shakods/realtime

shakods/realtime-hf-jobs · Hugging Face Finetune Voxtral for ASR 🤗 Fine-tune the Voxtral speech model for automatic speech recognition (ASR) using Hugging Face transformers and datasets. The recommended way to run training is Hugging Face Jobs: push your dataset to the Hub, then launch training on HF infrastructure (default a100-large GPU) with one script—no local GPU required. --- Train with Hugging Face Jobs (recommended) The`scripts/launch_hf_job.py` script submits your training run to Hugging Face Jobs. Training runs on HF’s cloud (default GPU: a100-large), so you don’t need a local GPU. Requirements - Hugging Face account with Jobs access(Pro, Team, or Enterprise; pre-paid credits). - Dataset on the Hub: your data must be a Hugging Face dataset (slug format`username/dataset-name`). The job loads it from the Hub; local JSONL is not used. - Token: set`HF_TOKEN` or`HUGGINGFACE_HUB_TOKEN` so the job can read private datasets and (optionally) push the model. 1. Push your dataset to the Hub If you have a local JSONL with`{audio_path, text}` and audio files, push it first: ``` python scripts/push_to_huggingface.py dataset datasets/voxtral_user/data.jsonl username/voxtral-asr-data ``` Or use the Gradio interface: Advanced options → Push dataset to HF Hub, then use the repo name (e.g.`username/voxtral-dataset-20250225`) as the dataset slug below. 2. Launch the training job ``` LoRA (default) – fast, parameter-efficient python scripts/launch_hf_job.py --dataset username/voxtral-asr-data Full fine-tuning python scripts/launch_hf_job.py --dataset username/voxtral-asr-data --no-lora With options: config, timeout, hardware, hyperparameters python scripts/launch_hf_job.py \ --dataset username/voxtral-asr-data \ --dataset-config voxpopuli \ --model-checkpoint mistralai/Voxtral-Mini-3B-2507 \ --train-count 500 \ --eval-count 100 \ --epochs 3 \ --timeout 8h \ --flavor a100-large ``` The script prints the job URL and job ID