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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1410
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m101.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1411
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m92.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1412
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m57.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1413
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1414
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1415
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1416
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1417
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1418
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m84.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1419
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.9/9.9 MB\u001b[0m \u001b[31m93.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1420
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m864.1/864.1 kB\u001b[0m \u001b[31m42.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1421
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.9/6.9 MB\u001b[0m \u001b[31m132.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1422
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.1/79.1 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1423
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.3/118.3 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1424
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m739.1/739.1 kB\u001b[0m \u001b[31m53.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1425
+ "\u001b[?25h"
1426
+ ]
1427
+ }
1428
+ ],
1429
+ "source": [
1430
+ "# Install needed libraries (run this cell first!)\n",
1431
+ "!pip install --quiet yt-dlp ffmpeg-python torch torchaudio transformers streamlit speechbrain\n"
1432
+ ]
1433
+ },
1434
+ {
1435
+ "cell_type": "code",
1436
+ "source": [
1437
+ "import os\n",
1438
+ "import subprocess\n",
1439
+ "import torchaudio\n",
1440
+ "import torch\n",
1441
+ "from speechbrain.pretrained import EncoderClassifier\n",
1442
+ "import yt_dlp\n"
1443
+ ],
1444
+ "metadata": {
1445
+ "colab": {
1446
+ "base_uri": "https://localhost:8080/"
1447
+ },
1448
+ "id": "BDKT_c4rI07R",
1449
+ "outputId": "8083d2c5-eade-4424-db57-a640ce85bcb8"
1450
+ },
1451
+ "execution_count": 5,
1452
+ "outputs": [
1453
+ {
1454
+ "output_type": "stream",
1455
+ "name": "stderr",
1456
+ "text": [
1457
+ "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint save hook for _speechbrain_save\n",
1458
+ "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint load hook for _speechbrain_load\n",
1459
+ "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint save hook for save\n",
1460
+ "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint load hook for load\n",
1461
+ "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint save hook for _save\n",
1462
+ "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint load hook for _recover\n",
1463
+ "<ipython-input-5-0f12fb55d26c>:5: UserWarning: Module 'speechbrain.pretrained' was deprecated, redirecting to 'speechbrain.inference'. Please update your script. This is a change from SpeechBrain 1.0. See: https://github.com/speechbrain/speechbrain/releases/tag/v1.0.0\n",
1464
+ " from speechbrain.pretrained import EncoderClassifier\n"
1465
+ ]
1466
+ }
1467
+ ]
1468
+ },
1469
+ {
1470
+ "cell_type": "code",
1471
+ "source": [
1472
+ "# Paste your video URL here (YouTube or direct MP4 link)\n",
1473
+ "VIDEO_URL = \"https://youtu.be/DDjWTWHHkpk?si=oIj6Fuy8Hg2E8U_l\" # Example: Replace with your actual link!\n"
1474
+ ],
1475
+ "metadata": {
1476
+ "id": "dco77Q2CI45K"
1477
+ },
1478
+ "execution_count": 2,
1479
+ "outputs": []
1480
+ },
1481
+ {
1482
+ "cell_type": "code",
1483
+ "source": [
1484
+ "def download_video(url, out_path=\"input_video.mp4\"):\n",
1485
+ " \"\"\"\n",
1486
+ " Downloads a video from YouTube or direct MP4 link.\n",
1487
+ " Returns the filename of the downloaded video.\n",
1488
+ " \"\"\"\n",
1489
+ " # If it's a YouTube link, use yt-dlp\n",
1490
+ " if \"youtube.com\" in url or \"youtu.be\" in url:\n",
1491
+ " ydl_opts = {'outtmpl': out_path}\n",
1492
+ " with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n",
