{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Qwen3-ASR-0.6B — Multi-Language Fine-tuning\n", "### Dataset: `ghananlpcommunity/ghana-bible-combined-90k-twi-ewe-dagbani`\n", "\n", "Trains on **all three languages** (Twi, Ewe, Dagbani) with language-prefixed targets so the model learns both transcription and language identification simultaneously.\n", "\n", "**JSONL target format used:**\n", "```\n", "language Twime din de Kwame\n", "language Eweŋdi na mi\n", "language Dagbanin yuli di Alhassan\n", "```\n", "\n", "**Runtime:** T4 GPU minimum → *Runtime → Change runtime type → T4 GPU*\n", "\n", "---\n", "| Section | Description |\n", "|---------|-------------|\n", "| 1 | GPU check |\n", "| 2 | Install dependencies |\n", "| 3 | Mount Google Drive |\n", "| 4 | Config — paths & hyperparameters |\n", "| 5 | Load HuggingFace dataset → build JSONL |\n", "| 6 | Write fine-tuning script |\n", "| 7 | Run training |\n", "| 8 | Resume from checkpoint |\n", "| 9 | Inference test |\n", "| 10 | Push to HuggingFace Hub |" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "accepted Terms of Service for \u001b[4;94mhttps://repo.anaconda.com/pkgs/main\u001b[0m\n", "accepted Terms of Service for \u001b[4;94mhttps://repo.anaconda.com/pkgs/r\u001b[0m\n" ] } ], "source": [ "!conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main\n", "!conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;33mJupyter detected\u001b[0m\u001b[1;33m...\u001b[0m\n", "\u001b[1;32m2\u001b[0m\u001b[1;32m channel Terms of Service accepted\u001b[0m\n", "Retrieving notices: done\n", "Channels:\n", " - defaults\n", "Platform: linux-64\n", "Collecting package metadata (repodata.json): done\n", "Solving environment: done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "\n", "==> WARNING: A newer version of conda exists. <==\n", " current version: 26.1.1\n", " latest version: 26.3.2\n", "\n", "Please update conda by running\n", "\n", " $ conda update -n base -c defaults conda\n", "\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "## Package Plan ##\n", "\n", " environment location: /home/user/miniconda/envs/ghana_speech\n", "\n", " added / updated specs:\n", " - python=3.11\n", "\n", "\n", "The following packages will be downloaded:\n", "\n", " package | build\n", " ---------------------------|-----------------\n", " _libgcc_mutex-0.1 | main 3 KB\n", " _openmp_mutex-5.1 | 1_gnu 21 KB\n", " ca-certificates-2026.3.19 | h06a4308_0 126 KB\n", " ld_impl_linux-64-2.44 | h9e0c5a2_3 725 KB\n", " libexpat-2.7.5 | h7354ed3_0 122 KB\n", " libffi-3.4.8 | hc5d346e_2 136 KB\n", " libgcc-ng-15.2.0 | h166f726_7 28 KB\n", " libnsl-2.0.0 | h5eee18b_0 31 KB\n", " libzlib-1.3.1 | h47b2149_1 59 KB\n", " openssl-3.5.6 | h1b28b03_0 5.6 MB\n", " packaging-26.0 | py311h06a4308_0 197 KB\n", " pip-26.0.1 | pyhc872135_1 1.1 MB\n", " pthread-stubs-0.3 | h0ce48e5_1 5 KB\n", " python-3.11.15 | h741d88c_0 29.5 MB\n", " setuptools-82.0.1 | py311h06a4308_0 1.6 MB\n", " sqlite-3.51.2 | h3e8d24a_0 1.2 MB\n", " tzdata-2026a | he532380_0 117 KB\n", " wheel-0.46.3 | py311h06a4308_0 71 KB\n", " zlib-1.3.1 | h47b2149_1 89 KB\n", " ------------------------------------------------------------\n", " Total: 40.7 MB\n", "\n", "The following NEW packages will be INSTALLED:\n", "\n", " _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main \n", " _openmp_mutex pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu \n", " bzip2 pkgs/main/linux-64::bzip2-1.0.8-h5eee18b_6 \n", " ca-certificates pkgs/main/linux-64::ca-certificates-2026.3.19-h06a4308_0 \n", " ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.44-h9e0c5a2_3 \n", " libexpat pkgs/main/linux-64::libexpat-2.7.5-h7354ed3_0 \n", " libffi pkgs/main/linux-64::libffi-3.4.8-hc5d346e_2 \n", " libgcc pkgs/main/linux-64::libgcc-15.2.0-h69a1729_7 \n", " libgcc-ng pkgs/main/linux-64::libgcc-ng-15.2.0-h166f726_7 \n", " libgomp pkgs/main/linux-64::libgomp-15.2.0-h4751f2c_7 \n", " libnsl pkgs/main/linux-64::libnsl-2.0.0-h5eee18b_0 \n", " libstdcxx pkgs/main/linux-64::libstdcxx-15.2.0-h39759b7_7 \n", " libuuid pkgs/main/linux-64::libuuid-1.41.5-h5eee18b_0 \n", " libxcb pkgs/main/linux-64::libxcb-1.17.0-h9b100fa_0 \n", " libzlib pkgs/main/linux-64::libzlib-1.3.1-h47b2149_1 \n", " ncurses pkgs/main/linux-64::ncurses-6.5-h7934f7d_0 \n", " openssl pkgs/main/linux-64::openssl-3.5.6-h1b28b03_0 \n", " packaging pkgs/main/linux-64::packaging-26.0-py311h06a4308_0 \n", " pip pkgs/main/noarch::pip-26.0.1-pyhc872135_1 \n", " pthread-stubs pkgs/main/linux-64::pthread-stubs-0.3-h0ce48e5_1 \n", " python pkgs/main/linux-64::python-3.11.15-h741d88c_0 \n", " readline pkgs/main/linux-64::readline-8.3-hc2a1206_0 \n", " setuptools pkgs/main/linux-64::setuptools-82.0.1-py311h06a4308_0 \n", " sqlite pkgs/main/linux-64::sqlite-3.51.2-h3e8d24a_0 \n", " tk pkgs/main/linux-64::tk-8.6.15-h54e0aa7_0 \n", " tzdata pkgs/main/noarch::tzdata-2026a-he532380_0 \n", " wheel pkgs/main/linux-64::wheel-0.46.3-py311h06a4308_0 \n", " xorg-libx11 pkgs/main/linux-64::xorg-libx11-1.8.12-h9b100fa_1 \n", " xorg-libxau pkgs/main/linux-64::xorg-libxau-1.0.12-h9b100fa_0 \n", " xorg-libxdmcp pkgs/main/linux-64::xorg-libxdmcp-1.1.5-h9b100fa_0 \n", " xorg-xorgproto pkgs/main/linux-64::xorg-xorgproto-2024.1-h5eee18b_1 \n", " xz pkgs/main/linux-64::xz-5.8.2-h448239c_0 \n", " zlib pkgs/main/linux-64::zlib-1.3.1-h47b2149_1 \n", "\n", "\n", "\n", "python-3.11.15 | 29.5 MB | | 0% \n", "openssl-3.5.6 | 5.6 MB | | 0% \u001b[A\n", "\n", "setuptools-82.0.1 | 1.6 MB | | 0% \u001b[A\u001b[A\n", "\n", "\n", "sqlite-3.51.2 | 1.2 MB | | 0% \u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", 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package metadata (repodata.json): done\n", "Solving environment: done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "\n", "==> WARNING: A newer version of conda exists. <==\n", " current version: 26.1.1\n", " latest version: 26.3.2\n", "\n", "Please update conda by running\n", "\n", " $ conda update -n base -c defaults conda\n", "\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "## Package Plan ##\n", "\n", " environment location: /home/user/miniconda/envs/ghana_speech\n", "\n", " added / updated specs:\n", " - ffmpeg=7\n", "\n", "\n", "The following packages will be downloaded:\n", "\n", " package | build\n", " ---------------------------|-----------------\n", " alsa-lib-1.2.15.3 | hb03c661_0 571 KB conda-forge\n", " aom-3.9.1 | hac33072_0 2.6 MB conda-forge\n", " ca-certificates-2026.4.22 | hbd8a1cb_0 128 KB conda-forge\n", " cairo-1.18.4 | he90730b_1 966 KB conda-forge\n", " dav1d-1.2.1 | hd590300_0 742 KB conda-forge\n", " dbus-1.16.2 | h24cb091_1 437 KB conda-forge\n", " ffmpeg-7.1.1 | gpl_hbbdf940_911 10.0 MB conda-forge\n", " font-ttf-dejavu-sans-mono-2.37| hab24e00_0 388 KB conda-forge\n", " font-ttf-inconsolata-3.000 | h77eed37_0 94 KB conda-forge\n", " font-ttf-source-code-pro-2.038| h77eed37_0 684 KB conda-forge\n", " font-ttf-ubuntu-0.83 | h77eed37_3 1.5 MB conda-forge\n", " fontconfig-2.17.1 | h062c814_0 269 KB\n", " fonts-conda-ecosystem-1 | 0 4 KB conda-forge\n", " fonts-conda-forge-1 | hc364b38_1 4 KB conda-forge\n", " freetype-2.14.2 | ha770c72_0 170 KB conda-forge\n", " fribidi-1.0.16 | hb03c661_0 60 KB conda-forge\n", " gdk-pixbuf-2.44.5 | h2b0a6b4_0 562 KB conda-forge\n", " gmp-6.3.0 | hac33072_2 449 KB conda-forge\n", " graphite2-1.3.14 | hecca717_2 97 KB conda-forge\n", " harfbuzz-12.3.2 | h6083320_0 1.9 MB conda-forge\n", " icu-78.3 | h33c6efd_0 12.1 MB conda-forge\n", " intel-gmmlib-22.10.0 | hb700be7_0 990 KB conda-forge\n", " intel-media-driver-25.3.4 | hecca717_0 8.0 MB conda-forge\n", " lame-3.100 | h166bdaf_1003 496 KB conda-forge\n", " lerc-4.1.0 | hdb68285_0 255 KB conda-forge\n", " level-zero-1.28.4 | hb700be7_0 838 KB conda-forge\n", " libabseil-20250512.1 | cxx17_hba17884_0 1.2 MB conda-forge\n", " libass-0.17.4 | h96ad9f0_0 149 KB conda-forge\n", " libcap-2.77 | hd0affe5_1 122 KB conda-forge\n", " libdeflate-1.25 | h17f619e_0 72 KB conda-forge\n", " libdrm-2.4.125 | hb03c661_1 304 KB conda-forge\n", " libegl-1.7.0 | ha4b6fd6_2 44 KB conda-forge\n", " libflac-1.5.0 | he200343_1 415 KB conda-forge\n", " libfreetype-2.14.2 | ha770c72_0 8 KB conda-forge\n", " libfreetype6-2.14.2 | h73754d4_0 377 KB conda-forge\n", " libgl-1.7.0 | ha4b6fd6_2 132 KB conda-forge\n", " libglib-2.86.3 | h8b17d9a_0 3.9 MB\n", " libglvnd-1.7.0 | ha4b6fd6_2 129 KB conda-forge\n", " libglx-1.7.0 | ha4b6fd6_2 74 KB conda-forge\n", " libhwloc-2.12.2 |default_hafda6a7_1000 2.3 MB conda-forge\n", " libiconv-1.18 | h3b78370_2 772 KB conda-forge\n", " libjpeg-turbo-3.1.4.1 | hb03c661_0 619 KB conda-forge\n", " liblzma-5.8.2 | hb03c661_0 111 KB conda-forge\n", " libogg-1.3.5 | hd0c01bc_1 213 KB conda-forge\n", " libopenvino-2025.2.0 | hb617929_1 6.0 MB conda-forge\n", " libopenvino-auto-batch-plugin-2025.2.0| hed573e4_1 112 KB conda-forge\n", " libopenvino-auto-plugin-2025.2.0| hed573e4_1 245 KB conda-forge\n", " libopenvino-hetero-plugin-2025.2.0| hd41364c_1 190 KB conda-forge\n", " libopenvino-intel-cpu-plugin-2025.2.0| hb617929_1 11.8 MB conda-forge\n", " libopenvino-intel-gpu-plugin-2025.2.0| hb617929_1 10.3 MB conda-forge\n", " libopenvino-intel-npu-plugin-2025.2.0| hb617929_1 1.2 MB conda-forge\n", " libopenvino-ir-frontend-2025.2.0| hd41364c_1 200 KB conda-forge\n", " libopenvino-onnx-frontend-2025.2.0| h1862bb8_1 1.6 MB conda-forge\n", " libopenvino-paddle-frontend-2025.2.0| h1862bb8_1 727 KB conda-forge\n", " libopenvino-pytorch-frontend-2025.2.0| hecca717_1 1.2 MB conda-forge\n", " libopenvino-tensorflow-frontend-2025.2.0| h0767aad_1 1.3 MB conda-forge\n", " libopenvino-tensorflow-lite-frontend-2025.2.0| hecca717_1 485 KB conda-forge\n", " libopus-1.6.1 | h280c20c_0 317 KB conda-forge\n", " libpciaccess-0.18 | hb9d3cd8_0 28 KB conda-forge\n", " libpng-1.6.55 | h421ea60_0 310 KB conda-forge\n", " libprotobuf-6.31.1 | h49aed37_4 4.2 MB conda-forge\n", " librsvg-2.60.0 | h61e6d4b_0 3.3 MB conda-forge\n", " libsndfile-1.2.2 | hc7d488a_2 347 KB conda-forge\n", " libstdcxx-ng-15.2.0 | hc03a8fd_7 28 KB\n", " libsystemd0-257.13 | hd0affe5_0 481 KB conda-forge\n", " libtiff-4.7.1 | h9d88235_1 425 KB conda-forge\n", " libudev1-257.13 | hd0affe5_0 142 KB conda-forge\n", " libunwind-1.8.3 | h65a8314_0 74 KB conda-forge\n", " liburing-2.13 | hb700be7_0 129 KB conda-forge\n", " libusb-1.0.29 | h73b1eb8_0 87 KB conda-forge\n", " libva-2.23.0 | he1eb515_0 216 KB conda-forge\n", " libvorbis-1.3.7 | h54a6638_2 279 KB conda-forge\n", " libvpl-2.15.0 | h54a6638_1 281 KB conda-forge\n", " libvpx-1.14.1 | hac33072_0 999 KB conda-forge\n", " libvulkan-loader-1.4.341.0 | h5279c79_0 195 KB conda-forge\n", " libwebp-base-1.6.0 | hd42ef1d_0 419 KB conda-forge\n", " libxkbcommon-1.13.1 | hca5e8e5_0 818 KB conda-forge\n", " libxml2-2.14.6 | he237659_3 46 KB conda-forge\n", " libxml2-16-2.14.6 | hca6bf5a_3 555 KB conda-forge\n", " mpg123-1.32.9 | hc50e24c_0 480 KB conda-forge\n", " ocl-icd-2.3.3 | hb9d3cd8_0 104 KB conda-forge\n", " opencl-headers-2025.06.13 | hecca717_0 54 KB conda-forge\n", " openh264-2.6.0 | hc22cd8d_0 714 KB conda-forge\n", " openssl-3.6.2 | h35e630c_0 3.0 MB conda-forge\n", " pango-1.56.4 | hadf4263_0 445 KB conda-forge\n", " pcre2-10.46 | h1321c63_0 1.2 MB conda-forge\n", " pixman-0.46.4 | h54a6638_1 440 KB conda-forge\n", " pugixml-1.15 | h3f63f65_0 116 KB conda-forge\n", " pulseaudio-client-17.0 | h9a6aba3_3 733 KB conda-forge\n", " sdl2-2.32.56 | h54a6638_0 575 KB conda-forge\n", " sdl3-3.4.0 | h3b84278_0 2.0 MB conda-forge\n", " snappy-1.2.2 | h03e3b7b_1 45 KB conda-forge\n", " svt-av1-3.1.2 | hecca717_0 2.6 MB conda-forge\n", " tbb-2022.3.0 | hb700be7_2 177 KB conda-forge\n", " wayland-1.24.0 | h3e06ad9_0 323 KB conda-forge\n", " wayland-protocols-1.47 | hd8ed1ab_0 137 KB conda-forge\n", " x264-1!164.3095 | h166bdaf_2 877 KB conda-forge\n", " x265-3.5 | h924138e_3 3.2 MB conda-forge\n", " xkeyboard-config-2.46 | hb03c661_0 388 KB conda-forge\n", " xorg-libice-1.1.2 | hb9d3cd8_0 57 KB conda-forge\n", " xorg-libsm-1.2.6 | h9b100fa_0 26 KB\n", " xorg-libxcursor-1.2.3 | hb9d3cd8_0 32 KB conda-forge\n", " xorg-libxext-1.3.7 | hb03c661_0 49 KB conda-forge\n", " xorg-libxfixes-6.0.2 | hb03c661_0 20 KB conda-forge\n", " xorg-libxi-1.8.2 | hb9d3cd8_0 46 KB conda-forge\n", " xorg-libxrandr-1.5.5 | hb03c661_0 30 KB conda-forge\n", " xorg-libxrender-0.9.12 | hb9d3cd8_0 32 KB conda-forge\n", " xorg-libxscrnsaver-1.2.4 | hb9d3cd8_0 14 KB conda-forge\n", " xorg-libxtst-1.2.5 | hb9d3cd8_3 32 KB conda-forge\n", " zstd-1.5.7 | hb78ec9c_6 587 KB conda-forge\n", " ------------------------------------------------------------\n", " Total: 122.4 MB\n", "\n", "The following NEW packages will be INSTALLED:\n", "\n", " alsa-lib conda-forge/linux-64::alsa-lib-1.2.15.3-hb03c661_0 \n", " aom conda-forge/linux-64::aom-3.9.1-hac33072_0 \n", " cairo conda-forge/linux-64::cairo-1.18.4-he90730b_1 \n", " dav1d conda-forge/linux-64::dav1d-1.2.1-hd590300_0 \n", " dbus conda-forge/linux-64::dbus-1.16.2-h24cb091_1 \n", " ffmpeg conda-forge/linux-64::ffmpeg-7.1.1-gpl_hbbdf940_911 \n", " font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0 \n", " font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0 \n", " font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0 \n", " font-ttf-ubuntu conda-forge/noarch::font-ttf-ubuntu-0.83-h77eed37_3 \n", " fontconfig pkgs/main/linux-64::fontconfig-2.17.1-h062c814_0 \n", " fonts-conda-ecosy~ conda-forge/noarch::fonts-conda-ecosystem-1-0 \n", " fonts-conda-forge conda-forge/noarch::fonts-conda-forge-1-hc364b38_1 \n", " freetype conda-forge/linux-64::freetype-2.14.2-ha770c72_0 \n", " fribidi conda-forge/linux-64::fribidi-1.0.16-hb03c661_0 \n", " gdk-pixbuf conda-forge/linux-64::gdk-pixbuf-2.44.5-h2b0a6b4_0 \n", " gmp conda-forge/linux-64::gmp-6.3.0-hac33072_2 \n", " graphite2 conda-forge/linux-64::graphite2-1.3.14-hecca717_2 \n", " harfbuzz conda-forge/linux-64::harfbuzz-12.3.2-h6083320_0 \n", " icu conda-forge/linux-64::icu-78.3-h33c6efd_0 \n", " intel-gmmlib conda-forge/linux-64::intel-gmmlib-22.10.0-hb700be7_0 \n", " intel-media-driver conda-forge/linux-64::intel-media-driver-25.3.4-hecca717_0 \n", " lame conda-forge/linux-64::lame-3.100-h166bdaf_1003 \n", " lerc conda-forge/linux-64::lerc-4.1.0-hdb68285_0 \n", " level-zero conda-forge/linux-64::level-zero-1.28.4-hb700be7_0 \n", " libabseil conda-forge/linux-64::libabseil-20250512.1-cxx17_hba17884_0 \n", " libass conda-forge/linux-64::libass-0.17.4-h96ad9f0_0 \n", " libcap conda-forge/linux-64::libcap-2.77-hd0affe5_1 \n", " libdeflate conda-forge/linux-64::libdeflate-1.25-h17f619e_0 \n", " libdrm conda-forge/linux-64::libdrm-2.4.125-hb03c661_1 \n", " libegl conda-forge/linux-64::libegl-1.7.0-ha4b6fd6_2 \n", " libflac conda-forge/linux-64::libflac-1.5.0-he200343_1 \n", " libfreetype conda-forge/linux-64::libfreetype-2.14.2-ha770c72_0 \n", " libfreetype6 conda-forge/linux-64::libfreetype6-2.14.2-h73754d4_0 \n", " libgl conda-forge/linux-64::libgl-1.7.0-ha4b6fd6_2 \n", " libglib pkgs/main/linux-64::libglib-2.86.3-h8b17d9a_0 \n", " libglvnd conda-forge/linux-64::libglvnd-1.7.0-ha4b6fd6_2 \n", " libglx conda-forge/linux-64::libglx-1.7.0-ha4b6fd6_2 \n", " libhwloc conda-forge/linux-64::libhwloc-2.12.2-default_hafda6a7_1000 \n", " libiconv conda-forge/linux-64::libiconv-1.18-h3b78370_2 \n", " libjpeg-turbo conda-forge/linux-64::libjpeg-turbo-3.1.4.1-hb03c661_0 \n", " liblzma conda-forge/linux-64::liblzma-5.8.2-hb03c661_0 \n", " libogg conda-forge/linux-64::libogg-1.3.5-hd0c01bc_1 \n", " libopenvino conda-forge/linux-64::libopenvino-2025.2.0-hb617929_1 \n", " libopenvino-auto-~ conda-forge/linux-64::libopenvino-auto-batch-plugin-2025.2.0-hed573e4_1 \n", " libopenvino-auto-~ conda-forge/linux-64::libopenvino-auto-plugin-2025.2.0-hed573e4_1 \n", " libopenvino-heter~ conda-forge/linux-64::libopenvino-hetero-plugin-2025.2.0-hd41364c_1 \n", " libopenvino-intel~ conda-forge/linux-64::libopenvino-intel-cpu-plugin-2025.2.0-hb617929_1 \n", " libopenvino-intel~ conda-forge/linux-64::libopenvino-intel-gpu-plugin-2025.2.0-hb617929_1 \n", " libopenvino-intel~ conda-forge/linux-64::libopenvino-intel-npu-plugin-2025.2.0-hb617929_1 \n", " libopenvino-ir-fr~ conda-forge/linux-64::libopenvino-ir-frontend-2025.2.0-hd41364c_1 \n", " libopenvino-onnx-~ conda-forge/linux-64::libopenvino-onnx-frontend-2025.2.0-h1862bb8_1 \n", " libopenvino-paddl~ conda-forge/linux-64::libopenvino-paddle-frontend-2025.2.0-h1862bb8_1 \n", " libopenvino-pytor~ conda-forge/linux-64::libopenvino-pytorch-frontend-2025.2.0-hecca717_1 \n", " libopenvino-tenso~ conda-forge/linux-64::libopenvino-tensorflow-frontend-2025.2.0-h0767aad_1 \n", " libopenvino-tenso~ conda-forge/linux-64::libopenvino-tensorflow-lite-frontend-2025.2.0-hecca717_1 \n", " libopus conda-forge/linux-64::libopus-1.6.1-h280c20c_0 \n", " libpciaccess conda-forge/linux-64::libpciaccess-0.18-hb9d3cd8_0 \n", " libpng conda-forge/linux-64::libpng-1.6.55-h421ea60_0 \n", " libprotobuf conda-forge/linux-64::libprotobuf-6.31.1-h49aed37_4 \n", " librsvg conda-forge/linux-64::librsvg-2.60.0-h61e6d4b_0 \n", " libsndfile conda-forge/linux-64::libsndfile-1.2.2-hc7d488a_2 \n", " libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-15.2.0-hc03a8fd_7 \n", " libsystemd0 conda-forge/linux-64::libsystemd0-257.13-hd0affe5_0 \n", " libtiff conda-forge/linux-64::libtiff-4.7.1-h9d88235_1 \n", " libudev1 conda-forge/linux-64::libudev1-257.13-hd0affe5_0 \n", " libunwind conda-forge/linux-64::libunwind-1.8.3-h65a8314_0 \n", " liburing conda-forge/linux-64::liburing-2.13-hb700be7_0 \n", " libusb conda-forge/linux-64::libusb-1.0.29-h73b1eb8_0 \n", " libva conda-forge/linux-64::libva-2.23.0-he1eb515_0 \n", " libvorbis conda-forge/linux-64::libvorbis-1.3.7-h54a6638_2 \n", " libvpl conda-forge/linux-64::libvpl-2.15.0-h54a6638_1 \n", " libvpx conda-forge/linux-64::libvpx-1.14.1-hac33072_0 \n", " libvulkan-loader conda-forge/linux-64::libvulkan-loader-1.4.341.0-h5279c79_0 \n", " libwebp-base conda-forge/linux-64::libwebp-base-1.6.0-hd42ef1d_0 \n", " libxkbcommon conda-forge/linux-64::libxkbcommon-1.13.1-hca5e8e5_0 \n", " libxml2 conda-forge/linux-64::libxml2-2.14.6-he237659_3 \n", " libxml2-16 conda-forge/linux-64::libxml2-16-2.14.6-hca6bf5a_3 \n", " mpg123 conda-forge/linux-64::mpg123-1.32.9-hc50e24c_0 \n", " ocl-icd conda-forge/linux-64::ocl-icd-2.3.3-hb9d3cd8_0 \n", " opencl-headers conda-forge/linux-64::opencl-headers-2025.06.13-hecca717_0 \n", " openh264 conda-forge/linux-64::openh264-2.6.0-hc22cd8d_0 \n", " pango conda-forge/linux-64::pango-1.56.4-hadf4263_0 \n", " pcre2 conda-forge/linux-64::pcre2-10.46-h1321c63_0 \n", " pixman conda-forge/linux-64::pixman-0.46.4-h54a6638_1 \n", " pugixml conda-forge/linux-64::pugixml-1.15-h3f63f65_0 \n", " pulseaudio-client conda-forge/linux-64::pulseaudio-client-17.0-h9a6aba3_3 \n", " sdl2 conda-forge/linux-64::sdl2-2.32.56-h54a6638_0 \n", " sdl3 conda-forge/linux-64::sdl3-3.4.0-h3b84278_0 \n", " snappy conda-forge/linux-64::snappy-1.2.2-h03e3b7b_1 \n", " svt-av1 conda-forge/linux-64::svt-av1-3.1.2-hecca717_0 \n", " tbb conda-forge/linux-64::tbb-2022.3.0-hb700be7_2 \n", " wayland conda-forge/linux-64::wayland-1.24.0-h3e06ad9_0 \n", " wayland-protocols conda-forge/noarch::wayland-protocols-1.47-hd8ed1ab_0 \n", " x264 conda-forge/linux-64::x264-1!164.3095-h166bdaf_2 \n", " x265 conda-forge/linux-64::x265-3.5-h924138e_3 \n", " xkeyboard-config conda-forge/linux-64::xkeyboard-config-2.46-hb03c661_0 \n", " xorg-libice conda-forge/linux-64::xorg-libice-1.1.2-hb9d3cd8_0 \n", " xorg-libsm pkgs/main/linux-64::xorg-libsm-1.2.6-h9b100fa_0 \n", " xorg-libxcursor conda-forge/linux-64::xorg-libxcursor-1.2.3-hb9d3cd8_0 \n", " xorg-libxext conda-forge/linux-64::xorg-libxext-1.3.7-hb03c661_0 \n", " xorg-libxfixes conda-forge/linux-64::xorg-libxfixes-6.0.2-hb03c661_0 \n", " xorg-libxi conda-forge/linux-64::xorg-libxi-1.8.2-hb9d3cd8_0 \n", " xorg-libxrandr conda-forge/linux-64::xorg-libxrandr-1.5.5-hb03c661_0 \n", " xorg-libxrender conda-forge/linux-64::xorg-libxrender-0.9.12-hb9d3cd8_0 \n", " xorg-libxscrnsaver conda-forge/linux-64::xorg-libxscrnsaver-1.2.4-hb9d3cd8_0 \n", " xorg-libxtst conda-forge/linux-64::xorg-libxtst-1.2.5-hb9d3cd8_3 \n", " zstd conda-forge/linux-64::zstd-1.5.7-hb78ec9c_6 \n", "\n", "The following packages will be UPDATED:\n", "\n", " ca-certificates pkgs/main/linux-64::ca-certificates-2~ --> conda-forge/noarch::ca-certificates-2026.4.22-hbd8a1cb_0 \n", " openssl pkgs/main::openssl-3.5.6-h1b28b03_0 --> conda-forge::openssl-3.6.2-h35e630c_0 \n", "\n", "\n", "\n", "icu-78.3 | 12.1 MB | | .. 0% \n", "libopenvino-intel-cp | 11.8 MB | | 0% \u001b[A\n", "\n", "libopenvino-intel-gp | 10.3 MB | | 0% \u001b[A\u001b[A\n", "\n", "\n", "ffmpeg-7.1.1 | 10.0 MB | | 0% \u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "intel-media-driver-2 | 8.0 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "libopenvino-2025.2.0 | 6.0 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "libprotobuf-6.31.1 | 4.2 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "libglib-2.86.3 | 3.9 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "librsvg-2.60.0 | 3.3 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "x265-3.5 | 3.2 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "openssl-3.6.2 | 3.0 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "svt-av1-3.1.2 | 2.6 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "aom-3.9.1 | 2.6 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "libhwloc-2.12.2 | 2.3 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "sdl3-3.4.0 | 2.0 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "harfbuzz-12.3.2 | 1.9 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "libopenvino-onnx-fro | 1.6 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "font-ttf-ubuntu-0.83 | 1.5 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "libopenvino-tensorfl | 1.3 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "icu-78.3 | 12.1 MB | ### | 31% [A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "libopenvino-intel-cp | 11.8 MB | 3 | 3% \u001b[A\n", "\n", "\n", "ffmpeg-7.1.1 | 10.0 MB | 2 | 2% \u001b[A\u001b[A\u001b[A\n", "\n", "libopenvino-intel-gp | 10.3 MB | | 0% \u001b[A\u001b[A\n", "\n", "\n", "\n", "icu-78.3 | 12.1 MB | ########3 | 83% \u001b[A\u001b[A\u001b[A\u001b[A\n", "libopenvino-intel-cp | 11.8 MB | #####7 | 57% \u001b[A\n", "\n", "\n", "ffmpeg-7.1.1 | 10.0 MB | ######4 | 64% \u001b[A\u001b[A\u001b[A\n", "\n", "libopenvino-intel-gp | 10.3 MB | ####4 | 45% \u001b[A\u001b[A\n", "\n", "\n", "\n", "intel-media-driver-2 | 8.0 MB | ########5 | 86% \u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "intel-media-driver-2 | 8.0 MB | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "icu-78.3 | 12.1 MB | ########## | 100% \u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "libopenvino-2025.2.0 | 6.0 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "libopenvino-intel-gp | 10.3 MB | ########## | 100% \u001b[A\u001b[A\n", "\n", "libopenvino-intel-gp | 10.3 MB | ########## | 100% \u001b[A\u001b[A\n", "libopenvino-intel-cp | 11.8 MB | ########## | 100% \u001b[A\n", "libopenvino-intel-cp | 11.8 MB | ########## | 100% \u001b[A\n", "\n", "\n", "\n", "\n", "\n", "libprotobuf-6.31.1 | 4.2 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "librsvg-2.60.0 | 3.3 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "x265-3.5 | 3.2 MB | | 0% 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"libprotobuf-6.31.1 | 4.2 MB | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "libprotobuf-6.31.1 | 4.2 MB | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "libopenvino-2025.2.0 | 6.0 MB | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "libglib-2.86.3 | 3.9 MB | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "libglib-2.86.3 | 3.9 MB | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "svt-av1-3.1.2 | 2.6 MB | | 1% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "openssl-3.6.2 | 3.0 MB | | 1% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", 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comm-0.2.3 debugpy-1.8.20 decorator-5.2.1 executing-2.2.1 ipykernel-7.2.0 ipython-9.13.0 ipython-pygments-lexers-1.1.1 jedi-0.19.2 jupyter-client-8.8.0 jupyter-core-5.9.1 matplotlib-inline-0.2.1 nest-asyncio-1.6.0 parso-0.8.6 pexpect-4.9.0 platformdirs-4.9.6 prompt_toolkit-3.0.52 psutil-7.2.2 ptyprocess-0.7.0 pure-eval-0.2.3 pygments-2.20.0 python-dateutil-2.9.0.post0 pyzmq-27.1.0 six-1.17.0 stack_data-0.6.3 tornado-6.5.5 traitlets-5.14.3 wcwidth-0.6.0\n", "Installed kernelspec ghana_speech in /home/user/.local/share/jupyter/kernels/ghana_speech\n", "----------------------------------------------------------------\n", "✅ SETUP COMPLETE! Refresh browser, then Kernel -> Change Kernel -> Ghana Speech\n", "----------------------------------------------------------------\n" ] } ], "source": [ "%%bash\n", "# 1. Create the environment\n", "conda create -n ghana_speech python=3.11 -y\n", "\n", "# 2. Install AI stack\n", "/home/user/miniconda/envs/ghana_speech/bin/pip install torch torchvision torchaudio torchcodec --index-url https://download.pytorch.org/whl/cu121\n", "\n", "# 3. Install FFmpeg 7\n", "conda install -y -p /home/user/miniconda/envs/ghana_speech -c conda-forge ffmpeg=7\n", "\n", "# 4. Install kernel\n", "/home/user/miniconda/envs/ghana_speech/bin/pip install ipykernel\n", "/home/user/miniconda/envs/ghana_speech/bin/python -m ipykernel install --user --name ghana_speech --display-name \"Python 3.11 (Ghana Speech-2)\"\n", "\n", "echo \"----------------------------------------------------------------\"\n", "echo \"✅ SETUP COMPLETE! Refresh browser, then Kernel -> Change Kernel -> Ghana Speech\"\n", "echo \"----------------------------------------------------------------\"" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting transformers==5.5.0\n", " Downloading transformers-5.5.0-py3-none-any.whl.metadata (32 kB)\n", "Collecting trl==0.22.2\n", " Downloading trl-0.22.2-py3-none-any.whl.metadata (11 kB)\n", "Collecting bitsandbytes==0.49.2\n", " Downloading bitsandbytes-0.49.2-py3-none-manylinux_2_24_x86_64.whl.metadata (10 kB)\n", "Collecting xformers==0.0.34\n", " Downloading xformers-0.0.34-cp39-abi3-manylinux_2_28_x86_64.whl.metadata (1.2 kB)\n", "Collecting datasets==4.3.0\n", " Downloading datasets-4.3.0-py3-none-any.whl.metadata (18 kB)\n", "Collecting peft==0.18.1\n", " Downloading peft-0.18.1-py3-none-any.whl.metadata (14 kB)\n", "Collecting accelerate==1.13.0\n", " Downloading accelerate-1.13.0-py3-none-any.whl.metadata (19 kB)\n", "Collecting timm==1.0.26\n", " Downloading timm-1.0.26-py3-none-any.whl.metadata (39 kB)\n", "Collecting huggingface-hub\n", " Downloading huggingface_hub-1.13.0-py3-none-any.whl.metadata (14 kB)\n", "Collecting hf_transfer==0.1.9\n", " Downloading hf_transfer-0.1.9-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.7 kB)\n", "Collecting hf-xet==1.4.2\n", " Downloading hf_xet-1.4.2-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (4.9 kB)\n", "Collecting pyarrow==23.0.1\n", " Downloading pyarrow-23.0.1-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (3.1 kB)\n", "Collecting protobuf==5.29.6\n", " Downloading protobuf-5.29.6-cp38-abi3-manylinux2014_x86_64.whl.metadata (592 bytes)\n", "Collecting sentencepiece==0.2.1\n", " Downloading sentencepiece-0.2.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (10 kB)\n", "Collecting numpy==2.0.2\n", " Downloading numpy-2.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n", "Collecting pandas==2.2.2\n", " Downloading pandas-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)\n", "Collecting requests==2.32.4\n", " Downloading requests-2.32.4-py3-none-any.whl.metadata (4.9 kB)\n", "Collecting httpx==0.28.1\n", " Downloading httpx-0.28.1-py3-none-any.whl.metadata (7.1 kB)\n", "Collecting tqdm==4.67.3\n", " Downloading tqdm-4.67.3-py3-none-any.whl.metadata (57 kB)\n", "Collecting fsspec==2025.3.0\n", " Downloading fsspec-2025.3.0-py3-none-any.whl.metadata (11 kB)\n", "Collecting safetensors==0.7.0\n", " Downloading safetensors-0.7.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)\n", "Collecting regex==2025.11.3\n", " Downloading regex-2025.11.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (40 kB)\n", "Collecting triton==3.6.0\n", " Downloading triton-3.6.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (1.7 kB)\n", "Collecting typer==0.24.1\n", " Downloading typer-0.24.1-py3-none-any.whl.metadata (16 kB)\n", "Collecting click==8.3.1\n", " Downloading click-8.3.1-py3-none-any.whl.metadata (2.6 kB)\n", "Collecting rich==13.9.4\n", " Downloading rich-13.9.4-py3-none-any.whl.metadata (18 kB)\n", "Collecting pyyaml==6.0.3\n", " Downloading pyyaml-6.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (2.4 kB)\n", "Requirement already satisfied: python-dateutil==2.9.0.post0 in /home/user/miniconda/envs/ghana_speech/lib/python3.11/site-packages (2.9.0.post0)\n", "Requirement already satisfied: typing-extensions==4.15.0 in /home/user/miniconda/envs/ghana_speech/lib/python3.11/site-packages (4.15.0)\n", "Collecting jiwer\n", " Downloading jiwer-4.0.0-py3-none-any.whl.metadata (3.3 kB)\n", "Collecting unsloth\n", " Downloading unsloth-2026.4.8-py3-none-any.whl.metadata (56 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torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl.metadata (1.7 kB)\n", "Collecting nvidia-cuda-runtime-cu12==12.8.90 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB)\n", "Collecting nvidia-cuda-cupti-cu12==12.8.90 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB)\n", "Collecting nvidia-cudnn-cu12==9.10.2.21 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl.metadata (1.8 kB)\n", "Collecting nvidia-cublas-cu12==12.8.4.1 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl.metadata (1.7 kB)\n", "Collecting nvidia-cufft-cu12==11.3.3.83 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB)\n", "Collecting nvidia-curand-cu12==10.3.9.90 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl.metadata (1.7 kB)\n", "Collecting nvidia-cusolver-cu12==11.7.3.90 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl.metadata (1.8 kB)\n", "Collecting nvidia-cusparse-cu12==12.5.8.93 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.8 kB)\n", "Collecting nvidia-cusparselt-cu12==0.7.1 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_x86_64.whl.metadata (7.0 kB)\n", "Collecting nvidia-nccl-cu12==2.27.5 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_nccl_cu12-2.27.