Delete .ipynb_checkpoints

.ipynb_checkpoints/README-checkpoint.md

@@ -1,41 +0,0 @@
----
-license: apache-2.0
----
-# Positive Transfer Of The Whisper Speech Transformer To Human And Animal Voice Activity Detection
-We proposed WhisperSeg, utilizing the Whisper Transformer pre-trained for Automatic Speech Recognition (ASR) for both human and animal Voice Activity Detection (VAD). For more details, please refer to our paper
-
->
-> [**Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection**](https://doi.org/10.1101/2023.09.30.560270)
-> 
-> Nianlong Gu, Kanghwi Lee, Maris Basha, Sumit Kumar Ram, Guanghao You, Richard H. R. Hahnloser <br>
-> University of Zurich and ETH Zurich
-
-This animals dataset was customized Animal Voice Activity Detection (vocal segmentation) when training the WhisperSeg segmenter.
-
-## Download Dataset
-```python
-from huggingface_hub import snapshot_download
-snapshot_download('nccratliri/vad-animals', local_dir = "data/vad-animals", repo_type="dataset" )
-```
-
-For more usage details, please refer to the GitHub repository: https://github.com/nianlonggu/WhisperSeg
-
-## Citation
-When using this dataset for your work, please cite:
-```
-@article {Gu2023.09.30.560270,
-	author = {Nianlong Gu and Kanghwi Lee and Maris Basha and Sumit Kumar Ram and Guanghao You and Richard Hahnloser},
-	title = {Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection},
-	elocation-id = {2023.09.30.560270},
-	year = {2023},
-	doi = {10.1101/2023.09.30.560270},
-	publisher = {Cold Spring Harbor Laboratory},
-	abstract = {This paper introduces WhisperSeg, utilizing the Whisper Transformer pre-trained for Automatic Speech Recognition (ASR) for human and animal Voice Activity Detection (VAD). Contrary to traditional methods that detect human voice or animal vocalizations from a short audio frame and rely on careful threshold selection, WhisperSeg processes entire spectrograms of long audio and generates plain text representations of onset, offset, and type of voice activity. Processing a longer audio context with a larger network greatly improves detection accuracy from few labeled examples. We further demonstrate a positive transfer of detection performance to new animal species, making our approach viable in the data-scarce multi-species setting.Competing Interest StatementThe authors have declared no competing interest.},
-	URL = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270},
-	eprint = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270.full.pdf},
-	journal = {bioRxiv}
-}
-```
-
-## Contact
-nianlong.gu@uzh.ch

.ipynb_checkpoints/process_data-checkpoint.ipynb

@@ -1,152 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "841f9abb-bac7-421a-be91-20cd0e66b565",
-   "metadata": {},
-   "source": [
-    "## Download dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c686f678-f6be-4227-b3bb-4dc0974d8377",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "da88b797d92d44dba55077c5453eb1b4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Fetching 7908 files:   0%|          | 0/7908 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'/mnt/d360c7ec-336f-4a33-832d-86d6562ba9ab/work/NCCR/requests/WhisperSeg/data/datasets/animals/raw'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import snapshot_download\n",
-    "snapshot_download('nccratliri/vad-multi-species', local_dir = \"./\", repo_type=\"dataset\" )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a926f0c1-2c1d-4b4b-b633-2c54c1fc8928",
-   "metadata": {},
-   "source": [
-    "### Use a unified cluster name \"vocal\" for all species, to train a general-purpose VAD model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "dfd9b300-ac32-422a-b687-6a21f8d0bea9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from glob import glob\n",
-    "import json\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a9ab98c2-d445-4004-afb9-77614075005f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3953"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "csv_file_list = glob(\"./t*/*.json\")\n",
-    "len(csv_file_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "7b92691e-7dbc-482f-93e1-05054c847196",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "160"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n_removed = 0\n",
-    "for csv_name in csv_file_list:\n",
-    "    data = json.load(open(csv_name, \"r\"))\n",
-    "    if data[\"species\"] == \"human\":\n",
-    "        audio_name = csv_name[:-4] + \"wav\"\n",
-    "        os.remove( audio_name )\n",
-    "        os.remove( csv_name )\n",
-    "        n_removed +=1\n",
-    "    else:\n",
-    "        data[\"species\"] = \"animal\"\n",
-    "        data[\"cluster\"] = [ \"vocal\" for _ in data[\"cluster\"] ]\n",
-    "        json.dump( data, open( csv_name, \"w\") )\n",
-    "n_removed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d5bf7554-7faf-486b-9ec9-5f3ccc025757",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}