Mutox Classifier Model (facebookresearch#332)

README.md

@@ -303,7 +303,8 @@ The following non-generative components are MIT licensed as found in [MIT_LICENS
 - Code
 - Text only part of the mExpresso dataset found in the [SeamlessExpressive README](docs/expressive/README.md).
 - UnitY2 forced alignment extractor found in the [UnitY2 Aligner README](docs/m4t/unity2_aligner_README.md).
-- Speech toxicity tool with the etox dataset found in the [Toxicity README](src/seamless_communication/cli/toxicity).
+- Speech toxicity tool with the etox dataset found in the [ETOX README](src/seamless_communication/cli/toxicity/etox).
+- MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector [Mutox README](src/seamless_communication/cli/toxicity/mutox)
 
 The following models are CC-BY-NC 4.0 licensed as found in the [LICENSE](LICENSE):
 - SeamlessM4T models (v1 and v2).

setup.py

@@ -27,6 +27,7 @@
         "librosa",
         "openai-whisper",
         "simuleval~=1.1.3",
+        "sonar-space==0.2.*",
         "soundfile",
         "scipy",
         "torchaudio",

src/seamless_communication/cards/mutox.yaml

@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+name: mutox
+model_type: mutox_classifier
+model_arch: mutox
+checkpoint: "https://dl.fbaipublicfiles.com/seamless/models/mutox.pt"
+input_size: 1024

src/seamless_communication/cli/toxicity/mutox/README.md

@@ -0,0 +1,87 @@
+# MuTox: MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector
+
+MuTox, the first highly multilingual audio-based dataset with toxicity labels.
+The dataset consists of 20k audio utterances for English and Spanish, and 4k for
+the other 19 languages. To showcase the quality of this dataset, we train the
+MuTox audio-based toxicity classifier, which allows zero-shot toxicity detection
+across a broad range of languages. This classifier outperforms existing
+text-based trainable classifiers by more than 1% AUC, while increasing the
+language coverage from 8 to 100+ languages. When compared to a wordlist-based
+classifier that covers a similar number of languages, MuTox improves precision
+and recall by ∼2.5 times.
+
+## License
+
+The mutox code and model are licensed under the MIT license (see MIT_LICENSE
+file at the root of seamless_communication). The mutox model depends on SONAR
+encoders, most are under the MIT license but a few are under CC-BY-NC license.
+See the [SONAR repository](https://github.com/facebookresearch/SONAR) for
+details.
+
+## Dataset Languages.
+
+- English,
+- Spanish,
+- Arabic,
+- Bengali,
+- Mandarin Chinese,
+- Dutch,
+- French,
+- German,
+- Hindi,
+- Indonesian,
+- Italian,
+- Japanese,
+- Korean,
+- Portuguese,
+- Russian,
+- Swahili,
+- Tagalog,
+- Thai,
+- Turkish,
+- Urdu,
+- Vietnamese
+
+## Classifier details.
+
+We use multi-modal and multilingual
+[SONAR](https://github.com/facebookresearch/SONAR) encoders from (Duquenne et
+al., 2023). For the classifier, we use variable input sizes for the 3
+feedforward layers (1024, 512, and 128).
+
+## Classifier Quick Start
+
+This introduces the MuTox speech toxicity model, this relies on computing the
+sonar embedding and then classifying it through the MuTox model. The
+`cli/mutox/mutox.py` provides an example of reading a TSV, computing the SONAR
+embedding and running the classifier on the results:
+
+```bash
+python -m seamless_communication.cli.toxicity.mutox.mutox_speech --lang fra --audio_column ref_tgt_audio /checkpoint/bokai/seamless/toxity_mitigation/exps_v5/joined_etox/fleurs/s2t/en-xx/fra.tsv /tmp/tesmortt.tsv
+```
+
+You can also work with text:
+
+```bash
+python -m seamless_communication.cli.toxicity.mutox.mutox_text --lang fra_Latn sentences.txt
+```
+
+You can also check the mutox example notebook in this directory.
+
+## Dataset
+
+The dataset is available in this [file](https://dl.fbaipublicfiles.com/seamless/datasets/mutox.csv). The dataset is licensed under the MIT license (see MIT_LICENSE
+file at the root of seamless_communication).
+
+## Citation
+
+```bitex
+@misc{costajussà2023mutox,
+      title={MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector},
+      author={ Marta R. Costa-jussà, Mariano Coria Meglioli, Pierre Andrews, David Dale, Prangthip Hansanti, Elahe Kalbassi, Alex Mourachko, Christophe Ropers, Carleigh Wood},
+      year={2023},
+      eprint={},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

