ctc_asr_with_hf_tokenizer.ipynb

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        "id": "N08CyS41_znk",
        "outputId": "1217b33d-3792-4e08-92ee-9d2ea359a33d"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.42.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.5)\n",
            "Requirement already satisfied: numpy<2.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.4)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.6.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.7.4)\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XcTnCY6FANf_"
      },
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      "source": []
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9cANg7T8ANc7"
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      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y6xhhB_ATQzW",
        "outputId": "27530b73-ec13-4126-f8aa-3b22bd21a963"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-08-18 10:51:10--  https://us.openslr.org/resources/1/waves_yesno.tar.gz\n",
            "Resolving us.openslr.org (us.openslr.org)... 46.101.158.64\n",
            "Connecting to us.openslr.org (us.openslr.org)|46.101.158.64|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4703754 (4.5M) [application/x-gzip]\n",
            "Saving to: ‘waves_yesno.tar.gz’\n",
            "\n",
            "waves_yesno.tar.gz  100%[===================>]   4.49M  3.30MB/s    in 1.4s    \n",
            "\n",
            "2024-08-18 10:51:12 (3.30 MB/s) - ‘waves_yesno.tar.gz’ saved [4703754/4703754]\n",
            "\n",
            "waves_yesno/\n",
            "waves_yesno/1_0_0_0_0_0_1_1.wav\n",
            "waves_yesno/1_1_0_0_1_0_1_0.wav\n",
            "waves_yesno/1_0_1_1_1_1_0_1.wav\n",
            "waves_yesno/1_1_1_1_0_1_0_0.wav\n",
            "waves_yesno/0_0_1_1_1_0_0_0.wav\n",
            "waves_yesno/0_1_1_1_1_1_1_1.wav\n",
            "waves_yesno/0_1_0_1_1_1_0_0.wav\n",
            "waves_yesno/1_0_1_1_1_0_1_0.wav\n",
            "waves_yesno/1_0_0_1_0_1_1_1.wav\n",
            "waves_yesno/0_0_1_0_1_0_0_0.wav\n",
            "waves_yesno/0_1_0_1_1_0_1_0.wav\n",
            "waves_yesno/0_0_1_1_0_1_1_0.wav\n",
            "waves_yesno/1_0_0_0_1_0_0_1.wav\n",
            "waves_yesno/1_1_0_1_1_1_1_0.wav\n",
            "waves_yesno/0_0_1_1_1_1_0_0.wav\n",
            "waves_yesno/1_1_0_0_1_1_1_0.wav\n",
            "waves_yesno/0_0_1_1_0_1_1_1.wav\n",
            "waves_yesno/1_1_0_1_0_1_1_0.wav\n",
            "waves_yesno/0_1_0_0_0_1_1_0.wav\n",
            "waves_yesno/0_0_0_1_0_0_0_1.wav\n",
            "waves_yesno/0_0_1_0_1_0_1_1.wav\n",
            "waves_yesno/0_0_1_0_0_0_1_0.wav\n",
            "waves_yesno/1_1_0_1_1_0_0_1.wav\n",
            "waves_yesno/0_1_1_1_0_1_0_1.wav\n",
            "waves_yesno/0_1_1_1_0_0_0_0.wav\n",
            "waves_yesno/README~\n",
            "waves_yesno/0_1_0_0_0_1_0_0.wav\n",
            "waves_yesno/1_0_0_0_0_0_0_1.wav\n",
            "waves_yesno/1_1_0_1_1_0_1_1.wav\n",
            "waves_yesno/1_1_0_0_0_0_0_1.wav\n",
            "waves_yesno/1_0_0_0_0_0_0_0.wav\n",
            "waves_yesno/0_1_1_1_1_0_1_0.wav\n",
            "waves_yesno/0_0_1_1_0_1_0_0.wav\n",
            "waves_yesno/1_1_1_0_0_0_0_1.wav\n",
            "waves_yesno/1_0_1_0_1_0_0_1.wav\n",
            "waves_yesno/0_1_0_0_1_0_1_1.wav\n",
            "waves_yesno/0_0_1_1_1_1_1_0.wav\n",
            "waves_yesno/1_1_0_0_0_1_1_1.wav\n",
            "waves_yesno/0_1_1_1_0_0_1_0.wav\n",
            "waves_yesno/1_1_0_1_0_1_0_0.wav\n",
            "waves_yesno/1_1_1_1_1_1_1_1.wav\n",
            "waves_yesno/0_0_1_0_1_0_0_1.wav\n",
            "waves_yesno/1_1_1_1_0_0_1_0.wav\n",
            "waves_yesno/0_0_1_1_1_0_0_1.wav\n",
            "waves_yesno/0_1_0_1_0_0_0_0.wav\n",
            "waves_yesno/1_1_1_1_1_0_0_0.wav\n",
            "waves_yesno/README\n",
            "waves_yesno/0_1_1_0_0_1_1_1.wav\n",
            "waves_yesno/0_0_1_0_0_1_1_0.wav\n",
            "waves_yesno/1_1_0_0_1_0_1_1.wav\n",
            "waves_yesno/1_1_1_0_0_1_0_1.wav\n",
            "waves_yesno/0_0_1_0_0_1_1_1.wav\n",
            "waves_yesno/0_0_1_1_0_0_0_1.wav\n",
            "waves_yesno/1_0_1_1_0_1_1_1.wav\n",
            "waves_yesno/1_1_1_0_1_0_1_0.wav\n",
            "waves_yesno/1_1_1_0_1_0_1_1.wav\n",
            "waves_yesno/0_1_0_0_1_0_1_0.wav\n",
            "waves_yesno/1_1_1_0_0_1_1_1.wav\n",
            "waves_yesno/0_1_1_0_0_1_1_0.wav\n",
            "waves_yesno/0_0_0_1_0_1_1_0.wav\n",
            "waves_yesno/1_1_1_1_1_1_0_0.wav\n",
            "waves_yesno/0_0_0_0_1_1_1_1.wav\n",
            "Audio paths have been saved to paths.txt\n",
            "Transcriptions have been saved to transcriptions.txt\n"
          ]
        }
      ],
      "source": [
        "# @title prepare yes no dataset\n",
        "!wget https://us.openslr.org/resources/1/waves_yesno.tar.gz\n",
        "!mkdir working\n",
        "!tar -xzvf waves_yesno.tar.gz -C working\n",
        "\n",
        "import os\n",
        "import re\n",
        "\n",
        "# Directory containing the audio files\n",
        "audio_dir = '/content/working/waves_yesno/'\n",
        "\n",
        "# Create a dictionary to store audio paths and text transcriptions\n",
        "audio_transcription_dict = {}\n",
        "\n",
        "# Iterate through the audio files in the directory\n",
        "for filename in os.listdir(audio_dir):\n",
        "    if filename.endswith('.wav'):\n",
        "        # Extract the numeric labels from the filename\n",
        "        labels = re.findall(r'\\d+', filename)\n",
        "\n",
        "        labels = [int(label) for label in labels if label.isdigit()]\n",
        "\n",
        "        # Convert the numeric labels to \"yes\" and \"no\"\n",
