{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Load RTTM" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# !wget https://raw.githubusercontent.com/pyannote/pyannote-audio/develop/tutorials/data_preparation/AMI/MixHeadset.test.rttm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "This tutorial is available as an IPython notebook at [malaya-speech/example/load-rrtm](https://github.com/huseinzol05/malaya-speech/tree/master/example/load-rttm).\n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import malaya_speech" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load .rttm\n", "\n", "```python\n", "def load(file: str):\n", " \"\"\"\n", " Load RTTM file.\n", "\n", " Parameters\n", " ----------\n", " file: str\n", "\n", " Returns\n", " -------\n", " result : Dict[str, malaya_speech.model.annotation.Annotation]\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "annotations = malaya_speech.extra.rttm.load('example/load-rttm/MixHeadset.test.rttm')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get available samples" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['EN2002a.Mix-Headset', 'EN2002b.Mix-Headset', 'EN2002c.Mix-Headset', 'EN2002d.Mix-Headset', 'ES2004a.Mix-Headset', 'ES2004b.Mix-Headset', 'ES2004c.Mix-Headset', 'ES2004d.Mix-Headset', 'ES2014a.Mix-Headset', 'ES2014b.Mix-Headset', 'ES2014c.Mix-Headset', 'ES2014d.Mix-Headset', 'IS1009a.Mix-Headset', 'IS1009b.Mix-Headset', 'IS1009c.Mix-Headset', 'IS1009d.Mix-Headset', 'TS3003a.Mix-Headset', 'TS3003b.Mix-Headset', 'TS3003c.Mix-Headset', 'TS3003d.Mix-Headset', 'TS3007a.Mix-Headset', 'TS3007b.Mix-Headset', 'TS3007c.Mix-Headset', 'TS3007d.Mix-Headset'])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "annotations.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get a sample" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample = annotations['ES2004a.Mix-Headset']\n", "sample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get minimum frame" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample.min" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get maximum frame" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1048.672" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample.max" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get tracks" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 5531 FEE013\n", " 5530 MEO015\n", " 5532 FEE013\n", " 5533 MEO015\n", " 5534 FEE013\n", " 5535 FEE013\n", " 5536 FEE016\n", " 5537 FEE013\n", " 5538 FEE016\n", " 5539 FEE013\n", " 5540 FEE016\n", " 5541 FEE013\n", " 5542 FEE016\n", " 5543 FEE013\n", " 5544 FEE016\n", " 5545 MEO015\n", " 5546 MEO015\n", " 5547 FEE013\n", " 5548 MEO015\n", " 5549 MEO015\n", " 5550 FEE013\n", " 5551 FEE013\n", " 5552 FEE013\n", " 5553 MEO015\n", " 5554 FEE013\n", " 5555 MEO015\n", " 5556 FEE016\n", " 5557 MEO015\n", " 5558 FEE013\n", " 5559 FEE013\n", " 5560 FEE013\n", " 5561 FEE013\n", " 5562 MEO015\n", " 5563 FEE013\n", " 5564 MEO015\n", " 5565 MEO015\n", " 5566 