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8415189576557ef37bfc0faffcf8675775aed3416881ba556212c05e.wav +3 -0
README.md +130 -0
config.yaml +20 -0
create_handler.ipynb +229 -0
handler.py +47 -0
reproducible_research/2022.07/AISHELL.SpeakerDiarization.Full.test.eval +24 -0
reproducible_research/2022.07/AISHELL.SpeakerDiarization.Full.test.rttm +0 -0
reproducible_research/2022.07/AMI-SDM.SpeakerDiarization.only_words.test.eval +20 -0
reproducible_research/2022.07/AMI-SDM.SpeakerDiarization.only_words.test.rttm +0 -0
reproducible_research/2022.07/AMI.SpeakerDiarization.only_words.test.eval +20 -0
reproducible_research/2022.07/AMI.SpeakerDiarization.only_words.test.rttm +0 -0
reproducible_research/2022.07/CALLHOME.SpeakerDiarization.CALLHOME.test.eval +254 -0
reproducible_research/2022.07/CALLHOME.SpeakerDiarization.CALLHOME.test.rttm +0 -0
reproducible_research/2022.07/DIHARD.SpeakerDiarization.Full.test.eval +263 -0
reproducible_research/2022.07/DIHARD.SpeakerDiarization.Full.test.rttm +0 -0
reproducible_research/2022.07/REPERE.SpeakerDiarization.Full.test.eval +65 -0
reproducible_research/2022.07/REPERE.SpeakerDiarization.Full.test.rttm +0 -0
reproducible_research/2022.07/VoxConverse.SpeakerDiarization.VoxConverse.test.eval +236 -0
reproducible_research/2022.07/VoxConverse.SpeakerDiarization.VoxConverse.test.rttm +0 -0
requirements.txt +2 -0

