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"source": [ + "!pip install datasets soundfile speechbrain\n", + "!pip install git+https://github.com/huggingface/transformers.git\n", + "!pip install --upgrade accelerate" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**GPU**" + ], + "metadata": { + "id": "CbBQUSqMBOAB" + } + }, + { + "cell_type": "code", + "source": [ + "!nvidia-smi" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yptV3W_yh5TZ", + "outputId": "0f2f7fe9-c6ad-4da7-c270-1080fea6ca88" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Wed Oct 23 11:29:21 2024 \n", + "+---------------------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n", + "|-----------------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|=========================================+======================+======================|\n", + "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 49C P8 10W / 70W | 0MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-----------------------------------------+----------------------+----------------------+\n", + " \n", + "+---------------------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=======================================================================================|\n", + "| No running processes found |\n", + "+---------------------------------------------------------------------------------------+\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "6c3dd62b8cd14e91b8d47d734dbbaced", + "34d191d760cc4c0888e08bab1a99e281", + "13dc6c4c01924acaa1ec5d8f629aeff0", + "c4eabe0167904f2da1d4d8accfbec968", + "2328af5aac93411fb1e03299de1ef3d0", + "d870a4a63a2646208563e80d60827086", + "a13585e1bcd84f5a8786d34882e3c762", + "2cef5d7da100426e8dfd33dbffa658fa", + "d9225211b21744ffb45276704bbe809c", + "6bc41785e2c047dcbf0d54e044ca11cb", + "8bb007e29d684ebeb5a1534c3578e3e8", + "1b5e7b36642242f3b79e7bb973910dc2", + "22053c7abb8b4c83abd77914334e808c", + "255023bba97a4d51a655c95d96b8f721", + "1a197129e7464d5690abba256adb7fb9", + "a1908468428b4a4f82a2965d457aa4ac", + "46b9333eae2246ebbdfdd90b2a0be122", + "3e247ee7035948f1b9d9a7e9a7047f7d", + "e088ce10ad4142c3876cef8bb769cd9a", + "3f3a93136de8495b946397b257a67a42", + "9d8f8793e20c4fa2a3c371f3b3812611", + "41a1a574b4b249d89f5f8760f6050a2d", + "ed8cedae4db6442f9937d2ffb5233ffa", + "8bccf75943dc43f985572f454ee7b09c", + "bac1c49f90484777b7b4f4e895bd2d30", + "ec38b28d457841ac9021c16fcba34541", + "76608f61b19d4245af7a906ab19069ce", + "2daa7e85503a46df886bfb8e08f57c0b", + "6daf75ea41aa4f99ba48f1841d588cce", + "acaca1f57e8e44a1b22375c31e8a2e66", + "066d22d0e9384037976d67d8ca7e8560", + "aed52dfb2e774540a72bd13e9f431701" + ] + }, + "id": "1aryCwT6hmRC", + "outputId": "3903394c-e202-4bb1-adb1-ca6167ce1996" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='
:3: UserWarning: Module 'speechbrain.pretrained' was deprecated, redirecting to 'speechbrain.inference'. Please update your script. This is a change from SpeechBrain 1.0. See: https://github.com/speechbrain/speechbrain/releases/tag/v1.0.0\n", + " from speechbrain.pretrained import EncoderClassifier\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "hyperparams.yaml: 0%| | 0.00/2.04k [00:00 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", + " ) # 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" + ], + "metadata": { + "id": "-Ko_cVuimiAa" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "data_collator = TTSDataCollatorWithPadding(processor=processor)" + ], + "metadata": { + "id": "bzIfZf5Omr78" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "features = [\n", + " dataset[\"train\"][0],\n", + " dataset[\"train\"][1],\n", + " dataset[\"train\"][20],\n", + "]\n", + "\n", + "batch = data_collator(features)" + ], + "metadata": { + "id": "bAdXpSJvnDOQ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "{k:v.shape