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whisper-large-v3

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Hi, I have the following Python (3.10.12) packages installed:

• torch === 2.3.1
• torchaudio === 2.3.1
• torchvision == 0.18.1
• transformers == 4.40.2

and the following specs:

• 32 gb ram ddr5
• 4070 super 12 gb vram
• ryzen 5 7600x

I'm trying to run whisper-large-v3 locally on my GPU for a data transcription task (from audio to text), but I get a cuda out of memory error.

Here is the core part of source code that I use to do that:

### import

from datasets import load_dataset, Audio, DatasetDict, Dataset
import multiprocess as mp
from transformers import WhisperFeatureExtractor, WhisperProcessor, WhisperForConditionalGeneration, GenerationConfig, WhisperTokenizer
import torch
from dataclasses import dataclass
from typing import Any, Dict, List, Union
import evaluate
from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer


### dataset preparation

dataset=load_dataset("huggingface_dataset_space", streaming=True)
dd = DatasetDict()

for ds_name, iterable_ds in dataset.items():
    ds=Dataset.from_generator(lambda: (yield from iterable_ds), features=iterable_ds.features, num_proc=mp.cpu_count())
    dd[ds_name] = ds


datasets=dd.cast_column("audio",  Audio(sampling_rate=16000))

feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-large-v3")

tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-large-v3", language="italian", task="transcribe")


def prepare_dataset(batch):
    audio = batch["audio"]
    batch["input_features"] = feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0]
    batch["labels"] = tokenizer(batch["transcription"]).input_ids
    return batch


final_ds=datasets.map(prepare_dataset, num_proc=mp.cpu_count())
final_ds=final_ds.remove_columns(["audio", "transcription"])


### model training

model=WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3")
processor=WhisperProcessor.from_pretrained("openai/whisper-large-v3", language="italian", task="transcribe")


device=("cuda" if torch.cuda.is_available() else "cpu")
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True


@dataclass
class DataCollatorSpeechSeq2SeqWithPadding:
    processor: Any

    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
        input_features=[{"input_features": feature["input_features"]} for feature in features]
        batch=self.processor.feature_extractor.pad(input_features, return_tensors="pt")

        label_features=[{"input_ids": feature["labels"]} for feature in features]

        labels_batch=self.processor.tokenizer.pad(label_features, return_tensors="pt")

        labels=labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)

        if (labels[:, 0]==self.processor.tokenizer.bos_token_id).all().cpu().item():
            labels=labels[:, 1:]

        batch["labels"]=labels

        return batch


data_collator=DataCollatorSpeechSeq2SeqWithPadding(processor=processor)

### word error rate as evaluation metric
metric=evaluate.load("wer")

def compute_metrics(pred):
    pred_ids=pred.predictions
    label_ids=pred.label_ids

    label_ids[label_ids == -100]=tokenizer.pad_token_id

    pred_str=tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
    label_str=tokenizer.batch_decode(label_ids, skip_special_tokens=True)

    wer=100 * metric.compute(predictions=pred_str, references=label_str)

    return {"wer": wer}

forced_decoder_ids = processor.get_decoder_prompt_ids(language="italian", task="transcribe")
model.config.forced_decoder_ids = forced_decoder_ids
model.config.suppress_tokens = []
model.generation_config.language = "it"

gen_cnf = GenerationConfig.from_pretrained(
            "openai/whisper-large-v3",
            language="it",
            num_beams=2,
            early_stopping=True,
            early_stopping_patience=1,
            early_stopping_metric="loss"
        )
gen_cnf.save_pretrained("resources/generation_config/")

### training arguments

training_args = Seq2SeqTrainingArguments(
    output_dir="huggingface_model_space",
    generation_config=gen_cnf,
    per_device_train_batch_size=2,
    per_device_eval_batch_size=2,
    gradient_accumulation_steps=1,
    gradient_checkpointing=True,
    max_steps=1000,
    eval_steps=100,
    save_steps=100,
    evaluation_strategy="steps",
    save_strategy="steps",
    fp16=True,
    log_level='debug',
    logging_strategy="steps",
    logging_steps=1,
    report_to=["tensorboard"],
    predict_with_generate=True,
    generation_max_length=225,
    load_best_model_at_end=True,
    metric_for_best_model="wer",
    greater_is_better=False,
    push_to_hub=False,
)

trainer = Seq2SeqTrainer(
    args=training_args,
    model=model,
    train_dataset=final_ds["train"],
    eval_dataset=final_ds["test"],
    data_collator=data_collator,
    compute_metrics=compute_metrics,
    tokenizer=processor,
)

trainer.train()

I have about 900 training audios and 250 test audios, but I think I cannot fit the model in memory and so I got CUDA memory error.

WHAT I TRIED:

• sample few examples from the dataset (about 50-20) but the situation didn't change,
• tried to run on CPU and in that case my 32 gb of RAM became saturated and the system got stucked
• tried the small version of Whisper on my GPU and it worked
• tried the medium version of Whisper on my GPU and it worked for five minutes and then again CUDA memory error

So it's like that the RAM became saturated while the training is ongoing. Do you have some advices to optimize memory or I need a machine with at least 64 gb of vram?

Thank you for your time!

EDIT: this is fine-tuning task of whisper