1493
+ " ydl.download([url])\n",
1494
+ " else:\n",
1495
+ " # For direct links, use wget/curl fallback\n",
1496
+ " os.system(f\"wget -O {out_path} {url}\")\n",
1497
+ " return out_path\n",
1498
+ "\n",
1499
+ "video_file = download_video(VIDEO_URL)\n",
1500
+ "print(f\"Downloaded video: {video_file}\")\n"
1501
+ ],
1502
+ "metadata": {
1503
+ "colab": {
1504
+ "base_uri": "https://localhost:8080/"
1505
+ },
1506
+ "id": "RpWlsFnUJECZ",
1507
+ "outputId": "c8795ebf-01de-40ae-c80c-02cfa09a1357"
1508
+ },
1509
+ "execution_count": 6,
1510
+ "outputs": [
1511
+ {
1512
+ "output_type": "stream",
1513
+ "name": "stdout",
1514
+ "text": [
1515
+ "[youtube] Extracting URL: https://youtu.be/DDjWTWHHkpk?si=oIj6Fuy8Hg2E8U_l\n",
1516
+ "[youtube] DDjWTWHHkpk: Downloading webpage\n",
1517
+ "[youtube] DDjWTWHHkpk: Downloading tv client config\n",
1518
+ "[youtube] DDjWTWHHkpk: Downloading player f203bbc8-main\n",
1519
+ "[youtube] DDjWTWHHkpk: Downloading tv player API JSON\n",
1520
+ "[youtube] DDjWTWHHkpk: Downloading ios player API JSON\n",
1521
+ "[youtube] DDjWTWHHkpk: Downloading m3u8 information\n",
1522
+ "[info] DDjWTWHHkpk: Downloading 1 format(s): 399+251\n",
1523
+ "[download] Destination: input_video.mp4.f399.mp4\n",
1524
+ "[download] 100% of 62.45MiB in 00:00:01 at 31.78MiB/s \n",
1525
+ "[download] Destination: input_video.mp4.f251.webm\n",
1526
+ "[download] 100% of 4.57MiB in 00:00:00 at 9.99MiB/s \n",
1527
+ "[Merger] Merging formats into \"input_video.mp4.webm\"\n",
1528
+ "Deleting original file input_video.mp4.f399.mp4 (pass -k to keep)\n",
1529
+ "Deleting original file input_video.mp4.f251.webm (pass -k to keep)\n",
1530
+ "Downloaded video: input_video.mp4\n"
1531
+ ]
1532
+ }
1533
+ ]
1534
+ },
1535
+ {
1536
+ "cell_type": "code",
1537
+ "source": [
1538
+ "def extract_audio(video_path, audio_path=\"audio.wav\"):\n",
1539
+ " \"\"\"\n",
1540
+ " Extracts audio from a video file using ffmpeg.\n",
1541
+ " Returns the filename of the audio file.\n",
1542
+ " \"\"\"\n",
1543
+ " # Remove if already exists\n",
1544
+ " if os.path.exists(audio_path):\n",
1545
+ " os.remove(audio_path)\n",
1546
+ " # Extract audio with ffmpeg\n",
1547
+ " cmd = f\"ffmpeg -y -i {video_path} -ar 16000 -ac 1 -vn {audio_path}\"\n",
1548
+ " subprocess.call(cmd, shell=True)\n",
1549
+ " return audio_path\n",
1550
+ "\n",
1551
+ "audio_file = extract_audio(video_file)\n",
1552
+ "print(f\"Extracted audio file: {audio_file}\")\n"
1553
+ ],
1554
+ "metadata": {
1555
+ "colab": {
1556
+ "base_uri": "https://localhost:8080/"
1557
+ },
1558
+ "id": "4a40jh5sJLMR",
1559
+ "outputId": "9756fbf1-2666-4271-bfd4-78de81d92b27"
1560
+ },
1561
+ "execution_count": 7,
1562
+ "outputs": [
1563
+ {
1564
+ "output_type": "stream",
1565
+ "name": "stdout",
1566
+ "text": [
1567
+ "Extracted audio file: audio.wav\n"
1568
+ ]
1569
+ }
1570
+ ]
1571
+ },
1572
+ {
1573
+ "cell_type": "code",
1574
+ "source": [
1575
+ "def extract_audio(video_path, audio_path=\"/content/audio.wav\"):\n",
1576
+ " \"\"\"\n",
1577
+ " Extracts audio from a video file using ffmpeg.\n",
1578
+ " Returns the filename of the audio file.\n",
1579
+ " \"\"\"\n",
1580
+ " # Remove if already exists\n",
1581
+ " if os.path.exists(audio_path):\n",
1582
+ " os.remove(audio_path)\n",
1583
+ " # Extract audio with ffmpeg\n",
1584
+ " cmd = f\"ffmpeg -y -i {video_path} -ar 16000 -ac 1 -vn {audio_path}\"\n",
1585
+ " # Use subprocess.run to capture output and check the return code\n",
1586
+ " result = subprocess.run(cmd, shell=True, capture_output=True, text=True)\n",
1587
+ "\n",
1588
+ " if result.returncode != 0:\n",
1589
+ " print(f\"FFmpeg command failed with error code {result.returncode}\")\n",
1590
+ " print(\"FFmpeg stderr:\")\n",
1591
+ " print(result.stderr)\n",
1592
+ " # Optionally, raise an error or exit if audio extraction fails\n",
1593
+ " raise RuntimeError(f\"Failed to extract audio using FFmpeg. See stderr above.\")\n",
1594
+ " else:\n",
1595
+ " print(\"FFmpeg stdout:\")\n",
1596
+ " print(result.stdout)\n",
1597
+ " print(\"FFmpeg stderr:\")\n",
1598
+ " print(result.stderr) # ffmpeg often outputs info/warnings to stderr\n",
1599
+ "\n",
1600
+ " # Check if the audio file was actually created\n",
1601
+ " if not os.path.exists(audio_path):\n",
1602
