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| }, | |
| "id": "zXz9ro6jBjcI", | |
| "outputId": "acae6ea3-88c5-4d44-8b12-db3666a924b4" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: 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" | |
| ] | |
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| "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n", | |
| " warnings.warn(\n" | |
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| { | |
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| "name": "stdout", | |
| "text": [ | |
| "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n", | |
| "* Running on public URL: https://2408cd2fa71e692785.gradio.live\n", | |
| "\n", | |
| "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" | |
| ] | |
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| ], | |
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| "<div><iframe src=\"https://2408cd2fa71e692785.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" | |
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| { | |
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| }, | |
| "metadata": {}, | |
| "execution_count": 4 | |
| } | |
| ], | |
| "source": [ | |
| "import gradio as gr\n", | |
| "from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan\n", | |
| "from datasets import load_dataset\n", | |
| "import torch\n", | |
| "import soundfile as sf\n", | |
| "import numpy as np\n", | |
| "\n", | |
| "# Load models and processor\n", | |
| "processor = SpeechT5Processor.from_pretrained(\"microsoft/speecht5_tts\")\n", | |
| "model = SpeechT5ForTextToSpeech.from_pretrained(\"microsoft/speecht5_tts\")\n", | |
| "vocoder = SpeechT5HifiGan.from_pretrained(\"microsoft/speecht5_hifigan\")\n", | |
| "\n", | |
| "# Load speaker embeddings\n", | |
| "embeddings_dataset = load_dataset(\"Matthijs/cmu-arctic-xvectors\", split=\"validation\")\n", | |
| "speaker_embeddings = torch.tensor(embeddings_dataset[7306][\"xvector\"]).unsqueeze(0)\n", | |
| "\n", | |
| "def text_to_speech(text):\n", | |
| " inputs = processor(text=text, return_tensors=\"pt\")\n", | |
| " speech = model.generate_speech(inputs[\"input_ids\"], speaker_embeddings, vocoder=vocoder)\n", | |
| "\n", | |
| " # Convert to numpy array and ensure it's in the correct format for Gradio's Audio output\n", | |
| " speech_np = speech.numpy()\n", | |
| " speech_np = (speech_np * 32767).astype(np.int16)\n", | |
| "\n", | |
| " return (16000, speech_np) # Return tuple of (sample_rate, speech_data)\n", | |
| "\n", | |
| "# Create Gradio interface\n", | |
| "iface = gr.Interface(\n", | |
| " fn=text_to_speech,\n", | |
| " inputs=gr.Textbox(lines=3, placeholder=\"Enter text to convert to speech...\"),\n", | |
| " outputs=gr.Audio(type=\"numpy\", label=\"Generated Speech\"),\n", | |
| " title=\"Text-to-Speech using SpeechT5\",\n", | |
| " description=\"Enter text and listen to the generated speech using the SpeechT5 model.\"\n", | |
| ")\n", | |
| "\n", | |
| "# Launch the interface with share=True to create a public link\n", | |
| "iface.launch(share=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import torch\n", | |
| "from torch.utils.data import Dataset, DataLoader\n", | |
| "from transformers import (\n", | |
| " SpeechT5Processor,\n", | |
| " SpeechT5ForTextToSpeech,\n", | |
| " Trainer,\n", | |
| " TrainingArguments\n", | |
| ")\n", | |
| "import numpy as np\n", | |
| "from tqdm import tqdm\n", | |
| "\n", | |
| "class SyntheticAudioDataset(Dataset):\n", | |
| " def __init__(self, processor, num_samples=100, max_length=200):\n", | |
| " \"\"\"\n", | |
| " Create synthetic data for TTS training\n", | |
| " num_samples: Number of synthetic samples to generate\n", | |
| " max_length: Maximum sequence length for text\n", | |
| " \"\"\"\n", | |
| " self.processor = processor\n", | |
| " self.num_samples = num_samples\n", | |