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+---
+tags:
+- pyannote
+- pyannote-audio
+- pyannote-audio-pipeline
+- audio
+- voice
+- speech
+- speaker
+- speaker-diarization
+- speaker-change-detection
+- voice-activity-detection
+- overlapped-speech-detection
+datasets:
+- ami
+- dihard
+- voxconverse
+- aishell
+- repere
+- voxceleb
+license: mit
+---
+# 🎹 Speaker diarization
+Relies on pyannote.audio 2.0: see [installation instructions](https://github.com/pyannote/pyannote-audio/tree/develop#installation).
+## TL;DR
+```python
+# load the pipeline from Hugginface Hub
+from pyannote.audio import Pipeline
+pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2022.07")
+# apply the pipeline to an audio file
+diarization = pipeline("audio.wav")
+# dump the diarization output to disk using RTTM format
+with open("audio.rttm", "w") as rttm:
+    diarization.write_rttm(rttm)
+```
+## Advanced usage
+In case the number of speakers is known in advance, one can use the `num_speakers` option:
+```python
+diarization = pipeline("audio.wav", num_speakers=2)
+```
+One can also provide lower and/or upper bounds on the number of speakers using `min_speakers` and `max_speakers` options:
+```python
+diarization = pipeline("audio.wav", min_speakers=2, max_speakers=5)
+```
+If you feel adventurous, you can try and play with the various pipeline hyper-parameters.
+For instance, one can use a more aggressive voice activity detection by increasing the value of `segmentation_onset` threshold:
+```python
+hparams = pipeline.parameters(instantiated=True)
+hparams["segmentation_onset"] += 0.1
+pipeline.instantiate(hparams)
+```
+## Benchmark
+### Real-time factor
+Real-time factor is around 5% using one Nvidia Tesla V100 SXM2 GPU (for the neural inference part) and one Intel Cascade Lake 6248 CPU (for the clustering part).
+In other words, it takes approximately 3 minutes to process a one hour conversation.
+### Accuracy
+This pipeline is benchmarked on a growing collection of datasets.
+Processing is fully automatic:
+* no manual voice activity detection (as is sometimes the case in the literature)
+* no manual number of speakers (though it is possible to provide it to the pipeline)
+* no fine-tuning of the internal models nor tuning of the pipeline hyper-parameters to each dataset
+... with the least forgiving diarization error rate (DER) setup (named *"Full"* in [this paper](https://doi.org/10.1016/j.csl.2021.101254)):
+* no forgiveness collar
+* evaluation of overlapped speech
+| Benchmark                                                                                                                          | [DER%](. "Diarization error rate") | [FA%](. "False alarm rate") | [Miss%](. "Missed detection rate") | [Conf%](. "Speaker confusion rate") | Expected output                                                                            | File-level evaluation                                                                      |
+| ---------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | --------------------------- | ---------------------------------- | ----------------------------------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------ |
+| [AISHELL-4](http://www.openslr.org/111/)                                                                                           | 14.61                              | 3.31                        | 4.35                               | 6.95                                | [RTTM](reproducible_research/AISHELL.SpeakerDiarization.Full.test.rttm)                    | [eval](reproducible_research/AISHELL.SpeakerDiarization.Full.test.eval)                    |
+| [AMI *Mix-Headset*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words*](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 18.21                              | 3.28                        | 11.07                              | 3.87                                | [RTTM](reproducible_research/2022.07/AMI.SpeakerDiarization.only_words.test.rttm)          | [eval](reproducible_research/2022.07/AMI.SpeakerDiarization.only_words.test.eval)          |
+| [AMI *Array1-01*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words*](https://github.com/BUTSpeechFIT/AMI-diarization-setup)   | 29.00                              | 2.71                        | 21.61                              | 4.68                                | [RTTM](reproducible_research/2022.07/AMI-SDM.SpeakerDiarization.only_words.test.rttm)      | [eval](reproducible_research/2022.07/AMI-SDM.SpeakerDiarization.only_words.test.eval)      |
+| [CALLHOME](https://catalog.ldc.upenn.edu/LDC2001S97) [*Part2*](https://github.com/BUTSpeechFIT/CALLHOME_sublists/issues/1)         | 30.24                              | 3.71                        | 16.86                              | 9.66                                | [RTTM](reproducible_research/2022.07/CALLHOME.SpeakerDiarization.CALLHOME.test.rttm)       | [eval](reproducible_research/2022.07/CALLHOME.SpeakerDiarization.CALLHOME.test.eval)       |
+| [DIHARD 3 *Full*](https://arxiv.org/abs/2012.01477)                                                                                | 20.99                              | 4.25                        | 10.74                              | 6.00                                | [RTTM](reproducible_research/2022.07/DIHARD.SpeakerDiarization.Full.test.rttm)             | [eval](reproducible_research/2022.07/DIHARD.SpeakerDiarization.Full.test.eval)             |
+| [REPERE *Phase 2*](https://islrn.org/resources/360-758-359-485-0/)                                                                 | 12.62                              | 1.55                        | 3.30                               | 7.76                                | [RTTM](reproducible_research/2022.07/REPERE.SpeakerDiarization.Full.test.rttm)             | [eval](reproducible_research/2022.07/REPERE.SpeakerDiarization.Full.test.eval)             |
+| [VoxConverse *v0.0.2*](https://github.com/joonson/voxconverse)                                                                     | 12.76                              | 3.45                        | 3.85                               | 5.46                                | [RTTM](reproducible_research/2022.07/VoxConverse.SpeakerDiarization.VoxConverse.test.rttm) | [eval](reproducible_research/2022.07/VoxConverse.SpeakerDiarization.VoxConverse.test.eval) |
+## Support
+For commercial enquiries and scientific consulting, please contact [me](mailto:herve@niderb.fr).
+For [technical questions](https://github.com/pyannote/pyannote-audio/discussions) and [bug reports](https://github.com/pyannote/pyannote-audio/issues), please check [pyannote.audio](https://github.com/pyannote/pyannote-audio) Github repository.
+## Citations
+```bibtex
+@inproceedings{Bredin2021,
+  Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}},
+  Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine},
+  Booktitle = {Proc. Interspeech 2021},
+  Address = {Brno, Czech Republic},
+  Month = {August},
+  Year = {2021},
+}
+```
+```bibtex
+@inproceedings{Bredin2020,
+  Title = {{pyannote.audio: neural building blocks for speaker diarization}},
+  Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe},
+  Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
+  Address = {Barcelona, Spain},
+  Month = {May},
+  Year = {2020},
+}
+```