for k,v in batch.items()}" + ], + "metadata": { + "id": "QIpryotGnGUB", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4aaf1ad2-5c15-477f-8597-019fc60a45e9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'input_ids': torch.Size([3, 122]),\n", + " 'attention_mask': torch.Size([3, 122]),\n", + " 'labels': torch.Size([3, 630, 80]),\n", + " 'speaker_embeddings': torch.Size([3, 512])}" + ] + }, + "metadata": {}, + "execution_count": 29 + } + ] + }, + { + "cell_type": "code", + "source": [ + "model.config.use_cache = False" + ], + "metadata": { + "id": "w8njZncSnOx3" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from functools import partial\n", + "model.generate = partial(model.generate, use_cache=True)" + ], + "metadata": { + "id": "bCYCFobHnWQ6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from transformers import Seq2SeqTrainingArguments\n", + "\n", + "training_args = Seq2SeqTrainingArguments(\n", + " output_dir=\"speecht5_finetuned_English\", # change to a repo name of your choice\n", + " per_device_train_batch_size=4,\n", + " gradient_accumulation_steps=8,\n", + " learning_rate=1e-4,\n", + " warmup_steps=100,\n", + " max_steps=1500,\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": "gHS-M3k_nbm3", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "2da30d64-973f-44fa-ec1a-ddad91ea02d1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1559: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "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": "d8QAvcpBnguZ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0eb789e8-b1ce-4974-e7bb-8b560b0328a6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":3: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Seq2SeqTrainer.__init__`. Use `processing_class` instead.\n", + " trainer = Seq2SeqTrainer(\n", + "max_steps is given, it will override any value given in num_train_epochs\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "trainer.train()" + ], + "metadata": { + "id": "5l8e4PG7njLo", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "b5a14394-ee99-462a-d8e5-416ba9403515" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
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" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:2816: UserWarning: Moving the following attributes in the config to the generation config: {'max_length': 1876}. You are seeing this warning because you've set generation parameters in the model config, as opposed to in the generation config.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n", + "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n", + " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=1500, training_loss=3.39983762105306, metrics={'train_runtime': 4837.6694, 'train_samples_per_second': 9.922, 'train_steps_per_second': 0.31, 'total_flos': 5483826441583776.0, 'train_loss': 3.39983762105306, 'epoch': 4.580152671755725})" + ] + }, + "metadata": {}, + "execution_count": 34 + } + ] + }, + { + "cell_type": "code", + "source": [ + "trainer.push_to_hub()" + ], + "metadata": { + "id": "T3aPr-chnqM_", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "outputId": "288fef0f-ba56-478a-8dcc-264dd6ddd90a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "CommitInfo(commit_url='https://huggingface.co/DeepDiveDev/speecht5_finetuned_English/commit/8bbc92b5968125e1ff51b8fea16e90aa40c5f267', commit_message='End of training', commit_description='', oid='8bbc92b5968125e1ff51b8fea16e90aa40c5f267', pr_url=None, pr_revision=None, pr_num=None)" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 35 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#Dataset on Technical Term" + ], + "metadata": { + "id": "yoOXf1BXOdg4" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install