+ " raise FileNotFoundError(f\"Audio file '{audio_path}' was not created after FFmpeg execution.\")\n",
1603
+ "\n",
1604
+ " return audio_path"
1605
+ ],
1606
+ "metadata": {
1607
+ "id": "ID_TzSsMJsqF"
1608
+ },
1609
+ "execution_count": 8,
1610
+ "outputs": []
1611
+ },
1612
+ {
1613
+ "cell_type": "code",
1614
+ "source": [
1615
+ "# Download the pre-trained English accent classifier (SpeechBrain)\n",
1616
+ "accent_model = EncoderClassifier.from_hparams(\n",
1617
+ " source=\"speechbrain/lang-id-commonlanguage_ecapa\",\n",
1618
+ " savedir=\"tmp_accent_model\"\n",
1619
+ ")\n"
1620
+ ],
1621
+ "metadata": {
1622
+ "colab": {
1623
+ "base_uri": "https://localhost:8080/",
1624
+ "height": 599,
1625
+ "referenced_widgets": [
1626
+ "025b845ba49e423cbfc757252db02381",
1627
+ "e29ef8d387a94e6990343111c600d40f",
1628
+ "bd085d627a404aac90bb6336f74d22a9",
1629
+ "06c62bd2fcc346a197c3579d00ca0d44",
1630
+ "df3990918f424c60be35b4c5be90ac34",
1631
+ "3c88eea7ce64430e94e998a81463143e",
1632
+ "0f38dc6f89064ec49950502e32a5cc53",
1633
+ "f6d0a0357ac048d9ad5ef31056fb65a1",
1634
+ "a570c3b7f01a4906a51eff36d264bb26",
1635
+ "4e352cff53fa49069d0131578fd3248d",
1636
+ "2ca94275b52b488fa7755de3c7b03216",
1637
+ "d7a05ee4c7c24eacbddf57ab6644639b",
1638
+ "9e2406e1e42d42068d99c706484bab9a",
1639
+ "2d49939f1d264b59906278bf4939ab2a",
1640
+ "87a3a050b04e4e5b941951780926d36c",
1641
+ "f0d5a89ad850448abcd506d6de86fff9",
1642
+ "16ccf523e9c24390ba654e282241831d",
1643
+ "25da015c20f8426db38b3853245b37a4",
1644
+ "77c6272fc49448529d7a7a919b1cc418",
1645
+ "a9c2643b4c3f4af7b54b9c3940377519",
1646
+ "75b859dc0f5642408cf318d6583a8da3",
1647
+ "424c140f22c142988c5e95114c4680d7",
1648
+ "a0ae653cdf364d528ff132a48c1020c4",
1649
+ "353f33d613d34b13944aaca5017752e1",
1650
+ "bb24cf37e9d948bb81527968af1194c9",
1651
+ "c2e2a5f07c594707a215a3f3c5e673b9",
1652
+ "ae843746277c43e5bbe68390ad8ea887",
1653
+ "901c650e39ea4ddc8148e80f087b40e5",
1654
+ "71ffe21e8fe04be3ab6bfb4c10ae2403",
1655
+ "a749c4a8500c45c4b083918cac225bc0",
1656
+ "9182ff55244542aa9f77a7fefa181f49",
1657
+ "a9c504808b7c4b87919dfc976342fe0e",
1658
+ "7f6e9e7d12574620a04e3ce7040f40d6",
1659
+ "0028087e124840ce93d36b8bd482bb57",
1660
+ "b1e9160b4b4b45798d9519bdfc9495a8",
1661
+ "0bc64a9e7911471f87a0d96b1c7f0e92",
1662
+ "b1c1e8f2fc854d0699cb1db59cfae2a1",
1663
+ "82d76c4cdcf14a4ba5f34b49e9bd66d8",
1664
+ "a39ac82e411b481893061534b82783d6",
1665
+ "f49d6a8841544fbeb1b3a01826d5d230",
1666
+ "8ef83c7b9f49497dbe1cfaa7a4f898b2",
1667
+ "68339024f0bc410c8f1bd95184ff7d0b",
1668
+ "93516c9e85bb4f82a493be0bcb25120a",
1669
+ "57b73755ea0246d98995c774e129e422"
1670
+ ]
1671
+ },
1672
+ "id": "NioBBuqiJSyA",
1673
+ "outputId": "01d37290-1a05-42d0-b74c-587eb8a71885"
1674
+ },
1675
+ "execution_count": 9,
1676
+ "outputs": [
1677
+ {
1678
+ "output_type": "stream",
1679
+ "name": "stderr",
1680
+ "text": [
1681
+ "INFO:speechbrain.utils.fetching:Fetch hyperparams.yaml: Fetching from HuggingFace Hub 'speechbrain/lang-id-commonlanguage_ecapa' if not cached\n"
1682
+ ]
1683
+ },
1684
+ {
1685
+ "output_type": "display_data",
1686
+ "data": {
1687
+ "text/plain": [
1688
+ "hyperparams.yaml: 0%| | 0.00/1.67k [00:00<?, ?B/s]"
1689
+ ],
1690
+ "application/vnd.jupyter.widget-view+json": {
1691
+ "version_major": 2,
1692
+ "version_minor": 0,
1693
+ "model_id": "025b845ba49e423cbfc757252db02381"
1694
+ }
1695
+ },
1696
+ "metadata": {}
1697
+ },
1698
+ {
1699
+ "output_type": "stream",
1700
+ "name": "stderr",
1701
+ "text": [
1702
+ "DEBUG:speechbrain.utils.fetching:Fetch: Local file found, creating symlink '/root/.cache/huggingface/hub/models--speechbrain--lang-id-commonlanguage_ecapa/snapshots/9d809a7e1fdf48afa53e1b41ee8ae0af9ce46308/hyperparams.yaml' -> '/content/tmp_accent_model/hyperparams.yaml'\n",
1703
+ "INFO:speechbrain.utils.fetching:Fetch custom.py: Fetching from HuggingFace Hub 'speechbrain/lang-id-commonlanguage_ecapa' if not cached\n",
1704
+ "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint save hook for _save\n",
1705
+ "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint load hook for _load\n",
1706
+ "DEBUG:speechbrain.utils.checkpoints:Registered parameter transfer hook for _load\n",
1707
+ "/usr/local/lib/python3.11/dist-packages/speechbrain/utils/autocast.py:188: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n",
1708