config.yaml ADDED Viewed

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+pipeline:
+  name: pyannote.audio.pipelines.SpeakerDiarization
+  params:
+    segmentation: philschmid/pyannote-segmentation
+    segmentation_step: 0.1
+    segmentation_batch_size: 32
+    # embedding: speechbrain/spkrec-ecapa-voxceleb@5c0be3875fda05e81f3c004ed8c7c06be308de1e
+    embedding: speechbrain/spkrec-ecapa-voxceleb
+    embedding_exclude_overlap: True
+    embedding_batch_size: 32
+    clustering: HiddenMarkovModelClustering
+params:
+  segmentation_onset: 0.58
+  clustering:
+    covariance_type: diag
+    threshold: 0.35
+    single_cluster_detection:
+      quantile: 0.05
+      threshold: 1.15

create_handler.ipynb ADDED Viewed

	@@ -0,0 +1,229 @@

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Setup & Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting requirements.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile requirements.txt\n",
+    "torchaudio==0.11.*\n",
+    "git+https://github.com/philschmid/pyannote-audio.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -r requirements.txt --upgrade"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Create Custom Handler for Inference Endpoints\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting handler.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile handler.py\n",
+    "from typing import  Dict\n",
+    "from pyannote.audio import Pipeline\n",
+    "from transformers.pipelines.audio_utils import ffmpeg_read\n",
+    "import torch \n",
+    "\n",
+    "SAMPLE_RATE = 16000\n",
+    "\n",
+    "\n",
+    "\n",
+    "class EndpointHandler():\n",
+    "    def __init__(self, path=\"\"):\n",
+    "        # load the model\n",
+    "        self.pipeline = Pipeline.from_pretrained(\"pyannote/speaker-diarization\")\n",
+    "\n",
+    "\n",
+    "    def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]:\n",
+    "        \"\"\"\n",
+    "        Args:\n",
+    "            data (:obj:):\n",
+    "                includes the deserialized audio file as bytes\n",
+    "        Return:\n",
+    "            A :obj:`dict`:. base64 encoded image\n",
+    "        \"\"\"\n",
+    "        # process input\n",
+    "        inputs = data.pop(\"inputs\", data)\n",
+    "        parameters = data.pop(\"parameters\", None) #  min_speakers=2, max_speakers=5\n",
+    "\n",
+    "        \n",
+    "        # prepare pynannote input\n",
+    "        audio_nparray = ffmpeg_read(inputs, SAMPLE_RATE)\n",
+    "        audio_tensor= torch.from_numpy(audio_nparray).unsqueeze(0)\n",
+    "        pyannote_input = {\"waveform\": audio_tensor, \"sample_rate\": SAMPLE_RATE}\n",
+    "        \n",
+    "        # apply pretrained pipeline\n",
+    "        # pass inputs with all kwargs in data\n",
+    "        if parameters is not None:\n",
+    "            diarization = self.pipeline(pyannote_input, **parameters)\n",
+    "        else:\n",
+    "            diarization = self.pipeline(pyannote_input)\n",
+    "\n",
+    "        # postprocess the prediction\n",
+    "        processed_diarization = [\n",
+    "            {\"label\": str(label), \"start\": str(segment.start), \"stop\": str(segment.end)}\n",
+    "            for segment, _, label in diarization.itertracks(yield_label=True)\n",
+    "        ]\n",
+    "        \n",
