Seminar_5_DLUB21_Wave2Vec_Tutorial.ipynb

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        "!pip install datasets\n",
        "!pip install transformers\n",
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            "Collecting torchaudio\n",
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            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Installing collected packages: sacremoses, tokenizers, huggingface-hub, transformers\n",
            "  Found existing installation: huggingface-hub 0.0.13\n",
            "    Uninstalling huggingface-hub-0.0.13:\n",
            "      Successfully uninstalled huggingface-hub-0.0.13\n",
            "Successfully installed huggingface-hub-0.0.12 sacremoses-0.0.45 tokenizers-0.10.3 transformers-4.8.2\n",
            "\u001b[K     - 10kB 25.7MB/s\n",
            "\u001b[?25h  Building wheel for Deep-Learning-Colab-Notebook-Utils (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5St_EeCGKDSt"
      },
      "source": [
        "from transformers import Wav2Vec2Processor\n",
        "from transformers import Wav2Vec2ForCTC\n",
        "from datasets import load_dataset\n",
        "import torch\n",
        "import torchaudio\n",
        "import librosa"
      ],
      "execution_count": null,
      "outputs": []
    },
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        "id": "ka0f7jwOKFbV",
        "outputId": "e3f3e3ea-c05d-4416-da65-5cbc6d173fbe"
      },
      "source": [
        "# load the model\n",
        "processor = Wav2Vec2Processor.from_pretrained(\"tugstugi/wav2vec2-large-xlsr-53-mongolian\")\n",
        "model = Wav2Vec2ForCTC.from_pretrained(\"tugstugi/wav2vec2-large-xlsr-53-mongolian\")"
      ],
      "execution_count": null,
      "outputs": [
        {
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            "\n"
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        {
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ],
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        },
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rIS7zWC1X1Xw",
        "outputId": "8b636a2c-1918-46c9-8737-bb698bb100c4"
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      "source": [
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Wav2Vec2ForCTC(\n",
              "  (wav2vec2): Wav2Vec2Model(\n",
              "    (feature_extractor): Wav2Vec2FeatureExtractor(\n",
              "      (conv_layers): ModuleList(\n",
              "        (0): Wav2Vec2LayerNormConvLayer(\n",
              "          (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,))\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (1): Wav2Vec2LayerNormConvLayer(\n",
              "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (2): Wav2Vec2LayerNormConvLayer(\n",
              "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (3): Wav2Vec2LayerNormConvLayer(\n",
              "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (4): Wav2Vec2LayerNormConvLayer(\n",
              "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (5): Wav2Vec2LayerNormConvLayer(\n",
              "          (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (6): Wav2Vec2LayerNormConvLayer(\n",
              "          (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))\n",
              "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (feature_projection): Wav2Vec2FeatureProjection(\n",
              "      (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "      (projection): Linear(in_features=512, out_features=1024, bias=True)\n",
              "      (dropout): Dropout(p=0.0, inplace=False)\n",
              "    )\n",
              "    (encoder): Wav2Vec2EncoderStableLayerNorm(\n",
              "      (pos_conv_embed): Wav2Vec2PositionalConvEmbedding(\n",
              "        (conv): Conv1d(1024, 1024, kernel_size=(128,), stride=(1,), padding=(64,), groups=16)\n",
              "        (padding): Wav2Vec2SamePadLayer()\n",
              "      )\n",
              "      (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "      (layers): ModuleList(\n",
              "        (0): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (1): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (2): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (3): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (4): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (5): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (6): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (7): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (8): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (9): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (10): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (11): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (12): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (13): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (14): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (15): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (16): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (17): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (18): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (19): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (20): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (21): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (22): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (23): Wav2Vec2EncoderLayerStableLayerNorm(\n",
