TTS_FineTune.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aryan-401/GoogleColabProjects/blob/main/TTS_FineTune.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A6ljaoS9hwgq",
        "outputId": "5353b636-b46a-4783-c492-f49e3e753f2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
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            "Requirement already satisfied: speechbrain in /usr/local/lib/python3.10/dist-packages (0.5.16)\n",
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            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from speechbrain) (0.1.99)\n",
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            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain) (2.1.0)\n",
            "Requirement already satisfied: ruamel.yaml>=0.17.28 in /usr/local/lib/python3.10/dist-packages (from hyperpyyaml->speechbrain) (0.18.5)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from ruamel.yaml>=0.17.28->hyperpyyaml->speechbrain) (0.2.8)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.9->speechbrain) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.9->speechbrain) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers datasets soundfile speechbrain accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162,
          "referenced_widgets": [
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            "7b64f9c99bd84265ac907f3b973cc1d6",
            "bfb01f66ccc84ea7a993b4175b7ea992"
          ]
        },
        "id": "LImCYem3h5GZ",
        "outputId": "2cec35e3-f7e5-4175-af6f-deee0d518fc7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d72fae9a39a4e4f9034e75b68ef41a4"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruIwVQQNh98v",
        "outputId": "efa6d027-b50c-44ff-f15d-c376a19144d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1429: FutureWarning: The repository for facebook/voxpopuli contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/facebook/voxpopuli\n",
            "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
            "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20968"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "from datasets import load_dataset, Audio\n",
        "\n",
        "dataset = load_dataset(\"facebook/voxpopuli\", \"nl\", split=\"train\")\n",
        "len(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "KCcWxgh-jR4g"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.cast_column(\"audio\", Audio(sampling_rate=16000))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "uCUv2vvukdQp"
      },
      "outputs": [],
      "source": [
        "from transformers import SpeechT5Processor\n",
        "\n",
        "checkpoint = \"microsoft/speecht5_tts\"\n",
        "processor = SpeechT5Processor.from_pretrained(checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "UiVI59zqkfrL"
      },
      "outputs": [],
      "source": [
        "tokenizer = processor.tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MCoajLegki2i",
        "outputId": "7c6660c9-d0a2-428b-d608-aab889d07b32"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'audio_id': '20100210-0900-PLENARY-3-nl_20100210-09:06:43_4',\n",
              " 'language': 9,\n",
              " 'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/02ec6a19d5b97c03e1379250378454dbf3fa2972943504a91c7da5045aa26a89/train_part_0/20100210-0900-PLENARY-3-nl_20100210-09:06:43_4.wav',\n",
              "  'array': array([ 4.27246094e-04,  1.31225586e-03,  1.03759766e-03, ...,\n",
              "         -9.15527344e-05,  7.62939453e-04, -2.44140625e-04]),\n",
              "  'sampling_rate': 16000},\n",
              " 'raw_text': 'Dat kan naar mijn gevoel alleen met een brede meerderheid die wij samen zoeken.',\n",
              " 'normalized_text': 'dat kan naar mijn gevoel alleen met een brede meerderheid die wij samen zoeken.',\n",
              " 'gender': 'female',\n",
              " 'speaker_id': '1122',\n",
              " 'is_gold_transcript': True,\n",
              " 'accent': 'None'}"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "dataset[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "068001cf30f04691b6038dba40e4f341",
            "e16f6a6690514f118c9eb9f99e2c41aa",
            "766f22a3251a4d24a16c5985d9199cac",
            "89b182f2ec334d14bd8adb12382f0cd5",
            "93e231cf4bc242119cbee1df1475dc71",
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            "c4661b03d6a346e69e59fcad544f97f0",
            "a4abcb6c82e94ff498a7122313573465",
            "13242550f02a40d5a6444374b499dea8"
          ]
        },
        "id": "P8pbABrRkmxY",
        "outputId": "f0122756-155e-4e46-f20f-4e35fd9502cf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/20968 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "068001cf30f04691b6038dba40e4f341"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def extract_all_chars(batch):\n",
