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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import notebook_login\n",
"notebook_login()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset, DatasetDict"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dataset({\n",
" features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],\n",
" num_rows: 563\n",
"})"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minds14_train = load_dataset(\n",
" \"PolyAI/minds14\", \n",
" \"en-US\",\n",
" split=\"train\"\n",
")\n",
"minds14_train"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],\n",
" num_rows: 450\n",
" })\n",
" test: Dataset({\n",
" features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],\n",
" num_rows: 113\n",
" })\n",
"})"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minds14 = DatasetDict()\n",
"\n",
"minds14[\"train\"] = minds14_train.select(range(450))\n",
"minds14[\"test\"] = minds14_train.select(range(450, 563))\n",
"\n",
"minds14"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['audio', 'transcription'],\n",
" num_rows: 450\n",
" })\n",
" test: Dataset({\n",
" features: ['audio', 'transcription'],\n",
" num_rows: 113\n",
" })\n",
"})"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minds14 = minds14.select_columns(['audio', 'transcription'])\n",
"minds14"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from transformers import WhisperProcessor\n",
"\n",
"processor = WhisperProcessor.from_pretrained(\n",
" \"openai/whisper-tiny\", language=\"english\", task=\"transcribe\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'audio': Audio(sampling_rate=8000, mono=True, decode=True, id=None),\n",
" 'transcription': Value(dtype='string', id=None)}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minds14[\"train\"].features"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from datasets import Audio\n",
"\n",
"sampling_rate = processor.feature_extractor.sampling_rate\n",
"minds14 = minds14.cast_column(\"audio\", Audio(sampling_rate=sampling_rate))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def prepare_dataset(example):\n",
" audio = example[\"audio\"]\n",
"\n",
" example = processor(\n",
" audio=audio[\"array\"],\n",
" sampling_rate=audio[\"sampling_rate\"],\n",
" text=example[\"transcription\"],\n",
" )\n",
"\n",
" # compute input length of audio sample in seconds\n",
" example[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\n",
"\n",
" return example"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "356d0ccec48f41b9ad10504ae0ca4813",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/450 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ef753a60316c4115924c49052eeb411d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/113 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"minds14 = minds14.map(\n",
" prepare_dataset, remove_columns=minds14.column_names[\"train\"], num_proc=1\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"max_input_length = 30.0\n",
"def is_audio_in_length_range(length):\n",
" return length < max_input_length"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2292d10d955d4d958e07849f0abb57c8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Filter: 0%| | 0/450 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"minds14[\"train\"] = minds14[\"train\"].filter(\n",
" is_audio_in_length_range,\n",
" input_columns=[\"input_length\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dataset({\n",
" features: ['input_features', 'labels', 'input_length'],\n",
" num_rows: 445\n",
"})"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minds14['train']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training and Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"\n",
"from dataclasses import dataclass\n",
"from typing import Any, Dict, List, Union\n",
"\n",
"\n",
"@dataclass\n",
"class DataCollatorSpeechSeq2SeqWithPadding:\n",
" processor: Any\n",
"\n",
" def __call__(\n",
" self, features: List[Dict[str, Union[List[int], torch.Tensor]]]\n",
" ) -> Dict[str, torch.Tensor]:\n",
" # split inputs and labels since they have to be of different lengths and need different padding methods\n",
" # first treat the audio inputs by simply returning torch tensors\n",
" input_features = [\n",
" {\"input_features\": feature[\"input_features\"][0]} for feature in features\n",
" ]\n",
" batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
"\n",
" # get the tokenized label sequences\n",
" label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
" # pad the labels to max length\n",
" labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
"\n",
" # replace padding with -100 to ignore loss correctly\n",
" labels = labels_batch[\"input_ids\"].masked_fill(\n",
" labels_batch.attention_mask.ne(1), -100\n",
" )\n",
"\n",
" # if bos token is appended in previous tokenization step,\n",
" # cut bos token here as it's append later anyways\n",
" if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
" labels = labels[:, 1:]\n",
"\n",
" batch[\"labels\"] = labels\n",
"\n",