| " self.max_length = max_length\n", | |
| "\n", | |
| " # Generate synthetic data\n", | |
| " self.data = self._generate_synthetic_data()\n", | |
| " print(f\"Generated {len(self.data)} synthetic samples\")\n", | |
| "\n", | |
| " def _generate_synthetic_data(self):\n", | |
| " synthetic_data = []\n", | |
| " print(\"Generating synthetic data...\")\n", | |
| "\n", | |
| " # Sample sentences for training\n", | |
| " sample_texts = [\n", | |
| " \"Hello world\",\n", | |
| " \"This is a test\",\n", | |
| " \"Artificial intelligence is amazing\",\n", | |
| " \"I love programming\",\n", | |
| " \"Machine learning is fun\"\n", | |
| " ]\n", | |
| "\n", | |
| " for _ in tqdm(range(self.num_samples)):\n", | |
| " try:\n", | |
| " # Randomly select a text\n", | |
| " text = np.random.choice(sample_texts)\n", | |
| "\n", | |
| " # Generate synthetic mel spectrogram with correct dimensions\n", | |
| " # SpeechT5 expects 80-dimensional mel spectrogram features\n", | |
| " sequence_length = 200 # Arbitrary sequence length\n", | |
| " synthetic_spectrogram = torch.randn(80, sequence_length) # Shape: [80, sequence_length]\n", | |
| "\n", | |
| " synthetic_data.append({\n", | |
| " 'text': text,\n", | |
| " 'spectrogram': synthetic_spectrogram\n", | |
| " })\n", | |
| "\n", | |
| " except Exception as e:\n", | |
| " print(f\"Error generating synthetic sample: {str(e)}\")\n", | |
| " continue\n", | |
| "\n", | |
| " return synthetic_data\n", | |
| "\n", | |
| " def __len__(self):\n", | |
| " return len(self.data)\n", | |
| "\n", | |
| " def __getitem__(self, idx):\n", | |
| " item = self.data[idx]\n", | |
| "\n", | |
| " # Process text\n", | |
| " text_inputs = self.processor(\n", | |
| " text=item[\"text\"],\n", | |
| " return_tensors=\"pt\",\n", | |
| " padding=\"max_length\",\n", | |
| " max_length=self.max_length,\n", | |
| " truncation=True\n", | |
| " )\n", | |
| "\n", | |
| " return {\n", | |
| " \"input_ids\": text_inputs.input_ids.squeeze(),\n", | |
| " \"attention_mask\": text_inputs.attention_mask.squeeze(),\n", | |
| " \"labels\": item[\"spectrogram\"], # Shape: [80, sequence_length]\n", | |
| " }\n", | |
| "\n", | |
| "class TTSTrainer:\n", | |
| " def __init__(self, model_path=\"microsoft/speecht5_tts\", output_dir=\"./tts_finetuned\"):\n", | |
| " self.processor = SpeechT5Processor.from_pretrained(model_path)\n", | |
| " self.model = SpeechT5ForTextToSpeech.from_pretrained(model_path)\n", | |
| " self.output_dir = output_dir\n", | |
| "\n", | |
| " def train(self, train_dataset, eval_dataset=None):\n", | |
| " training_args = TrainingArguments(\n", | |
| " output_dir=self.output_dir,\n", | |
| " num_train_epochs=1, # Reduced epochs\n", | |
| " per_device_train_batch_size=2,\n", | |
| " per_device_eval_batch_size=2,\n", | |
| " warmup_steps=50,\n", | |
| " weight_decay=0.01,\n", | |
| " logging_dir='./logs',\n", | |
| " logging_steps=10,\n", | |
| " evaluation_strategy=\"steps\" if eval_dataset else \"no\",\n", | |
| " eval_steps=50 if eval_dataset else None,\n", | |
| " save_steps=100,\n", | |
| " fp16=False,\n", | |
| " gradient_accumulation_steps=2,\n", | |
| " learning_rate=1e-5,\n", | |
| " )\n", | |
| "\n", | |
| " trainer = Trainer(\n", | |
| " model=self.model,\n", | |
| " args=training_args,\n", | |
| " train_dataset=train_dataset,\n", | |
| " eval_dataset=eval_dataset,\n", | |
| " )\n", | |
| "\n", | |
| " trainer.train()\n", | |
| "\n", | |
| " def save_model(self):\n", | |
| " self.model.save_pretrained(self.output_dir)\n", | |
| " self.processor.save_pretrained(self.output_dir)\n", | |
| "\n", | |
| "def main():\n", | |
| " # Initialize trainer\n", | |
| " output_dir = \"./tts_finetuned\"\n", | |
| " trainer = TTSTrainer(output_dir=output_dir)\n", | |
| "\n", | |
| " # Create synthetic dataset with fewer samples\n", | |
| " print(\"Creating synthetic dataset...\")\n", | |