        "    all_text = \" \".join(batch[\"normalized_text\"])\n",
        "    vocab = list(set(all_text))\n",
        "    return {\"vocab\": [vocab], \"all_text\": [all_text]}\n",
        "\n",
        "\n",
        "vocabs = dataset.map(\n",
        "    extract_all_chars,\n",
        "    batched=True,\n",
        "    batch_size=-1,\n",
        "    keep_in_memory=True,\n",
        "    remove_columns=dataset.column_names,\n",
        ")\n",
        "\n",
        "dataset_vocab = set(vocabs[\"vocab\"][0])\n",
        "tokenizer_vocab = {k for k, _ in tokenizer.get_vocab().items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DecEeCKhk3Fh",
        "outputId": "5219d904-778b-406a-f5d3-77b208c45651"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{' ', 'à', 'ç', 'è', 'ë', 'í', 'ï', 'ö', 'ü'}"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "dataset_vocab - tokenizer_vocab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "4Pl8IN2vk5YN"
      },
      "outputs": [],
      "source": [
        "replacements = [\n",
        "    (\"à\", \"a\"),\n",
        "    (\"ç\", \"c\"),\n",
        "    (\"è\", \"e\"),\n",
        "    (\"ë\", \"e\"),\n",
        "    (\"í\", \"i\"),\n",
        "    (\"ï\", \"i\"),\n",
        "    (\"ö\", \"o\"),\n",
        "    (\"ü\", \"u\"),\n",
        "]\n",
        "\n",
        "\n",
        "def cleanup_text(inputs):\n",
        "    for src, dst in replacements:\n",
        "        inputs[\"normalized_text\"] = inputs[\"normalized_text\"].replace(src, dst)\n",
        "    return inputs\n",
        "\n",
        "\n",
        "dataset = dataset.map(cleanup_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "WCYGvThTk7kI"
      },
      "outputs": [],
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "speaker_counts = defaultdict(int)\n",
        "\n",
        "for speaker_id in dataset[\"speaker_id\"]:\n",
        "    speaker_counts[speaker_id] += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "wbmjFKlqk9JJ",
        "outputId": "dd2eb94c-bf98-497c-8a71-8b4a2d4152b7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure()\n",
        "plt.hist(speaker_counts.values(), bins=20)\n",
        "plt.ylabel(\"Speakers\")\n",
        "plt.xlabel(\"Examples\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "0888569681ad4ffaa38e091885511479",
            "542d373f9ef14f1e8e4fad19449d901d",
            "17e8f070b7e0472f8a96a317f90b696c",
            "750b75ac84ea403184403e5d1b1f0447",
            "1b49bfdec00e43daa3ad120500fc2cae",
            "26dfbc05665c4cd1890f8580567f3f99",
            "15bf1d423e614233991660e80531f03b",
            "571907f0219e4b4bb2fce267aa9a438f",
            "87283d9f863e45c1a3f7dc3468317034",
            "23e952009b564e108e3bc56eecee9686",
            "92fb9f2491e2464db630273048869c73"
          ]
        },
        "id": "z_EkQ_8ylywQ",
        "outputId": "6a2b82a2-f72a-43a9-b447-563716002aae"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/20968 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0888569681ad4ffaa38e091885511479"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def select_speaker(speaker_id):\n",
        "    return 300 <= speaker_counts[speaker_id] <= 400\n",
        "\n",
        "\n",
        "dataset = dataset.filter(select_speaker, input_columns=[\"speaker_id\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zeGiiqnrlJ6A",
        "outputId": "a337beaa-d6d6-4a35-9569-7794df6ee877"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "len(set(dataset[\"speaker_id\"]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dSiti5UOlNM9",
        "outputId": "016b2e07-e348-4205-82fd-53824dcc4d51"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4128"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "len(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "-jcdCqXDlQpJ"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import torch\n",
        "from speechbrain.pretrained import EncoderClassifier\n",
        "\n",
        "spk_model_name = \"speechbrain/spkrec-xvect-voxceleb\"\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "speaker_model = EncoderClassifier.from_hparams(\n",
        "    source=spk_model_name,\n",
        "    run_opts={\"device\": device},\n",
        "    savedir=os.path.join(\"/tmp\", spk_model_name),\n",
        ")\n",
        "\n",
        "\n",
        "def create_speaker_embedding(waveform):\n",
        "    with torch.no_grad():\n",
        "        speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))\n",
        "        speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)\n",
        "        speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()\n",
