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{
"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\n",
"pyannote.audio"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Successfully built antlr4-python3-runtime docopt julius hyperpyyaml pyperclip\n",
"Installing collected packages: singledispatchmethod, pyperclip, primePy, einops, docopt, commonmark, antlr4-python3-runtime, typer, torch, stevedore, simplejson, shellingham, semver, scipy, ruamel.yaml.clib, rich, pyDeprecate, PrettyTable, omegaconf, networkx, Mako, kiwisolver, greenlet, fonttools, cycler, contourpy, cmd2, cmaes, backports.cached-property, autopage, torchvision, torchmetrics, torchaudio, sqlalchemy, ruamel.yaml, pyannote.core, matplotlib, julius, huggingface-hub, cliff, asteroid-filterbanks, torch-pitch-shift, pytorch-metric-learning, pyannote.database, hyperpyyaml, hmmlearn, alembic, torch-audiomentations, speechbrain, pyannote.metrics, optuna, pytorch-lightning, pyannote.pipeline, pyannote.audio\n",
" Attempting uninstall: torch\n",
" Found existing installation: torch 1.11.0+cu113\n",
" Uninstalling torch-1.11.0+cu113:\n",
" Successfully uninstalled torch-1.11.0+cu113\n",
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" Found existing installation: scipy 1.9.0\n",
" Uninstalling scipy-1.9.0:\n",
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"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"huggingface-inference-toolkit 0.1.0 requires torchvision<=0.12.0, but you have torchvision 0.13.1 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed Mako-1.2.3 PrettyTable-3.4.1 alembic-1.8.1 antlr4-python3-runtime-4.9.3 asteroid-filterbanks-0.4.0 autopage-0.5.1 backports.cached-property-1.0.2 cliff-4.0.0 cmaes-0.8.2 cmd2-2.4.2 commonmark-0.9.1 contourpy-1.0.5 cycler-0.11.0 docopt-0.6.2 einops-0.3.2 fonttools-4.37.4 greenlet-1.1.3 hmmlearn-0.2.8 huggingface-hub-0.8.1 hyperpyyaml-1.0.1 julius-0.2.7 kiwisolver-1.4.4 matplotlib-3.6.0 networkx-2.8.7 omegaconf-2.2.3 optuna-3.0.2 primePy-1.3 pyDeprecate-0.3.2 pyannote.audio-2.0.1 pyannote.core-4.5 pyannote.database-4.1.3 pyannote.metrics-3.2.1 pyannote.pipeline-2.3 pyperclip-1.8.2 pytorch-lightning-1.6.5 pytorch-metric-learning-1.6.2 rich-12.6.0 ruamel.yaml-0.17.21 ruamel.yaml.clib-0.2.6 scipy-1.8.1 semver-2.13.0 shellingham-1.5.0 simplejson-3.17.6 singledispatchmethod-1.0 speechbrain-0.5.13 sqlalchemy-1.4.41 stevedore-4.0.0 torch-1.12.1 torch-audiomentations-0.11.0 torch-pitch-shift-1.2.2 torchaudio-0.12.1 torchmetrics-0.10.0 torchvision-0.13.1 typer-0.6.1\n"
]
}
],
"source": [
"!pip install -r requirements.txt --upgrade"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"start=0.5s stop=1.4s speaker_SPEAKER_01\n",
"start=1.9s stop=2.8s speaker_SPEAKER_01\n",
"start=3.0s stop=3.5s speaker_SPEAKER_02\n",
"start=3.6s stop=4.3s speaker_SPEAKER_01\n",
"start=4.6s stop=6.8s speaker_SPEAKER_02\n",
"start=7.1s stop=7.6s speaker_SPEAKER_00\n",
"start=7.6s stop=9.5s speaker_SPEAKER_02\n",
"start=9.8s stop=10.6s speaker_SPEAKER_02\n",
"start=9.9s stop=10.4s speaker_SPEAKER_00\n",
"start=12.4s stop=15.6s speaker_SPEAKER_03\n",
"start=15.8s stop=16.1s speaker_SPEAKER_00\n",
"start=16.1s stop=16.2s speaker_SPEAKER_01\n",
"start=17.2s stop=17.4s speaker_SPEAKER_00\n",
"start=17.7s stop=20.4s speaker_SPEAKER_01\n",
"start=20.6s stop=20.7s speaker_SPEAKER_01\n",
"start=20.7s stop=20.8s speaker_SPEAKER_00\n",
"start=20.8s stop=20.9s speaker_SPEAKER_01\n",
"start=21.1s stop=22.1s speaker_SPEAKER_01\n",
"start=22.5s stop=22.7s speaker_SPEAKER_02\n",
"start=23.2s stop=23.5s speaker_SPEAKER_02\n",
"start=23.5s stop=24.0s speaker_SPEAKER_01\n",
"start=24.3s stop=25.5s speaker_SPEAKER_02\n",
"start=25.8s stop=27.3s speaker_SPEAKER_01\n",
"start=27.3s stop=27.5s speaker_SPEAKER_02\n",
"start=29.7s stop=30.0s speaker_SPEAKER_01\n"
]
}
],
"source": [
"from pyannote.audio import Pipeline\n",
"pipeline = Pipeline.from_pretrained(\"pyannote/speaker-diarization\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from transformers.pipelines.audio_utils import ffmpeg_read\n",
"import torch\n",
"\n",
"\n",
"\n",
"\n",
"audio_nparray = ffmpeg_read(request[\"inputs\"], 16000)\n",
"audio_tensor= torch.from_numpy(audio_nparray).unsqueeze(0)\n",
"f = {\"waveform\": audio_tensor, \"sample_rate\": 16000}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Create Custom Handler for Inference Endpoints\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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"
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"orig_nbformat": 4,
"vscode": {
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