        "        #transcription = ' '.join(['yes' if label == 1 else 'no' for label in labels])\n",
        "        transcription = ' '.join(['ඔව්' if label == 1 else 'නැහැ' for label in labels])\n",
        "\n",
        "\n",
        "        # Store the audio path and transcription in the dictionary\n",
        "        audio_path = os.path.join(audio_dir, filename)\n",
        "        audio_transcription_dict[audio_path] = transcription\n",
        "\n",
        "# Sort the dictionary by transcription (which ensures both lists are in the same order)\n",
        "sorted_audio_transcription = sorted(audio_transcription_dict.items(), key=lambda x: x[1])\n",
        "\n",
        "# Separate the sorted data into audio paths and transcriptions\n",
        "sorted_audio_paths, sorted_transcriptions = zip(*sorted_audio_transcription)\n",
        "\n",
        "# Create a text file to store the audio paths\n",
        "paths_output_file = 'paths.txt'\n",
        "with open(paths_output_file, 'w') as f_paths:\n",
        "    # Write each audio path on a separate line\n",
        "    f_paths.write('\\n'.join(sorted_audio_paths))\n",
        "\n",
        "print(f\"Audio paths have been saved to {paths_output_file}\")\n",
        "\n",
        "# Create a text file to store the transcriptions\n",
        "transcriptions_output_file = 'transcriptions.txt'\n",
        "with open(transcriptions_output_file, 'w') as f_transcriptions:\n",
        "    # Write each transcription on a separate line\n",
        "    f_transcriptions.write('\\n'.join(sorted_transcriptions))\n",
        "\n",
        "print(f\"Transcriptions have been saved to {transcriptions_output_file}\")\n"
      ]
    },
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/39.3k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "bada416a60eb4f77b56f2d5e23b24cfa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentencepiece.bpe.model:   0%|          | 0.00/4.85M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/361k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c4d105927f84be4973d09bd86ccf082"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/4.23k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "08c9fe4fe4dd48b498e6db9ef34917e4"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import librosa\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "\n",
        "mdl = \"zaanind/deeptf_stt_tokenizer\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(mdl)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b44n2KUxpzEP"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k3LjD1rd8aTl"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f5enate8qgky"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rya-e-KYTQju",
        "outputId": "e56fca26-0a7c-42c1-b3ba-fc6764886412"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                         Audio_Path  \\\n",
            "0  /content/working/waves_yesno/1_1_1_1_1_1_1_1.wav   \n",
            "1  /content/working/waves_yesno/1_1_1_1_1_1_0_0.wav   \n",
            "2  /content/working/waves_yesno/1_1_1_1_1_0_0_0.wav   \n",
            "3  /content/working/waves_yesno/1_1_1_1_0_1_0_0.wav   \n",
            "4  /content/working/waves_yesno/1_1_1_1_0_0_1_0.wav   \n",
            "\n",
            "                        Transcription  \n",
            "0     ඔව් ඔව් ඔව් ඔව් ඔව් ඔව් ඔව් ඔව්  \n",
            "1   ඔව් ඔව් ඔව් ඔව් ඔව් ඔව් නැහැ නැහැ  \n",
            "2  ඔව් ඔව් ඔව් ඔව් ඔව් නැහැ නැහැ නැහැ  \n",
            "3  ඔව් ඔව් ඔව් ඔව් නැහැ ඔව් නැහැ නැහැ  \n",
            "4  ඔව් ඔව් ඔව් ඔව් නැහැ නැහැ ඔව් නැහැ  \n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "\n",
        "audio_files_path = \"/content/paths.txt\"\n",
        "texttranscribe_path = \"/content/transcriptions.txt\"\n",
        "\n",
        "# Load the audio file paths and corresponding text labels\n",
        "with open(audio_files_path, 'r') as f:\n",
        "    audio_files = f.read().splitlines()\n",
        "with open(texttranscribe_path, 'r') as f:\n",
        "    audio_labels_text = f.read().splitlines()\n",
        "# Create a dictionary with the data\n",
        "data = {'Audio_Path': audio_files, 'Transcription': audio_labels_text}\n",
        "\n",
        "# Create a Pandas DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "metadata_df  = df\n",
        "\n",
        "# Display the first few rows of the DataFrame\n",
        "print(df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FWWepzWtM67T"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Zk5wNJlg96hr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login  --token \"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\""
      ],
      "metadata": {
        "id": "vIbXDUOQ96Xz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0618ad2c-32bf-4bb6-ed26-d47a22ba9791"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j6GJ2muSc_jp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title tokenizer\n",
        "def batch_iterator(batch_size=10, size=8):\n",
        "        #df = pd.DataFrame({\"note_text\": ['fghijk', 'wxyz']})\n",
        "        for x in range(0, size, batch_size):\n",
        "            yield df['Transcription'].to_list()\n",
        "\n",
        "new_tokenizer = tokenizer.train_new_from_iterator(batch_iterator(), vocab_size=500)\n",
        "new_tokenizer.save_pretrained(\"deeptf_stt_tokenizer\")\n",
        "new_tokenizer.push_to_hub(\"zaanind/deeptf_stt_tokenizer\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6eXhVlb9mIL",
        "outputId": "234d1e2d-67f4-4d17-bdac-2f05aba94395",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('deeptf_stt_tokenizer/tokenizer_config.json',\n",
              " 'deeptf_stt_tokenizer/special_tokens_map.json',\n",