              "          (attention): Wav2Vec2Attention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "          (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (feed_forward): Wav2Vec2FeedForward(\n",
              "            (intermediate_dropout): Dropout(p=0.0, inplace=False)\n",
              "            (intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (output_dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (output_dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.0, inplace=False)\n",
              "  (lm_head): Linear(in_features=1024, out_features=38, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218,
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            "dd8fa44cdba44533b54c24d05ff829fa",
            "e90c13881586415cbcea399d5f276ce5",
            "e29b79b798854e6a89e1ffea2c6da243",
            "a648ab82f268450fb32aadc6a2f35b87",
            "0baae60efeaf4a92a436ea156c8d329a"
          ]
        },
        "id": "gLHIusBDKbyQ",
        "outputId": "5126d2b5-a427-4864-9ca7-e74745ee1e8a"
      },
      "source": [
        "test_dataset = load_dataset(\"common_voice\", \"mn\", split=\"test[:2%]\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a858b8143a7542e19b09700ebedbebcb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=4284.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b212e6e588fb43fcac2ae7272842af2b",
              "version_minor": 0,
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=9954.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Downloading and preparing dataset common_voice/mn (download: 463.84 MiB, generated: 4.65 MiB, post-processed: Unknown size, total: 468.49 MiB) to /root/.cache/huggingface/datasets/common_voice/mn/6.1.0/bb59ce0bb532485ab64b5d488a8dd2addc3104f694e06bcd2c272dc608bb1112...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7fd21494052745a7b2b8f514225f91ca",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=486369317.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2606dd6fc6c54608b0fcd8b0a5060bdb",
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              "version_major": 2
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dbf73310324f452fa42d9226d5b8b8c7",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "823ee0a039cb438d8f4838a75d939daf",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9556debbf93f44e6b222469e8fe8df46",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "409aec8c1e864caeb1985421feba5edd",
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              "version_major": 2
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset common_voice downloaded and prepared to /root/.cache/huggingface/datasets/common_voice/mn/6.1.0/bb59ce0bb532485ab64b5d488a8dd2addc3104f694e06bcd2c272dc608bb1112. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I9dAQyITO916",
        "outputId": "fb97abd8-bf44-42b1-83ca-339e2b185c7f"
      },
      "source": [
        "test_dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['accent', 'age', 'client_id', 'down_votes', 'gender', 'locale', 'path', 'segment', 'sentence', 'up_votes'],\n",
              "    num_rows: 37\n",
              "})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7-JajnDOu7v"
      },
      "source": [
        "resampler = torchaudio.transforms.Resample(48_000, 16_000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvMiVsPJQAee"
      },
      "source": [
        "?resampler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "41ba4f68e9824cfe99d1fb2c3e57faa0",
            "e3ac573588fa4e39a280c23b20abe6d2",
            "0f0c93e8f48f494595309a9d837c0541",
            "693b9d48c1cd4ff39f51af27c2eaf8f4",
            "a3536940c6b346dc8e923c133745ecbb",
            "aedbc00453cb42c4884faa14625113fb",
            "c9ad18915f5143f98c54961331eb6665",
            "e0ae1bd033ea486ca106d257696146ef"
          ]
        },
        "id": "1-KsSoYO6sv6",
        "outputId": "903975f3-d004-400f-d734-f92f3509ec42"
      },
      "source": [
        "# Preprocessing the datasets.\n",
        "# We need to read the audio files as arrays\n",
        "def speech_file_to_array_fn(batch):\n",
        "    speech_array, sampling_rate = torchaudio.load(batch[\"path\"])\n",
        "    batch[\"speech\"] = resampler(speech_array).squeeze().numpy()\n",
        "    return batch\n",
        "\n",
        "test_dataset = test_dataset.map(speech_file_to_array_fn)\n",
        "inputs = processor(test_dataset[\"speech\"][:2], sampling_rate=16_000, return_tensors=\"pt\", padding=True)\n",
        "\n",
        "with torch.no_grad():\n",
        "    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits\n",
        "\n",
        "predicted_ids = torch.argmax(logits, dim=-1)\n",
        "\n",
        "print(\"Prediction:\", processor.batch_decode(predicted_ids))\n",