+    "        return {\"diarization\": processed_diarization}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "test custom pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from handler import EndpointHandler\n",
+    "\n",
+    "# init handler\n",
+    "my_handler = EndpointHandler(path=\".\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "from PIL import Image\n",
+    "from io import BytesIO\n",
+    "import json\n",
+    "\n",
+    "# file reader\n",
+    "with open(\"sample.wav\", \"rb\") as f:\n",
+    "  request = {\"inputs\": f.read()}\n",
+    "\n",
+    "# test the handler\n",
+    "pred = my_handler(request)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'diarization': [{'label': 'SPEAKER_01',\n",
+       "   'start': '0.4978125',\n",
+       "   'stop': '1.3921875'},\n",
+       "  {'label': 'SPEAKER_01', 'start': '1.8984375', 'stop': '2.7590624999999998'},\n",
+       "  {'label': 'SPEAKER_02', 'start': '2.9953125', 'stop': '3.5015625000000004'},\n",
+       "  {'label': 'SPEAKER_01',\n",
+       "   'start': '3.5690625000000002',\n",
+       "   'stop': '4.311562500000001'},\n",
+       "  {'label': 'SPEAKER_02', 'start': '4.6153125', 'stop': '6.7753125'},\n",
+       "  {'label': 'SPEAKER_00', 'start': '7.1128125', 'stop': '7.551562500000001'},\n",
+       "  {'label': 'SPEAKER_02',\n",
+       "   'start': '7.551562500000001',\n",
+       "   'stop': '9.475312500000001'},\n",
+       "  {'label': 'SPEAKER_02',\n",
+       "   'start': '9.812812500000003',\n",
+       "   'stop': '10.555312500000003'},\n",
+       "  {'label': 'SPEAKER_00',\n",
+       "   'start': '9.863437500000003',\n",
+       "   'stop': '10.420312500000001'},\n",
+       "  {'label': 'SPEAKER_03', 'start': '12.411562500000002', 'stop': '15.5503125'},\n",
+       "  {'label': 'SPEAKER_00', 'start': '15.786562500000002', 'stop': '16.1409375'},\n",
+       "  {'label': 'SPEAKER_01', 'start': '16.1409375', 'stop': '16.1578125'},\n",
+       "  {'label': 'SPEAKER_00', 'start': '17.1534375', 'stop': '17.4234375'},\n",
+       "  {'label': 'SPEAKER_01', 'start': '17.7440625', 'stop': '20.3596875'},\n",
+       "  {'label': 'SPEAKER_01', 'start': '20.6128125', 'stop': '20.6634375'},\n",
+       "  {'label': 'SPEAKER_00', 'start': '20.6634375', 'stop': '20.8490625'},\n",
+       "  {'label': 'SPEAKER_01', 'start': '20.8490625', 'stop': '20.8828125'},\n",
+       "  {'label': 'SPEAKER_01', 'start': '21.1021875', 'stop': '22.1315625'},\n",
+       "  {'label': 'SPEAKER_02', 'start': '22.4521875', 'stop': '22.7053125'},\n",
+       "  {'label': 'SPEAKER_02', 'start': '23.2115625', 'stop': '23.4815625'},\n",
+       "  {'label': 'SPEAKER_01', 'start': '23.4815625', 'stop': '24.0215625'},\n",
+       "  {'label': 'SPEAKER_02', 'start': '24.3253125', 'stop': '25.5065625'},\n",
+       "  {'label': 'SPEAKER_01', 'start': '25.8440625', 'stop': '27.3121875'},\n",
+       "  {'label': 'SPEAKER_02', 'start': '27.3121875', 'stop': '27.4978125'},\n",
+       "  {'label': 'SPEAKER_01', 'start': '29.7253125', 'stop': '29.9615625'}]}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.13 ('dev': conda)",
+   "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": "3.9.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "f6dd96c16031089903d5a31ec148b80aeb0d39c32affb1a1080393235fbfa2fc"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

