Delete create_handler.ipynb

create_handler.ipynb

@@ -1,280 +0,0 @@
-{
- "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
-
-from typing import Dict
-from pyannote.audio import Pipeline
-import torch 
-import base64
-import numpy as np
-
-SAMPLE_RATE = 16000
-
-class EndpointHandler():
-    def __init__(self, path=""):
-        # load the model
-        self.pipeline = Pipeline.from_pretrained("KIFF/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
-
-        # decode the base64 audio data
-        audio_data = base64.b64decode(inputs)
-        audio_nparray = np.frombuffer(audio_data, dtype=np.int16)
-
-        # prepare pynannote input
-        audio_tensor= torch.from_numpy(audio_nparray).float().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}