datasets\n", + "import pandas as pd\n", + "from datasets import Dataset, load_dataset\n", + "\n", + "# Load your dataset from Excel file using pd.read_excel\n", + "df = pd.read_excel('/content/drive/MyDrive/TTS_Eng/TTS-English.xlsx')\n", + "# Convert the pandas DataFrame to a Hugging Face Dataset\n", + "dataset = Dataset.from_pandas(df)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K8YzZRrPf6mz", + "outputId": "109e7328-faa4-4274-fef3-ae2c1e628106" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting datasets\n", + " Downloading datasets-3.0.2-py3-none-any.whl.metadata (20 kB)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n", + "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (16.1.0)\n", + "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n", + " Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.2)\n", + "Requirement already satisfied: requests>=2.32.2 in 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"data": { + "text/plain": [ + "133" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install transformers\n", + "from transformers import AutoProcessor\n", + "\n", + "# Assuming you want to use a specific processor, replace \"facebook/wav2vec2-base-960h\" with the desired model\n", + "processor = AutoProcessor.from_pretrained(\"facebook/wav2vec2-base-960h\")\n", + "\n", + "tokenizer = processor.tokenizer" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 527, + "referenced_widgets": [ + "bf0d33ef88d6454a9a4ea5a78883d9e9", + "b0700ca20203401fa7e2eb9a2b60aa0f", + "4225f3ecaa9046519340846aaba8616a", + "09b941636ca64b238d3e0a761573f45e", + "f2c3e3edbf5040be97bc87c1711d31bc", + "9b6cbb6586b24fa095476410bc1d80b9", + "cd3f004541194be29b29c99864fcf146", + "d4f6744cd7ce4a57aa784fbbee4c0621", + "7aaba4f57f7d4effbbf66933c1ebc61b", + "9f8c7e1a0b5e4942855b6a2143e1eed7", + "3805153ecfb743ffbb0e337a77004560", + "36f8c5fce0fb4b93be7295042913e208", + "7f3217a296c04423804d8290a17e946a", + "0429e54da0404f4298c47efe1c6b66e2", + "50b8cc6f892b4a149494951839bd3993", + "42e0f0238e9149468a716b765dc8baff", + "a81d5a2e37f945fca8b0cda4bdab0b91", + "22c4ce4eac7542409c62d058825e761c", + "2294bcb50eef48e698d6723958af9c94", + "56dc4123de544d50a9d5dde76876e00b", + "151b4d2db3bc4797ad977f722806ed64", + "692a45c766aa43acb1b7f7ad7ee1772d", + "4f20b5912c994e3986d3c4d30eb99143", + "a1c4cf9ad32b49a4996fbb3f321745a9", + "60183325ec9c4bb1967e83fe3be5dd32", + "369ff789de224c6f910cf8758420f0c8", + "f42a309ff1994451b50cf9d49d1f7a6d", + "f115fc561c044c8c9d79f1a189766fcf", + "5f5a995405d84c1b80ee0b22ad955696", + "79c5b6542ba34375a8f44d54709bab45", + "95ce8dec3e7f49b4b3311e7739da70d5", + "0d5030b6ed24403da75989467066f260", + "ef78e68136b448d3b8b603040e2de130", + "520715b2218b45068326603a6639746c", + "d30d15c3d0b745eda19f46e4c9c1d6c6", + "9f452748eb134bc0ac00f4038fd45a88", + "a3d6d1ae1a764fa782c5e42bc07823ba", + "db4f5325442b47f68cc0dfe18a4113b1", + "37dc19476c7c466482cac9c4509b3ad5", + "d01f1c3db49845a8bdb69a2cd8613018", + "48b70ae7b3a346ceac5a48b36fbddffc", + "e7716f8922284f30bd33b605ba90940e", + "38c3bbe6735e4bdf99263c6251a79eeb", + "6e90888770ce4e919567ec299fe5c28e", + "ef48464237604789b200054eaa4a23d9", + "5bc9d68ab2a04312a5a9d4b5977fd691", + "7586d1a50f924338887b3d66b6a7947a", + "c6cbd5b7493746299e01d7b7a3a1e3be", + "ab76bf76b190487b96317ce4f4583ae6", + "b37be5cbddd94530bac98f7f2301ac6d", + "8f51b4c1f2b043d4b640baf4a8cec808", + "c62b866ecf204cb3803f0226d166d213", + "f0aa4b397e3c402cbfb4d1c3af8aacf6", + "d05fe02294354059a6ce512f94ff1b17", + "1397290d20c44f67b51176023c0090bb" + ] + }, + "id": "ZyUozQcJV0w2", + "outputId": "44da5f87-59e3-4bc1-a64f-3582c58602af" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: transformers in 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"\u001b[?25hInstalling collected