              " 'deeptf_stt_tokenizer/sentencepiece.bpe.model',\n",
              " 'deeptf_stt_tokenizer/added_tokens.json',\n",
              " 'deeptf_stt_tokenizer/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hb0rJyjD9mA_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1mB__u04HYEs"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "FOVO8XDvBj32"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "def pad_mfccs(mfccs, max_length):\n",
        "    num_rows_to_pad = max_length - mfccs.shape[0]\n",
        "    if num_rows_to_pad > 0:\n",
        "        padding = np.zeros((num_rows_to_pad, mfccs.shape[1]))\n",
        "        return np.vstack((mfccs, padding))\n",
        "    return mfccs\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def char_to_num(label):\n",
        "  #print(label)\n",
        "  tkns = tokenizer(label,return_tensors=\"tf\",\n",
        "                   #padding='max_length'\n",
        "                   )\n",
        "  tkns = tkns['input_ids']#.squeeze()\n",
        "\n",
        "  tkns = tf.squeeze(tkns, axis=0)\n",
        "\n",
        "  return tkns\n",
        "\n",
        "\n",
        "def num_to_char(label):\n",
        "  out = tokenizer.decode(label, skip_special_tokens=True)\n",
        "  return out\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def gmfcc(audio_path, sr=16000, n_mfcc=40):\n",
        "    signal, sr = librosa.load(audio_path, sr=sr)\n",
        "    mfccs = librosa.feature.mfcc(y=signal, sr=sr, n_mfcc=n_mfcc)\n",
        "    mfccs = mfccs.T #disable this to fix shape error. cnn needs 40,219 not 219,40\n",
        "    return mfccs\n",
        "\n",
        "\n",
        "def enc_label(label):\n",
        "  label = char_to_num(label)\n",
        "  return label\n",
        "\n",
        "def enc_aud(Audio_Path):\n",
        "  audio =  gmfcc(Audio_Path)\n",
        "  return audio\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z0KbzAVHRBw4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8KxNjpPLBV7c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be195361-e8db-4550-9ca3-89ce37411b8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor([ 50 213   3   2], shape=(4,), dtype=int32)\n",
            "[ 50 213   3   2]\n"
          ]
        }
      ],
      "source": [
        "kkk = enc_label(\"test\")\n",
        "\n",
        "print(kkk)\n",
        "# Convert tensor to NumPy array\n",
        "numpy_array = kkk.numpy()\n",
        "\n",
        "np.set_printoptions(threshold=np.inf)\n",
        "\n",
        "# Print the NumPy array\n",
        "print(numpy_array)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Tc8wYrFBXKy"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BDrbwYK0Mr61"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XtmWjVCUztVm"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "oCfCP_BdgR54"
      },
      "outputs": [],
      "source": [
        "# An integer scalar Tensor. The window length in samples.\n",
        "frame_length = 256\n",
        "# An integer scalar Tensor. The number of samples to step.\n",
        "frame_step = 160\n",
        "# An integer scalar Tensor. The size of the FFT to apply.\n",
        "# If not provided, uses the smallest power of 2 enclosing frame_length.\n",
        "fft_length = 384\n",
        "\n",
        "\n",
        "def encode_single_sample(wav_file, label):\n",
        "    ###########################################\n",
        "    ##  Process the Audio\n",
        "    ##########################################\n",
        "    # 1. Read wav file\n",
        "    #file = tf.io.read_file(wavs_path + wav_file + \".wav\")\n",
        "    file = tf.io.read_file(wav_file)\n",
        "    # 2. Decode the wav file\n",
        "    audio, _ = tf.audio.decode_wav(file)\n",
        "    audio = tf.squeeze(audio, axis=-1)\n",
        "    # 3. Change type to float\n",
        "    audio = tf.cast(audio, tf.float32)\n",
        "    # 4. Get the spectrogram\n",
        "    spectrogram = tf.signal.stft(\n",
        "        audio, frame_length=frame_length, frame_step=frame_step, fft_length=fft_length\n",
        "    )\n",
        "    # 5. We only need the magnitude, which can be derived by applying tf.abs\n",
        "    spectrogram = tf.abs(spectrogram)\n",
        "    spectrogram = tf.math.pow(spectrogram, 0.5)\n",
        "    # 6. normalisation\n",
        "    means = tf.math.reduce_mean(spectrogram, 1, keepdims=True)\n",
        "    stddevs = tf.math.reduce_std(spectrogram, 1, keepdims=True)\n",
        "    spectrogram = (spectrogram - means) / (stddevs + 1e-10)\n",
        "    ###########################################\n",
        "    ##  Process the label\n",
        "    ##########################################\n",
        "\n",
        "    #label = char_to_num(label)\n",
        "\n",
        "    return spectrogram, label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ODBBRjudRkYP"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "7Lxsv0Cqud3P"
      },
      "outputs": [],
      "source": [
        "batch_size = 2\n",
        "df[\"Transcription\"] = df[\"Transcription\"].apply(enc_label)\n",
        "\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices(\n",
        "    #old(list(df_train[\"file_name\"]), list(df_train[\"normalized_transcription\"]))\n",
        "    (list(df[\"Audio_Path\"]), list(df[\"Transcription\"]))\n",
        "\n",
        ")\n",
        "\n",
        "train_dataset = (\n",
        "    train_dataset.map(encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE)\n",
        "    .padded_batch(batch_size)\n",
