerator.device}.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "da9e928e",
"metadata": {},
"outputs": [],
"source": [
"model, optimizer, train_dl, val_dl = accelerator.prepare(model, optimizer, train_dl, val_dl)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f191f256",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"with open(log_dir / 'config.json', 'w') as config_file:\n",
" json.dump(config, config_file, indent=4)\n",
" \n",
"writer = SummaryWriter(log_dir)\n",
"val_golds = [eg['sentence'] for eg in val_ds]\n",
"best_val_wer = 10.\n",
"global_train_step = 0\n",
"\n",
"for epoch in range(config['max_epochs']):\n",
" \n",
" model.train()\n",
" model.encoder.cpu() # Model gets moved to gpu for evaluation (see below).\n",
" torch.cuda.empty_cache()\n",
" for batch_step, (encoder_hidden_states, att_mask, input_ids) in enumerate(train_dl):\n",
" if encoder_hidden_states.shape[1] > 1024:\n",
" # That's too long for the position embeddings. \n",
" # TODO: handle this in model code.\n",
" print(f'SKIPPED: {encoder_hidden_states.shape}')\n",
" continue\n",
" global_train_step += 1\n",
" \n",
" out = model(labels=input_ids, encoder_outputs=Wav2Vec2BaseModelOutput(encoder_hidden_states))\n",
" accelerator.backward(out.loss)\n",
" writer.add_scalar('train_loss', out.loss.item(), global_train_step)\n",
" \n",
" if (batch_step + 1) % config['accumulate_grad'] == 0:\n",
" optimizer.step()\n",
" optimizer.zero_grad()\n",
" \n",
" if batch_step % 300 == 0:\n",
" print(out.loss.item())\n",
" \n",
" model.eval()\n",
" model.cuda() # Necessary for input_ids to be initialized on the correct device.\n",
" val_preds = []\n",
" for encoder_hidden_states, att_mask, _ in val_dl:\n",
" with torch.no_grad():\n",
" generated = model.generate(\n",
" encoder_outputs=Wav2Vec2BaseModelOutput(last_hidden_state=encoder_hidden_states)\n",
" )\n",
" val_preds += tokenizer.batch_decode(generated)\n",
" val_preds = [pred.lstrip('~').rstrip('_') for pred in val_preds]\n",
" wer = calculate_wer(val_preds, val_golds)\n",
" writer.add_scalar('val_wer', wer, epoch)\n",
" print('WER: ', wer)\n",
" \n",
" if wer < best_val_wer:\n",
" torch.save(model.state_dict(), model_dir / 'model.pt')\n",
" print('Saved Model.')\n",
" best_val_wer = wer"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d84a7e5c",
"metadata": {},
"outputs": [],
"source": [
"# # Load saved pytorch model and save with all necessary model files.\n",
"# output_path = model_dir /'full_model'\n",
"# \n",
"# model.load_state_dict(torch.load(model_dir / 'model.pt'))\n",
"# \n",
"# tokenizer.save_pretrained(output_path)\n",
"# wave2vec_extractor = Wav2Vec2FeatureExtractor.from_pretrained(config['encoder_id'])\n",
"# wave2vec_extractor.save_pretrained(output_path)\n",
"# model.save_pretrained(output_path)"
]
}
],
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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