" return batch"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"import evaluate\n",
"from transformers.models.whisper.english_normalizer import BasicTextNormalizer\n",
"\n",
"metric = evaluate.load(\"wer\")\n",
"normalizer = BasicTextNormalizer()\n",
"\n",
"def compute_metrics(pred):\n",
" pred_ids = pred.predictions\n",
" label_ids = pred.label_ids\n",
"\n",
" # replace -100 with the pad_token_id\n",
" label_ids[label_ids == -100] = processor.tokenizer.pad_token_id\n",
"\n",
" # we do not want to group tokens when computing the metrics\n",
" pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True)\n",
" label_str = processor.batch_decode(label_ids, skip_special_tokens=True)\n",
"\n",
" # compute orthographic wer\n",
" wer_ortho = 100 * metric.compute(predictions=pred_str, references=label_str)\n",
"\n",
" # compute normalised WER\n",
" pred_str_norm = [normalizer(pred) for pred in pred_str]\n",
" label_str_norm = [normalizer(label) for label in label_str]\n",
" # filtering step to only evaluate the samples that correspond to non-zero references:\n",
" pred_str_norm = [\n",
" pred_str_norm[i] for i in range(len(pred_str_norm)) if len(label_str_norm[i]) > 0\n",
" ]\n",
" label_str_norm = [\n",
" label_str_norm[i]\n",
" for i in range(len(label_str_norm))\n",
" if len(label_str_norm[i]) > 0\n",
" ]\n",
"\n",
" wer = 100 * metric.compute(predictions=pred_str_norm, references=label_str_norm)\n",
"\n",
" return {\"wer_ortho\": wer_ortho, \"wer\": wer}"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"from transformers import WhisperForConditionalGeneration\n",
"model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-tiny\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"from functools import partial\n",
"\n",
"# disable cache during training since it's incompatible with gradient checkpointing\n",
"model.config.use_cache = False\n",
"\n",
"# set language and task for generation and re-enable cache\n",
"model.generate = partial(\n",
" model.generate, language=\"english\", task=\"transcribe\", use_cache=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from transformers import Seq2SeqTrainingArguments\n",
"\n",
"training_args = Seq2SeqTrainingArguments(\n",
" output_dir=\"./whisper-tiny-en-us-minds14\", # name on the HF Hub\n",
" per_device_train_batch_size=16,\n",
" gradient_accumulation_steps=1, # increase by 2x for every 2x decrease in batch size\n",
" learning_rate=1e-5,\n",
" lr_scheduler_type=\"constant_with_warmup\",\n",
" warmup_steps=50,\n",
" max_steps=4000, # increase to 4000 if you have your own GPU or a Colab paid plan\n",
" gradient_checkpointing=True,\n",
" # fp16=True,\n",
" # fp16_full_eval=True,\n",
" evaluation_strategy=\"steps\",\n",
" per_device_eval_batch_size=16,\n",
" predict_with_generate=True,\n",
" generation_max_length=225,\n",
" save_steps=500,\n",
" eval_steps=500,\n",
" logging_steps=25,\n",
" report_to=[\"tensorboard\"],\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"wer\",\n",
" greater_is_better=False,\n",
" # push_to_hub=False,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"from transformers import Seq2SeqTrainer\n",
"\n",
"trainer = Seq2SeqTrainer(\n",
" args=training_args,\n",
" model=model,\n",
" train_dataset=minds14[\"train\"],\n",
" eval_dataset=minds14[\"test\"],\n",
" data_collator=data_collator,\n",
" compute_metrics=compute_metrics,\n",
" tokenizer=processor,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
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" 0%| | 0/4000 [00:00<?, ?it/s]"
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mkhojira/Projects/mml/audio-course/venv/lib/python3.8/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'loss': 1.584, 'learning_rate': 5e-06, 'epoch': 0.89}\n",
"{'loss': 0.6567, 'learning_rate': 1e-05, 'epoch': 1.79}\n",
"{'loss': 0.1857, 'learning_rate': 1e-05, 'epoch': 2.68}\n",
"{'loss': 0.1218, 'learning_rate': 1e-05, 'epoch': 3.57}\n",
"{'loss': 0.0876, 'learning_rate': 1e-05, 'epoch': 4.46}\n",
"{'loss': 0.0512, 'learning_rate': 1e-05, 'epoch': 5.36}\n",
"{'loss': 0.0299, 'learning_rate': 1e-05, 'epoch': 6.25}\n",
"{'loss': 0.016, 'learning_rate': 1e-05, 'epoch': 7.14}\n",
"{'loss': 0.0085, 'learning_rate': 1e-05, 'epoch': 8.04}\n",
"{'loss': 0.0038, 'learning_rate': 1e-05, 'epoch': 8.93}\n",
"{'loss': 0.0028, 'learning_rate': 1e-05, 'epoch': 9.82}\n",
"{'loss': 0.0023, 'learning_rate': 1e-05, 'epoch': 10.71}\n",
"{'loss': 0.0015, 'learning_rate': 1e-05, 'epoch': 11.61}\n",
"{'loss': 0.0012, 'learning_rate': 1e-05, 'epoch': 12.5}\n",
"{'loss': 0.0011, 'learning_rate': 1e-05, 'epoch': 13.39}\n",
"{'loss': 0.0009, 'learning_rate': 1e-05, 'epoch': 14.29}\n",
"{'loss': 0.0008, 'learning_rate': 1e-05, 'epoch': 15.18}\n",
"{'loss': 0.0007, 'learning_rate': 1e-05, 'epoch': 16.07}\n",
"{'loss': 0.0007, 'learning_rate': 1e-05, 'epoch': 16.96}\n",
"{'loss': 0.0006, 'learning_rate': 1e-05, 'epoch': 17.86}\n"
]
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"text": [
"{'eval_loss': 0.25609758496284485, 'eval_wer_ortho': 35.90376310919186, 'eval_wer': 35.30106257378985, 'eval_runtime': 27.7439, 'eval_samples_per_second': 4.073, 'eval_steps_per_second': 0.288, 'epoch': 17.86}\n"