        "    return speaker_embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "bFLrOSwYl7lw"
      },
      "outputs": [],
      "source": [
        "def prepare_dataset(example):\n",
        "    audio = example[\"audio\"]\n",
        "\n",
        "    example = processor(\n",
        "        text=example[\"normalized_text\"],\n",
        "        audio_target=audio[\"array\"],\n",
        "        sampling_rate=audio[\"sampling_rate\"],\n",
        "        return_attention_mask=False,\n",
        "    )\n",
        "\n",
        "    # strip off the batch dimension\n",
        "    example[\"labels\"] = example[\"labels\"][0]\n",
        "\n",
        "    # use SpeechBrain to obtain x-vector\n",
        "    example[\"speaker_embeddings\"] = create_speaker_embedding(audio[\"array\"])\n",
        "\n",
        "    return example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLYnzttFl9wm",
        "outputId": "ee7ddd41-701b-4e31-93f5-894a5d88960e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['input_ids', 'labels', 'speaker_embeddings']"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "processed_example = prepare_dataset(dataset[0])\n",
        "list(processed_example.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t01EMt-sl_4W",
        "outputId": "ba90d5e0-20d4-4f81-bf9e-eadf1f5e3448"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512,)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "processed_example[\"speaker_embeddings\"].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "id": "8hGVyd_NmCEW",
        "outputId": "e0434141-eae9-4a12-b221-c46f8a5c7412"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure()\n",
        "plt.imshow(processed_example[\"labels\"].T)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "P3t8j_AGmLbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
            "d74f738428b4442eba3c03403544f592",
            "769bd1441ef348ee89a4ab9a54229f44",
            "3774a1e81a1149c7b992cfc0583da091",
            "9442830a09544c2caf65e81ad711422a",
            "891804a4115d4ace8c11003539afa121",
            "2b64edc1de314dc094f82bfbb2595508",
            "2cf5a1e6c6a0449294aaa2bae83dfce7",
            "869dd61569ee4de4b9c8f7f91098340c",
            "3267d77dee7644f1a748b007a7443641",
            "03c98f0d09474f249aad41787584c031",
            "f8654f4d9eb6445b9c51aaeb7480a9fc"
          ]
        },
        "outputId": "385afdc9-8815-476a-eb4f-6cf559da89be"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4128 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d74f738428b4442eba3c03403544f592"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (750 > 600). Running this sequence through the model will result in indexing errors\n"
          ]
        }
      ],
      "source": [
        "dataset = dataset.map(prepare_dataset, remove_columns=dataset.column_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "fDmbQZGZmOaW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "dc884651475b45cbad78c69e13632e4f",
            "8e8038e2319f4e97bfce9811de342f3c",
            "ea2b63014bf44d378bb0d0259aa85df7",
            "2d690c3d4009448d9596796f4385bda0",
            "5f52a3c77d1d49f3b5534a0e34e02d6d",
            "15b94ca7ac0c40e89011cf18a4268b9c",
            "d419f32318f642f6918ab47f03387699",
            "ff6a19bda3e8407a8bdae8ffa54883c0",
            "5b69836b1ec446198b550e3c8eb5034e",
            "da072a4ce729409e9a2b178281431310",
            "1da72abeefe04e0183fbc8e5dea94eb6"
          ]
        },
        "outputId": "eeb84d07-19cf-4972-b991-fc56fbbed975"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/4128 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc884651475b45cbad78c69e13632e4f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3433"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "def is_not_too_long(input_ids):\n",
        "    input_length = len(input_ids)\n",
        "    return input_length < 200\n",
        "\n",
        "\n",
        "dataset = dataset.filter(is_not_too_long, input_columns=[\"input_ids\"])\n",
        "len(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.train_test_split(test_size=0.1)"
      ],
      "metadata": {
        "id": "a9xnnfz2-qqo"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "pQoger_Km1fP"
      },
      "outputs": [],
      "source": [
        "from dataclasses import dataclass\n",
        "from typing import Any, Dict, List, Union\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class TTSDataCollatorWithPadding:\n",
        "    processor: Any\n",
        "\n",
        "    def __call__(\n",
        "        self, features: List[Dict[str, Union[List[int], torch.Tensor]]]\n",
        "    ) -> Dict[str, torch.Tensor]:\n",