+ " wrapped_fwd = torch.cuda.amp.custom_fwd(fwd, cast_inputs=cast_inputs)\n",
1709
+ "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint save hook for save\n",
1710
+ "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint load hook for load_if_possible\n",
1711
+ "DEBUG:speechbrain.utils.parameter_transfer:Collecting files (or symlinks) for pretraining in tmp_accent_model.\n",
1712
+ "INFO:speechbrain.utils.fetching:Fetch embedding_model.ckpt: Fetching from HuggingFace Hub 'speechbrain/lang-id-commonlanguage_ecapa' if not cached\n"
1713
+ ]
1714
+ },
1715
+ {
1716
+ "output_type": "display_data",
1717
+ "data": {
1718
+ "text/plain": [
1719
+ "embedding_model.ckpt: 0%| | 0.00/83.3M [00:00<?, ?B/s]"
1720
+ ],
1721
+ "application/vnd.jupyter.widget-view+json": {
1722
+ "version_major": 2,
1723
+ "version_minor": 0,
1724
+ "model_id": "d7a05ee4c7c24eacbddf57ab6644639b"
1725
+ }
1726
+ },
1727
+ "metadata": {}
1728
+ },
1729
+ {
1730
+ "output_type": "stream",
1731
+ "name": "stderr",
1732
+ "text": [
1733
+ "DEBUG:speechbrain.utils.fetching:Fetch: Local file found, creating symlink '/root/.cache/huggingface/hub/models--speechbrain--lang-id-commonlanguage_ecapa/snapshots/9d809a7e1fdf48afa53e1b41ee8ae0af9ce46308/embedding_model.ckpt' -> '/content/tmp_accent_model/embedding_model.ckpt'\n",
1734
+ "DEBUG:speechbrain.utils.parameter_transfer:Set local path in self.paths[\"embedding_model\"] = /content/tmp_accent_model/embedding_model.ckpt\n",
1735
+ "INFO:speechbrain.utils.fetching:Fetch classifier.ckpt: Fetching from HuggingFace Hub 'speechbrain/lang-id-commonlanguage_ecapa' if not cached\n"
1736
+ ]
1737
+ },
1738
+ {
1739
+ "output_type": "display_data",
1740
+ "data": {
1741
+ "text/plain": [
1742
+ "classifier.ckpt: 0%| | 0.00/35.4k [00:00<?, ?B/s]"
1743
+ ],
1744
+ "application/vnd.jupyter.widget-view+json": {
1745
+ "version_major": 2,
1746
+ "version_minor": 0,
1747
+ "model_id": "a0ae653cdf364d528ff132a48c1020c4"
1748
+ }
1749
+ },
1750
+ "metadata": {}
1751
+ },
1752
+ {
1753
+ "output_type": "stream",
1754
+ "name": "stderr",
1755
+ "text": [
1756
+ "DEBUG:speechbrain.utils.fetching:Fetch: Local file found, creating symlink '/root/.cache/huggingface/hub/models--speechbrain--lang-id-commonlanguage_ecapa/snapshots/9d809a7e1fdf48afa53e1b41ee8ae0af9ce46308/classifier.ckpt' -> '/content/tmp_accent_model/classifier.ckpt'\n",
1757
+ "DEBUG:speechbrain.utils.parameter_transfer:Set local path in self.paths[\"classifier\"] = /content/tmp_accent_model/classifier.ckpt\n",
1758
+ "INFO:speechbrain.utils.fetching:Fetch label_encoder.txt: Fetching from HuggingFace Hub 'speechbrain/lang-id-commonlanguage_ecapa' if not cached\n"
1759
+ ]
1760
+ },
1761
+ {
1762
+ "output_type": "display_data",
1763
+ "data": {
1764
+ "text/plain": [
1765
+ "label_encoder.txt: 0%| | 0.00/788 [00:00<?, ?B/s]"
1766
+ ],
1767
+ "application/vnd.jupyter.widget-view+json": {
1768
+ "version_major": 2,
1769
+ "version_minor": 0,
1770
+ "model_id": "0028087e124840ce93d36b8bd482bb57"
1771
+ }
1772
+ },
1773
+ "metadata": {}
1774
+ },
1775
+ {
1776
+ "output_type": "stream",
1777
+ "name": "stderr",
1778
+ "text": [
1779
+ "DEBUG:speechbrain.utils.fetching:Fetch: Local file found, creating symlink '/root/.cache/huggingface/hub/models--speechbrain--lang-id-commonlanguage_ecapa/snapshots/9d809a7e1fdf48afa53e1b41ee8ae0af9ce46308/label_encoder.txt' -> '/content/tmp_accent_model/label_encoder.ckpt'\n",
1780
+ "DEBUG:speechbrain.utils.parameter_transfer:Set local path in self.paths[\"label_encoder\"] = /content/tmp_accent_model/label_encoder.ckpt\n",
1781
+ "INFO:speechbrain.utils.parameter_transfer:Loading pretrained files for: embedding_model, classifier, label_encoder\n",
1782
+ "DEBUG:speechbrain.utils.parameter_transfer:Redirecting (loading from local path): embedding_model -> /content/tmp_accent_model/embedding_model.ckpt\n",
1783
+ "DEBUG:speechbrain.utils.parameter_transfer:Redirecting (loading from local path): classifier -> /content/tmp_accent_model/classifier.ckpt\n",
1784
+ "DEBUG:speechbrain.utils.parameter_transfer:Redirecting (loading from local path): label_encoder -> /content/tmp_accent_model/label_encoder.ckpt\n",
1785
+ "DEBUG:speechbrain.dataio.encoder:Loaded categorical encoding from /content/tmp_accent_model/label_encoder.ckpt\n"
1786
+ ]
1787
+ }
1788
+ ]
1789
+ },
1790
+ {
1791
+ "cell_type": "markdown",