        "    .prefetch(buffer_size=tf.data.AUTOTUNE)\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tOlDOXMMucay"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0hV4DqeVjuN9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "galQqNwiz16n"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bgB4QK8Rz2p3"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "wJFNyTpAz2nj"
      },
      "outputs": [],
      "source": [
        "def CTCLoss(y_true, y_pred):\n",
        "    #print(y_pred)\n",
        "    batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n",
        "    input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n",
        "    label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n",
        "\n",
        "    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
        "    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
        "\n",
        "    loss = keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gQybUTwBCdrh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "def build_modelnw(input_dim, output_dim):\n",
        "    input_spectrogram = layers.Input((None, input_dim), name=\"input\")\n",
        "    x = layers.Reshape((-1, input_dim, 1), name=\"expand_dim\")(input_spectrogram)\n",
        "\n",
        "    # Single convolutional layer for feature extraction\n",
        "    x = layers.Conv2D(\n",
        "        filters=32,\n",
        "        kernel_size=[3, 3],\n",
        "        strides=[1, 1],\n",
        "        padding=\"same\",\n",
        "        use_bias=False,\n",
        "        name=\"conv_1\",\n",
        "    )(x)\n",
        "    x = layers.BatchNormalization(name=\"conv_1_bn\")(x)\n",
        "    x = layers.ReLU(name=\"conv_1_relu\")(x)\n",
        "    x = layers.Reshape((-1, x.shape[-2] * x.shape[-1]))(x)\n",
        "\n",
        "    # Single bidirectional LSTM layer\n",
        "    x = layers.Bidirectional(layers.LSTM(128, return_sequences=True))(x)\n",
        "\n",
        "    # Dense layer for output\n",
        "    output = layers.Dense(output_dim + 1, activation=\"softmax\")(x)\n",
        "\n",
        "    model = tf.keras.Model(input_spectrogram, output, name=\"Simplified_DeepSpeech\")\n",
        "    opt = tf.keras.optimizers.Adam(learning_rate=1e-4)\n",
        "    model.compile(optimizer=opt, loss=CTCLoss)\n",
        "    return model"
      ],
      "metadata": {
        "id": "o1KhinvKCdoP"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EVrkPwlflGVJ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 913
        },
        "id": "l1-Gq2KmlGv2",
        "outputId": "fae0107b-5d71-463c-cb6c-9d6db0ab2a30"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"DeepSpeech_2\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"DeepSpeech_2\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                                  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape                       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m            Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input (\u001b[38;5;33mInputLayer\u001b[0m)                             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m193\u001b[0m)                   │                   \u001b[38;5;34m0\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ expand_dim (\u001b[38;5;33mReshape\u001b[0m)                           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m193\u001b[0m, \u001b[38;5;34m1\u001b[0m)                │                   \u001b[38;5;34m0\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ conv_1 (\u001b[38;5;33mConv2D\u001b[0m)                                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m97\u001b[0m, \u001b[38;5;34m32\u001b[0m)                │              \u001b[38;5;34m14,432\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ conv_1_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m97\u001b[0m, \u001b[38;5;34m32\u001b[0m)                │                 \u001b[38;5;34m128\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ conv_1_relu (\u001b[38;5;33mReLU\u001b[0m)                             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m97\u001b[0m, \u001b[38;5;34m32\u001b[0m)                │                   \u001b[38;5;34m0\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ conv_2 (\u001b[38;5;33mConv2D\u001b[0m)                                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m32\u001b[0m)                │             \u001b[38;5;34m236,544\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ conv_2_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m32\u001b[0m)                │                 \u001b[38;5;34m128\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ conv_2_relu (\u001b[38;5;33mReLU\u001b[0m)                             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m32\u001b[0m)                │                   \u001b[38;5;34m0\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ reshape (\u001b[38;5;33mReshape\u001b[0m)                              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1568\u001b[0m)                  │                   \u001b[38;5;34m0\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ bidirectional_1 (\u001b[38;5;33mBidirectional\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                  │           \u001b[38;5;34m6,395,904\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                  │                   \u001b[38;5;34m0\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ bidirectional_2 (\u001b[38;5;33mBidirectional\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                  │           \u001b[38;5;34m4,724,736\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                  │                   \u001b[38;5;34m0\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ bidirectional_3 (\u001b[38;5;33mBidirectional\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                  │           \u001b[38;5;34m4,724,736\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)                            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                  │                   \u001b[38;5;34m0\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ bidirectional_4 (\u001b[38;5;33mBidirectional\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                  │           \u001b[38;5;34m4,724,736\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)                            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                  │                   \u001b[38;5;34m0\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ bidirectional_5 (\u001b[38;5;33mBidirectional\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                  │           \u001b[38;5;34m4,724,736\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                  │           \u001b[38;5;34m1,049,600\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dense_1_relu (\u001b[38;5;33mReLU\u001b[0m)                            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                  │                   \u001b[38;5;34m0\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)                            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                  │                   \u001b[38;5;34m0\u001b[0m │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m)                   │             \u001b[38;5;34m227,550\u001b[0m │\n",
              "└────────────────────────────────────────────────┴─────────────────────────────────────┴─────────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                                   </span>┃<span style=\"font-weight: bold\"> Output Shape                        </span>┃<span style=\"font-weight: bold\">             Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)                             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">193</span>)                   │                   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ expand_dim (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)                           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">193</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                │                   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ conv_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">97</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                │              <span style=\"color: #00af00; text-decoration-color: #00af00\">14,432</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ conv_1_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">97</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                │                 <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ conv_1_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">97</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                │                   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ conv_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                │             <span style=\"color: #00af00; text-decoration-color: #00af00\">236,544</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ conv_2_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                │                 <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ conv_2_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                │                   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ reshape (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)                              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1568</span>)                  │                   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ bidirectional_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,395,904</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                  │                   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ bidirectional_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,724,736</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                  │                   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ bidirectional_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,724,736</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                  │                   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ bidirectional_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,724,736</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                  │                   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ bidirectional_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,724,736</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,049,600</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dense_1_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                  │                   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                  │                   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├────────────────────────────────────────────────┼─────────────────────────────────────┼─────────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">227,550</span> │\n",
              "└────────────────────────────────────────────────┴─────────────────────────────────────┴─────────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m26,823,230\u001b[0m (102.32 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">26,823,230</span> (102.32 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m26,823,102\u001b[0m (102.32 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">26,823,102</span> (102.32 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m128\u001b[0m (512.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> (512.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "\n",
