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+# Finetune Voxtral for ASR with Transformers 🤗
+
+This repository fine-tunes the [Voxtral](https://huggingface.co/Deep-unlearning/Voxtral) speech model on conversational speech datasets using the Hugging Face `transformers` and `datasets` libraries.
+
+## Installation
+
+### Step 1: Clone the repository
+
+```bash
+git clone https://github.com/Deep-unlearning/Finetune-Voxtral-ASR.git
+cd Finetune-Voxtral-ASR
+```
+
+### Step 2: Set up environment
+
+Choose your preferred package manager:
+
+<details>
+<summary>📦 Using UV (recommended)</summary>
+
+[Install `uv`](https://docs.astral.sh/uv/getting-started/installation/)
+
+```bash
+uv venv .venv --python 3.10 && source .venv/bin/activate
+uv pip install -r requirements.txt
+```
+
+</details>
+
+<details>
+<summary>🐍 Using pip</summary>
+
+```bash
+python -m venv .venv --python 3.10 && source .venv/bin/activate
+pip install --upgrade pip
+pip install -r requirements.txt
+```
+
+</details>
+
+## Dataset Preparation
+
+Perfect — here’s a **drop-in replacement** for your README’s “Dataset Preparation” that matches your script (uses **`hf-audio/esb-datasets-test-only-sorted`** with the **`voxpopuli`** config, 16 kHz casting, and a small train/eval slice), and explains the Voxtral/LLaMA-style prompt+label masking your collator implements.
+
+---
+
+## Dataset Preparation
+
+For ASR fine-tuning, inputs look like:
+
+* **Inputs**: `[AUDIO] … [AUDIO] <transcribe>  <reference transcription>`
+* **Labels**: same sequence, but the prefix `[AUDIO] … [AUDIO] <transcribe>` is **masked with `-100`** so loss is computed **only** on the transcription tokens.
+
+The `VoxtralDataCollator` already builds this sequence (prompt expansion via the processor and label masking).
+The dataset only needs two fields:
+
+```python
+{
+  "audio": {"array": <float32 numpy array>, "sampling_rate": 16000, ...},
+  "text":  "<reference transcription>"
+}
+```
+
+
+If you want to swap to a different dataset, ensure after loading you still have:
+
+* an **`audio`** column (cast to `Audio(sampling_rate=16000)`), and
+* a **`text`** column (the reference transcription).
+
+If your dataset uses different column names, map them to `audio` and `text` before returning.
+
+## Training
+
+Run the training script:
+
+```bash
+uv run train.py
+```
+
+Logs and checkpoints will be saved under the `outputs/` directory by default.
+
+## Training with LoRA
+
+You can also run the training script with LoRA:
+
+```bash
+uv run train_lora.py
+```
+
+**Happy fine-tuning Voxtral!** 🚀