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.0 kB)\n", "Collecting nvidia-nvshmem-cu12==3.4.5 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_nvshmem_cu12-3.4.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.1 kB)\n", "Collecting nvidia-nvtx-cu12==12.8.90 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.8 kB)\n", "Collecting nvidia-nvjitlink-cu12==12.8.93 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl.metadata (1.7 kB)\n", "Collecting nvidia-cufile-cu12==1.13.1.3 (from torch<3,>=2.3->bitsandbytes==0.49.2)\n", " Downloading nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata 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pydantic-core, pyarrow, protobuf, propcache, nvidia-nvtx-cu12, nvidia-nvshmem-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufile-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, multidict, msgspec, mdurl, kiwisolver, idna, hf-xet, hf_transfer, h11, fsspec, frozenlist, fonttools, docstring-parser, dill, cycler, cuda-pathfinder, click, charset_normalizer, certifi, attrs, annotated-types, annotated-doc, aiohappyeyeballs, yarl, tyro, requests, pydantic, pandas, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, multiprocess, markdown-it-py, jiwer, importlib-metadata, httpcore, cuda-bindings, contourpy, anyio, aiosignal, rich, nvidia-cusolver-cu12, matplotlib, httpx, aiohttp, typer, torch, xformers, torchvision, huggingface-hub, cut_cross_entropy, bitsandbytes, tokenizers, timm, diffusers, datasets, accelerate, transformers, trl, peft, unsloth_zoo, unsloth\n", "\u001b[2K Attempting uninstall: triton━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 6/95\u001b[0m [tzdata]t-cu12]\n", "\u001b[2K Found existing installation: triton 3.1.0━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 6/95\u001b[0m [tzdata]\n", "\u001b[2K Uninstalling triton-3.1.0:━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 6/95\u001b[0m [tzdata]\n", "\u001b[2K Successfully uninstalled triton-3.1.0━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 6/95\u001b[0m [tzdata]\n", "\u001b[2K Attempting uninstall: sympym━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 9/95\u001b[0m [triton]\n", "\u001b[2K Found existing installation: sympy 1.13.1━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 9/95\u001b[0m [triton]\n", "\u001b[2K Uninstalling sympy-1.13.1:m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11/95\u001b[0m [sympy]\n", "\u001b[2K Successfully uninstalled sympy-1.13.1━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11/95\u001b[0m [sympy]\n", "\u001b[2K Attempting uninstall: nvidia-nvtx-cu12━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21/95\u001b[0m [protobuf]\n", "\u001b[2K Found existing installation: nvidia-nvtx-cu12 12.1.105━━━━\u001b[0m \u001b[32m21/95\u001b[0m [protobuf]\n", "\u001b[2K Uninstalling nvidia-nvtx-cu12-12.1.105:━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21/95\u001b[0m [protobuf]\n", "\u001b[2K Successfully uninstalled nvidia-nvtx-cu12-12.1.105━━━━━━\u001b[0m \u001b[32m21/95\u001b[0m [protobuf]\n", "\u001b[2K Attempting uninstall: nvidia-nvjitlink-cu12━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24/95\u001b[0m [nvidia-nvshmem-cu12]\n", "\u001b[2K Found existing installation: nvidia-nvjitlink-cu12 12.9.86\u001b[0m \u001b[32m24/95\u001b[0m [nvidia-nvshmem-cu12]\n", "\u001b[2K Uninstalling nvidia-nvjitlink-cu12-12.9.86:━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24/95\u001b[0m [nvidia-nvshmem-cu12]\n", "\u001b[2K Successfully uninstalled nvidia-nvjitlink-cu12-12.9.86━━\u001b[0m \u001b[32m24/95\u001b[0m [nvidia-nvshmem-cu12]\n", "\u001b[2K Attempting 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\u001b[32m31/95\u001b[0m [nvidia-cuda-cupti-cu12]\n", "\u001b[2K Successfully uninstalled nvidia-cublas-cu12-12.1.3.1━━━━\u001b[0m \u001b[32m31/95\u001b[0m [nvidia-cuda-cupti-cu12]\n", "\u001b[2K Attempting uninstall: numpym╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m32/95\u001b[0m [nvidia-cublas-cu12]\n", "\u001b[2K Found existing installation: numpy 2.4.3━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m32/95\u001b[0m [nvidia-cublas-cu12]\n", "\u001b[2K Uninstalling numpy-2.4.3:0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m32/95\u001b[0m [nvidia-cublas-cu12]\n", "\u001b[2K Successfully uninstalled numpy-2.4.3━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m32/95\u001b[0m [nvidia-cublas-cu12]\n", "\u001b[2K Attempting uninstall: fsspec91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40/95\u001b[0m [hf_transfer]12]\n", "\u001b[2K Found existing installation: fsspec 2026.2.0━━━━━━━━━━━━━━\u001b[0m \u001b[32m40/95\u001b[0m [hf_transfer]\n", "\u001b[2K Uninstalling fsspec-2026.2.0:m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40/95\u001b[0m [hf_transfer]\n", "\u001b[2K Successfully uninstalled fsspec-2026.2.0━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40/95\u001b[0m [hf_transfer]\n", "\u001b[2K Attempting uninstall: nvidia-cusparse-cu12[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m \u001b[32m60/95\u001b[0m [pandas]c]]\n", "\u001b[2K Found existing installation: nvidia-cusparse-cu12 12.1.0.1060m \u001b[32m60/95\u001b[0m [pandas]\n", "\u001b[2K Uninstalling nvidia-cusparse-cu12-12.1.0.106:━━━━━━━━━━━━━\u001b[0m \u001b[32m60/95\u001b[0m [pandas]\n", "\u001b[2K Successfully uninstalled nvidia-cusparse-cu12-12.1.0.106\u001b[0m \u001b[32m60/95\u001b[0m [pandas]\n", "\u001b[2K Attempting uninstall: nvidia-cufft-cu12m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m \u001b[32m61/95\u001b[0m [nvidia-cusparse-cu12]\n", "\u001b[2K Found existing installation: nvidia-cufft-cu12 11.0.2.54━━\u001b[0m \u001b[32m61/95\u001b[0m [nvidia-cusparse-cu12]\n", "\u001b[2K Uninstalling nvidia-cufft-cu12-11.0.2.54:90m━━━━━━━━━━━━━━\u001b[0m \u001b[32m61/95\u001b[0m [nvidia-cusparse-cu12]\n", "\u001b[2K Successfully uninstalled nvidia-cufft-cu12-11.0.2.54━━━━━━━━\u001b[0m \u001b[32m62/95\u001b[0m [nvidia-cufft-cu12]\n", "\u001b[2K Attempting uninstall: nvidia-cudnn-cu120m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m62/95\u001b[0m [nvidia-cufft-cu12]\n", "\u001b[2K Found existing installation: nvidia-cudnn-cu12 9.1.0.70━━━\u001b[0m \u001b[32m62/95\u001b[0m [nvidia-cufft-cu12]\n", "\u001b[2K Uninstalling nvidia-cudnn-cu12-9.1.0.70:\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m62/95\u001b[0m [nvidia-cufft-cu12]\n", "\u001b[2K Successfully uninstalled nvidia-cudnn-cu12-9.1.0.70━━━━━━━━━\u001b[0m \u001b[32m63/95\u001b[0m [nvidia-cudnn-cu12]\n", "\u001b[2K Attempting uninstall: nvidia-cusolver-cu1291m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m73/95\u001b[0m [rich]]indings]u12]\n", "\u001b[2K Found existing installation: nvidia-cusolver-cu12 11.4.5.1070m \u001b[32m73/95\u001b[0m [rich]\n", "\u001b[2K Uninstalling nvidia-cusolver-cu12-11.4.5.107:[90m━━━━━━━━━\u001b[0m \u001b[32m73/95\u001b[0m [rich]\n", "\u001b[2K Successfully uninstalled nvidia-cusolver-cu12-11.4.5.107\u001b[0m \u001b[32m73/95\u001b[0m [rich]\n", "\u001b[2K Attempting uninstall: torch━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m77/95\u001b[0m [aiohttp]ib]lver-cu12]\n", "\u001b[2K Found existing installation: torch 2.5.1+cu121\u001b[90m━━━━━━━\u001b[0m \u001b[32m77/95\u001b[0m [aiohttp]\n", "\u001b[2K Uninstalling torch-2.5.1+cu121:━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m79/95\u001b[0m [torch]\n", "\u001b[2K Successfully uninstalled torch-2.5.1+cu121[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m79/95\u001b[0m [torch]\n", "\u001b[2K Attempting uninstall: torchvision━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m80/95\u001b[0m [xformers]\n", "\u001b[2K Found existing installation: torchvision 0.20.1+cu121━━━━━\u001b[0m \u001b[32m80/95\u001b[0m [xformers]\n", "\u001b[2K Uninstalling torchvision-0.20.1+cu121:[91m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m80/95\u001b[0m [xformers]\n", "\u001b[2K Successfully uninstalled torchvision-0.20.1+cu121m━━━━━━\u001b[0m \u001b[32m80/95\u001b[0m [xformers]\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m95/95\u001b[0m [unsloth][unsloth]zoo]t]formers]ub]\n", "\u001b[1A\u001b[2K\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", "torchaudio 2.5.1+cu121 requires torch==2.5.1, but you have torch 2.10.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed accelerate-1.13.0 aiohappyeyeballs-2.6.1 aiohttp-3.13.5 aiosignal-1.4.0 annotated-doc-0.0.4 annotated-types-0.7.0 anyio-4.13.0 attrs-26.1.0 bitsandbytes-0.49.2 certifi-2026.4.22 charset_normalizer-3.4.7 click-8.3.1 contourpy-1.3.3 cuda-bindings-12.9.4 cuda-pathfinder-1.5.4 cut_cross_entropy-25.1.1 cycler-0.12.1 datasets-4.3.0 diffusers-0.37.1 dill-0.4.0 docstring-parser-0.18.0 fonttools-4.62.1 frozenlist-1.8.0 fsspec-2025.3.0 h11-0.16.0 hf-xet-1.4.2 hf_transfer-0.1.9 httpcore-1.0.9 httpx-0.28.1 huggingface-hub-1.8.0 idna-3.13 importlib-metadata-9.0.0 jiwer-4.0.0 kiwisolver-1.5.0 markdown-it-py-4.0.0 matplotlib-3.10.9 mdurl-0.1.2 msgspec-0.21.1 multidict-6.7.1 multiprocess-0.70.16 numpy-2.0.2 nvidia-cublas-cu12-12.8.4.1 nvidia-cuda-cupti-cu12-12.8.90 nvidia-cuda-nvrtc-cu12-12.8.93 nvidia-cuda-runtime-cu12-12.8.90 nvidia-cudnn-cu12-9.10.2.21 nvidia-cufft-cu12-11.3.3.83 nvidia-cufile-cu12-1.13.1.3 nvidia-curand-cu12-10.3.9.90 nvidia-cusolver-cu12-11.7.3.90 nvidia-cusparse-cu12-12.5.8.93 nvidia-cusparselt-cu12-0.7.1 nvidia-nccl-cu12-2.27.5 nvidia-nvjitlink-cu12-12.8.93 nvidia-nvshmem-cu12-3.4.5 nvidia-nvtx-cu12-12.8.90 pandas-2.2.2 peft-0.18.1 propcache-0.4.1 protobuf-5.29.6 pyarrow-23.0.1 pydantic-2.13.3 pydantic-core-2.46.3 pyparsing-3.3.2 pytz-2026.1.post1 pyyaml-6.0.3 rapidfuzz-3.14.5 regex-2025.11.3 requests-2.32.4 rich-13.9.4 safetensors-0.7.0 sentencepiece-0.2.1 shellingham-1.5.4 sympy-1.14.0 timm-1.0.26 tokenizers-0.22.2 torch-2.10.0 torchao-0.17.0 torchvision-0.25.0 tqdm-4.67.3 transformers-5.5.0 triton-3.6.0 trl-0.22.2 typeguard-4.5.1 typer-0.24.1 typing-inspection-0.4.2 tyro-1.0.13 tzdata-2026.2 unsloth-2026.4.8 unsloth_zoo-2026.4.9 urllib3-2.6.3 xformers-0.0.34 xxhash-3.7.0 yarl-1.23.0 zipp-3.23.1\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install transformers==5.5.0 trl==0.22.2 bitsandbytes==0.49.2 \\\n", "xformers==0.0.34 datasets==4.3.0 peft==0.18.1 accelerate==1.13.0 \\\n", "timm==1.0.26 huggingface-hub hf_transfer==0.1.9 \\\n", "hf-xet==1.4.2 pyarrow==23.0.1 protobuf==5.29.6 \\\n", "sentencepiece==0.2.1 numpy==2.0.2 pandas==2.2.2 \\\n", "requests==2.32.4 httpx==0.28.1 tqdm==4.67.3 \\\n", "fsspec==2025.3.0 safetensors==0.7.0 regex==2025.11.3 \\\n", "triton==3.6.0 typer==0.24.1 click==8.3.1 \\\n", "rich==13.9.4 pyyaml==6.0.3 python-dateutil==2.9.0.post0 \\\n", "typing-extensions==4.15.0 jiwer unsloth matplotlib pandas unsloth_zoo setuptools" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://download.pytorch.org/whl/cu128\n", "Requirement already satisfied: torch==2.10.0 in /home/user/miniconda/lib/python3.13/site-packages (2.10.0)\n", "Collecting torchaudio==2.10.0\n", " Downloading https://download-r2.pytorch.org/whl/cu128/torchaudio-2.10.0%2Bcu128-cp313-cp313-manylinux_2_28_x86_64.whl.metadata (6.9 kB)\n", "Requirement already satisfied: torchvision==0.25.0 in /home/user/miniconda/lib/python3.13/site-packages (0.25.0)\n", "Collecting torchcodec==0.10.0\n", " Downloading https://download.pytorch.org/whl/cu128/torchcodec-0.10.0%2Bcu128-cp313-cp313-manylinux_2_28_x86_64.whl.metadata (11 kB)\n", "Requirement already satisfied: filelock in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (3.29.0)\n", "Requirement already satisfied: typing-extensions>=4.10.0 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (4.15.0)\n", "Requirement already satisfied: setuptools in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (80.10.2)\n", "Requirement already satisfied: sympy>=1.13.3 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (1.14.0)\n", "Requirement already satisfied: networkx>=2.5.1 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (3.6.1)\n", "Requirement already satisfied: jinja2 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (3.1.6)\n", "Requirement already satisfied: fsspec>=0.8.5 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (2025.3.0)\n", "Requirement already satisfied: cuda-bindings==12.9.4 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (12.9.4)\n", "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (12.8.93)\n", "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (12.8.90)\n", "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (12.8.90)\n", "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (9.10.2.21)\n", "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (12.8.4.1)\n", "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (11.3.3.83)\n", "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (10.3.9.90)\n", "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (11.7.3.90)\n", "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (12.5.8.93)\n", "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (0.7.1)\n", "Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (2.27.5)\n", "Requirement already satisfied: nvidia-nvshmem-cu12==3.4.5 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (3.4.5)\n", "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (12.8.90)\n", "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (12.8.93)\n", "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (1.13.1.3)\n", "Requirement already satisfied: triton==3.6.0 in /home/user/miniconda/lib/python3.13/site-packages (from torch==2.10.0) (3.6.0)\n", "Requirement already satisfied: numpy in /home/user/miniconda/lib/python3.13/site-packages (from torchvision==0.25.0) (2.0.2)\n", "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /home/user/miniconda/lib/python3.13/site-packages (from torchvision==0.25.0) (12.2.0)\n", "Requirement already satisfied: cuda-pathfinder~=1.1 in /home/user/miniconda/lib/python3.13/site-packages (from cuda-bindings==12.9.4->torch==2.10.0) (1.5.4)\n", "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /home/user/miniconda/lib/python3.13/site-packages (from sympy>=1.13.3->torch==2.10.0) (1.3.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /home/user/miniconda/lib/python3.13/site-packages (from jinja2->torch==2.10.0) (3.0.3)\n", "Downloading https://download-r2.pytorch.org/whl/cu128/torchaudio-2.10.0%2Bcu128-cp313-cp313-manylinux_2_28_x86_64.whl (1.8 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m102.9 MB/s\u001b[0m \u001b[33m0:00:00\u001b[0m\n", "\u001b[?25hDownloading https://download.pytorch.org/whl/cu128/torchcodec-0.10.0%2Bcu128-cp313-cp313-manylinux_2_28_x86_64.whl (2.4 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m26.7 MB/s\u001b[0m \u001b[33m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: torchcodec, torchaudio\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [torchaudio]2\u001b[0m [torchaudio]\n", "\u001b[1A\u001b[2KSuccessfully installed torchaudio-2.10.0+cu128 torchcodec-0.10.0+cu128\n", "Note: you may need to restart the kernel to use updated packages.\n", "\u001b[1;33mJupyter detected\u001b[0m\u001b[1;33m...\u001b[0m\n", "\u001b[1;32m2\u001b[0m\u001b[1;32m channel Terms of Service accepted\u001b[0m\n", "Channels:\n", " - conda-forge\n", " - defaults\n", "Platform: linux-64\n", "Collecting package metadata (repodata.json): done\n", "Solving environment: done\n", "\n", "\n", "==> WARNING: A newer version of conda exists. <==\n", " current version: 26.1.1\n", " latest version: 26.3.2\n", "\n", "Please update conda by running\n", "\n", " $ conda update -n base -c defaults conda\n", "\n", "\n", "\n", "## Package Plan ##\n", "\n", " environment location: /home/user/miniconda\n", "\n", " added / updated specs:\n", " - ffmpeg=7\n", "\n", "\n", "The following packages will be downloaded:\n", "\n", " package | build\n", " ---------------------------|-----------------\n", " cairo-1.18.4 | h44eff21_0 728 KB\n", " certifi-2026.4.22 | pyhd8ed1ab_0 132 KB conda-forge\n", " ffmpeg-7.1.1 | gpl_ha0aeed6_910 10.1 MB conda-forge\n", " fontconfig-2.15.0 | h2c49b7f_0 262 KB\n", " harfbuzz-12.3.0 | h79d275a_1 2.0 MB\n", " libhwloc-2.12.1 |default_h3d81e11_1000 2.3 MB conda-forge\n", " librsvg-2.58.4 | h49af25d_2 6.0 MB conda-forge\n", " liburing-2.12 | hb700be7_0 125 KB conda-forge\n", " libxkbcommon-1.11.0 | he8b52b9_0 773 KB conda-forge\n", " sdl3-3.2.24 | h68140b3_0 1.8 MB conda-forge\n", " tbb-2022.3.0 | h8d10470_1 177 KB conda-forge\n", " wayland-1.24.0 | hdac8c69_0 356 KB\n", " ------------------------------------------------------------\n", " Total: 24.7 MB\n", "\n", "The following NEW packages will be INSTALLED:\n", "\n", " alsa-lib conda-forge/linux-64::alsa-lib-1.2.15.3-hb03c661_0 \n", " aom conda-forge/linux-64::aom-3.9.1-hac33072_0 \n", " cairo pkgs/main/linux-64::cairo-1.18.4-h44eff21_0 \n", " dav1d conda-forge/linux-64::dav1d-1.2.1-hd590300_0 \n", " ffmpeg conda-forge/linux-64::ffmpeg-7.1.1-gpl_ha0aeed6_910 \n", " font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0 \n", " font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0 \n", " font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0 \n", " font-ttf-ubuntu conda-forge/noarch::font-ttf-ubuntu-0.83-h77eed37_3 \n", " fontconfig pkgs/main/linux-64::fontconfig-2.15.0-h2c49b7f_0 \n", " fonts-conda-ecosy~ conda-forge/noarch::fonts-conda-ecosystem-1-0 \n", " fonts-conda-forge conda-forge/noarch::fonts-conda-forge-1-hc364b38_1 \n", " freetype conda-forge/linux-64::freetype-2.14.2-ha770c72_0 \n", " fribidi conda-forge/linux-64::fribidi-1.0.16-hb03c661_0 \n", " gdk-pixbuf conda-forge/linux-64::gdk-pixbuf-2.44.5-h2b0a6b4_0 \n", " gmp conda-forge/linux-64::gmp-6.3.0-hac33072_2 \n", " graphite2 conda-forge/linux-64::graphite2-1.3.14-hecca717_2 \n", " harfbuzz pkgs/main/linux-64::harfbuzz-12.3.0-h79d275a_1 \n", " intel-gmmlib conda-forge/linux-64::intel-gmmlib-22.10.0-hb700be7_0 \n", " intel-media-driver conda-forge/linux-64::intel-media-driver-25.3.4-hecca717_0 \n", " lame conda-forge/linux-64::lame-3.100-h166bdaf_1003 \n", " lerc conda-forge/linux-64::lerc-4.1.0-hdb68285_0 \n", " level-zero conda-forge/linux-64::level-zero-1.28.4-hb700be7_0 \n", " libabseil conda-forge/linux-64::libabseil-20250512.1-cxx17_hba17884_0 \n", " libass conda-forge/linux-64::libass-0.17.4-h96ad9f0_0 \n", " libcap conda-forge/linux-64::libcap-2.77-hd0affe5_1 \n", " libdeflate conda-forge/linux-64::libdeflate-1.25-h17f619e_0 \n", " libdrm conda-forge/linux-64::libdrm-2.4.125-hb03c661_1 \n", " libegl conda-forge/linux-64::libegl-1.7.0-ha4b6fd6_2 \n", " libflac conda-forge/linux-64::libflac-1.5.0-he200343_1 \n", " libfreetype conda-forge/linux-64::libfreetype-2.14.2-ha770c72_0 \n", " libfreetype6 conda-forge/linux-64::libfreetype6-2.14.2-h73754d4_0 \n", " libgl conda-forge/linux-64::libgl-1.7.0-ha4b6fd6_2 \n", " libglib pkgs/main/linux-64::libglib-2.86.3-h8b17d9a_0 \n", " libglvnd conda-forge/linux-64::libglvnd-1.7.0-ha4b6fd6_2 \n", " libglx conda-forge/linux-64::libglx-1.7.0-ha4b6fd6_2 \n", " libhwloc conda-forge/linux-64::libhwloc-2.12.1-default_h3d81e11_1000 \n", " libjpeg-turbo conda-forge/linux-64::libjpeg-turbo-3.1.4.1-hb03c661_0 \n", " liblzma conda-forge/linux-64::liblzma-5.8.2-hb03c661_0 \n", " libogg conda-forge/linux-64::libogg-1.3.5-hd0c01bc_1 \n", " libopenvino conda-forge/linux-64::libopenvino-2025.2.0-hb617929_1 \n", " libopenvino-auto-~ conda-forge/linux-64::libopenvino-auto-batch-plugin-2025.2.0-hed573e4_1 \n", " libopenvino-auto-~ conda-forge/linux-64::libopenvino-auto-plugin-2025.2.0-hed573e4_1 \n", " libopenvino-heter~ conda-forge/linux-64::libopenvino-hetero-plugin-2025.2.0-hd41364c_1 \n", " libopenvino-intel~ conda-forge/linux-64::libopenvino-intel-cpu-plugin-2025.2.0-hb617929_1 \n", " libopenvino-intel~ conda-forge/linux-64::libopenvino-intel-gpu-plugin-2025.2.0-hb617929_1 \n", " libopenvino-intel~ conda-forge/linux-64::libopenvino-intel-npu-plugin-2025.2.0-hb617929_1 \n", " libopenvino-ir-fr~ conda-forge/linux-64::libopenvino-ir-frontend-2025.2.0-hd41364c_1 \n", " libopenvino-onnx-~ conda-forge/linux-64::libopenvino-onnx-frontend-2025.2.0-h1862bb8_1 \n", " libopenvino-paddl~ conda-forge/linux-64::libopenvino-paddle-frontend-2025.2.0-h1862bb8_1 \n", " libopenvino-pytor~ conda-forge/linux-64::libopenvino-pytorch-frontend-2025.2.0-hecca717_1 \n", " libopenvino-tenso~ conda-forge/linux-64::libopenvino-tensorflow-frontend-2025.2.0-h0767aad_1 \n", " libopenvino-tenso~ conda-forge/linux-64::libopenvino-tensorflow-lite-frontend-2025.2.0-hecca717_1 \n", " libopus conda-forge/linux-64::libopus-1.6.1-h280c20c_0 \n", " libpciaccess conda-forge/linux-64::libpciaccess-0.18-hb9d3cd8_0 \n", " libpng conda-forge/linux-64::libpng-1.6.55-h421ea60_0 \n", " libprotobuf conda-forge/linux-64::libprotobuf-6.31.1-h49aed37_4 \n", " librsvg conda-forge/linux-64::librsvg-2.58.4-h49af25d_2 \n", " libsndfile conda-forge/linux-64::libsndfile-1.2.2-hc7d488a_2 \n", " libsystemd0 conda-forge/linux-64::libsystemd0-257.13-hd0affe5_0 \n", " libtiff conda-forge/linux-64::libtiff-4.7.1-h9d88235_1 \n", " libudev1 conda-forge/linux-64::libudev1-257.13-hd0affe5_0 \n", " libunwind conda-forge/linux-64::libunwind-1.8.3-h65a8314_0 \n", " liburing conda-forge/linux-64::liburing-2.12-hb700be7_0 \n", " libusb conda-forge/linux-64::libusb-1.0.29-h73b1eb8_0 \n", " libva conda-forge/linux-64::libva-2.23.0-he1eb515_0 \n", " libvorbis conda-forge/linux-64::libvorbis-1.3.7-h54a6638_2 \n", " libvpl conda-forge/linux-64::libvpl-2.15.0-h54a6638_1 \n", " libvpx conda-forge/linux-64::libvpx-1.14.1-hac33072_0 \n", " libvulkan-loader conda-forge/linux-64::libvulkan-loader-1.4.341.0-h5279c79_0 \n", " libwebp-base conda-forge/linux-64::libwebp-base-1.6.0-hd42ef1d_0 \n", " libxkbcommon conda-forge/linux-64::libxkbcommon-1.11.0-he8b52b9_0 \n", " mpg123 conda-forge/linux-64::mpg123-1.32.9-hc50e24c_0 \n", " ocl-icd conda-forge/linux-64::ocl-icd-2.3.3-hb9d3cd8_0 \n", " opencl-headers conda-forge/linux-64::opencl-headers-2025.06.13-hecca717_0 \n", " openh264 conda-forge/linux-64::openh264-2.6.0-hc22cd8d_0 \n", " pango conda-forge/linux-64::pango-1.56.4-hadf4263_0 \n", " pixman conda-forge/linux-64::pixman-0.46.4-h54a6638_1 \n", " pugixml conda-forge/linux-64::pugixml-1.15-h3f63f65_0 \n", " pulseaudio-client conda-forge/linux-64::pulseaudio-client-17.0-h9a6aba3_3 \n", " sdl2 conda-forge/linux-64::sdl2-2.32.56-h54a6638_0 \n", " sdl3 conda-forge/linux-64::sdl3-3.2.24-h68140b3_0 \n", " snappy conda-forge/linux-64::snappy-1.2.2-h03e3b7b_1 \n", " svt-av1 conda-forge/linux-64::svt-av1-3.1.2-hecca717_0 \n", " tbb conda-forge/linux-64::tbb-2022.3.0-h8d10470_1 \n", " wayland pkgs/main/linux-64::wayland-1.24.0-hdac8c69_0 \n", " wayland-protocols conda-forge/noarch::wayland-protocols-1.47-hd8ed1ab_0 \n", " x264 conda-forge/linux-64::x264-1!164.3095-h166bdaf_2 \n", " x265 conda-forge/linux-64::x265-3.5-h924138e_3 \n", " xkeyboard-config conda-forge/linux-64::xkeyboard-config-2.46-hb03c661_0 \n", " xorg-libxcursor conda-forge/linux-64::xorg-libxcursor-1.2.3-hb9d3cd8_0 \n", " xorg-libxext conda-forge/linux-64::xorg-libxext-1.3.7-hb03c661_0 \n", " xorg-libxfixes conda-forge/linux-64::xorg-libxfixes-6.0.2-hb03c661_0 \n", " xorg-libxrandr conda-forge/linux-64::xorg-libxrandr-1.5.5-hb03c661_0 \n", " xorg-libxrender conda-forge/linux-64::xorg-libxrender-0.9.12-hb9d3cd8_0 \n", " xorg-libxscrnsaver conda-forge/linux-64::xorg-libxscrnsaver-1.2.4-hb9d3cd8_0 \n", "\n", "The following packages will be UPDATED:\n", "\n", " ca-certificates pkgs/main/linux-64::ca-certificates-2~ --> conda-forge/noarch::ca-certificates-2026.4.22-hbd8a1cb_0 \n", " certifi pkgs/main/linux-64::certifi-2026.01.0~ --> conda-forge/noarch::certifi-2026.4.22-pyhd8ed1ab_0 \n", " openssl pkgs/main::openssl-3.5.5-h1b28b03_0 --> conda-forge::openssl-3.6.2-h35e630c_0 \n", "\n", "\n", "\n", "Downloading and Extracting Packages:\n", "ffmpeg-7.1.1 | 10.1 MB | | 0% \n", "librsvg-2.58.4 | 6.0 MB | | 0% \u001b[A\n", "\n", "libhwloc-2.12.1 | 2.3 MB | | 0% \u001b[A\u001b[A\n", "\n", "\n", "harfbuzz-12.3.0 | 2.0 MB | | 0% \u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "sdl3-3.2.24 | 1.8 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "libxkbcommon-1.11.0 | 773 KB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "cairo-1.18.4 | 728 KB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "wayland-1.24.0 | 356 KB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "fontconfig-2.15.0 | 262 KB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "tbb-2022.3.0 | 177 KB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "certifi-2026.4.22 | 132 KB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "liburing-2.12 | 125 KB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "ffmpeg-7.1.1 | 10.1 MB | #################3 | 47% \u001b[A\u001b[A\n", "librsvg-2.58.4 | 6.0 MB | ######## | 22% \u001b[A\n", "\n", "\n", "harfbuzz-12.3.0 | 2.0 MB | ############################2 | 76% \u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "sdl3-3.2.24 | 1.8 MB | #######2 | 19% \u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "libxkbcommon-1.11.0 | 773 KB | 7 | 2% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "harfbuzz-12.3.0 | 2.0 MB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "libxkbcommon-1.11.0 | 773 KB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "sdl3-3.2.24 | 1.8 MB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "cairo-1.18.4 | 728 KB | 8 | 2% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "libxkbcommon-1.11.0 | 773 KB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "cairo-1.18.4 | 728 KB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "librsvg-2.58.4 | 6.0 MB | ##################################### | 100% \u001b[A\n", "librsvg-2.58.4 | 6.0 MB | ##################################### | 100% \u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "fontconfig-2.15.0 | 262 KB | ##2 | 6% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "fontconfig-2.15.0 | 262 KB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "wayland-1.24.0 | 356 KB | #6 | 4% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "certifi-2026.4.22 | 132 KB | ####4 | 12% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "certifi-2026.4.22 | 132 KB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "ffmpeg-7.1.1 | 10.1 MB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "wayland-1.24.0 | 356 KB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "tbb-2022.3.0 | 177 KB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "liburing-2.12 | 125 KB | ####7 | 13% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "liburing-2.12 | 125 KB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "harfbuzz-12.3.0 | 2.0 MB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "sdl3-3.2.24 | 1.8 MB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "sdl3-3.2.24 | 1.8 MB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "cairo-1.18.4 | 728 KB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "cairo-1.18.4 | 728 KB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", 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\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", "Preparing transaction: done\n", "Verifying transaction: done\n", "Executing transaction: / \n", "/ \n", "done\n", "\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install torch==2.10.0 torchaudio==2.10.0 torchvision==0.25.0 torchcodec==0.10.0 \\\n", "--index-url https://download.pytorch.org/whl/cu128\n", "\n", "%conda install -y -c conda-forge ffmpeg=7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. GPU Check" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Thu Apr 30 16:55:20 2026 \n", "+-----------------------------------------------------------------------------------------+\n", "| NVIDIA-SMI 575.57.08 Driver Version: 575.57.08 CUDA Version: 12.9 |\n", "|-----------------------------------------+------------------------+----------------------+\n", "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|=========================================+========================+======================|\n", "| 0 NVIDIA A100-SXM4-80GB On | 00000000:20:1D.0 Off | 0 |\n", "| N/A 25C P0 57W / 400W | 0MiB / 81920MiB | 0% Default |\n", "| | | Disabled |\n", "+-----------------------------------------+------------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=========================================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------------------+\n", "\n" ] } ], "source": [ "import subprocess\n", "r = subprocess.run(['nvidia-smi'], capture_output=True, text=True)\n", "print(r.stdout if r.returncode == 0 else '❌ No GPU — switch to GPU runtime first!')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Install Dependencies\n", "\n", "> **FlashAttention 2** is skipped — T4 is Turing (compute 7.5) and incompatible with FA2. \n", "> On an A100 Colab Pro runtime, uncomment the flash-attn line for a significant speedup." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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", "unsloth-zoo 2026.4.9 requires datasets!=4.0.*,!=4.1.0,<4.4.0,>=3.4.1, but you have datasets 4.8.5 which is incompatible.\n", "unsloth 2026.4.8 requires datasets!=4.0.*,!=4.1.0,<4.4.0,>=3.4.1, but you have datasets 4.8.5 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install -q -U qwen-asr datasets librosa transformers accelerate soundfile\n", "\n", "# Uncomment on A100 / Colab Pro:\n", "%pip install -q -U flash-attn --no-build-isolation\n", "\n", "import importlib\n", "for pkg in ['qwen_asr', 'datasets', 'librosa', 'transformers', 'accelerate']:\n", " try:\n", " m = importlib.import_module(pkg)\n", " print(f' ✅ {pkg}: {getattr(m, \"__version__\", \"ok\")}')\n", " except ImportError:\n", " print(f' ❌ {pkg}: MISSING — re-run this cell')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Config — Paths & Hyperparameters\n", "\n", "**Edit this cell** before running anything else." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ Config ready\n", " Model : Qwen/Qwen3-ASR-0.6B\n", " Dataset : ghananlpcommunity/ghana-bible-combined-90k-twi-ewe-dagbani\n", " Train JSONL: outputs/QWEN-ASR-fintuning/train.jsonl\n", " Eval JSONL: outputs/QWEN-ASR-fintuning/eval.jsonl\n", " Output dir : outputs/QWEN-ASR-fintuning/qwen3-asr-0.6b-ghana-out\n" ] } ], "source": [ "import os\n", "\n", "# ── Model ─────────────────────────────────────────────────────────────────────\n", "MODEL_PATH = 'Qwen/Qwen3-ASR-0.6B'\n", "\n", "# ── HuggingFace dataset ───────────────────────────────────────────────────────\n", "HF_DATASET = 'ghananlpcommunity/ghana-bible-combined-90k-twi-ewe-dagbani'\n", "HF_SPLIT = 'train' # dataset only has a train split\n", "\n", "# Language column values → Qwen3-ASR language prefix mapping\n", "# Adjust if the column uses different casing / spellings\n", "LANG_MAP = {\n", " 'twi': 'Twi',\n", " 'ewe': 'Ewe',\n", " 'dagbani': 'Dagbani',\n", "}\n", "\n", "# ── Local paths ───────────────────────────────────────────────────────────────\n", "BASE_DIR = 'outputs/QWEN-ASR-fintuning'\n", "AUDIO_DIR = 'ghana_audio' # temp audio cache (Colab local, fast I/O)\n", "TRAIN_FILE = f'{BASE_DIR}/train.jsonl'\n", "EVAL_FILE = f'{BASE_DIR}/eval.jsonl'\n", "OUTPUT_DIR = f'{BASE_DIR}/qwen3-asr-0.6b-ghana-out'\n", "\n", "# ── Data split ────────────────────────────────────────────────────────────────\n", "EVAL_RATIO = 0.02 # 2 % held out for eval (~1800 samples)\n", "MAX_SAMPLES = None # set e.g. 5000 to do a quick smoke-test; None = use all\n", "\n", "# ── Hyperparameters ───────────────────────────────────────────────────────────\n", "BATCH_SIZE = 4 # per-device; reduce to 1 if OOM on T4\n", "GRAD_ACC = 16 # effective batch = 2 * 16 = 32\n", "LR = 2e-5\n", "EPOCHS = 10\n", "SAVE_STEPS = 500\n", "SAVE_LIMIT = 100\n", "LOG_STEPS = 20\n", "WARMUP_RATIO = 0.05\n", "SR = 16000 # sampling rate expected by Qwen3-ASR\n", "\n", "for d in [BASE_DIR, AUDIO_DIR, OUTPUT_DIR]:\n", " os.makedirs(d, exist_ok=True)\n", "\n", "print('✅ Config ready')\n", "print(f' Model : {MODEL_PATH}')\n", "print(f' Dataset : {HF_DATASET}')\n", "print(f' Train JSONL: {TRAIN_FILE}')\n", "print(f' Eval JSONL: {EVAL_FILE}')\n", "print(f' Output dir : {OUTPUT_DIR}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Load HuggingFace Dataset → Build JSONL\n", "\n", "This cell:\n", "1. Streams the dataset from HuggingFace\n", "2. Saves each audio clip as a `.wav` file to `AUDIO_DIR`\n", "3. Writes `train.jsonl` and `eval.jsonl` with the format:\n", " ```json\n", " {\"audio\": \"/content/ghana_audio/00001.wav\", \"text\": \"language Twi...\"}\n", " ```\n", "\n", "**Runtime:** ~20-40 min for the full 90k dataset on Colab. Set `MAX_SAMPLES` in cell 4 to a small number for a quick test." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/user/miniconda/envs/ghana_speech/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ JSONL already built — train=87840, eval=1793\n", " Delete files and re-run to rebuild.\n", "\n", "Language distribution in train:\n", " language Dagbani: 29393\n", " language Ewe: 29334\n", " language Twi: 29113\n" ] } ], "source": [ "import json, os, soundfile as sf, numpy as np, librosa\n", "from datasets import load_dataset, Audio\n", "from tqdm.auto import tqdm\n", "\n", "# ── Skip if JSONL already exists ──────────────────────────────────────────\n", "if os.path.exists(TRAIN_FILE) and os.path.exists(EVAL_FILE):\n", " n_train = sum(1 for _ in open(TRAIN_FILE))\n", " n_eval = sum(1 for _ in open(EVAL_FILE))\n", " print(f'✅ JSONL already built — train={n_train}, eval={n_eval}')\n", " print(' Delete files and re-run to rebuild.')\n", "else:\n", " print(f'Loading {HF_DATASET} (streaming)...')\n", " ds = load_dataset(\n", " HF_DATASET,\n", " split=HF_SPLIT,\n", " streaming=True,\n", " ).cast_column('audio', Audio(sampling_rate=SR))\n", " # .cast_column forces AudioDecoder to decode + resample in one step\n", "\n", " train_records, eval_records = [], []\n", " skipped = 0\n", " idx = 0\n", "\n", " for ex in tqdm(ds, desc='Processing'):\n", " if MAX_SAMPLES and idx >= MAX_SAMPLES:\n", " break\n", "\n", " # ── Language ──────────────────────────────────────────────────────────\n", " raw_lang = str(ex.get('language', '')).strip().lower()\n", " qwen_lang = LANG_MAP.get(raw_lang)\n", " if qwen_lang is None:\n", " skipped += 1\n", " continue\n", "\n", " # ── Text ──────────────────────────────────────────────────────────────\n", " text = str(ex.get('text', '')).strip()\n", " if not text:\n", " skipped += 1\n", " continue\n", "\n", " # ── Audio — AudioDecoder is now decoded by cast_column ─────────────\n", " audio_col = ex.get('audio')\n", " if audio_col is None:\n", " skipped += 1\n", " continue\n", "\n", " # After cast_column, audio_col is a plain dict: {'array': ..., 'sampling_rate': ...}\n", " array = np.array(audio_col['array'], dtype=np.float32)\n", " orig_sr = audio_col['sampling_rate']\n", "\n", " if orig_sr != SR:\n", " array = librosa.resample(array, orig_sr=orig_sr, target_sr=SR)\n", "\n", " # ── Save wav ──────────────────────────────────────────────────────────\n", " wav_path = os.path.join(AUDIO_DIR, f'{idx:06d}.wav')\n", " sf.write(wav_path, array, SR)\n", "\n", " # ── Build JSONL record ────────────────────────────────────────────────\n", " target = f'language {qwen_lang}{text}'\n", " record = {'audio': wav_path, 'text': target}\n", "\n", " if idx % 50 == 0:\n", " eval_records.append(record)\n", " else:\n", " train_records.append(record)\n", "\n", " idx += 1\n", "\n", " # ── Write JSONL ───────────────────────────────────────────────────────\n", " os.makedirs(os.path.dirname(TRAIN_FILE), exist_ok=True)\n", " with open(TRAIN_FILE, 'w', encoding='utf-8') as f:\n", " for r in train_records:\n", " f.write(json.dumps(r, ensure_ascii=False) + '\\n')\n", " with open(EVAL_FILE, 'w', encoding='utf-8') as f:\n", " for r in eval_records:\n", " f.write(json.dumps(r, ensure_ascii=False) + '\\n')\n", "\n", " print(f'\\n✅ Done')\n", " print(f' Train: {len(train_records)}, Eval: {len(eval_records)}, Skipped: {skipped}')\n", "\n", "# ── Language distribution ─────────────────────────────────────────────────\n", "from collections import Counter\n", "lang_counts = Counter()\n", "with open(TRAIN_FILE) as f:\n", " for line in f:\n", " rec = json.loads(line)\n", " prefix = rec['text'].split('')[0]\n", " lang_counts[prefix] += 1\n", "print('\\nLanguage distribution in train:')\n", "for lang, count in lang_counts.most_common():\n", " print(f' {lang}: {count}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Write Fine-tuning Script\n", "\n", "Writes `qwen3_asr_sft.py` to `/content/`. Based on the official Qwen3-ASR example, adapted for single-GPU Colab." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ qwen3_asr_sft.py written to /content/\n" ] } ], "source": [ "script = r'''\n", "# coding=utf-8\n", "# Qwen3-ASR SFT script — single-GPU Colab edition\n", "# Adapted from: https://github.com/QwenLM/Qwen3-ASR\n", "import argparse, os, re, shutil\n", "from dataclasses import dataclass\n", "from typing import Any, Dict, List, Optional\n", "\n", "import librosa\n", "import torch\n", "from datasets import load_dataset\n", "from qwen_asr import Qwen3ASRModel\n", "from transformers import (GenerationConfig, Trainer, TrainerCallback,\n", " TrainingArguments)\n", "\n", "\n", "# ── Forward patch ─────────────────────────────────────────────────────────────\n", "def patch_outer_forward(model):\n", " cls = model.__class__\n", " if getattr(cls, \"_forward_patched\", False):\n", " return\n", " if not hasattr(model, \"thinker\") or not hasattr(model.thinker, \"forward\"):\n", " raise RuntimeError(\n", " \"Cannot patch forward: model has no .thinker.forward. \"\n", " \"Check your qwen-asr version.\"\n", " )\n", " def forward(self, input_ids=None, attention_mask=None, input_features=None,\n", " feature_attention_mask=None, labels=None, **kwargs):\n", " return self.thinker.forward(\n", " input_ids=input_ids, attention_mask=attention_mask,\n", " input_features=input_features,\n", " feature_attention_mask=feature_attention_mask,\n", " labels=labels, **kwargs,\n", " )\n", " cls.forward = forward\n", " cls._forward_patched = True\n", "\n", "\n", "# ── Checkpoint utils ──────────────────────────────────────────────────────────\n", "_CKPT_RE = re.compile(r\"^checkpoint-(\\d+)$\")\n", "\n", "def find_latest_checkpoint(output_dir: str) -> Optional[str]:\n", " if not output_dir or not os.path.isdir(output_dir):\n", " return None\n", " best_step, best_path = None, None\n", " for name in os.listdir(output_dir):\n", " m = _CKPT_RE.match(name)\n", " if not m:\n", " continue\n", " step = int(m.group(1))\n", " path = os.path.join(output_dir, name)\n", " if os.path.isdir(path) and (best_step is None or step > best_step):\n", " best_step, best_path = step, path\n", " return best_path\n", "\n", "\n", "# ── Audio ─────────────────────────────────────────────────────────────────────\n", "def load_audio(path: str, sr: int = 16000):\n", " wav, _ = librosa.load(path, sr=sr, mono=True)\n", " return wav\n", "\n", "\n", "# ── Preprocessing ─────────────────────────────────────────────────────────────\n", "def build_prefix_messages(prompt: str, audio_array):\n", " return [\n", " {\"role\": \"system\", \"content\": prompt or \"\"},\n", " {\"role\": \"user\", \"content\": [{\"type\": \"audio\", \"audio\": audio_array}]},\n", " ]\n", "\n", "def make_preprocess_fn(processor):\n", " def _preprocess(ex: Dict[str, Any]) -> Dict[str, Any]:\n", " prompt = ex.get(\"prompt\", \"\")\n", " prefix_msgs = build_prefix_messages(prompt, None)\n", " prefix_text = processor.apply_chat_template(\n", " [prefix_msgs], add_generation_prompt=True, tokenize=False\n", " )[0]\n", " return {\n", " \"prompt\": prompt,\n", " \"audio\": ex[\"audio\"],\n", " \"target\": ex[\"text\"],\n", " \"prefix_text\": prefix_text,\n", " }\n", " return _preprocess\n", "\n", "\n", "# ── Collator ──────────────────────────────────────────────────────────────────\n", "@dataclass\n", "class DataCollatorQwen3ASR:\n", " processor: Any\n", " sampling_rate: int = 16000\n", "\n", " def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]:\n", " audio_paths = [f[\"audio\"] for f in features]\n", " prefix_texts = [f[\"prefix_text\"] for f in features]\n", " targets = [f[\"target\"] for f in features]\n", "\n", " eos = self.processor.tokenizer.eos_token or \"\"\n", " full_texts = [p + t + eos for p, t in zip(prefix_texts, targets)]\n", " audios = [load_audio(p, self.sampling_rate) for p in audio_paths]\n", "\n", " full_inp = self.processor(text=full_texts, audio=audios,\n", " return_tensors=\"pt\", padding=True, truncation=False)\n", " prefix_inp = self.processor(text=prefix_texts, audio=audios,\n", " return_tensors=\"pt\", padding=True, truncation=False)\n", "\n", " prefix_lens = prefix_inp[\"attention_mask\"].sum(dim=1).tolist()\n", " labels = full_inp[\"input_ids\"].clone()\n", " for i, pl in enumerate(prefix_lens):\n", " labels[i, :pl] = -100 # mask prompt tokens from loss\n", "\n", " pad_id = self.processor.tokenizer.pad_token_id\n", " if pad_id is not None:\n", " labels[labels == pad_id] = -100\n", "\n", " full_inp[\"labels\"] = labels\n", " return full_inp\n", "\n", "\n", "# ── Trainer ───────────────────────────────────────────────────────────────────\n", "class CastFloatTrainer(Trainer):\n", " \"\"\"Cast all float inputs to the model's dtype (handles fp16 / bf16 mismatches).\"\"\"\n", " def _prepare_inputs(self, inputs):\n", " inputs = super()._prepare_inputs(inputs)\n", " model_dtype = getattr(self.model, \"dtype\", None)\n", " if model_dtype is not None:\n", " for k, v in list(inputs.items()):\n", " if torch.is_tensor(v) and v.is_floating_point():\n", " inputs[k] = v.to(dtype=model_dtype)\n", " return inputs\n", "\n", "\n", "# ── Callback: copy HF config files into every checkpoint ─────────────────────\n", "def copy_hf_config_files(src_dir: str, dst_dir: str):\n", " os.makedirs(dst_dir, exist_ok=True)\n", " for fn in [\n", " \"config.json\", \"generation_config.json\", \"preprocessor_config.json\",\n", " \"processor_config.json\", \"tokenizer_config.json\", \"tokenizer.json\",\n", " \"special_tokens_map.json\", \"chat_template.json\", \"merges.txt\", \"vocab.json\",\n", " ]:\n", " src = os.path.join(src_dir, fn)\n", " if os.path.exists(src):\n", " shutil.copy2(src, os.path.join(dst_dir, fn))\n", "\n", "class MakeCheckpointInferableCallback(TrainerCallback):\n", " def __init__(self, base_model_path: str):\n", " self.base_model_path = base_model_path\n", "\n", " def on_save(self, args: TrainingArguments, state, control, **kwargs):\n", " if args.process_index != 0:\n", " return control\n", " ckpt_dir = os.path.join(args.output_dir, f\"checkpoint-{state.global_step}\")\n", " copy_hf_config_files(self.base_model_path, ckpt_dir)\n", " return control\n", "\n", "\n", "# ── Argument parsing ──────────────────────────────────────────────────────────\n", "def parse_args():\n", " p = argparse.ArgumentParser(\"Qwen3-ASR SFT\")\n", " p.add_argument(\"--model_path\", default=\"Qwen/Qwen3-ASR-0.6B\")\n", " p.add_argument(\"--train_file\", required=True)\n", " p.add_argument(\"--eval_file\", default=\"\")\n", " p.add_argument(\"--output_dir\", default=\"./qwen3-asr-out\")\n", " p.add_argument(\"--sr\", type=int, default=16000)\n", " p.add_argument(\"--batch_size\", type=int, default=2)\n", " p.add_argument(\"--grad_acc\", type=int, default=16)\n", " p.add_argument(\"--lr\", type=float, default=2e-5)\n", " p.add_argument(\"--epochs\", type=float, default=3)\n", " p.add_argument(\"--log_steps\", type=int, default=20)\n", " p.add_argument(\"--warmup_ratio\", type=float, default=0.05)\n", " p.add_argument(\"--save_strategy\", default=\"steps\")\n", " p.add_argument(\"--save_steps\", type=int, default=200)\n", " p.add_argument(\"--save_total_limit\",type=int, default=3)\n", " p.add_argument(\"--num_workers\", type=int, default=2)\n", " p.add_argument(\"--resume_from\", default=\"\")\n", " p.add_argument(\"--resume\", type=int, default=0)\n", " return p.parse_args()\n", "\n", "\n", "# ── Main ──────────────────────────────────────────────────────────────────────\n", "def main():\n", " args = parse_args()\n", "\n", " use_bf16 = (\n", " torch.cuda.is_available()\n", " and torch.cuda.get_device_capability(0)[0] >= 8\n", " )\n", " print(f\"Using {'bf16' if use_bf16 else 'fp16'}\")\n", "\n", " asr_wrapper = Qwen3ASRModel.from_pretrained(\n", " args.model_path,\n", " dtype=torch.bfloat16 if use_bf16 else torch.float16,\n", " device_map=None,\n", " )\n", " model = asr_wrapper.model\n", " processor = asr_wrapper.processor\n", "\n", " patch_outer_forward(model)\n", " model.generation_config = GenerationConfig.from_model_config(model.config)\n", "\n", " # ── Dataset ───────────────────────────────────────────────────────────────\n", " data_files = {\"train\": args.train_file}\n", " if args.eval_file:\n", " data_files[\"validation\"] = args.eval_file\n", "\n", " raw_ds = load_dataset(\"json\", data_files=data_files)\n", " ds = raw_ds.map(make_preprocess_fn(processor), num_proc=1)\n", "\n", " keep = {\"prompt\", \"audio\", \"target\", \"prefix_text\"}\n", " for split in ds.keys():\n", " drop = [c for c in ds[split].column_names if c not in keep]\n", " if drop:\n", " ds[split] = ds[split].remove_columns(drop)\n", "\n", " # ── Training args ─────────────────────────────────────────────────────────\n", " training_args = TrainingArguments(\n", " output_dir = args.output_dir,\n", " per_device_train_batch_size = args.batch_size,\n", " gradient_accumulation_steps = args.grad_acc,\n", " learning_rate = args.lr,\n", " num_train_epochs = args.epochs,\n", " logging_steps = args.log_steps,\n", " lr_scheduler_type = \"cosine\",\n", " warmup_ratio = args.warmup_ratio,\n", " dataloader_num_workers = args.num_workers,\n", " save_strategy = args.save_strategy,\n", " save_steps = args.save_steps,\n", " save_total_limit = args.save_total_limit,\n", " save_safetensors = True,\n", " eval_strategy = \"steps\" if args.eval_file else \"no\",\n", " eval_steps = args.save_steps if args.eval_file else None,\n", " do_eval = bool(args.eval_file),\n", " bf16 = use_bf16,\n", " fp16 = not use_bf16,\n", " ddp_find_unused_parameters = False,\n", " remove_unused_columns = False,\n", " report_to = \"none\",\n", " )\n", "\n", " collator = DataCollatorQwen3ASR(processor=processor, sampling_rate=args.sr)\n", "\n", " trainer = CastFloatTrainer(\n", " model = model,\n", " args = training_args,\n", " train_dataset = ds[\"train\"],\n", " eval_dataset = ds.get(\"validation\", None),\n", " data_collator = collator,\n", " tokenizer = processor.tokenizer,\n", " callbacks = [MakeCheckpointInferableCallback(args.model_path)],\n", " )\n", "\n", " resume = (args.resume_from or \"\").strip()\n", " if not resume and args.resume == 1:\n", " resume = find_latest_checkpoint(training_args.output_dir) or \"\"\n", " if resume:\n", " print(f\"Resuming from: {resume}\")\n", " trainer.train(resume_from_checkpoint=resume)\n", " else:\n", " trainer.train()\n", "\n", "\n", "if __name__ == \"__main__\":\n", " main()\n", "'''\n", "\n", "with open('qwen3_asr_sft.py', 'w') as f:\n", " f.write(script.strip())\n", "\n", "print('✅ qwen3_asr_sft.py written to /content/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Run Training\n", "\n", "Single-GPU training. All config variables come from Section 4.\n", "\n", "**Expected throughput on T4:** ~0.3–0.5 it/s at batch_size=2, grad_acc=16. \n", "Full 90k dataset × 3 epochs ≈ 15–25 hours — use Colab Pro or train in multiple sessions with resume (Section 8)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running: /home/user/miniconda/envs/ghana_speech/bin/python qwen3_asr_sft.py --model_path Qwen/Qwen3-ASR-0.6B --train_file outputs/QWEN-ASR-fintuning/train.jsonl --eval_file outputs/QWEN-ASR-fintuning/eval.jsonl --output_dir outputs/QWEN-ASR-fintuning/qwen3-asr-0.6b-ghana-out --sr 16000 --batch_size 4 --grad_acc 16 --lr 2e-05 --epochs 10 --save_steps 500 --save_total_limit 100 --log_steps 20 --warmup_ratio 0.05 --num_workers 8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Skipping import of cpp extensions due to incompatible torch version. Please upgrade to torch >= 2.11.0 (found 2.10.0+cu128).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Using bf16\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "The following generation flags are not valid and may be ignored: ['temperature']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n", "/data/qwen3_asr_sft.py:240: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `CastFloatTrainer.__init__`. Use `processing_class` instead.\n", " trainer = CastFloatTrainer(\n", "The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. 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Set `TRANSFORMERS_VERBOSITY=info` for more details.\n", "/data/qwen3_asr_sft.py:240: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `CastFloatTrainer.__init__`. Use `processing_class` instead.\n", " trainer = CastFloatTrainer(\n", "The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'eos_token_id': 151645, 'pad_token_id': 151643}.\n", " 0%| | 14/8235 [01:00<9:21:06, 4.10s/it]" ] } ], "source": [ "import subprocess, sys\n", "\n", "cmd = [\n", " sys.executable, 'qwen3_asr_sft.py',\n", " '--model_path', MODEL_PATH,\n", " '--train_file', TRAIN_FILE,\n", " '--eval_file', EVAL_FILE,\n", " '--output_dir', OUTPUT_DIR,\n", " '--sr', str(SR),\n", " '--batch_size', str(BATCH_SIZE),\n", " '--grad_acc', str(GRAD_ACC),\n", " '--lr', str(LR),\n", " '--epochs', str(EPOCHS),\n", " '--save_steps', str(SAVE_STEPS),\n", " '--save_total_limit', str(SAVE_LIMIT),\n", " '--log_steps', str(LOG_STEPS),\n", " '--warmup_ratio', str(WARMUP_RATIO),\n", " '--num_workers', '2',\n", " '--resume', '1', # ← auto-resume from latest checkpoint\n", "]\n", "\n", "print('Resuming training...')\n", "result = subprocess.run(cmd)\n", "if result.returncode != 0:\n", " raise RuntimeError(f'Training failed with exit code {result.returncode}')\n", "print('\\n✅ Training complete!')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 9. Inference Test\n", "\n", "Test the fine-tuned model on a sample audio file. Expects the model to output the language prefix + transcript." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ Processor files copied into checkpoint.\n" ] } ], "source": [ "# ── Fix: copy processor files from base model into checkpoint ─────────────────\n", "import shutil, os\n", "\n", "BASE_MODEL = \"Qwen/Qwen3-ASR-0.6B\" # the original base model\n", "CKPT_PATH = \"outputs/QWEN-ASR-fintuning/qwen3-asr-0.6b-ghana-out/checkpoint-4000\"\n", "\n", "PROCESSOR_FILES = [\n", " \"preprocessor_config.json\",\n", " \"tokenizer.json\",\n", " \"tokenizer_config.json\",\n", " \"special_tokens_map.json\",\n", " \"vocab.json\", # may not exist, that's fine\n", " \"merges.txt\", # may not exist, that's fine\n", "]\n", "\n", "from transformers import AutoProcessor\n", "# Download/cache processor from base model then copy its files\n", "processor = AutoProcessor.from_pretrained(BASE_MODEL)\n", "processor.save_pretrained(CKPT_PATH) # writes all processor files into ckpt dir\n", "print(\"✅ Processor files copied into checkpoint.\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading checkpoint: outputs/QWEN-ASR-fintuning/qwen3-asr-0.6b-ghana-out/checkpoint-4000\n", "\n", "Reference : language Dagbanibɛn daa dolila Dauda. Saha shɛli Dauda ni daa di nam ʒi Hiburɔn n-su Judanim maa\n", "Audio file: ghana_audio/033650.wav\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Predicted language : Dagbani\n", "Predicted text : bɛn daa doli la Dauda saha shɛli Dauda ni daa di nam ʒi Hiburɔn n-su Judanim maa\n" ] } ], "source": [ "import torch, os\n", "from qwen_asr import Qwen3ASRModel\n", "\n", "# ── Set checkpoint path directly ──────────────────────────────────────────────\n", "# Option 1: Hardcode a specific checkpoint\n", "CKPT_PATH = \"outputs/QWEN-ASR-fintuning/qwen3-asr-0.6b-ghana-out/checkpoint-4000\" # <-- change this\n", "\n", "if not os.path.isdir(CKPT_PATH):\n", " raise FileNotFoundError(f\"Checkpoint not found: {CKPT_PATH}\")\n", "\n", "print(f'Loading checkpoint: {CKPT_PATH}')\n", "\n", "# ── Load model ────────────────────────────────────────────────────────────────\n", "use_bf16 = torch.cuda.is_available() and torch.cuda.get_device_capability(0)[0] >= 8\n", "model = Qwen3ASRModel.from_pretrained(\n", " CKPT_PATH,\n", " dtype=torch.bfloat16 if use_bf16 else torch.float16,\n", " device_map='cuda:0',\n", ")\n", "\n", "# ── Pick a sample from eval JSONL ─────────────────────────────────────────────\n", "import json, random\n", "with open(EVAL_FILE) as f:\n", " samples = [json.loads(l) for l in f]\n", "\n", "sample = random.choice(samples)\n", "print(f'\\nReference : {sample[\"text\"]}')\n", "print(f'Audio file: {sample[\"audio\"]}')\n", "\n", "# ── Transcribe ────────────────────────────────────────────────────────────────\n", "results = model.transcribe(audio=sample['audio'])\n", "print(f'\\nPredicted language : {results[0].language}')\n", "print(f'Predicted text : {results[0].text}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 10. Push Checkpoint to HuggingFace Hub (Optional)\n", "\n", "Upload your best checkpoint to `ghananlpcommunity` or your personal HF org." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# ── Set your HF repo details ──────────────────────────────────────────────────\n", "HF_REPO_ID = 'ghananlpcommunity/qwen3-asr-0.6b-ghana-multilang'\n", "HF_TOKEN = '' # paste your HF write token, or use: from google.colab import userdata; userdata.get('HF_TOKEN')\n", "PUSH_CKPT = ckpt # or set manually: '/content/drive/MyDrive/GhanaNLP_ASR/qwen3-asr-0.6b-ghana-out/checkpoint-600'\n", "\n", "# ──────────────────────────────────────────────────────────────────────────────\n", "from huggingface_hub import HfApi\n", "import os\n", "\n", "if not HF_TOKEN:\n", " raise ValueError('Set HF_TOKEN above before running this cell.')\n", "\n", "api = HfApi(token=HF_TOKEN)\n", "api.create_repo(repo_id=HF_REPO_ID, repo_type='model', exist_ok=True)\n", "\n", "print(f'Uploading {PUSH_CKPT} → {HF_REPO_ID} ...')\n", "api.upload_folder(\n", " folder_path=PUSH_CKPT,\n", " repo_id=HF_REPO_ID,\n", " repo_type='model',\n", " commit_message=f'Upload fine-tuned checkpoint: {os.path.basename(PUSH_CKPT)}',\n", ")\n", "print(f'✅ Uploaded to https://huggingface.co/{HF_REPO_ID}')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install \"datasets==2.19.2\" --quiet" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# ── CELL 2: Config ────────────────────────────────────────────────────────────\n", "import os\n", "\n", "BASE_MODEL = \"Qwen/Qwen3-ASR-0.6B\"\n", "CKPT_PATH = \"outputs/QWEN-ASR-fintuning/qwen3-asr-0.6b-ghana-out/checkpoint-4000\" # adjust\n", "\n", "HF_TOKEN = \"\" # paste your HF read token if datasets are private\n", "\n", "DATASETS = {\n", " \"Twi\": \"ghananlpcommunity/ghana-nlp-health-UNICEF-asr-twi\",\n", " \"Dagbani\": \"ghananlpcommunity/ghana-nlp-health-UNICEF-asr-dagbani\",\n", " \"Ewe\": \"ghananlpcommunity/ghana-nlp-health-UNICEF-asr-ewe\",\n", "}\n", "\n", "# Column names in the HF datasets — adjust if different\n", "AUDIO_COL = \"audio\"\n", "TEXT_COL = \"text\"\n", "\n", "# How many samples to evaluate per language (None = all)\n", "MAX_SAMPLES = None" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading BASE model...\n", "✅ Base model loaded.\n", "\n", "Loading FINE-TUNED model from outputs/QWEN-ASR-fintuning/qwen3-asr-0.6b-ghana-out/checkpoint-4000...\n", "✅ Fine-tuned model loaded.\n" ] } ], "source": [ "# ── CELL 3: Load both models ──────────────────────────────────────────────────\n", "import torch\n", "from qwen_asr import Qwen3ASRModel\n", "from transformers import AutoProcessor\n", "\n", "use_bf16 = torch.cuda.is_available() and torch.cuda.get_device_capability(0)[0] >= 8\n", "dtype = torch.bfloat16 if use_bf16 else torch.float16\n", "\n", "print(\"Loading BASE model...\")\n", "base_model = Qwen3ASRModel.from_pretrained(\n", " BASE_MODEL,\n", " dtype=dtype,\n", " device_map=\"cuda:0\",\n", ")\n", "print(\"✅ Base model loaded.\")\n", "\n", "print(f\"\\nLoading FINE-TUNED model from {CKPT_PATH}...\")\n", "ft_model = Qwen3ASRModel.from_pretrained(\n", " CKPT_PATH,\n", " dtype=dtype,\n", " device_map=\"cuda:0\",\n", ")\n", "ft_model.processor = AutoProcessor.from_pretrained(BASE_MODEL, fix_mistral_regex=True)\n", "print(\"✅ Fine-tuned model loaded.\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading Twi: ghananlpcommunity/ghana-nlp-health-UNICEF-asr-twi\n", " → 200 samples, columns: ['audio', 'text', 'audio_file_name']\n", "Loading Dagbani: ghananlpcommunity/ghana-nlp-health-UNICEF-asr-dagbani\n", " → 200 samples, columns: ['audio', 'text', 'audio_file_name']\n", "Loading Ewe: ghananlpcommunity/ghana-nlp-health-UNICEF-asr-ewe\n", " → 200 samples, columns: ['audio', 'text', 'audio_file_name']\n", "\n", "✅ All datasets loaded.\n" ] } ], "source": [ "# ── CELL 4 (FIXED): Load datasets ─────────────────────────────────────────────\n", "from datasets import load_dataset, Audio\n", "\n", "loaded = {}\n", "for lang, repo in DATASETS.items():\n", " print(f\"Loading {lang}: {repo}\")\n", " ds = load_dataset(repo, token=HF_TOKEN if HF_TOKEN else None, split=\"train\")\n", " ds = ds.cast_column(\"audio\", Audio(sampling_rate=16000)) # decode via soundfile\n", " if MAX_SAMPLES:\n", " ds = ds.select(range(min(MAX_SAMPLES, len(ds))))\n", " loaded[lang] = ds\n", " print(f\" → {len(ds)} samples, columns: {ds.column_names}\")\n", "\n", "print(\"\\n✅ All datasets loaded.\")" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Twi:\n", " Columns : ['audio', 'text', 'audio_file_name']\n" ] }, { "ename": "ImportError", "evalue": "To support decoding audio data, please install 'torchcodec'.", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[30]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[38;5;66;03m# Run this to inspect what columns your dataset actually has\u001b[39;00m\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m lang, ds \u001b[38;5;28;01min\u001b[39;00m loaded.items():\n\u001b[32m 4\u001b[39m print(\u001b[33mf\"\\n{lang}:\"\u001b[39m)\n\u001b[32m 5\u001b[39m print(\u001b[33mf\" Columns : {ds.column_names}\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m print(\u001b[33mf\" Sample : { {k: str(v)[:80] for k, v in ds[0].items()} }\"\u001b[39m)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/arrow_dataset.py:3157\u001b[39m, in \u001b[36mDataset.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 3155\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._format_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m._format_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[33m\"\u001b[39m\u001b[33marrow\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mpandas\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mpolars\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m 3156\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m Column(\u001b[38;5;28mself\u001b[39m, key)\n\u001b[32m-> \u001b[39m\u001b[32m3157\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_getitem\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mkey\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/arrow_dataset.py:3139\u001b[39m, in \u001b[36mDataset._getitem\u001b[39m\u001b[34m(self, key, **kwargs)\u001b[39m\n\u001b[32m 3137\u001b[39m formatter = get_formatter(format_type, features=\u001b[38;5;28mself\u001b[39m._info.features, **format_kwargs)\n\u001b[32m 3138\u001b[39m pa_subtable = query_table(\u001b[38;5;28mself\u001b[39m._data, key, indices=\u001b[38;5;28mself\u001b[39m._indices)\n\u001b[32m-> \u001b[39m\u001b[32m3139\u001b[39m formatted_output = \u001b[30;43mformat_table\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 3140\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mpa_subtable\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mkey\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mformatter\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mformatter\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mformat_columns\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mformat_columns\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43moutput_all_columns\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43moutput_all_columns\u001b[39;49m\n\u001b[32m 3141\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 3142\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m formatted_output\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/formatting/formatting.py:658\u001b[39m, in \u001b[36mformat_table\u001b[39m\u001b[34m(table, key, formatter, format_columns, output_all_columns)\u001b[39m\n\u001b[32m 656\u001b[39m python_formatter = PythonFormatter(features=formatter.features)\n\u001b[32m 657\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m format_columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m658\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mformatter\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpa_table\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mquery_type\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mquery_type\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 659\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m query_type == \u001b[33m\"\u001b[39m\u001b[33mcolumn\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 660\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m format_columns:\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/formatting/formatting.py:411\u001b[39m, in \u001b[36mFormatter.