        "def build_model(input_dim, output_dim, rnn_layers=5, rnn_units=128):\n",
        "    \"\"\"Model similar to DeepSpeech2.\"\"\"\n",
        "    # Model's input\n",
        "    input_spectrogram = layers.Input((None, input_dim), name=\"input\")\n",
        "    # Expand the dimension to use 2D CNN.\n",
        "    x = layers.Reshape((-1, input_dim, 1), name=\"expand_dim\")(input_spectrogram)\n",
        "    # Convolution layer 1\n",
        "    x = layers.Conv2D(\n",
        "        filters=32,\n",
        "        kernel_size=[11, 41],\n",
        "        strides=[2, 2],\n",
        "        padding=\"same\",\n",
        "        use_bias=False,\n",
        "        name=\"conv_1\",\n",
        "    )(x)\n",
        "    x = layers.BatchNormalization(name=\"conv_1_bn\")(x)\n",
        "    x = layers.ReLU(name=\"conv_1_relu\")(x)\n",
        "    # Convolution layer 2\n",
        "    x = layers.Conv2D(\n",
        "        filters=32,\n",
        "        kernel_size=[11, 21],\n",
        "        strides=[1, 2],\n",
        "        padding=\"same\",\n",
        "        use_bias=False,\n",
        "        name=\"conv_2\",\n",
        "    )(x)\n",
        "    x = layers.BatchNormalization(name=\"conv_2_bn\")(x)\n",
        "    x = layers.ReLU(name=\"conv_2_relu\")(x)\n",
        "    # Reshape the resulted volume to feed the RNNs layers\n",
        "    x = layers.Reshape((-1, x.shape[-2] * x.shape[-1]))(x)\n",
        "    # RNN layers\n",
        "    for i in range(1, rnn_layers + 1):\n",
        "        recurrent = layers.GRU(\n",
        "            units=rnn_units,\n",
        "            activation=\"tanh\",\n",
        "            recurrent_activation=\"sigmoid\",\n",
        "            use_bias=True,\n",
        "            return_sequences=True,\n",
        "            reset_after=True,\n",
        "            name=f\"gru_{i}\",\n",
        "        )\n",
        "        x = layers.Bidirectional(\n",
        "            recurrent, name=f\"bidirectional_{i}\", merge_mode=\"concat\"\n",
        "        )(x)\n",
        "        if i < rnn_layers:\n",
        "            x = layers.Dropout(rate=0.5)(x)\n",
        "    # Dense layer\n",
        "    x = layers.Dense(units=rnn_units * 2, name=\"dense_1\")(x)\n",
        "    x = layers.ReLU(name=\"dense_1_relu\")(x)\n",
        "    x = layers.Dropout(rate=0.5)(x)\n",
        "    # Classification layer\n",
        "    output = layers.Dense(units=output_dim + 1, activation=\"softmax\")(x)\n",
        "    # Model\n",
        "    model = keras.Model(input_spectrogram, output, name=\"DeepSpeech_2\")\n",
        "    # Optimizer\n",
        "    opt = keras.optimizers.Adam(learning_rate=1e-4)\n",
        "    # Compile the model and return\n",
        "    model.compile(optimizer=opt, loss=CTCLoss)\n",
        "    return model\n",
        "\n",
        "\n",
        "# Get the model\n",
        "model = build_model(\n",
        "    input_dim=fft_length // 2 + 1,\n",
        "    output_dim= tokenizer.vocab_size ,#char_to_num.vocabulary_size(),\n",
        "    rnn_units=512,\n",
        ")\n",
        "model.summary(line_length=110)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HXkkrwU9kkjb"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RXlyEwVjo6lR"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NGaYcx4EteEf",
        "outputId": "b7990d26-f4ec-41ee-a68d-ece03ba36573"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 260ms/step - loss: 221.4749\n",
            "Epoch 2/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 132ms/step - loss: 18.6845\n",
            "Epoch 3/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 10.9995\n",
            "Epoch 4/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 132ms/step - loss: 9.1329\n",
            "Epoch 5/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 133ms/step - loss: 8.7360\n",
            "Epoch 6/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 138ms/step - loss: 8.4339\n",
            "Epoch 7/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 133ms/step - loss: 8.5219\n",
            "Epoch 8/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 133ms/step - loss: 8.6905\n",
            "Epoch 9/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 139ms/step - loss: 8.0950\n",
            "Epoch 10/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 134ms/step - loss: 8.6194\n",
            "Epoch 11/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 133ms/step - loss: 8.0672\n",
            "Epoch 12/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 8.2867\n",
            "Epoch 13/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 135ms/step - loss: 8.1524\n",
            "Epoch 14/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 133ms/step - loss: 8.0480\n",
            "Epoch 15/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 8.1278\n",
            "Epoch 16/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 133ms/step - loss: 8.0282\n",
            "Epoch 17/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 134ms/step - loss: 7.9350\n",
            "Epoch 18/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 140ms/step - loss: 7.8659\n",
            "Epoch 19/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 134ms/step - loss: 7.9570\n",
            "Epoch 20/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 141ms/step - loss: 7.8960\n",
            "Epoch 21/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 141ms/step - loss: 7.3398\n",