1792
+ "source": [
1793
+ "Used to Debuging the code"
1794
+ ],
1795
+ "metadata": {
1796
+ "id": "Tu9Wo4k6K-EK"
1797
+ }
1798
+ },
1799
+ {
1800
+ "cell_type": "code",
1801
+ "source": [
1802
+ "# List the files to see if input_video.mp4 is present\n",
1803
+ "import os\n",
1804
+ "print(os.listdir('.'))\n"
1805
+ ],
1806
+ "metadata": {
1807
+ "colab": {
1808
+ "base_uri": "https://localhost:8080/"
1809
+ },
1810
+ "id": "iuY4MTUmKDmx",
1811
+ "outputId": "e325dbb9-3e01-4e17-a832-3180a8c809e3"
1812
+ },
1813
+ "execution_count": 10,
1814
+ "outputs": [
1815
+ {
1816
+ "output_type": "stream",
1817
+ "name": "stdout",
1818
+ "text": [
1819
+ "['.config', 'tmp_accent_model', 'input_video.mp4.webm', 'sample_data']\n"
1820
+ ]
1821
+ }
1822
+ ]
1823
+ },
1824
+ {
1825
+ "cell_type": "markdown",
1826
+ "source": [
1827
+ "TO check the debug file path"
1828
+ ],
1829
+ "metadata": {
1830
+ "id": "H2pjikAiLCqM"
1831
+ }
1832
+ },
1833
+ {
1834
+ "cell_type": "code",
1835
+ "source": [
1836
+ "# Try extracting audio again, but print output to check for errors\n",
1837
+ "video_path = \"/content/input_video.mp4.webm\" # or whatever your filename is!\n",
1838
+ "audio_path = \"audio.wav\"\n",
1839
+ "\n",
1840
+ "os.system(f\"ffmpeg -y -i {video_path} -ar 16000 -ac 1 -vn {audio_path}\")\n",
1841
+ "\n",
1842
+ "# See if audio.wav was created\n",
1843
+ "print(os.listdir('.'))\n"
1844
+ ],
1845
+ "metadata": {
1846
+ "colab": {
1847
+ "base_uri": "https://localhost:8080/"
1848
+ },
1849
+ "id": "_5huLKQoKMec",
1850
+ "outputId": "fca4a365-4745-46f0-cf29-80f754af6c59"
1851
+ },
1852
+ "execution_count": 11,
1853
+ "outputs": [
1854
+ {
1855
+ "output_type": "stream",
1856
+ "name": "stdout",
1857
+ "text": [
1858
+ "['.config', 'tmp_accent_model', 'input_video.mp4.webm', 'audio.wav', 'sample_data']\n"
1859
+ ]
1860
+ }
1861
+ ]
1862
+ },
1863
+ {
1864
+ "cell_type": "markdown",
1865
+ "source": [
1866
+ "Check the Size of the file"
1867
+ ],
1868
+ "metadata": {
1869
+ "id": "6tbUtgFDLGQq"
1870
+ }
1871
+ },
1872
+ {
1873
+ "cell_type": "code",
1874
+ "source": [
1875
+ "# Check if the file now exists and get its size\n",
1876
+ "import os\n",
1877
+ "print(\"audio.wav exists:\", os.path.exists(audio_path))\n",
1878
+ "if os.path.exists(audio_path):\n",
1879
+ " print(\"audio.wav size (bytes):\", os.path.getsize(audio_path))\n"
1880
+ ],
1881
+ "metadata": {
1882
+ "colab": {
1883
+ "base_uri": "https://localhost:8080/"
1884
+ },
1885
+ "id": "dnyXSxAmKZN2",
1886
+ "outputId": "9616b8da-62c3-4a77-8983-16a193f7ee0f"
1887
+ },
1888
+ "execution_count": 12,
1889
+ "outputs": [
1890
+ {
1891
+ "output_type": "stream",
1892
+ "name": "stdout",
1893
+ "text": [
1894
+ "audio.wav exists: True\n",
1895
+ "audio.wav size (bytes): 9308494\n"
1896
+ ]
1897
+ }
1898
+ ]
1899
+ },
1900
+ {
1901
+ "cell_type": "code",
1902
+ "source": [
1903
+ "# Load the audio file (must be 16kHz mono)\n",
1904
+ "signal, fs = torchaudio.load(audio_file)\n",
1905
+ "\n",
1906
+ "# If stereo, take only the first channel\n",
1907
+ "if signal.shape[0] > 1:\n",
1908
+ " signal = signal[0].unsqueeze(0)\n",
1909
+ "\n",
1910
+ "# Run classification\n",
1911
+ "prediction = accent_model.classify_batch(signal)\n",
1912
+ "pred_label = prediction[3][0]\n",
1913
+ "pred_scores = prediction[1][0]\n",
1914
+ "\n",
1915
+ "# Convert score to percentage\n",
1916
+ "confidence = float(pred_scores.max()) * 100\n",
1917
+ "\n",
1918
+ "# Display top label and score\n",
1919
+ "print(f\"Predicted Accent: {pred_label}\")\n",
1920
+ "print(f\"Confidence: {confidence:.1f}%\")\n",
1921
+ "print(\"Possible accent labels:\", accent_model.hparams.label_encoder.lab2ind.keys())\n",
1922
+ "\n"
1923
+ ],
1924
+ "metadata": {
1925
+ "colab": {
1926
+ "base_uri": "https://localhost:8080/"
1927
+ },
1928
+ "id": "dib87e25JZkd",
1929
+ "outputId": "1ddd1991-148b-461b-94d4-845c401365d7"
1930
+ },
1931
+ "execution_count": 13,
1932
+ "outputs": [
1933
+ {
1934
+ "output_type": "stream",
1935
+ "name": "stderr",
1936
+ "text": [
1937
+ "WARNING:speechbrain.dataio.encoder:CategoricalEncoder.expect_len was never called: assuming category count of 45 to be correct! Sanity check your encoder using `.expect_len`. Ensure that downstream code also uses the correct size. If you are sure this does not apply to you, use `.ignore_len`.\n"
1938
+ ]
1939
+ },
1940
+ {
1941
+ "output_type": "stream",
1942
+ "name": "stdout",
1943
+ "text": [
1944
+ "Predicted Accent: English\n",
1945
+ "Confidence: 66.8%\n",
1946
+ "Possible accent labels: dict_keys(['Basque', 'Romansh_Sursilvan', 'Sakha', 'Georgian', 'Greek', 'Hakha_Chin', 'Ukrainian', 'Interlingua', 'Persian', 'Polish', 'Dutch', 'Chinese_Hongkong', 'Japanese', 'Portuguese', 'Italian', 'Catalan', 'Chuvash', 'Swedish', 'Spanish', 'Slovenian', 'Tamil', 'Breton', 'Russian', 'Czech', 'English', 'French', 'Tatar', 'Welsh', 'Kyrgyz', 'Esperanto', 'Kinyarwanda', 'Dhivehi', 'Turkish', 'Latvian', 'Estonian', 'Arabic', 'Frisian', 'Mongolian', 'Chinese_Taiwan', 'Indonesian', 'Maltese', 'Kabyle', 'Romanian', 'Chinese_China', 'German'])\n"
1947
+ ]
1948
+ }
1949
+ ]
1950
+ },
1951
+ {
1952
+ "cell_type": "code",
1953
+ "source": [
1954
+ "explanation = f\"The speaker's English accent was classified as '{pred_label}' with a confidence score of {confidence:.1f}%. This means the model is {confidence:.0f}% sure the person sounds most similar to this accent group.\"\n",
1955
+ "\n",
1956
+ "print(explanation)\n"
1957
+ ],
1958
+ "metadata": {
1959
+ "colab": {
1960
+ "base_uri": "https://localhost:8080/"
1961
+ },
1962
+ "id": "YchZqUzSLna9",
1963
+ "outputId": "91d6f6ae-0247-4e0e-a9c1-d0e9d379a8e0"
1964
+ },
1965
+ "execution_count": 14,
1966
+ "outputs": [
1967
+ {
1968
+ "output_type": "stream",
1969
+ "name": "stdout",
1970
+ "text": [
1971
+ "The speaker's English accent was classified as 'English' with a confidence score of 66.8%. This means the model is 67% sure the person sounds most similar to this accent group.\n"
1972
+ ]
1973
+ }
1974
+ ]
1975
+ },
1976
+ {
1977
+ "cell_type": "code",
1978
+ "source": [
1979
+ "# Save as app.py in Colab for launching a simple web UI\n",
1980
+ "with open(\"app.py\", \"w\") as f:\n",
1981
+ " f.write('''\n",
1982
+ "import streamlit as st\n",
1983
+ "import os\n",
1984
+ "import subprocess\n",
1985
+ "import torchaudio\n",
1986
+ "from speechbrain.pretrained import EncoderClassifier\n",
1987
+ "\n",
1988
+ "st.title(\"🗣️ English Accent Classifier (Proof of Concept)\")\n",
1989
+ "\n",
1990
+ "url = st.text_input(\"Enter public video URL (YouTube or direct MP4):\")\n",
1991
+ "if st.button(\"Analyze\"):\n",
1992
+ " with st.spinner(\"Downloading video...\"):\n",
1993
+ " if \"youtube.com\" in url or \"youtu.be\" in url:\n",
1994
+ " os.system(f'yt-dlp -o input_video.mp4 \"{url}\"')\n",
1995
+ " else:\n",
1996
+ " os.system(f'wget -O input_video.mp4 \"{url}\"')\n",
1997
+ " with st.spinner(\"Extracting audio...\"):\n",
1998
+ " os.system(\"ffmpeg -y -i input_video.mp4 -ar 16000 -ac 1 -vn audio.wav\")\n",
1999
+ " with st.spinner(\"Classifying accent...\"):\n",
2000
+ " accent_model = EncoderClassifier.from_hparams(\n",
2001
+ " source=\"speechbrain/lang-id-commonlanguage_ecapa\",\n",
2002
+ " savedir=\"tmp_accent_model\"\n",
2003
+ " )\n",
2004
+ " signal, fs = torchaudio.load(\"audio.wav\")\n",
2005
+ " if signal.shape[0] > 1:\n",
2006
+ " signal = signal[0].unsqueeze(0)\n",
2007
+ " prediction = accent_model.classify_batch(signal)\n",
2008
+ " pred_label = prediction[3][0]\n",
2009
+ " pred_scores = prediction[1][0]\n",
2010
+ " confidence = float(pred_scores.max()) * 100\n",
2011
+ " st.success(f\"Predicted Accent: {pred_label} ({confidence:.1f}%)\")\n",
2012
+ " st.info(f\"The model is {confidence:.0f}% confident this is a {pred_label} English accent.\")\n",
2013
+ "''')\n",
2014
+ "\n",
2015
+ "print(\"Streamlit app code saved as app.py!\")\n",
2016
+ "print(\"To launch the UI, run: !streamlit run app.py --server.headless true --server.port 8501\")\n"
2017
+ ],
2018
+ "metadata": {
2019
+ "colab": {
2020
+ "base_uri": "https://localhost:8080/"
2021
+ },
2022
+ "id": "K7TCPSn8MLcN",
2023
+ "outputId": "5c4d3e3a-191e-4b61-97e0-739626eb6263"
2024
+ },
2025
+ "execution_count": 15,
2026
+ "outputs": [
2027
+ {
2028
+ "output_type": "stream",
2029
+ "name": "stdout",
2030
+ "text": [
2031
+ "Streamlit app code saved as app.py!\n",
2032
+ "To launch the UI, run: !streamlit run app.py --server.headless true --server.port 8501\n"
2033
+ ]
2034
+ }
2035
+ ]
2036
+ }
2037
+ ]
2038
+ }
accent.py ADDED
@@ -0,0 +1,180 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """Accent.ipynb
3
+
4
+ Automatically generated by Colab.
