Update AudioEditingCode_Demo.ipynb

AudioEditingCode_Demo.ipynb

@@ -1,81 +1,160 @@
-# AudioEditingCode Colab Demo
-
-This notebook demonstrates how to use the `AudioEditingCode` repository in Google Colab.
-
-## 1. Clone the repository
-
-
-
-```bash
-!git clone https://github.com/HilaManor/AudioEditingCode.git
-%cd AudioEditingCode
-```
-
-## 2. Install dependencies
-
-
-
-```bash
-!pip install -r requirements.txt
-```
-
-## 3. Demo Usage
-
-Here you can add examples of how to use the code. You might need to download some audio files for demonstration.
-
-
-
-### Download example audio
-
-```bash
-!wget https://www.soundhelix.com/examples/mp3/SoundHelix-Song-1.mp3 -O input_audio.mp3
-```
-
-### Text-Based Editing Example
-
-This example uses `main_run.py` for text-based audio editing. You will need a Hugging Face token to use models like Stable Audio Open. Please visit [Hugging Face](https://huggingface.co/settings/tokens) to get your token and replace `<YOUR_HF_TOKEN>` below.
-
-```python
-import os
-
-# Replace with your actual Hugging Face token
-os.environ["HF_TOKEN"] = "<YOUR_HF_TOKEN>"
-
-!python code/main_run.py \
-    --cfg_tar 1.5 \
-    --cfg_src 0.5 \
-    --init_aud input_audio.mp3 \
-    --target_prompt "a dog barking" \
-    --tstart 100 \
-    --model_id audioldm \
-    --results_path results_text_based
-```
-
-
-
-### Unsupervised Editing Example
-
-First, extract the principal components:
-
-```bash
-!python code/main_pc_extract_inv.py \
-    --init_aud input_audio.mp3 \
-    --model_id audioldm \
-    --results_path results_unsupervised_extract \
-    --drift_start 0 \
-    --drift_end 200 \
-    --n_evs 5
-```
-
-Then, apply the principal components:
-
-```bash
-!python code/main_pc_apply_drift.py \
-    --extraction_path results_unsupervised_extract/input_audio_audioldm_inversion_data.pt \
-    --drift_start 0 \
-    --drift_end 200 \
-    --amount 1.0 \
-    --evs 0
-```
-
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AudioEditingCode Colab Demo\n",
+    "\n",
+    "This notebook demonstrates how to use the `AudioEditingCode` repository in Google Colab.\n",
+    "\n",
+    "## 1. Clone the repository\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/HilaManor/AudioEditingCode.git\n",
+    "%cd AudioEditingCode\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Install dependencies\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -r requirements.txt\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Demo Usage\n",
+    "\n",
+    "Here you can add examples of how to use the code. You might need to download some audio files for demonstration.\n",
+    "\n",
+    "### Download example audio\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!wget https://www.soundhelix.com/examples/mp3/SoundHelix-Song-1.mp3 -O input_audio.mp3\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Text-Based Editing Example\n",
+    "\n",
+    "This example uses `main_run.py` for text-based audio editing. You will need a Hugging Face token to use models like Stable Audio Open. Please visit [Hugging Face](https://huggingface.co/settings/tokens) to get your token and replace `<YOUR_HF_TOKEN>` below.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Replace with your actual Hugging Face token\n",
+    "os.environ[\"HF_TOKEN\"] = \"<YOUR_HF_TOKEN>\"\n",
+    "\n",
+    "!python code/main_run.py \\\n",
+    "    --cfg_tar 1.5 \\\n",
+    "    --cfg_src 0.5 \\\n",
+    "    --init_aud input_audio.mp3 \\\n",
+    "    --target_prompt \"a dog barking\" \\\n",
+    "    --tstart 100 \\\n",
+    "    --model_id cvssp/audioldm-s-full-v2 \\\n",
+    "    --results_path results_text_based\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unsupervised Editing Example\n",
+    "\n",
+    "First, extract the principal components:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python code/main_pc_extract_inv.py \\\n",
+    "    --init_aud input_audio.mp3 \\\n",
+    "    --model_id cvssp/audioldm-s-full-v2 \\\n",
+    "    --results_path results_unsupervised_extract \\\n",
+    "    --drift_start 0 \\\n",
+    "    --drift_end 200 \\\n",
+    "    --n_evs 5\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, apply the principal components:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python code/main_pc_apply_drift.py \\\n",
+    "    --extraction_path results_unsupervised_extract/input_audio_cvssp_audioldm-s-full-v2_inversion_data.pt \\\n",
+    "    --drift_start 0 \\\n",
+    "    --drift_end 200 \\\n",
+    "    --amount 1.0 \\\n",
+    "    --evs 0\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}