FEE013\n", " 5567 FEE013\n", " 5568 FEE013\n", " 5569 FEE013\n", " 5570 MEO015\n", " 5571 FEE016\n", " 5572 MEO015\n", " 5573 MEO015\n", " 5574 FEE013\n", " 5575 MEO015\n", " 5576 FEE016\n", " 5577 FEE013\n", " 5578 MEO015\n", " 5579 MEO015\n", " 5580 FEE016\n", " 5581 MEE014\n", " 5582 FEE016\n", " 5583 MEO015\n", " 5584 FEE016\n", " 5585 FEE013\n", " 5586 MEE014\n", " 5587 MEO015\n", " 5588 MEO015\n", " 5589 FEE016\n", " 5590 MEE014\n", " 5591 FEE013\n", " 5592 MEE014\n", " 5593 MEO015\n", " 5594 FEE013\n", " 5595 MEE014\n", " 5596 FEE016\n", " 5597 FEE013\n", " 5598 MEO015\n", " 5599 FEE016\n", " 5600 FEE013\n", " 5601 MEE014\n", " 5602 FEE013\n", " 5603 MEE014\n", " 5604 FEE016\n", " 5605 FEE013\n", " 5606 FEE013\n", " 5607 FEE016\n", " 5608 MEO015\n", " 5609 FEE016\n", " 5610 FEE016\n", " 5611 FEE013\n", " 5612 FEE013\n", " 5613 FEE013\n", " 5614 FEE016\n", " 5615 MEE014\n", " 5616 MEE014\n", " 5617 MEE014\n", " 5618 MEE014\n", " 5619 FEE013\n", " 5620 FEE016\n", " 5621 FEE016\n", " 5622 MEE014\n", " 5623 MEE014\n", " 5624 FEE016\n", " 5625 MEE014\n", " 5626 FEE013\n", " 5627 FEE016\n", " 5628 FEE013\n", " 5629 MEE014\n", " 5630 FEE016\n", " 5631 MEE014\n", " 5632 FEE016\n", " 5633 MEE014\n", " 5634 FEE016\n", " 5635 FEE013\n", " 5636 MEE014\n", " 5637 FEE016\n", " 5638 MEE014\n", " 5639 FEE016\n", " 5640 MEE014\n", " 5641 FEE013\n", " 5642 FEE016\n", " 5643 MEE014\n", " 5644 FEE013\n", " 5645 MEO015\n", " 5646 MEE014\n", " 5647 MEE014\n", " 5648 FEE016\n", " 5649 FEE013\n", " 5650 MEE014\n", " 5651 FEE013\n", " 5652 FEE016\n", " 5653 FEE013\n", " 5654 FEE013\n", " 5655 FEE016\n", " 5656 FEE013\n", " 5657 FEE013\n", " 5658 FEE016\n", " 5659 MEE014\n", " 5660 FEE013\n", " 5661 FEE013\n", " 5662 MEE014\n", " 5663 FEE016\n", " 5664 MEO015\n", " 5665 MEE014\n", " 5666 MEO015\n", " 5667 FEE013\n", " 5668 FEE016\n", " 5669 FEE016\n", " 5670 MEE014\n", " 5671 FEE013\n", " 5672 FEE016\n", " 5673 MEE014\n", " 5674 FEE016\n", " 5675 FEE013\n", " 5676 FEE016\n", " 5677 FEE013\n", " 5678 FEE016\n", " 5679 FEE013\n", " 5680 FEE013\n", " 5681 MEE014\n", " 5682 MEO015\n", " 5683 FEE013\n", " 5684 MEE014\n", " 5685 FEE016\n", " 5686 FEE016\n", " 5687 FEE016\n", " 5688 FEE013\n", " 5689 FEE016\n", " 5690 MEO015\n", " 5691 FEE016\n", " 5692 FEE013\n", " 5693 FEE013\n", " 5694 FEE016\n", " 5695 FEE013\n", " 5696 FEE016\n", " 5697 FEE016\n", " 5698 FEE013\n", " 5699 MEO015\n", " 5700 MEE014\n", " 5701 MEE014\n", " 5702 MEO015\n", " 5703 FEE016\n", " 5704 MEE014\n", " 5705 FEE016\n", " 5706 MEE014\n", " 5707 FEE013\n", " 5708 FEE016\n", " 5709 MEO015\n", " 5710 MEE014\n", " 5711 FEE016\n", " 5712 MEO015\n", " 5713 FEE013\n", " 5714 FEE013\n", " 5715 FEE016\n", " 5716 FEE016\n", " 5717 MEE014\n", " 5718 FEE016\n", " 5719 FEE013\n", " 5720 FEE013\n", " 5721 MEO015\n", " 5722 FEE013\n", " 5723 FEE016\n", " 5724 FEE016\n", " 5725 FEE016\n", " 5726 FEE016\n", " 5727 MEE014\n", " 5728 FEE016\n", " 5729 MEO015\n", " 5730 MEO015\n", " 5731 MEE014\n", " 5732 MEO015\n", " 