{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "c61232ac", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "%matplotlib inline\n", "\n", "import json, os, glob" ] }, { "cell_type": "code", "execution_count": 3, "id": "3ef17ff3", "metadata": {}, "outputs": [], "source": [ "from suno_utils.audio import Audio\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "17872d88", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "75" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "audio_files = glob.glob(\"/mnt/data-ssd-1/data/private/customer/sanas/ERC-PH-ASR-1/ERC-PH-ASR-1/*.wav\")\n", "len(audio_files)" ] }, { "cell_type": "code", "execution_count": 11, "id": "5c767105", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Audio(391.9 secs, 44100 Hz, 16 bits, 1 ch)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = Audio.from_file(audio_files[0])\n", "a" ] }, { "cell_type": "code", "execution_count": 142, "id": "2ee191e0", "metadata": { "scrolled": false }, "outputs": [], "source": [ "#audio_files" ] }, { "cell_type": "code", "execution_count": 32, "id": "1c26284c", "metadata": {}, "outputs": [], "source": [ "bad_files = {'/mnt/data-ssd-1/data/private/customer/sanas/ERC-PH-ASR-1/ERC-PH-ASR-1/JuanCarlos_Ragon_Male_PH-Tagalog_1658753625818.wav'}" ] }, { "cell_type": "code", "execution_count": 35, "id": "22a51a0d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "838.7297597190733" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from suno_utils.audio.conversion import get_audio_properties\n", "sum([get_audio_properties(af)[\"duration_s\"] for af in audio_files if af not in bad_files]) / 3600" ] }, { "cell_type": "code", "execution_count": 45, "id": "de36d4d8", "metadata": {}, "outputs": [], "source": [ "from suno_utils.utils.slicer import get_vad_gaps, predict_vad_speech, get_annotated_segments, get_segments\n" ] }, { "cell_type": "code", "execution_count": 198, "id": "c121d873", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "153" ] }, "execution_count": 198, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gaps = get_vad_gaps(a, vad_level=3, min_gap_s=.1)\n", "len(gaps)" ] }, { "cell_type": "code", "execution_count": 199, "id": "2706b27a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.05,6.54)" ] }, "execution_count": 199, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gaps[0]" ] }, { "cell_type": "code", "execution_count": 201, "id": "96c85708", "metadata": {}, "outputs": [], "source": [ "#a.play()" ] }, { "cell_type": "code", "execution_count": 202, "id": "863fdb4e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.05,6.54) | [6.73,8.14) | [8.35,11.24) | [11.53,11.75) | [11.93,13.16) | [13.4,13.54) | [13.88,13.98) | [14.43,14.57) | [15.21,15.36) | [15.54,15.63)" ] }, "execution_count": 202, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gaps[:10]\n", "#gaps[4], gaps[5], gaps[6]" ] }, { "cell_type": "code", "execution_count": 203, "id": "f2b80bb8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13.4" ] }, "execution_count": 203, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g = gaps[5]\n", "g.lower" ] }, { "cell_type": "code", "execution_count": 204, "id": "767d4319", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 205, "id": "53d85c27", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(10000000.0, 11000000.0)" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = pd.Series(a.array_float).plot(figsize=(12, 7))\n", "for gap in gaps:\n", " span = seg[1]\n", " ax.axvline(gap.lower * sample_factor, color='g')\n", " ax.axvline(gap.upper * sample_factor, color='r')\n", "plt.xlim(1e7, 1.1e7)" ] }, { "cell_type": "code", "execution_count": 206, "id": "e0a26fde", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.05,6.54)" ] }, "execution_count": 206, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gaps[0]" ] }, { "cell_type": "code", "execution_count": 207, "id": "4ee6d5d7", "metadata": {}, "outputs": [], "source": [ "freq_a = 440 * 4\n", "beep_array = np.sin(np.linspace(0, 1, a.sample_rate) * freq_a)\n", "#beep_array" ] }, { "cell_type": "code", "execution_count": 208, "id": "8fc263ca", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "beep_audio = Audio.from_array_float(beep_array, sample_rate=a.sample_rate)\n", "beep_audio.play()" ] }, { "cell_type": "code", "execution_count": 209, "id": "402c3514", "metadata": {}, "outputs": [], "source": [ "pieces = []\n", "for gap1, gap2 in zip(gaps[:-1], gaps[1:]):\n", " st, ed = gap1.upper, gap2.lower\n", " if ed - st < 0.2:\n", " continue\n", " pieces.append(a.get_slice(st, ed).array)\n", "# if gap2.upper - gap2.lower > 2:\n", "# pieces.append(beep_audio.array)\n", "new_audio = Audio.from_array(np.concatenate(pieces), sample_rate=a.sample_rate)\n", "del pieces\n", "\n", "pieces = []\n", "for gap in gaps:\n", " pieces.append(a.get_slice(gap.lower, gap.upper).array)\n", "deleted_audio = Audio.from_array(np.concatenate(pieces), sample_rate=a.sample_rate)\n", "del pieces" ] }, { "cell_type": "code", "execution_count": 210, "id": "ac68081a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(Audio(156.6 secs, 44100 Hz, 16 bits, 1 ch),\n", " Audio(225.6 secs, 44100 Hz, 16 bits, 1 ch),\n", " Audio(391.9 secs, 44100 Hz, 16 bits, 1 ch))" ] }, "execution_count": 210, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_audio, deleted_audio, a" ] }, { "cell_type": "code", "execution_count": 211, "id": "421ea47a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_audio.play()" ] }, { "cell_type": "code", "execution_count": 212, "id": "94047716", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "deleted_audio.play()" ] }, { "cell_type": "code", "execution_count": 136, "id": "9b00f167", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "([11.58,11.7), [11.98,13.11), [13.45,13.49))" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gaps[3], gaps[4], gaps[5]" ] }, { "cell_type": "code", "execution_count": null, "id": "cad0914a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "bb892b2b", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 69, "id": "91dcb3db", "metadata": {}, "outputs": [], "source": [ "segments = get_segments(a, vad_level=3)" ] }, { "cell_type": "code", "execution_count": 70, "id": "bce53a8b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(Audio(20.0 secs, 44100 Hz, 16 bits, 1 ch), (120.03, 140.03))" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#segments[3][0].play()\n", "segments[6]" ] }, { "cell_type": "code", "execution_count": null, "id": "1239e258", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 85, "id": "06d82281", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "21" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(segments)" ] }, { "cell_type": "code", "execution_count": 86, "id": "ce5a8c31", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = pd.Series(a.array_float).plot(figsize=(12, 7))\n", "for seg in segments:\n", " span = seg[1]\n", " ax.axvline(span[0] * sample_factor, color='g')\n", " ax.axvline(span[1] * sample_factor, color='r')" ] }, { "cell_type": "code", "execution_count": 89, "id": "c5c3301a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "i = 2\n", "segments[i][0].play()" ] }, { "cell_type": "code", "execution_count": null, "id": "bd589747", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "863197bf", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "eda1142e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "04db8cb7", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }