test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################################### 0\n",
    "\n",
    "input_file = \"Recording.wav\"\n",
    "\n",
    "# user defined\n",
    "output_vad_file = \"frame_vad_outputs/manifest_vad_output.json\"\n",
    "manifest_name = 'manifest.json'\n",
    "vad_folder = \"VAD_wavs\"\n",
    "\n",
    "from pathlib import Path\n",
    "input_file_path = Path(input_file)\n",
    "prep_file = str(f\"{input_file_path.stem}_preprocessed{input_file_path.suffix}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Конвертация вавок в нужный формат - 16000 Гц sample rate, Mono channel. На выходе имеем преобразованные вавки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ffmpeg version 4.4.2-0ubuntu0.22.04.1 Copyright (c) 2000-2021 the FFmpeg developers\n",
      "  built with gcc 11 (Ubuntu 11.2.0-19ubuntu1)\n",
      "  configuration: --prefix=/usr --extra-version=0ubuntu0.22.04.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librabbitmq --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-pocketsphinx --enable-librsvg --enable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\n",
      "  libavutil      56. 70.100 / 56. 70.100\n",
      "  libavcodec     58.134.100 / 58.134.100\n",
      "  libavformat    58. 76.100 / 58. 76.100\n",
      "  libavdevice    58. 13.100 / 58. 13.100\n",
      "  libavfilter     7.110.100 /  7.110.100\n",
      "  libswscale      5.  9.100 /  5.  9.100\n",
      "  libswresample   3.  9.100 /  3.  9.100\n",
      "  libpostproc    55.  9.100 / 55.  9.100\n",
      "Guessed Channel Layout for Input Stream #0.0 : stereo\n",
      "Input #0, wav, from 'Recording.wav':\n",
      "  Duration: 00:00:14.25, bitrate: 1536 kb/s\n",
      "  Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 48000 Hz, stereo, s16, 1536 kb/s\n",
      "Stream mapping:\n",
      "  Stream #0:0 -> #0:0 (pcm_s16le (native) -> pcm_s16le (native))\n",
      "Press [q] to stop, [?] for help\n",
      "Output #0, wav, to 'Recording_preprocessed.wav':\n",
      "  Metadata:\n",
      "    ISFT            : Lavf58.76.100\n",
      "  Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 16000 Hz, mono, s16, 256 kb/s\n",
      "    Metadata:\n",
      "      encoder         : Lavc58.134.100 pcm_s16le\n",
      "size=     445kB time=00:00:14.25 bitrate= 256.1kbits/s speed= 870x    \n",
      "video:0kB audio:445kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.017101%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "########################################################### 1\n",
    "import ffmpeg\n",
    "\n",
    "audio_input = ffmpeg.input(input_file)\n",
    "audio_output = ffmpeg.output(audio_input, prep_file, format='wav', ar=16000, ac=1)\n",
    "ffmpeg.run(audio_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создание манифеста, по которому работает ВАД. На выходе имеем файл манифеста:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################################### 2\n",
    "manifest = {\n",
    "    \"audio_filepath\": prep_file,\n",
    "    \"offset\": 0,\n",
    "    \"duration\": None\n",
    "}\n",
    "\n",
    "import json\n",
    "with open(manifest_name, \"w+\") as user_file:\n",
    "    json.dump(manifest, user_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Используя файл манифесра, делаем VAD. Получаем файл vad_out.json:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NeMo W 2023-10-03 15:31:07 nemo_logging:349] /tmp/ipykernel_25596/161577061.py:5: UserWarning: \n",
      "    The version_base parameter is not specified.\n",
      "    Please specify a compatability version level, or None.\n",
      "    Will assume defaults for version 1.1\n",
      "      initialize(config_path=\".\", job_name=\"test_app\")\n",
      "    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NeMo I 2023-10-03 15:31:07 frame_vad_infer:76] Loading manifest file manifest.json\n",
      "[NeMo I 2023-10-03 15:31:07 frame_vad_infer:82] Split long audio file to avoid CUDA memory issue\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "splitting manifest:   0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    }
   ],
   "source": [
    "########################################################### 3\n",
    "from src.nemo.frame_vad_infer import main as vad\n",
    "from hydra import compose, initialize\n",
    "\n",
    "initialize(config_path=\".\", job_name=\"test_app\")\n",
    "cfg = compose(config_name=\"frame_vad_infer_postprocess.yaml\", return_hydra_config=True, overrides=[f\"input_manifest={manifest_name}\", f\"dataset={manifest_name}\"])\n",
    "vad(cfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализируем это дело"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NeMo I 2023-10-03 02:19:18 cloud:58] Found existing object /home/imeshcheryakov/.cache/torch/NeMo/NeMo_1.20.0/stt_enes_contextnet_large/bab2e1126d9c3eca50f26e03d9e8eb3b/stt_enes_contextnet_large.nemo.\n",
      "[NeMo I 2023-10-03 02:19:18 cloud:64] Re-using file from: /home/imeshcheryakov/.cache/torch/NeMo/NeMo_1.20.0/stt_enes_contextnet_large/bab2e1126d9c3eca50f26e03d9e8eb3b/stt_enes_contextnet_large.nemo\n",
      "[NeMo I 2023-10-03 02:19:18 common:913] Instantiating model from pre-trained checkpoint\n",
      "[NeMo I 2023-10-03 02:19:23 mixins:194] _setup_tokenizer: detected an aggregate tokenizer\n",
      "[NeMo I 2023-10-03 02:19:23 mixins:322] Tokenizer SentencePieceTokenizer initialized with 1024 tokens\n",
      "[NeMo I 2023-10-03 02:19:23 mixins:322] Tokenizer SentencePieceTokenizer initialized with 1024 tokens\n",
      "[NeMo I 2023-10-03 02:19:23 aggregate_tokenizer:72] Aggregate vocab size: 2048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NeMo W 2023-10-03 02:19:25 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n",
      "    Train config : \n",
      "    manifest_filepath: /datatrain/tarred_audio_manifest.json\n",
      "    sample_rate: 16000\n",
      "    batch_size: 16\n",
      "    trim_silence: false\n",
      "    max_duration: 20\n",
      "    shuffle: true\n",
      "    is_tarred: true\n",
      "    tarred_audio_filepaths: /datatrain/audio__OP_0..511_CL_.tar\n",
      "    tarred_shard_strategy: scatter\n",
      "    use_start_end_token: false\n",
      "    num_workers: 8\n",
      "    pin_memory: true\n",
      "    \n",
      "[NeMo W 2023-10-03 02:19:25 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). \n",
      "    Validation config : \n",
      "    manifest_filepath:\n",
      "    - /datadev/fisher/dev_fisher_mounted_files_manifest_es.json\n",
      "    - /datadev/mcv7/dev_mcv7_mounted_files_manifest_es.json\n",
      "    - /datadev/mls/dev_mls_mounted_files_manifest_es.json\n",
      "    - /datadev/voxpopuli/dev_voxpopuli_mounted_files_manifest_es.json\n",
      "    - /data/ls_dev_en/dev_clean_en.json\n",
      "    - /data/ls_dev_en/dev_other_en.json\n",
      "    sample_rate: 16000\n",
      "    batch_size: 512\n",
      "    shuffle: false\n",
      "    use_start_end_token: false\n",
      "    num_workers: 8\n",
      "    pin_memory: true\n",
      "    \n",
      "[NeMo W 2023-10-03 02:19:25 modelPT:174] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).\n",
      "    Test config : \n",
      "    manifest_filepath: null\n",
      "    sample_rate: 16000\n",
      "    batch_size: 8\n",
      "    shuffle: false\n",
      "    use_start_end_token: false\n",
      "    num_workers: 16\n",
      "    pin_memory: true\n",
      "    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NeMo I 2023-10-03 02:19:25 features:289] PADDING: 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NeMo W 2023-10-03 02:19:31 nemo_logging:349] /home/imeshcheryakov/anaconda3/envs/kvint/lib/python3.10/site-packages/torch/nn/modules/rnn.py:71: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.1 and num_layers=1\n",
      "      warnings.warn(\"dropout option adds dropout after all but last \"\n",
      "    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NeMo I 2023-10-03 02:19:32 rnnt_models:211] Using RNNT Loss : warprnnt_numba\n",
      "    Loss warprnnt_numba_kwargs: {'fastemit_lambda': 0.001}\n",
      "[NeMo I 2023-10-03 02:19:33 save_restore_connector:249] Model EncDecRNNTBPEModel was successfully restored from /home/imeshcheryakov/.cache/torch/NeMo/NeMo_1.20.0/stt_enes_contextnet_large/bab2e1126d9c3eca50f26e03d9e8eb3b/stt_enes_contextnet_large.nemo.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa908c63d1854e2ead497bafc2f5c5d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Transcribing:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[' this is the death aldure', ' one', ' do', ' free', \" i'm the best candidate you've ever seen\"]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "########################################################### 4\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import librosa\n",
    "import soundfile as sf\n",
    "import os\n",
    "import json\n",
    "\n",
    "with open(output_vad_file, \"r\") as user_file:\n",
    "    vad_file = json.load(user_file)\n",
    "\n",
    "manifest = []\n",
    "with open(vad_file['rttm_filepath']) as f:\n",
    "    for line in f:\n",
    "        manifest.append(line)\n",
    "\n",
    "Path(vad_folder).exists()\n",
    "\n",
    "os.makedirs(vad_folder, exist_ok=True)\n",
    "\n",
    "data, sr = librosa.load(prep_file, sr=16000)\n",
    "\n",
    "labels = np.zeros(len(data))\n",
    "\n",
    "filelist = []\n",
    "for idx, interval in enumerate(manifest):\n",
    "    file = f'{vad_folder}/wav_{idx}.wav'\n",
    "    start = float(interval.split(\" \")[3])\n",
    "    end = float(interval.split(\" \")[3]) + float(interval.split(\" \")[4])\n",
    "    labels[int(sr * start) : int(sr * end)] = [1] * (int(sr * end) - int(sr * start))\n",
    "    sf.write(file, data[int(sr * start) : int(sr * end)], sr, 'PCM_24')\n",
    "    filelist.append(file)\n",
    "\n",
    "\n",
    "import nemo.collections.asr as nemo_asr\n",
    "asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name=\"stt_enes_contextnet_large\")\n",
    "transcripts = asr_model.transcribe(paths2audio_files=filelist)[0]\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.subplots(1, 1, figsize=(12, 6))\n",
    "plt.plot(data / max(data), label=\"Signal\", color=\"b\")\n",
    "plt.plot(labels, label=\"LABELS\", color='r')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "\n",
    "print(transcripts)\n",
    "\n",
    "os.rmdir(Path(output_vad_file).parent)\n",
    "os.rmdir(vad_folder)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "kvint",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}