__call__\u001b[39m\u001b[34m(self, pa_table, query_type)\u001b[39m\n\u001b[32m 409\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, pa_table: pa.Table, query_type: \u001b[38;5;28mstr\u001b[39m) -> Union[RowFormat, ColumnFormat, BatchFormat]:\n\u001b[32m 410\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m query_type == \u001b[33m\"\u001b[39m\u001b[33mrow\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m411\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mformat_row\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpa_table\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 412\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m query_type == \u001b[33m\"\u001b[39m\u001b[33mcolumn\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 413\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.format_column(pa_table)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/formatting/formatting.py:460\u001b[39m, in \u001b[36mPythonFormatter.format_row\u001b[39m\u001b[34m(self, pa_table)\u001b[39m\n\u001b[32m 458\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m LazyRow(pa_table, \u001b[38;5;28mself\u001b[39m)\n\u001b[32m 459\u001b[39m row = \u001b[38;5;28mself\u001b[39m.python_arrow_extractor().extract_row(pa_table)\n\u001b[32m--> \u001b[39m\u001b[32m460\u001b[39m row = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mpython_features_decoder\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdecode_row\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mrow\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 461\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m row\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/formatting/formatting.py:224\u001b[39m, in \u001b[36mPythonFeaturesDecoder.decode_row\u001b[39m\u001b[34m(self, row)\u001b[39m\n\u001b[32m 223\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdecode_row\u001b[39m(\u001b[38;5;28mself\u001b[39m, row: \u001b[38;5;28mdict\u001b[39m) -> \u001b[38;5;28mdict\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m224\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mfeatures\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdecode_example\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mrow\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m)\u001b[39;49m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.features \u001b[38;5;28;01melse\u001b[39;00m row\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/features/features.py:2203\u001b[39m, in \u001b[36mFeatures.decode_example\u001b[39m\u001b[34m(self, example, token_per_repo_id)\u001b[39m\n\u001b[32m 2189\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdecode_example\u001b[39m(\u001b[38;5;28mself\u001b[39m, example: \u001b[38;5;28mdict\u001b[39m, token_per_repo_id: Optional[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Union[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m]]] = \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 2190\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Decode example with custom feature decoding.\u001b[39;00m\n\u001b[32m 2191\u001b[39m \n\u001b[32m 2192\u001b[39m \u001b[33;03m Args:\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 2200\u001b[39m \u001b[33;03m `dict[str, Any]`\u001b[39;00m\n\u001b[32m 2201\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m2203\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43m{\u001b[39;49m\n\u001b[32m 2204\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcolumn_name\u001b[39;49m\u001b[30;43m:\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mdecode_nested_example\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfeature\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mvalue\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 2205\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_column_requires_decoding\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43mcolumn_name\u001b[39;49m\u001b[30;43m]\u001b[39;49m\n\u001b[32m 2206\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01melse\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mvalue\u001b[39;49m\n\u001b[32m 2207\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01mfor\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mcolumn_name\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfeature\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mvalue\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01min\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mzip_dict\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 2208\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m{\u001b[39;49m\u001b[30;43mkey\u001b[39;49m\u001b[30;43m:\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mvalue\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mfor\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mkey\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mvalue\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01min\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mitems\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mkey\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01min\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mexample\u001b[39;49m\u001b[30;43m}\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mexample\u001b[39;49m\n\u001b[32m 2209\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 2210\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m}\u001b[39;49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/features/features.py:2204\u001b[39m, in \u001b[36m\u001b[39m\u001b[34m(.0)\u001b[39m\n\u001b[32m 2189\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdecode_example\u001b[39m(\u001b[38;5;28mself\u001b[39m, example: \u001b[38;5;28mdict\u001b[39m, token_per_repo_id: Optional[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Union[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m]]] = \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 2190\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Decode example with custom feature decoding.\u001b[39;00m\n\u001b[32m 2191\u001b[39m \n\u001b[32m 2192\u001b[39m \u001b[33;03m Args:\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 2200\u001b[39m \u001b[33;03m `dict[str, Any]`\u001b[39;00m\n\u001b[32m 2201\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m 2203\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[32m-> \u001b[39m\u001b[32m2204\u001b[39m column_name: \u001b[30;43mdecode_nested_example\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfeature\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mvalue\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 2205\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._column_requires_decoding[column_name]\n\u001b[32m 2206\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m value\n\u001b[32m 2207\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m column_name, (feature, value) \u001b[38;5;129;01min\u001b[39;00m zip_dict(\n\u001b[32m 2208\u001b[39m {key: value \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.items() \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m example}, example\n\u001b[32m 2209\u001b[39m )\n\u001b[32m 2210\u001b[39m }\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/features/features.py:1508\u001b[39m, in \u001b[36mdecode_nested_example\u001b[39m\u001b[34m(schema, obj, token_per_repo_id)\u001b[39m\n\u001b[32m 1505\u001b[39m \u001b[38;5;66;03m# Object with special decoding:\u001b[39;00m\n\u001b[32m 1506\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(schema, \u001b[33m\"\u001b[39m\u001b[33mdecode_example\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(schema, \u001b[33m\"\u001b[39m\u001b[33mdecode\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[32m 1507\u001b[39m \u001b[38;5;66;03m# we pass the token to read and decode files from private repositories in streaming mode\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1508\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mschema\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdecode_example\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mobj\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m)\u001b[39;49m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1509\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m obj\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/features/audio.py:186\u001b[39m, in \u001b[36mdecode_example\u001b[39m\u001b[34m(self, value, token_per_repo_id)\u001b[39m\n\u001b[32m 183\u001b[39m with xopen(path, \"rb\", download_config=download_config) as f:\n\u001b[32m 184\u001b[39m array, sampling_rate = sf.read(f)\n\u001b[32m--> \u001b[39m\u001b[32m186\u001b[39m else:\n\u001b[32m 187\u001b[39m array, sampling_rate = sf.read(file)\n\u001b[32m 189\u001b[39m array = array.T\n", "\u001b[31mImportError\u001b[39m: To support decoding audio data, please install 'torchcodec'." ] } ], "source": [ "# ── CELL 5: Verify column names ───────────────────────────────────────────────\n", "# Run this to inspect what columns your dataset actually has\n", "for lang, ds in loaded.items():\n", " print(f\"\\n{lang}:\")\n", " print(f\" Columns : {ds.column_names}\")\n", " print(f\" Sample : { {k: str(v)[:80] for k, v in ds[0].items()} }\")" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "=======================================================\n", " Evaluating: Twi (200 samples)\n", "=======================================================\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Twi: 0%| | 0/200 [00:00 \u001b[39m\u001b[32m34\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, sample \u001b[38;5;28;01min\u001b[39;00m enumerate(tqdm(ds, desc=lang)):\n\u001b[32m 35\u001b[39m pred_b, ref = transcribe_sample(base_model, sample, AUDIO_COL, TEXT_COL)\n\u001b[32m 36\u001b[39m base_preds.append(pred_b)\n\u001b[32m 37\u001b[39m base_refs.append(ref)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/tqdm/std.py:1181\u001b[39m, in \u001b[36mtqdm.__iter__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1178\u001b[39m time = \u001b[38;5;28mself\u001b[39m._time\n\u001b[32m 1180\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1181\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01mfor\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mobj\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01min\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43miterable\u001b[39;49m\u001b[30;43m:\u001b[39;49m\n\u001b[32m 1182\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01myield\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mobj\u001b[39;49m\n\u001b[32m 1183\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;03m# Update and possibly print the progressbar.\u001b[39;49;00m\n\u001b[32m 1184\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;49;00m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/arrow_dataset.py:2764\u001b[39m, in \u001b[36mDataset.__iter__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 2762\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(pa_subtable.num_rows):\n\u001b[32m 2763\u001b[39m pa_subtable_ex = pa_subtable.slice(i, \u001b[32m1\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m2764\u001b[39m formatted_output = \u001b[30;43mformat_table\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 2765\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mpa_subtable_ex\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 2766\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m0\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 2767\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mformatter\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mformatter\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 2768\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mformat_columns\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_format_columns\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 2769\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43moutput_all_columns\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_output_all_columns\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 2770\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 2771\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m formatted_output\n\u001b[32m 2772\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/formatting/formatting.py:658\u001b[39m, in \u001b[36mformat_table\u001b[39m\u001b[34m(table, key, formatter, format_columns, output_all_columns)\u001b[39m\n\u001b[32m 656\u001b[39m python_formatter = PythonFormatter(features=formatter.features)\n\u001b[32m 657\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m format_columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m658\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mformatter\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpa_table\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mquery_type\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mquery_type\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 659\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m query_type == \u001b[33m\"\u001b[39m\u001b[33mcolumn\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 660\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m format_columns:\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/formatting/formatting.py:411\u001b[39m, in \u001b[36mFormatter.__call__\u001b[39m\u001b[34m(self, pa_table, query_type)\u001b[39m\n\u001b[32m 409\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, pa_table: pa.Table, query_type: \u001b[38;5;28mstr\u001b[39m) -> Union[RowFormat, ColumnFormat, BatchFormat]:\n\u001b[32m 410\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m query_type == \u001b[33m\"\u001b[39m\u001b[33mrow\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m411\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mformat_row\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpa_table\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 412\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m query_type == \u001b[33m\"\u001b[39m\u001b[33mcolumn\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 413\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.format_column(pa_table)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/formatting/formatting.py:460\u001b[39m, in \u001b[36mPythonFormatter.format_row\u001b[39m\u001b[34m(self, pa_table)\u001b[39m\n\u001b[32m 458\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m LazyRow(pa_table, \u001b[38;5;28mself\u001b[39m)\n\u001b[32m 459\u001b[39m row = \u001b[38;5;28mself\u001b[39m.python_arrow_extractor().extract_row(pa_table)\n\u001b[32m--> \u001b[39m\u001b[32m460\u001b[39m row = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mpython_features_decoder\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdecode_row\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mrow\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 461\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m row\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/formatting/formatting.py:224\u001b[39m, in \u001b[36mPythonFeaturesDecoder.decode_row\u001b[39m\u001b[34m(self, row)\u001b[39m\n\u001b[32m 223\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdecode_row\u001b[39m(\u001b[38;5;28mself\u001b[39m, row: \u001b[38;5;28mdict\u001b[39m) -> \u001b[38;5;28mdict\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m224\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mfeatures\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdecode_example\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mrow\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m)\u001b[39;49m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.features \u001b[38;5;28;01melse\u001b[39;00m row\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/features/features.py:2203\u001b[39m, in \u001b[36mFeatures.decode_example\u001b[39m\u001b[34m(self, example, token_per_repo_id)\u001b[39m\n\u001b[32m 2189\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdecode_example\u001b[39m(\u001b[38;5;28mself\u001b[39m, example: \u001b[38;5;28mdict\u001b[39m, token_per_repo_id: Optional[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Union[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m]]] = \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 2190\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Decode example with custom feature decoding.\u001b[39;00m\n\u001b[32m 2191\u001b[39m \n\u001b[32m 2192\u001b[39m \u001b[33;03m Args:\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 2200\u001b[39m \u001b[33;03m `dict[str, Any]`\u001b[39;00m\n\u001b[32m 2201\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m2203\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43m{\u001b[39;49m\n\u001b[32m 2204\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcolumn_name\u001b[39;49m\u001b[30;43m:\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mdecode_nested_example\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfeature\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mvalue\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 2205\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_column_requires_decoding\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43mcolumn_name\u001b[39;49m\u001b[30;43m]\u001b[39;49m\n\u001b[32m 2206\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01melse\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mvalue\u001b[39;49m\n\u001b[32m 2207\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01mfor\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mcolumn_name\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfeature\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mvalue\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01min\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mzip_dict\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 2208\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m{\u001b[39;49m\u001b[30;43mkey\u001b[39;49m\u001b[30;43m:\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mvalue\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mfor\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mkey\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mvalue\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01min\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mitems\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mkey\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01min\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mexample\u001b[39;49m\u001b[30;43m}\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mexample\u001b[39;49m\n\u001b[32m 2209\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 2210\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m}\u001b[39;49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/features/features.py:2204\u001b[39m, in \u001b[36m\u001b[39m\u001b[34m(.0)\u001b[39m\n\u001b[32m 2189\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdecode_example\u001b[39m(\u001b[38;5;28mself\u001b[39m, example: \u001b[38;5;28mdict\u001b[39m, token_per_repo_id: Optional[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Union[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m]]] = \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 2190\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Decode example with custom feature decoding.\u001b[39;00m\n\u001b[32m 2191\u001b[39m \n\u001b[32m 2192\u001b[39m \u001b[33;03m Args:\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 2200\u001b[39m \u001b[33;03m `dict[str, Any]`\u001b[39;00m\n\u001b[32m 2201\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m 2203\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[32m-> \u001b[39m\u001b[32m2204\u001b[39m column_name: \u001b[30;43mdecode_nested_example\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfeature\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mvalue\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 2205\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._column_requires_decoding[column_name]\n\u001b[32m 2206\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m value\n\u001b[32m 2207\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m column_name, (feature, value) \u001b[38;5;129;01min\u001b[39;00m zip_dict(\n\u001b[32m 2208\u001b[39m {key: value \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.items() \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m example}, example\n\u001b[32m 2209\u001b[39m )\n\u001b[32m 2210\u001b[39m }\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/features/features.py:1508\u001b[39m, in \u001b[36mdecode_nested_example\u001b[39m\u001b[34m(schema, obj, token_per_repo_id)\u001b[39m\n\u001b[32m 1505\u001b[39m \u001b[38;5;66;03m# Object with special decoding:\u001b[39;00m\n\u001b[32m 1506\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(schema, \u001b[33m\"\u001b[39m\u001b[33mdecode_example\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(schema, \u001b[33m\"\u001b[39m\u001b[33mdecode\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[32m 1507\u001b[39m \u001b[38;5;66;03m# we pass the token to read and decode files from private repositories in streaming mode\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1508\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mschema\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdecode_example\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mobj\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtoken_per_repo_id\u001b[39;49m\u001b[30;43m)\u001b[39;49m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1509\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m obj\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda/envs/ghana_speech/lib/python3.11/site-packages/datasets/features/audio.py:186\u001b[39m, in \u001b[36mdecode_example\u001b[39m\u001b[34m(self, value, token_per_repo_id)\u001b[39m\n\u001b[32m 183\u001b[39m with xopen(path, \"rb\", download_config=download_config) as f:\n\u001b[32m 184\u001b[39m array, sampling_rate = sf.read(f)\n\u001b[32m--> \u001b[39m\u001b[32m186\u001b[39m else:\n\u001b[32m 187\u001b[39m array, sampling_rate = sf.read(file)\n\u001b[32m 189\u001b[39m array = array.T\n", "\u001b[31mImportError\u001b[39m: To support decoding audio data, please install 'torchcodec'." ] } ], "source": [ "# ── CELL 6 (FIXED): Inference ─────────────────────────────────────────────────\n", "import tempfile, os\n", "import soundfile as sf\n", "import numpy as np\n", "from tqdm.auto import tqdm\n", "\n", "def transcribe_sample(model, sample, audio_col, text_col):\n", " ref = sample[text_col].strip()\n", " arr = np.array(sample[audio_col][\"array\"], dtype=np.float32)\n", " sr = sample[audio_col][\"sampling_rate\"]\n", "\n", " with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=False) as tmp:\n", " sf.write(tmp.name, arr, sr)\n", " tmp_path = tmp.name\n", " try:\n", " result = model.transcribe(audio=tmp_path)\n", " pred = result[0].text.strip() if result else \"\"\n", " finally:\n", " os.unlink(tmp_path)\n", "\n", " return pred, ref\n", "\n", "\n", "results = {}\n", "\n", "for lang, ds in loaded.items():\n", " print(f\"\\n{'='*55}\")\n", " print(f\" Evaluating: {lang} ({len(ds)} samples)\")\n", " print(f\"{'='*55}\")\n", "\n", " base_preds, base_refs = [], []\n", " ft_preds, ft_refs = [], []\n", "\n", " for i, sample in enumerate(tqdm(ds, desc=lang)):\n", " pred_b, ref = transcribe_sample(base_model, sample, AUDIO_COL, TEXT_COL)\n", " base_preds.append(pred_b)\n", " base_refs.append(ref)\n", "\n", " pred_f, _ = transcribe_sample(ft_model, sample, AUDIO_COL, TEXT_COL)\n", " ft_preds.append(pred_f)\n", " ft_refs.append(ref)\n", "\n", " if i < 3:\n", " print(f\"\\n Sample {i+1}\")\n", " print(f\" REF : {ref}\")\n", " print(f\" BASE : {pred_b}\")\n", " print(f\" FT : {pred_f}\")\n", "\n", " results[lang] = {\n", " \"base\": (base_preds, base_refs),\n", " \"ft\": (ft_preds, ft_refs),\n", " }\n", "\n", "print(\"\\n✅ Inference complete.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# ── CELL 7: Compute WER / CER ─────────────────────────────────────────────────\n", "metrics = {} # metrics[lang] = {base_wer, ft_wer, base_cer, ft_cer}\n", "\n", "print(f\"\\n{'Lang':<10} {'Base WER':>10} {'FT WER':>10} {'ΔWER':>10} | {'Base CER':>10} {'FT CER':>10} {'ΔCER':>10}\")\n", "print(\"-\" * 75)\n", "\n", "for lang, res in results.items():\n", " base_preds, base_refs = res[\"base\"]\n", " ft_preds, ft_refs = res[\"ft\"]\n", "\n", " b_wer = wer(base_refs, base_preds)\n", " f_wer = wer(ft_refs, ft_preds)\n", " b_cer = cer(base_refs, base_preds)\n", " f_cer = cer(ft_refs, ft_preds)\n", "\n", " metrics[lang] = dict(base_wer=b_wer, ft_wer=f_wer,\n", " base_cer=b_cer, ft_cer=f_cer)\n", "\n", " d_wer = f_wer - b_wer\n", " d_cer = f_cer - b_cer\n", " arrow_w = \"↓\" if d_wer < 0 else \"↑\"\n", " arrow_c = \"↓\" if d_cer < 0 else \"↑\"\n", "\n", " print(f\"{lang:<10} {b_wer:>9.1%} {f_wer:>9.1%} {arrow_w}{abs(d_wer):>8.1%} | \"\n", " f\"{b_cer:>9.1%} {f_cer:>9.1%} {arrow_c}{abs(d_cer):>8.1%}\")\n", "\n", "print(\"\\n✅ Metrics computed.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# ── CELL 8: Plot WER and CER comparison charts ────────────────────────────────\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "import numpy as np\n", "\n", "langs = list(metrics.keys())\n", "x = np.arange(len(langs))\n", "bar_width = 0.32\n", "\n", "COLOR_BASE = \"#4C72B0\"\n", "COLOR_FT = \"#DD8452\"\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n", "fig.suptitle(\"Qwen3-ASR-0.6B: Base vs Fine-tuned — Ghana Health Domain (UNICEF)\",\n", " fontsize=13, fontweight=\"bold\", y=1.02)\n", "\n", "for ax, metric, title in [\n", " (axes[0], \"wer\", \"Word Error Rate (WER) ↓ is better\"),\n", " (axes[1], \"cer\", \"Character Error Rate (CER) ↓ is better\"),\n", "]:\n", " base_vals = [metrics[l][f\"base_{metric}\"] for l in langs]\n", " ft_vals = [metrics[l][f\"ft_{metric}\"] for l in langs]\n", "\n", " bars_base = ax.bar(x - bar_width/2, base_vals, bar_width,\n", " label=\"Base model\", color=COLOR_BASE, alpha=0.88)\n", " bars_ft = ax.bar(x + bar_width/2, ft_vals, bar_width,\n", " label=\"Fine-tuned\", color=COLOR_FT, alpha=0.88)\n", "\n", " # Value labels on bars\n", " for bar in bars_base:\n", " h = bar.get_height()\n", " ax.text(bar.get_x() + bar.get_width()/2, h + 0.005,\n", " f\"{h:.1%}\", ha=\"center\", va=\"bottom\", fontsize=9, color=COLOR_BASE)\n", " for bar in bars_ft:\n", " h = bar.get_height()\n", " ax.text(bar.get_x() + bar.get_width()/2, h + 0.005,\n", " f\"{h:.1%}\", ha=\"center\", va=\"bottom\", fontsize=9, color=COLOR_FT)\n", "\n", " # Delta annotations below each pair\n", " for i, lang in enumerate(langs):\n", " delta = metrics[lang][f\"ft_{metric}\"] - metrics[lang][f\"base_{metric}\"]\n", " sign = \"−\" if delta < 0 else \"+\"\n", " color = \"green\" if delta < 0 else \"red\"\n", " ax.text(i, -0.04, f\"{sign}{abs(delta):.1%}\", ha=\"center\",\n", " fontsize=8.5, color=color, transform=ax.get_xaxis_transform())\n", "\n", " ax.set_xticks(x)\n", " ax.set_xticklabels(langs, fontsize=11)\n", " ax.set_title(title, fontsize=11, pad=8)\n", " ax.set_ylabel(metric.upper(), fontsize=10)\n", " ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda v, _: f\"{v:.0%}\"))\n", " ax.set_ylim(0, max(max(base_vals), max(ft_vals)) * 1.18)\n", " ax.legend(fontsize=9)\n", " ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.4)\n", " ax.spines[[\"top\", \"right\"]].set_visible(False)\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"wer_cer_comparison.png\", dpi=150, bbox_inches=\"tight\")\n", "plt.show()\n", "print(\"✅ Chart saved as wer_cer_comparison.png\")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ Repo ready: https://huggingface.co/ghananlpcommunity/qwen3-asr-0.6b-ghana-twi-ewe-dagbani\n", "Uploading outputs/QWEN-ASR-fintuning/qwen3-asr-0.6b-ghana-out/checkpoint-4000 ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Processing Files (0 / 0): | | 0.00B / 0.00B \n", "Processing Files (6 / 6): 100%|██████████| 4.71GB / 4.71GB, 330MB/s \n", "Processing Files (6 / 6): 100%|██████████| 4.71GB / 4.71GB, 298MB/s \n", "New Data Upload: 100%|██████████| 60.0kB / 60.0kB, 6.00kB/s \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Done → https://huggingface.co/ghananlpcommunity/qwen3-asr-0.6b-ghana-twi-ewe-dagbani\n" ] } ], "source": [ "# ── Push fine-tuned checkpoint to HuggingFace Hub ─────────────────────────────\n", "from huggingface_hub import HfApi\n", "import os\n", "\n", "HF_TOKEN = \"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\" # your HF write token\n", "CKPT_PATH = \"outputs/QWEN-ASR-fintuning/qwen3-asr-0.6b-ghana-out/checkpoint-4000\" # adjust\n", "HF_REPO_ID = \"ghananlpcommunity/qwen3-asr-0.6b-ghana-twi-ewe-dagbani\"\n", "\n", "api = HfApi(token=HF_TOKEN)\n", "\n", "# Create repo if it doesn't exist\n", "api.create_repo(repo_id=HF_REPO_ID, repo_type=\"model\", exist_ok=True)\n", "print(f\"✅ Repo ready: https://huggingface.co/{HF_REPO_ID}\")\n", "\n", "# Upload the checkpoint folder\n", "print(f\"Uploading {CKPT_PATH} ...\")\n", "api.upload_folder(\n", " folder_path=CKPT_PATH,\n", " repo_id=HF_REPO_ID,\n", " repo_type=\"model\",\n", " commit_message=f\"Upload fine-tuned checkpoint: {os.path.basename(CKPT_PATH)}\",\n", ")\n", "print(f\"✅ Done → https://huggingface.co/{HF_REPO_ID}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3.11 (Ghana Speech-2)", "language": "python", "name": "ghana_speech" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.15" } }, "nbformat": 4, "nbformat_minor": 4 }