5
+
6
+ Original file is located at
7
+ https://colab.research.google.com/drive/1yprWdRUXGqD4QIFAZuMwdyTuwA2Hhdvj
8
+ """
9
+
10
+ # Install needed libraries (run this cell first!)
11
+ !pip install --quiet yt-dlp ffmpeg-python torch torchaudio transformers streamlit speechbrain
12
+
13
+ import os
14
+ import subprocess
15
+ import torchaudio
16
+ import torch
17
+ from speechbrain.pretrained import EncoderClassifier
18
+ import yt_dlp
19
+
20
+ # Paste your video URL here (YouTube or direct MP4 link)
21
+ VIDEO_URL = "https://youtu.be/DDjWTWHHkpk?si=oIj6Fuy8Hg2E8U_l" # Example: Replace with your actual link!
22
+
23
+ def download_video(url, out_path="input_video.mp4"):
24
+ """
25
+ Downloads a video from YouTube or direct MP4 link.
26
+ Returns the filename of the downloaded video.
27
+ """
28
+ # If it's a YouTube link, use yt-dlp
29
+ if "youtube.com" in url or "youtu.be" in url:
30
+ ydl_opts = {'outtmpl': out_path}
31
+ with yt_dlp.YoutubeDL(ydl_opts) as ydl:
32
+ ydl.download([url])
33
+ else:
34
+ # For direct links, use wget/curl fallback
35
+ os.system(f"wget -O {out_path} {url}")
36
+ return out_path
37
+
38
+ video_file = download_video(VIDEO_URL)
39
+ print(f"Downloaded video: {video_file}")
40
+
41
+ def extract_audio(video_path, audio_path="audio.wav"):
42
+ """
43
+ Extracts audio from a video file using ffmpeg.
44
+ Returns the filename of the audio file.
45
+ """
46
+ # Remove if already exists
47
+ if os.path.exists(audio_path):
48
+ os.remove(audio_path)
49
+ # Extract audio with ffmpeg
50
+ cmd = f"ffmpeg -y -i {video_path} -ar 16000 -ac 1 -vn {audio_path}"
51
+ subprocess.call(cmd, shell=True)
52
+ return audio_path
53
+
54
+ audio_file = extract_audio(video_file)
55
+ print(f"Extracted audio file: {audio_file}")
56
+
57
+ def extract_audio(video_path, audio_path="/content/audio.wav"):
58
+ """
59
+ Extracts audio from a video file using ffmpeg.
60
+ Returns the filename of the audio file.
61
+ """
62
+ # Remove if already exists
63
+ if os.path.exists(audio_path):
64
+ os.remove(audio_path)
65
+ # Extract audio with ffmpeg
66
+ cmd = f"ffmpeg -y -i {video_path} -ar 16000 -ac 1 -vn {audio_path}"
67
+ # Use subprocess.run to capture output and check the return code
68
+ result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
69
+
70
+ if result.returncode != 0:
71
+ print(f"FFmpeg command failed with error code {result.returncode}")
72
+ print("FFmpeg stderr:")
73
+ print(result.stderr)
74
+ # Optionally, raise an error or exit if audio extraction fails
75
+ raise RuntimeError(f"Failed to extract audio using FFmpeg. See stderr above.")
76
+ else:
77
+ print("FFmpeg stdout:")
78
+ print(result.stdout)
79
+ print("FFmpeg stderr:")
80
+ print(result.stderr) # ffmpeg often outputs info/warnings to stderr
81
+
82
+ # Check if the audio file was actually created
83
+ if not os.path.exists(audio_path):
84
+ raise FileNotFoundError(f"Audio file '{audio_path}' was not created after FFmpeg execution.")
85
+
86
+ return audio_path
87
+
88
+ # Download the pre-trained English accent classifier (SpeechBrain)
89
+ accent_model = EncoderClassifier.from_hparams(
90
+ source="speechbrain/lang-id-commonlanguage_ecapa",
91
+ savedir="tmp_accent_model"
92
+ )
93
+
94
+ """Used to Debuging the code"""
95
+
96
+ # List the files to see if input_video.mp4 is present
97
+ import os
98
+ print(os.listdir('.'))
99
+
100
+ """TO check the debug file path"""
101
+
102
+ # Try extracting audio again, but print output to check for errors
103
+ video_path = "/content/input_video.mp4.webm" # or whatever your filename is!
104
+ audio_path = "audio.wav"
105
+
106
+ os.system(f"ffmpeg -y -i {video_path} -ar 16000 -ac 1 -vn {audio_path}")
107
+
108
+ # See if audio.wav was created
109
+ print(os.listdir('.'))
110
+
111
+ """Check the Size of the file"""
112
+
113
+ # Check if the file now exists and get its size
114
+ import os
115
+ print("audio.wav exists:", os.path.exists(audio_path))
116
+ if os.path.exists(audio_path):
117
+ print("audio.wav size (bytes):", os.path.getsize(audio_path))
118
+
119
+ # Load the audio file (must be 16kHz mono)
120
+ signal, fs = torchaudio.load(audio_file)
121
+
122
+ # If stereo, take only the first channel
123
+ if signal.shape[0] > 1:
124
+ signal = signal[0].unsqueeze(0)
125
+
126
+ # Run classification
127
+ prediction = accent_model.classify_batch(signal)
128
+ pred_label = prediction[3][0]
129
+ pred_scores = prediction[1][0]
130
+
131
+ # Convert score to percentage
132
+ confidence = float(pred_scores.max()) * 100
133
+
134
+ # Display top label and score
135
+ print(f"Predicted Accent: {pred_label}")
136
+ print(f"Confidence: {confidence:.1f}%")
137
+ print("Possible accent labels:", accent_model.hparams.label_encoder.lab2ind.keys())
138
+
139
+ explanation = f"The speaker's English accent was classified as '{pred_label}' with a confidence score of {confidence:.1f}%. This means the model is {confidence:.0f}% sure the person sounds most similar to this accent group."
140
+
141
+ print(explanation)
142
+
143
+ # Save as app.py in Colab for launching a simple web UI
144
+ with open("app.py", "w") as f:
145
+ f.write('''
146
+ import streamlit as st
147
+ import os
148
+ import subprocess
149
+ import torchaudio
150
+ from speechbrain.pretrained import EncoderClassifier
151
+
152
+ st.title("🗣️ English Accent Classifier (Proof of Concept)")
153
+
154
+ url = st.text_input("Enter public video URL (YouTube or direct MP4):")
155
+ if st.button("Analyze"):
156
+ with st.spinner("Downloading video..."):
157
+ if "youtube.com" in url or "youtu.be" in url:
158
+ os.system(f'yt-dlp -o input_video.mp4 "{url}"')
159
+ else:
160
+ os.system(f'wget -O input_video.mp4 "{url}"')
161
+ with st.spinner("Extracting audio..."):
162
+ os.system("ffmpeg -y -i input_video.mp4 -ar 16000 -ac 1 -vn audio.wav")
163
+ with st.spinner("Classifying accent..."):
164
+ accent_model = EncoderClassifier.from_hparams(
165
+ source="speechbrain/lang-id-commonlanguage_ecapa",
166
+ savedir="tmp_accent_model"
167
+ )
168
+ signal, fs = torchaudio.load("audio.wav")
169
+ if signal.shape[0] > 1:
170
+ signal = signal[0].unsqueeze(0)
171
+ prediction = accent_model.classify_batch(signal)
172
+ pred_label = prediction[3][0]
173
+ pred_scores = prediction[1][0]
174
+ confidence = float(pred_scores.max()) * 100
175
+ st.success(f"Predicted Accent: {pred_label} ({confidence:.1f}%)")
176
+ st.info(f"The model is {confidence:.0f}% confident this is a {pred_label} English accent.")
177
+ ''')
178
+
179
+ print("Streamlit app code saved as app.py!")
180
+ print("To launch the UI, run: !streamlit run app.py --server.headless true --server.port 8501")
app.py ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import streamlit as st
3
+ import os
4
+ import subprocess
5
+ import torchaudio
6
+ from speechbrain.pretrained import EncoderClassifier
7
+
8
+ st.title("🗣️ English Accent Classifier (Proof of Concept)")
9
+
10
+ url = st.text_input("Enter public video URL (YouTube or direct MP4):")
11
+ if st.button("Analyze"):
12
+ with st.spinner("Downloading video..."):
13
+ if "youtube.com" in url or "youtu.be" in url:
14
+ os.system(f'yt-dlp -o input_video.mp4 "{url}"')
15
+ else:
16
+ os.system(f'wget -O input_video.mp4 "{url}"')
17
+ with st.spinner("Extracting audio..."):
18
+ os.system("ffmpeg -y -i input_video.mp4 -ar 16000 -ac 1 -vn audio.wav")
19
+ with st.spinner("Classifying accent..."):
20
+ accent_model = EncoderClassifier.from_hparams(
21
+ source="speechbrain/lang-id-commonlanguage_ecapa",
22
+ savedir="tmp_accent_model"
23
+ )
24
+ signal, fs = torchaudio.load("audio.wav")
25
+ if signal.shape[0] > 1:
26
+ signal = signal[0].unsqueeze(0)
27
+ prediction = accent_model.classify_batch(signal)
28
+ pred_label = prediction[3][0]
29
+ pred_scores = prediction[1][0]
30
+ confidence = float(pred_scores.max()) * 100
31
+ st.success(f"Predicted Accent: {pred_label} ({confidence:.1f}%)")
32
+ st.info(f"The model is {confidence:.0f}% confident this is a {pred_label} English accent.")