]
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"output_type": "stream",
"text": [
"/Users/mkhojira/Projects/mml/audio-course/venv/lib/python3.8/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
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"{'loss': 0.0006, 'learning_rate': 1e-05, 'epoch': 18.75}\n",
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"{'loss': 0.0005, 'learning_rate': 1e-05, 'epoch': 20.54}\n",
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"{'loss': 0.0004, 'learning_rate': 1e-05, 'epoch': 22.32}\n",
"{'loss': 0.0004, 'learning_rate': 1e-05, 'epoch': 23.21}\n",
"{'loss': 0.0004, 'learning_rate': 1e-05, 'epoch': 24.11}\n",
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"{'loss': 0.0003, 'learning_rate': 1e-05, 'epoch': 25.89}\n",
"{'loss': 0.0003, 'learning_rate': 1e-05, 'epoch': 26.79}\n",
"{'loss': 0.0003, 'learning_rate': 1e-05, 'epoch': 27.68}\n",
"{'loss': 0.0003, 'learning_rate': 1e-05, 'epoch': 28.57}\n",
"{'loss': 0.0003, 'learning_rate': 1e-05, 'epoch': 29.46}\n",
"{'loss': 0.0002, 'learning_rate': 1e-05, 'epoch': 30.36}\n",
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"\u001b[1;32m/Users/mkhojira/Projects/mml/audio-course/unit5/hands_on.ipynb Cell 22\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/mkhojira/Projects/mml/audio-course/unit5/hands_on.ipynb#X34sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m trainer\u001b[39m.\u001b[39;49mtrain()\n",
"File \u001b[0;32m~/Projects/mml/audio-course/venv/lib/python3.8/site-packages/transformers/trainer.py:1555\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1553\u001b[0m hf_hub_utils\u001b[39m.\u001b[39menable_progress_bars()\n\u001b[1;32m 1554\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1555\u001b[0m \u001b[39mreturn\u001b[39;00m inner_training_loop(\n\u001b[1;32m 1556\u001b[0m args\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m 1557\u001b[0m resume_from_checkpoint\u001b[39m=\u001b[39;49mresume_from_checkpoint,\n\u001b[1;32m 1558\u001b[0m trial\u001b[39m=\u001b[39;49mtrial,\n\u001b[1;32m 1559\u001b[0m ignore_keys_for_eval\u001b[39m=\u001b[39;49mignore_keys_for_eval,\n\u001b[1;32m 1560\u001b[0m )\n",
"File \u001b[0;32m~/Projects/mml/audio-course/venv/lib/python3.8/site-packages/transformers/trainer.py:1862\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 1859\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maccelerator\u001b[39m.\u001b[39maccumulate(model):\n\u001b[1;32m 1860\u001b[0m tr_loss_step \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtraining_step(model, inputs)\n\u001b[0;32m-> 1862\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[1;32m 1863\u001b[0m args\u001b[39m.\u001b[39mlogging_nan_inf_filter\n\u001b[1;32m 1864\u001b[0m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m is_torch_tpu_available()\n\u001b[1;32m 1865\u001b[0m \u001b[39mand\u001b[39;00m (torch\u001b[39m.\u001b[39misnan(tr_loss_step) \u001b[39mor\u001b[39;00m torch\u001b[39m.\u001b[39misinf(tr_loss_step))\n\u001b[1;32m 1866\u001b[0m ):\n\u001b[1;32m 1867\u001b[0m \u001b[39m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[1;32m 1868\u001b[0m tr_loss \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m tr_loss \u001b[39m/\u001b[39m (\u001b[39m1\u001b[39m \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mglobal_step \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_globalstep_last_logged)\n\u001b[1;32m 1869\u001b[0m \u001b[39melse\u001b[39;00m:\n",
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],
"source": [
"trainer.train()"
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},
{
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"metadata": {},
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"source": [
"# from transformers import GenerationConfig\n",
"# generation_config = GenerationConfig.from_pretrained(\"openai/whisper-tiny.en\")\n",
"# generation_config.push_to_hub('mirodil/whisper-tiny-en-us-minds14')"
]
},
{
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{
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{
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"model_id": "c4487700a97e42188f5bf27cf538c82d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Upload 3 LFS files: 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'https://huggingface.co/mirodil/whisper-tiny-en-us-minds14/tree/main/'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kwargs = {\n",
" \"dataset_tags\": \"PolyAI/minds14\",\n",
" \"finetuned_from\": \"openai/whisper-tiny\",\n",
" \"tasks\": \"automatic-speech-recognition\",\n",
"}\n",
"trainer.push_to_hub(**kwargs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.generation_config"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hasattr(generation_config, \"lang_to_id\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
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"version": 3
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"pygments_lexer": "ipython3",
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