5733 MEO015\n", " 5734 FEE016\n", " 5735 FEE016\n", " 5736 FEE013\n", " 5737 MEO015\n", " 5738 FEE013\n", " 5739 MEE014\n", " 5740 FEE016\n", " 5741 MEO015\n", " 5742 FEE013\n", " 5743 MEE014\n", " 5744 FEE016\n", " 5745 FEE016\n", " 5746 FEE016\n", " 5747 MEE014\n", " 5748 FEE013\n", " 5749 MEE014\n", " 5750 MEE014\n", " 5751 FEE013\n", " 5752 MEO015\n", " 5753 MEE014\n", " 5754 FEE013\n", " 5755 MEE014\n", " 5756 FEE016\n", " 5757 FEE013\n", " 5758 FEE016\n", " 5759 FEE013\n", " 5760 MEE014\n", " 5761 FEE016\n", " 5762 FEE013\n", " 5763 FEE016\n", " 5764 FEE016\n", " 5765 MEO015\n", " 5766 FEE013\n", " 5767 FEE013\n", " 5768 FEE013\n", " 5769 FEE016\n", " 5770 FEE016\n", " 5771 FEE016\n", " 5772 FEE016\n", " 5773 FEE013\n", " 5774 MEO015\n", " 5775 MEO015\n", " 5776 FEE013\n", " 5777 MEE014\n", " 5778 MEO015\n", " 5779 FEE016\n", " 5780 FEE016\n", " 5781 FEE013\n", " 5782 FEE016\n", " 5783 MEE014\n", " 5784 FEE013\n", " 5785 FEE016\n", " 5786 FEE013\n", " 5787 MEO015\n", " 5788 FEE013\n", " 5789 MEE014\n", " 5790 FEE013\n", " 5791 FEE013\n", " 5792 MEE014\n", " 5793 FEE013\n", " 5794 FEE016\n", " 5795 MEO015\n", " 5796 FEE013\n", " 5797 FEE016\n", " 5798 FEE016\n", " 5799 FEE016\n", " 5800 MEE014\n", " 5801 FEE013\n", " 5802 MEE014\n", " 5803 FEE013\n", " 5804 FEE013\n", " 5805 FEE016\n", " 5806 FEE016\n", " 5807 FEE016\n", " 5808 FEE013\n", " 5809 MEE014\n", " 5810 FEE016\n", " 5811 FEE013\n", " 5812 MEO015\n" ] } ], "source": [ "for segment, track, label in sample.itertracks():\n", " print(segment, track, label)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So we able to know which speakers for which segments." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Crop sample by time\n", "\n", "```python\n", "\n", "def crop(\n", " self,\n", " from_t: float = None,\n", " to_t: float = None,\n", " mode: str = 'intersection',\n", "):\n", "\n", " \"\"\"\n", " Crop sample by time.\n", "\n", " Parameters\n", " ----------\n", " from_t: float, optional (default=None)\n", " if None, will take self.min\n", " to_t: float, optional (default=None)\n", " if None, will take self.max\n", " mode: str, optional (default='intersection')\n", " crop mode supported. Allowed values:\n", "\n", " * ``'intersection'`` - sampling with crop if middle of the track.\n", " * ``'strict'`` - sampling with strictly method, will not crop the track.\n", " * ``'loose'`` - sampling with loose method, will take entire track.\n", "\n", " Returns\n", " -------\n", " result : malaya_speech.model.annotation.ANNOTATION class\n", " \"\"\"\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Visualize tracks" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a = sample.crop(600, 660, mode = 'strict')\n", "a.plot()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a = sample.crop(600, 660, mode = 'loose')\n", "a.plot()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a = sample.crop(600, 660, mode = 'intersection')\n", "a.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }