Upload folder using huggingface_hub

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,41 @@
+---
+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
+[email protected]

.ipynb_checkpoints/process_data-checkpoint.ipynb

@@ -0,0 +1,152 @@
+{
+ "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
+}

README.md

@@ -1,3 +1,41 @@
 ---
 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
+[email protected]

test/.ipynb_checkpoints/bengalese_finch_bl26lb16_190412_0721.20144_0-checkpoint.json

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test/.ipynb_checkpoints/bengalese_finch_bl26lb16_190412_0722.20147_0-checkpoint.json

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test/.ipynb_checkpoints/bengalese_finch_bl26lb16_190412_0918.20609_0-checkpoint.json

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test/bengalese_finch_bl26lb16_190412_0721.20144_0.json

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