        "        input_ids = [{\"input_ids\": feature[\"input_ids\"]} for feature in features]\n",
        "        label_features = [{\"input_values\": feature[\"labels\"]} for feature in features]\n",
        "        speaker_features = [feature[\"speaker_embeddings\"] for feature in features]\n",
        "\n",
        "        # collate the inputs and targets into a batch\n",
        "        batch = processor.pad(\n",
        "            input_ids=input_ids, labels=label_features, return_tensors=\"pt\"\n",
        "        )\n",
        "\n",
        "        # replace padding with -100 to ignore loss correctly\n",
        "        batch[\"labels\"] = batch[\"labels\"].masked_fill(\n",
        "            batch.decoder_attention_mask.unsqueeze(-1).ne(1), -100\n",
        "        )\n",
        "\n",
        "        # not used during fine-tuning\n",
        "        del batch[\"decoder_attention_mask\"]\n",
        "\n",
        "        # round down target lengths to multiple of reduction factor\n",
        "        if model.config.reduction_factor > 1:\n",
        "            target_lengths = torch.tensor(\n",
        "                [len(feature[\"input_values\"]) for feature in label_features]\n",
        "            )\n",
        "            target_lengths = target_lengths.new(\n",
        "                [\n",
        "                    length - length % model.config.reduction_factor\n",
        "                    for length in target_lengths\n",
        "                ]\n",
        "            )\n",
        "            max_length = max(target_lengths)\n",
        "            batch[\"labels\"] = batch[\"labels\"][:, :max_length]\n",
        "\n",
        "        # also add in the speaker embeddings\n",
        "        batch[\"speaker_embeddings\"] = torch.tensor(speaker_features)\n",
        "\n",
        "        return batch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = TTSDataCollatorWithPadding(processor=processor)"
      ],
      "metadata": {
        "id": "-usE2Osa-Hju"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import SpeechT5ForTextToSpeech\n",
        "\n",
        "model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)"
      ],
      "metadata": {
        "id": "gcjeh4sK-KU1"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from functools import partial\n",
        "\n",
        "# disable cache during training since it's incompatible with gradient checkpointing\n",
        "model.config.use_cache = False\n",
        "\n",
        "# set language and task for generation and re-enable cache\n",
        "model.generate = partial(model.generate, use_cache=True)"
      ],
      "metadata": {
        "id": "MLnZ3QkF-MNP"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainingArguments\n",
        "\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"speecht5_finetuned_voxpopuli_nl\",  # change to a repo name of your choice\n",
        "    per_device_train_batch_size=4,\n",
        "    gradient_accumulation_steps=8,\n",
        "    learning_rate=1e-5,\n",
        "    warmup_steps=50,\n",
        "    max_steps=100,\n",
        "    gradient_checkpointing=True,\n",
        "    fp16=True,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    per_device_eval_batch_size=2,\n",
        "    save_steps=100,\n",
        "    eval_steps=100,\n",
        "    logging_steps=25,\n",
        "    report_to=[\"tensorboard\"],\n",
        "    load_best_model_at_end=True,\n",
        "    greater_is_better=False,\n",
        "    label_names=[\"labels\"],\n",
        "    push_to_hub=True,\n",
        ")"
      ],
      "metadata": {
        "id": "Bb8cP6Vd-OSC"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainer\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    args=training_args,\n",
        "    model=model,\n",
        "    train_dataset=dataset[\"train\"],\n",
        "    eval_dataset=dataset[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=processor,\n",
        ")"
      ],
      "metadata": {
        "id": "9ZM1goyl-cso"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kwargs = {\n",
        "     \"dataset_tags\": \"facebook/voxpopuli\",\n",
        "    \"finetuned_from\": \"speechbrain/spkrec-xvect-voxceleb\",\n",
        "    \"tasks\": \"text-to-speech\",\n",
        "}"
      ],
      "metadata": {
        "id": "oE_g1cvi-enG"
      },
      "execution_count": 79,
      "outputs": []
    },
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        "trainer.train()\n",
        "trainer.push_to_hub(**kwargs)"
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
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              "      [100/100 04:27, Epoch 1/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
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