src/seamless_communication/cli/toxicity/mutox/mutox_example.ipynb

@@ -0,0 +1,245 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright (c) Meta Platforms, Inc. and affiliates\n",
+    "# All rights reserved.\n",
+    "#\n",
+    "# This source code is licensed under the license found in the\n",
+    "# MIT_LICENSE file in the root directory of this source tree."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MUTOX toxicity classification\n",
+    "\n",
+    "Mutox lets you score speech and text toxicity using a classifier that can score sonar embeddings. In this notebook, we provide an example of encoding speech and text and classifying that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from pathlib import Path\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device(\"cuda:0\")\n",
+    "    dtype = torch.float16\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
+    "    dtype = torch.float32"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Speech Scoring"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. download some demo audio segments\n",
+    "2. create a tsv file to feed to the speech scoring pipeline\n",
+    "3. load the model and build the pipeline\n",
+    "4. go through the batches in the pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get demo file\n",
+    "import urllib.request\n",
+    "import tempfile\n",
+    "\n",
+    "files = [\n",
+    "    (\"https://dl.fbaipublicfiles.com/seamless/tests/commonvoice_example_en_clocks.wav\", \"commonvoice_example_en_clocks.wav\"),\n",
+    "    (\"https://dl.fbaipublicfiles.com/seamlessM4T/LJ037-0171_sr16k.wav\", \"LJ037-0171_sr16k.wav\")\n",
+    "]\n",
+    "\n",
+    "tmpdir = Path(tempfile.mkdtemp())\n",
+    "tsv_file = (tmpdir / 'data.tsv')\n",
+    "with tsv_file.open('w') as tsv_file_p:\n",
+    "    print('path', file=tsv_file_p)\n",
+    "    for (uri, name) in files:\n",
+    "        dl = tmpdir / name\n",
+    "        urllib.request.urlretrieve(uri, dl)\n",
+    "        print(dl, file=tsv_file_p)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sonar.inference_pipelines.speech import SpeechInferenceParams\n",
+    "from seamless_communication.toxicity.mutox.speech_pipeline import MutoxSpeechClassifierPipeline\n",
+    "\n",
+    "pipeline_builder = MutoxSpeechClassifierPipeline.load_model_from_name(\n",
+    "    mutox_classifier_name =\"mutox\",\n",
+    "    encoder_name=f\"sonar_speech_encoder_eng\",\n",
+    "    device=device,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline = pipeline_builder.build_pipeline(SpeechInferenceParams(\n",
+    "    data_file=tsv_file,\n",
+    "    audio_root_dir=None,\n",
+    "    audio_path_index=0,\n",
+    "    target_lang=\"eng\",\n",
+    "    batch_size=4,\n",
+    "    pad_idx=0,\n",
+    "    device=device,\n",
+    "    fbank_dtype=torch.float32,\n",
+    "    n_parallel=4\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/tmp/tmpqasvhgx6/commonvoice_example_en_clocks.wav\t-42.40079116821289\n",
+      "/tmp/tmpqasvhgx6/LJ037-0171_sr16k.wav\t-47.90427780151367\n"
+     ]
+    }
+   ],
+   "source": [
+    "for batch in pipeline:\n",
+    "    ex = batch['audio']\n",
+    "    for idx, path in enumerate(ex['path']):\n",
+    "        print(str(path), ex[\"data\"][idx].item(), sep=\"\\t\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cleanup tmp dir\n",
+    "import shutil\n",
+    "shutil.rmtree(tmpdir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Text Scoring\n",
+    "\n",
+    "1. load the sonar text encoder\n",
+    "2. load the mutox classifier model\n",
+    "3. compute embedding for a sentence\n",
+    "4. score this embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using the cached checkpoint of mutox. Set `force` to `True` to download again.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from seamless_communication.toxicity.mutox.loader import load_mutox_model\n",
+    "from sonar.inference_pipelines.text import TextToEmbeddingModelPipeline\n",
+    "\n",
+    "t2vec_model = TextToEmbeddingModelPipeline(\n",
+    "    encoder=\"text_sonar_basic_encoder\",\n",
+    "    tokenizer=\"text_sonar_basic_encoder\",\n",
+    ")\n",
+    "text_column='lang_txt'\n",
+    "classifier = load_mutox_model(\n",
+    "    \"mutox\",\n",
+    "    device=device,\n",
+    "    dtype=dtype,\n",
+    ").eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-19.7812]], device='cuda:0', dtype=torch.float16)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with torch.inference_mode():\n",
+    "    emb = t2vec_model.predict([\"De peur que le pays ne se prostitue et ne se remplisse de crimes.\"], source_lang='fra_Latn')\n",
+    "    x = classifier(emb.to(device).half())\n",
+    "\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "sc_fr2",
+   "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": 2
+}