            "Epoch 22/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 134ms/step - loss: 6.5882\n",
            "Epoch 23/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 135ms/step - loss: 5.7179\n",
            "Epoch 24/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 142ms/step - loss: 5.6806\n",
            "Epoch 25/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 134ms/step - loss: 5.0214\n",
            "Epoch 26/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 134ms/step - loss: 4.9361\n",
            "Epoch 27/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 140ms/step - loss: 4.8460\n",
            "Epoch 28/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 134ms/step - loss: 4.7454\n",
            "Epoch 29/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 135ms/step - loss: 4.6585\n",
            "Epoch 30/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 141ms/step - loss: 4.7486\n",
            "Epoch 31/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 134ms/step - loss: 4.6349\n",
            "Epoch 32/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 135ms/step - loss: 4.5073\n",
            "Epoch 33/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 141ms/step - loss: 4.4113\n",
            "Epoch 34/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 135ms/step - loss: 3.4687\n",
            "Epoch 35/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 135ms/step - loss: 1.8910\n",
            "Epoch 36/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 141ms/step - loss: 0.4244\n",
            "Epoch 37/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 0.1465\n",
            "Epoch 38/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 135ms/step - loss: 0.1093\n",
            "Epoch 39/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 141ms/step - loss: 0.0778\n",
            "Epoch 40/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 136ms/step - loss: 0.0759\n",
            "Epoch 41/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 0.0687\n",
            "Epoch 42/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 141ms/step - loss: 0.0700\n",
            "Epoch 43/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 135ms/step - loss: 0.0602\n",
            "Epoch 44/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 0.0910\n",
            "Epoch 45/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 143ms/step - loss: 0.0529\n",
            "Epoch 46/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 0.0587\n",
            "Epoch 47/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 135ms/step - loss: 0.1279\n",
            "Epoch 48/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 143ms/step - loss: 1.1122\n",
            "Epoch 49/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 0.2662\n",
            "Epoch 50/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 0.5489\n",
            "Epoch 51/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 145ms/step - loss: 6.8528\n",
            "Epoch 52/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 137ms/step - loss: 0.2315\n",
            "Epoch 53/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 0.0946\n",
            "Epoch 54/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 143ms/step - loss: 0.0682\n",
            "Epoch 55/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 0.0640\n",
            "Epoch 56/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 0.0609\n",
            "Epoch 57/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 141ms/step - loss: 0.0669\n",
            "Epoch 58/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 0.0606\n",
            "Epoch 59/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 0.0540\n",
            "Epoch 60/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 142ms/step - loss: 0.0651\n",
            "Epoch 61/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 0.0622\n",
            "Epoch 62/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 0.0563\n",
            "Epoch 63/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 144ms/step - loss: 0.0610\n",
            "Epoch 64/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 0.0584\n",
            "Epoch 65/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 0.0607\n",
            "Epoch 66/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 144ms/step - loss: 0.0547\n",
            "Epoch 67/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 0.0549\n",
            "Epoch 68/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 138ms/step - loss: 0.0550\n",
            "Epoch 69/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 143ms/step - loss: 0.0538\n",
            "Epoch 70/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 0.0520\n",
            "Epoch 71/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 0.0576\n",
            "Epoch 72/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 142ms/step - loss: 0.0661\n",
            "Epoch 73/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 0.0567\n",
            "Epoch 74/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 0.0807\n",
            "Epoch 75/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 142ms/step - loss: 0.0535\n",
            "Epoch 76/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 136ms/step - loss: 0.0464\n",
            "Epoch 77/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 0.0583\n",
            "Epoch 78/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 140ms/step - loss: 0.0472\n",
            "Epoch 79/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 0.0536\n",
            "Epoch 80/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 138ms/step - loss: 0.0564\n",