        "print(\"Reference:\", test_dataset[\"sentence\"][:2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "41ba4f68e9824cfe99d1fb2c3e57faa0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=37.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\n",
            "To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at  /pytorch/aten/src/ATen/native/BinaryOps.cpp:467.)\n",
            "  return torch.floor_divide(self, other)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Prediction: ['бийв буг угаал эзсгүйд байгуулдэнээ түмүүхай сүт багаадагхюм юулаа', 'тэгэхээр нь мориныхоо хомыйг тотондог шүүрүхийх булив']\n",
            "Reference: ['Иймэрхүү хоол идсээр байгаад дэндүү туранхай, сул дорой болсон байлаа.', 'Тэгэхээр нь мориныхоо амыг татан ташуур өгөхийг болив.']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PGfJ7RpyExN8"
      },
      "source": [
        "# imports for uploading/recording\n",
        "import ipywidgets as widgets\n",
        "from IPython.display import Audio, display, clear_output\n",
        "from dl_colab_notebooks.audio import record_audio, upload_audio\n",
        "import numpy as np\n",
        "from scipy.io import wavfile\n",
        "\n",
        "SAMPLE_RATE = 16000\n",
        "# wav to text method\n",
        "def wav_to_text(f='test.wav'):\n",
        "  audio, _ = librosa.load('test.wav', sr=SAMPLE_RATE)\n",
        "  input_dict = processor(torch.tensor(audio), return_tensors=\"pt\", padding=True)\n",
        "  logits = model(input_dict.input_values).logits\n",
        "  pred_ids = torch.argmax(logits, dim=-1)[0]\n",
        "  return processor.decode(pred_ids)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuZz9-Nv21SJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211,
          "referenced_widgets": [
            "8f7a77e8de8a4c79908e5d8ab0022045",
            "8b65ef23deab4b628ede8227b5c3b3f5",
            "e9f9b442c1ff4b738f32345d24123965"
          ]
        },
        "outputId": "b557457c-2555-4d8a-e862-c143c76b8f47"
      },
      "source": [
        "#@markdown * Either record audio from microphone or upload audio from file (.mp3 or .wav) { run: \"auto\" }\n",
        "\n",
        "record_or_upload = \"Record\" #@param [\"Record\", \"Upload (.mp3 or .wav)\"]\n",
        "record_seconds =   10#@param {type:\"number\", min:1, max:10, step:1}\n",
        "\n",
        "def _recognize(audio):\n",
        "  display(Audio(audio, rate=SAMPLE_RATE, autoplay=True))\n",
        "  !rm -f test.wav\n",
        "  wavfile.write('test.wav', rate=SAMPLE_RATE, data=(32767*audio).astype(np.int16))\n",
        "\n",
        "  transcription = wav_to_text()\n",
        "  print('\\n\\nTRANSCRIPTION:\\n')\n",
        "  print(transcription)\n",
        "\n",
        "\n",
        "def _record_audio(b):\n",
        "  clear_output()\n",
        "  audio = record_audio(record_seconds, sample_rate=SAMPLE_RATE)\n",
        "  _recognize(audio)\n",
        "def _upload_audio(b):\n",
        "  clear_output()\n",
        "  audio = upload_audio(sample_rate=SAMPLE_RATE)\n",
        "  _recognize(audio)\n",
        "\n",
        "if record_or_upload == \"Record\":\n",
        "  button = widgets.Button(description=\"Record Speech\")\n",
        "  button.on_click(_record_audio)\n",
        "  display(button)\n",
        "else:\n",
        "  _upload_audio(\"\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Starting recording for 10 seconds...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "      const sleep  = time => new Promise(resolve => setTimeout(resolve, time))\n",
              "      const b2text = blob => new Promise(resolve => {\n",
              "        const reader = new FileReader()\n",
              "        reader.onloadend = e => resolve(e.srcElement.result)\n",
              "        reader.readAsDataURL(blob)\n",
              "      })\n",
              "      var record = time => new Promise(async resolve => {\n",
              "        stream = await navigator.mediaDevices.getUserMedia({ audio: true })\n",
              "        recorder = new MediaRecorder(stream)\n",
              "        chunks = []\n",
              "        recorder.ondataavailable = e => chunks.push(e.data)\n",
              "        recorder.start()\n",
              "        await sleep(time)\n",
              "        recorder.onstop = async ()=>{\n",
              "          blob = new Blob(chunks)\n",
              "          text = await b2text(blob)\n",
              "          resolve(text)\n",
              "        }\n",
              "        recorder.stop()\n",
              "      })\n",
              "      "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Finished recording!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
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              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "It is strongly recommended to pass the ``sampling_rate`` argument to this function.Failing to do so can result in silent errors that might be hard to debug.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "TRANSCRIPTION:\n",
            "\n",
            "цан уу намайг энх болд гэдэг зоны сургалтанд тавтай морил\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GadcjeuU5ON"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}