packages: ruamel.yaml.clib, ruamel.yaml, hyperpyyaml, speechbrain\n", + "Successfully installed hyperpyyaml-1.2.2 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.12 speechbrain-1.0.1\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":4: UserWarning: Module 'speechbrain.pretrained' was deprecated, redirecting to 'speechbrain.inference'. Please update your script. This is a change from SpeechBrain 1.0. See: https://github.com/speechbrain/speechbrain/releases/tag/v1.0.0\n", + " from speechbrain.pretrained import EncoderClassifier\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "hyperparams.yaml: 0%| | 0.00/2.04k [00:00 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", + " )# 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" + ], + "metadata": { + "id": "VPmLwOUIkXVJ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "data_collator = TTSDataCollatorWithPadding(processor=processor)" + ], + "metadata": { + "id": "t7TCP0SemAV4" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from transformers import SpeechT5ForTextToSpeech\n", + "\n", + "# Define the checkpoint variable with the desired model name or path\n", + "checkpoint = \"microsoft/speecht5_tts\" # Example checkpoint for SpeechT5 text-to-speech\n", + "\n", + "model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81, + "referenced_widgets": [ + "7e9f6e39ae5b4a67a6c3f6784c1b27fd", + "f9f524d062c94f8292546b8e7725e173", + "644230da1cf94c03a94fdc6f5d50e679", + "06b1f0f29e1443c2872991a640069f9a", + "7fdf330be2814e1ebaed555e6ffb741d", + "e764e6b820444e2dbc8662de23fe4fe2", + "1e96402bce724195888a2ee988c47949", + "a494bdb761414c678e6df357b5d17f33", + "ff767d13a25b4392ace435eefeead147", + "5d3d3d387ac54d6bb5127ade744b6ce7", + "e04eeb09c5d7469ea0737041d4166d20", + "1d9c4b97bb814dec804419862b9d4519", + "bbebee553dfa41a8b972c9b11ca28db1", + "9385cbb7ddec444c8de50ea8f19fe41a", + "8ff4fc045d6d4bb99431309f51e7f3f2", + "b96bf4c87a39442fa7321e1a6a17a668", + "8c21060511fa459b93af66c63ab89614", + "a687e10459064680a1b632a7e6d25253", + "fd0d2e45e27547d097870d268a37cc7b", + "0c0526a71b254c51b830c9544fd5e70f", + "1f69dc2914274109a9cb12d0567193a0", + "8e7329e6d9da48379e07cafe8633b8af" + ] + }, + "id": "o5jN1MH2mE0M", + "outputId": "85be9a6e-c759-445c-d288-6b552cc7aef7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/2.06k [00:00 =1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n", + "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (16.1.0)\n", + "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.2)\n", + "Requirement already 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] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install datasets transformers[torch] accelerate\n", + "!pip install sklearn\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "import datasets\n", + "import numpy as np\n", + "\n", + "# Assuming 'dataset' is your Hugging Face Dataset\n", + "# Rename or add the required columns to match the model's expected input.\n", + "# For example, if your dataset has a column named 'text' containing the target text,\n", + "# rename it to 'decoder_input_values':\n", + "dataset = dataset.rename_column('Text Example', 'decoder_input_values')\n", + "# Similarly, rename or add other necessary columns like 'input_values', 'attention_mask', and 'decoder_attention_mask'.