Fine-Tuning Wav2Vec2 on a Dedicated GPU

Whisper is the default transcriber for good reason. For narrow-domain speech recognition – a specific medical vocabulary, a particular accent family, or low-resource languages – a fine-tuned Wav2Vec2 often outperforms generic Whisper on your specific slice. On our dedicated GPU hosting the fine-tune is straightforward.

Contents

• When to fine-tune
• Data
• Training
• Serving

When

Fine-tune Wav2Vec2 over using Whisper when:

• You have 5+ hours of transcribed domain audio
• Your domain vocabulary or accent is systematically misrecognised by Whisper
• You need sub-200ms latency (Wav2Vec2 is faster than Whisper)

Stay with Whisper if your domain is well-covered and you have no labelled training data.

Data

You need audio files paired with text transcripts. Format the dataset as a Hugging Face Dataset with audio and text columns.

from datasets import load_dataset, Audio
ds = load_dataset("json", data_files="train.jsonl", split="train")
ds = ds.cast_column("audio", Audio(sampling_rate=16000))

Training

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, TrainingArguments, Trainer

processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-xlsr-53")
model = Wav2Vec2ForCTC.from_pretrained(
    "facebook/wav2vec2-large-xlsr-53",
    ctc_loss_reduction="mean",
    pad_token_id=processor.tokenizer.pad_token_id,
)

args = TrainingArguments(
    output_dir="./w2v2-finetune",
    per_device_train_batch_size=8,
    gradient_accumulation_steps=2,
    learning_rate=3e-4,
    warmup_steps=500,
    num_train_epochs=20,
    bf16=True,
    gradient_checkpointing=True,
    save_steps=500,
)
Trainer(model=model, args=args, train_dataset=ds, tokenizer=processor.feature_extractor).train()

On a 5080, 20 epochs on 10 hours of audio takes roughly 3-6 hours.

Serving

For serving, wrap the model in a FastAPI endpoint or use an ONNX export for speed. Inference is real-time on any GPU.

See Whisper Turbo for the generalist alternative.