handler.py ADDED Viewed

	@@ -0,0 +1,47 @@

+from typing import  Dict
+from pyannote.audio import Pipeline
+from transformers.pipelines.audio_utils import ffmpeg_read
+import torch
+SAMPLE_RATE = 16000
+class EndpointHandler():
+    def __init__(self, path=""):
+        # load the model
+        self.pipeline = Pipeline.from_pretrained("philschmid/pyannote-speaker-diarization-endpoint")
+    def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]:
+        """
+        Args:
+            data (:obj:):
+                includes the deserialized audio file as bytes
+        Return:
+            A :obj:`dict`:. base64 encoded image
+        """
+        # process input
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None) #  min_speakers=2, max_speakers=5
+        # prepare pynannote input
+        audio_nparray = ffmpeg_read(inputs, SAMPLE_RATE)
+        audio_tensor= torch.from_numpy(audio_nparray).unsqueeze(0)
+        pyannote_input = {"waveform": audio_tensor, "sample_rate": SAMPLE_RATE}
+        # apply pretrained pipeline
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            diarization = self.pipeline(pyannote_input, **parameters)
+        else:
+            diarization = self.pipeline(pyannote_input)
+        # postprocess the prediction
+        processed_diarization = [
+            {"label": str(label), "start": str(segment.start), "stop": str(segment.end)}
+            for segment, _, label in diarization.itertracks(yield_label=True)
+        ]
+        return {"diarization": processed_diarization}

reproducible_research/2022.07/AISHELL.SpeakerDiarization.Full.test.eval ADDED Viewed

	@@ -0,0 +1,24 @@

+             diarization error rate    total  correct correct false alarm false alarm missed detection missed detection confusion confusion
+                                  %                         %                       %                                 %                   %
+item
+L_R003S01C02                  18.89  2311.97  1980.25   85.65      104.95        4.54           144.55             6.25    187.17      8.10
+L_R003S02C02                  12.74  2036.79  1832.36   89.96       55.15        2.71            93.48             4.59    110.94      5.45
+L_R003S03C02                  16.46  2376.45  2045.30   86.07       59.97        2.52           206.09             8.67    125.06      5.26
+L_R003S04C02                  17.63  2011.85  1752.94   87.13       95.70        4.76            78.03             3.88    180.89      8.99
+L_R004S01C01                  10.96  2041.84  1877.74   91.96       59.67        2.92            53.53             2.62    110.58      5.42
+L_R004S02C01                  14.78  2204.89  1970.28   89.36       91.27        4.14            57.61             2.61    177.01      8.03
+L_R004S03C01                  12.31  2314.18  2103.86   90.91       74.52        3.22            86.45             3.74    123.87      5.35
+L_R004S06C01                   9.26  2029.95  1861.81   91.72       19.85        0.98            44.98             2.22    123.15      6.07
+M_R003S01C01                  15.04  2191.47  1935.43   88.32       73.45        3.35           126.17             5.76    129.87      5.93
+M_R003S02C01                  15.14  2376.79  2052.14   86.34       35.24        1.48           204.97             8.62    119.69      5.04
+M_R003S04C01                  37.43  2015.63  1444.81   71.68      183.54        9.11            93.33             4.63    477.50     23.69
+M_R003S05C01                  46.60  2249.68  1344.71   59.77      143.29        6.37           134.14             5.96    770.84     34.26
+S_R003S01C01                  11.02  2127.86  1984.73   93.27       91.34        4.29            69.86             3.28     73.27      3.44
+S_R003S02C01                   8.70  2222.87  2097.15   94.34       67.66        3.04            71.94             3.24     53.78      2.42
+S_R003S03C01                  10.01  2071.12  1957.05   94.49       93.19        4.50            71.56             3.45     42.51      2.05
+S_R003S04C01                  16.47  2615.78  2254.19   86.18       69.33        2.65           218.44             8.35    143.15      5.47
+S_R004S01C01                   4.65  2117.91  2043.76   96.50       24.30        1.15            64.34             3.04      9.81      0.46
+S_R004S02C01                   5.04  2095.08  2027.67   96.78       38.13        1.82            35.96             1.72     31.45      1.50
+S_R004S03C01                   2.13  2074.39  2046.66   98.66       16.48        0.79            26.72             1.29      1.01      0.05
+S_R004S04C01                   4.43  2078.85  2029.99   97.65       43.19        2.08            12.66             0.61     36.20      1.74
+TOTAL                         14.61 43565.36 38642.83   88.70     1440.22        3.31          1894.78             4.35   3027.75      6.95

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