\n", + "\n", + "# Convert the Hugging Face Dataset to a NumPy array or list\n", + "# before splitting. Assuming 'dataset' is a Hugging Face Dataset\n", + "dataset_np = dataset[:] # If dataset is a DatasetDict, select the desired split, e.g., dataset[\"train\"][:]\n", + "#dataset_np = np.array(dataset) #If you need a NumPy array specifically\n", + "\n", + "# Split the NumPy array using train_test_split\n", + "train_indices, test_indices = train_test_split(np.arange(len(dataset_np)), test_size=0.2, random_state=42) # Adjust test_size and random_state as needed\n", + "\n", + "# Create new Hugging Face Datasets using the split indices\n", + "train_dataset = dataset.select(train_indices)\n", + "test_dataset = dataset.select(test_indices)\n", + "\n", + "# You might need to define a data collator to prepare the data for the model:\n", + "def data_collator(features):\n", + " # Implement logic to collate data based on the columns present in your dataset\n", + " # and the format required by the SpeechT5ForTextToSpeech model.\n", + " # This might involve padding, creating attention masks, etc.\n", + " # You can refer to the documentation for examples of data collators:\n", + " # https://huggingface.co/docs/transformers/main_classes/data_collator\n", + " pass # Replace with your data collation logic\n", + "\n", + "# Now you can pass these datasets to the trainer\n", + "from transformers import Seq2SeqTrainer\n", + "trainer = Seq2SeqTrainer(\n", + " args=training_args,\n", + " model=model,\n", + " train_dataset=train_dataset, # Use train_dataset instead of dataset[\"train\"]\n", + " eval_dataset=test_dataset, # Use test_dataset instead of dataset[\"test\"]\n", + " data_collator=data_collator,\n", + " tokenizer=processor, # Assuming 'processor' is your SpeechT5Processor\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "f5200UGhmm1y", + "outputId": "589f5ef9-f923-4e43-db28-a2cb3187fae3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: 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not run successfully.\n", + " \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n", + " \u001b[31m╰─>\u001b[0m See above for output.\n", + " \n", + " \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n", + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25herror\n", + "\u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\n", + "\n", + "\u001b[31m×\u001b[0m Encountered error while generating package metadata.\n", + "\u001b[31m╰─>\u001b[0m See above for output.\n", + "\n", + "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n", + "\u001b[1;36mhint\u001b[0m: See above for details.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "max_steps is given, it will override any value given in num_train_epochs\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from transformers import SpeechT5HifiGan\n", + "vocoder = SpeechT5HifiGan.from_pretrained(\"microsoft/speecht5_hifigan\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81, + "referenced_widgets": [ + "f7f64603670540cca1cfa1a6bb95d8d4", + "eecd754bff9d41f5803b1d543d8eb859", + "af79f746f2d94ac9802b1f0e3bac9ef5", + "b15adb94a0544448aa19d7847e0bc3d2", + "2d6eef2155c94cd0aa87769b945f77cb", + "b57381a0a4b140118f4acb711ae0b930", + "39bab19c0cf64667b772f2292c5f70b7", + "54e297f441114a2eb00a418eee763bde", + "24a3f21743fe43e49b5eac5b13070564", + "b4316acb39974b45beae8e5adadfdbeb", + "ddbdb7af43a147eb9a4b74a6677fe0aa", + "7e85f12904de48beabb658b439d35fc4", + "211f9427940545128436e2472903176f", + "8cf800c4dfce4ea1b513881fd9e0a101", + "f887a58fdd7a495f8420760c9566deb1", + "b5991c8c911b46b3a4d7889b51df7759", + "442e7734e9874c72b085c11a49d1e710", + "f1858842eec545a9bfa48a8b49db3966", + "5787962f856f46e9b4d15b9b8f009a15", + "102b5eb12c8344f587cfeba6f70f78f8", + "20d218dcba4b48b6afc9ccbf43904cac", + "4452e9c0da20434b976c3719764ea341" + ] + }, + "id": "2VZIjVFjnCzW", + "outputId": "c6a477c3-5761-4606-ef64-ab907b1b1532" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/636 [00:00 1 else \"hundred\") + (\" and \" + number_to_words(remainder) if remainder else \"\")\n", + " elif number < 1000000:\n", + " thousands, remainder = divmod(number, 1000)\n", + " return number_to_words(thousands) + \" thousand\" + (\" \" + number_to_words(remainder) if remainder else \"\")\n", + " elif number < 1000000000:\n", + " millions, remainder = divmod(number, 1000000)\n", + " return number_to_words(millions) + \" million\" + (\" \" + number_to_words(remainder) if remainder else \"\")\n", + " elif number < 1000000000000:\n", + " billions, remainder = divmod(number, 1000000000)\n", + " return number_to_words(billions) + \" billion\" + (\" \" + number_to_words(remainder) if remainder else \"\")\n", + " else:\n", + " return str(number)\n", + "\n", + "def replace_numbers_with_words(text):\n", + " def replace(match):\n", + " number = int(match.group())\n", + " return number_to_words(number)\n", + "\n", + " # Find the numbers and change with words.\n", + " result = re.sub(r'\\b\\d+\\b', replace, text)\n", + "\n", + " return result" + ], + "metadata": { + "id": "WcG5qSO89itj" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import re\n", + "\n", + "# Dictionary of technical words and their spoken equivalents (each letter separated)\n", + "technical_words = {\n", + " \"API\": \"A P I\",\n", + " \"CUDA\": \"C U D A\",\n", + " \"OAuth\": \"O Auth\",\n", + " \"LLM\": \"L L M\",\n", + " \"HTTP\": \"H T T P\",\n", + " \"HTTPS\": \"H T T P S\",\n", + " \"URL\": \"U R L\",\n", + " \"SQL\": \"S Q L\",\n", + " \"JSON\": \"J S O N\",\n", + " \"XML\": \"X M L\",\n", + " \"REST\": \"R E S T\",\n", + " \"JWT\": \"J W T\",\n", + " \"FTP\": \"F T P\",\n", + " \"SSH\": \"S S H\",\n", + " \"GPU\": \"G P U\",\n", + " \"CPU\": \"C P U\",\n", + " \"IP\": \"I P\",\n", + " \"RAM\": \"R A M\",\n", + " \"ROM\": \"R O M\",\n", + " \"ID\": \"I D\",\n", + " \"UID\": \"U I D\",\n", + " \"UUID\": \"U U I D\",\n", + " \"NLP\": \"N L P\",\n", + " \"ML\": \"M L\",\n", + " \"AI\": \"A I\",\n", + " \"IoT\": \"I O T\",\n", + " \"VPN\": \"V P N\",\n", + " \"DNS\": \"D N S\",\n", + " \"SMTP\": \"S M T P\",\n", + " \"KNN\": \"K N N\",\n", + " \"CNN\": \"C N N\",\n", + " \"LSTM\": \"L S T M\",\n", + " \"GRU\": \"G R U\",\n", + " # Add more technical terms with letters separated\n", + "}\n", + "\n", + "def technical_words_to_speech(word):\n", + " # Convert technical words to their spoken form, with each letter separated by spaces\n", + " return technical_words.get(word, word) # If not in the dictionary, return the original word\n", + "\n", + "def replace_technical_words_with_speech(text):\n", + " # Split the text into words and check for technical terms\n", + " words = re.findall(r'\\b\\w+\\b', text)\n", + "\n", + " result = []\n", + " for word in words:\n", + " # Convert each word to its spoken form if it's a technical term\n", + " spoken_form = technical_words_to_speech(word)\n", + " result.append(spoken_form)\n", + "\n", + " return \" \".join(result)\n", + "\n", + "# Example usage\n", + "text = \"I will use an API with OAuth and CUDA to train the LLM model on a GPU.\"\n", + "spoken_text = replace_technical_words_with_speech(text)\n", + "print(spoken_text)" + ], + "metadata": { + "id": "WaHEQ1eX9nHx" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "inputs = processor(text=final_text, return_tensors=\"pt\")" + ], + "metadata": { + "id": "pGmsxMrG-I_9" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from transformers import SpeechT5HifiGan\n", + "\n", + "vocoder = SpeechT5HifiGan.from_pretrained(\"microsoft/speecht5_hifigan\")\n", + "speech = model.generate_speech(inputs[\"input_ids\"], speaker_embeddings, vocoder=vocoder)" + ], + "metadata": { + "id": "ayrv9MJu-N-c" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from IPython.display import Audio\n", + "import soundfile as sf\n", + "\n", + "Audio(speech.numpy(), rate=16000)\n", + "# Save the audio to a file (e.g., 'output.wav')\n", + "sf.write('output.wav', speech.numpy(), 16000)" + ], + "metadata": { + "id": "9znt0YGZ-UXw" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file