            "Epoch 81/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 141ms/step - loss: 0.0514\n",
            "Epoch 82/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 0.0513\n",
            "Epoch 83/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 145ms/step - loss: 0.0554\n",
            "Epoch 84/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 139ms/step - loss: 0.0506\n",
            "Epoch 85/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 138ms/step - loss: 0.0521\n",
            "Epoch 86/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 141ms/step - loss: 0.0433\n",
            "Epoch 87/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 141ms/step - loss: 0.0597\n",
            "Epoch 88/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 138ms/step - loss: 0.0434\n",
            "Epoch 89/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 142ms/step - loss: 0.0476\n",
            "Epoch 90/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 138ms/step - loss: 0.0402\n",
            "Epoch 91/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 138ms/step - loss: 0.0543\n",
            "Epoch 92/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 144ms/step - loss: 0.0326\n",
            "Epoch 93/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 138ms/step - loss: 0.0545\n",
            "Epoch 94/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 138ms/step - loss: 0.0461\n",
            "Epoch 95/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 143ms/step - loss: 0.0386\n",
            "Epoch 96/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 138ms/step - loss: 0.0579\n",
            "Epoch 97/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 138ms/step - loss: 0.0410\n",
            "Epoch 98/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 144ms/step - loss: 0.0431\n",
            "Epoch 99/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 137ms/step - loss: 0.0401\n",
            "Epoch 100/100\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 138ms/step - loss: 0.0407\n"
          ]
        }
      ],
      "source": [
        "# Define the number of epochs.\n",
        "epochs = 100\n",
        "# Callback function to check transcription on the val set.\n",
        "\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(\n",
        "    train_dataset,\n",
        "    epochs=epochs,\n",
        "   # callbacks=[validation_callback],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oUoJyv3alqEn"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oigZVYd26T4W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ORGg2ktxO7Ky"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [
        "def encode_aud(wav_file):\n",
        "    \"\"\"\n",
        "    audio file encoder\n",
        "    params : wav audio file path\n",
        "    return : spectogram\n",
        "     \"\"\"\n",
        "    file = tf.io.read_file(wav_file)\n",
        "    audio, _ = tf.audio.decode_wav(file)\n",
        "    audio = tf.squeeze(audio, axis=-1)\n",
        "    audio = tf.cast(audio, tf.float32)\n",
        "    spectrogram = tf.signal.stft(\n",
        "        audio, frame_length=frame_length, frame_step=frame_step, fft_length=fft_length\n",
        "    )\n",
        "    spectrogram = tf.abs(spectrogram)\n",
        "    spectrogram = tf.math.pow(spectrogram, 0.5)\n",
        "    means = tf.math.reduce_mean(spectrogram, 1, keepdims=True)\n",
        "    stddevs = tf.math.reduce_std(spectrogram, 1, keepdims=True)\n",
        "    spectrogram = (spectrogram - means) / (stddevs + 1e-10)\n",
        "\n",
        "    return spectrogram\n",
        "\n",
        "def decode_batch_predictions(pred):\n",
        "    input_len = np.ones(pred.shape[0]) * pred.shape[1]\n",
        "    # Use greedy search. For complex tasks, you can use beam search\n",
        "    results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0]\n",
        "    # Iterate over the results and get back the text\n",
        "    output_text = []\n",
        "    for result in results:\n",
        "\n",
        "        result = tf.boolean_mask(result, result != -1)\n",
        "      #  print(result)\n",
        "        result = num_to_char(result)#.numpy().decode(\"utf-8\")\n",
        "       # print(result)\n",
        "        #output_text.append(result)\n",
        "    return result"
      ],
      "metadata": {
        "id": "cGRk2eqo2gMl"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TyxXHV9S2fNH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#############################################################\n",
        "################### Give Audio Path And Run #################\n",
        "#############################################################\n",
        "\n",
        "aud = \"/content/working/waves_yesno/1_1_1_1_0_0_1_0.wav\"\n",
        "\n",
        "#############################################################\n",
        "\n",
        "\n",
        "aud = encode_aud(aud)\n",
        "aud = tf.expand_dims(aud, axis=0)\n",
        "#Make preds by ai\n",
        "pred = model.predict(aud)\n",
        "#decode and detokenize it\n",
        "pred = decode_batch_predictions(pred)\n",
        "print(pred)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ep6cMWqJ4hfE",
        "outputId": "d21a7ed8-a59e-4382-ca37-6ba3473cc0b8"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 894ms/step\n",
            "ඔව් ඔව් ඔව් ඔව් නැහැ නැහැ ඔව් නැහැ\n"
          ]
        }
      ]
    }
  ],
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      "gpuType": "T4"
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
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}