| " dataset = SyntheticAudioDataset(trainer.processor, num_samples=10)\n", | |
| "\n", | |
| " # Split dataset into train and evaluation sets\n", | |
| " train_size = int(0.8 * len(dataset))\n", | |
| " eval_size = len(dataset) - train_size\n", | |
| "\n", | |
| " print(f\"Splitting dataset into {train_size} training and {eval_size} evaluation samples\")\n", | |
| "\n", | |
| " train_dataset, eval_dataset = torch.utils.data.random_split(\n", | |
| " dataset, [train_size, eval_size])\n", | |
| "\n", | |
| " # Train model\n", | |
| " print(\"Starting training...\")\n", | |
| " try:\n", | |
| " trainer.train(train_dataset, eval_dataset)\n", | |
| " # Save model\n", | |
| " trainer.save_model()\n", | |
| " print(\"Training complete. Model saved.\")\n", | |
| " except Exception as e:\n", | |
| " print(f\"Training failed with error: {str(e)}\")\n", | |
| "\n", | |
| "if __name__ == \"__main__\":\n", | |
| " main()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "HNfRgPdidhYT", | |
| "outputId": "b18d5bc7-6aaf-47ef-9cca-8ef256076204" | |
| }, | |
| "execution_count": 39, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Creating synthetic dataset...\n", | |
| "Generating synthetic data...\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "100%|██████████| 10/10 [00:00<00:00, 1705.35it/s]" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Generated 10 synthetic samples\n", | |
| "Splitting dataset into 8 training and 2 evaluation samples\n", | |
| "Starting training...\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Training failed with error: mat1 and mat2 shapes cannot be multiplied (80x200 and 80x256)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# New Section" | |
| ], | |
| "metadata": { | |
| "id": "zNdShtuIjCZ4" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install python-Levenshtein" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "uDzu4LmveFI9", | |
| "outputId": "7d501857-70ba-43e7-c4c7-83ae928d08ff" | |
| }, | |
| "execution_count": 31, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Collecting python-Levenshtein\n", | |
| " Downloading python_Levenshtein-0.26.0-py3-none-any.whl.metadata (3.7 kB)\n", | |
| "Collecting Levenshtein==0.26.0 (from python-Levenshtein)\n", | |
| " Downloading levenshtein-0.26.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.2 kB)\n", | |
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| "\u001b[?25hInstalling collected packages: rapidfuzz, Levenshtein, python-Levenshtein\n", | |
| "Successfully installed Levenshtein-0.26.0 python-Levenshtein-0.26.0 rapidfuzz-3.10.0\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install pyloudnorm pesq pystoi torchaudio" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "_7ihdmUVdVUR", | |
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| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Collecting pyloudnorm\n", | |
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| " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
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| "Building wheels for collected packages: pesq\n", | |
| " Building wheel for pesq (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for pesq: filename=pesq-0.0.4-cp310-cp310-linux_x86_64.whl size=262948 sha256=c6bc9ffdb50f9239212374944fc2a70dc0e0f0bd87dc73c8c2a95c35ba49ea60\n", | |
| " Stored in directory: /root/.cache/pip/wheels/c5/4e/2c/251524370c0fdd659e99639a0fbd0ca5a782c3aafcd456b28d\n", | |
| "Successfully built pesq\n", | |
| "Installing collected packages: pesq, pystoi, pyloudnorm\n", | |
| "Successfully installed pesq-0.0.4 pyloudnorm-0.1.1 pystoi-0.4.1\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
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| } | |
| }, | |
| { | |
| "cell_type": "code", | |
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| "!pip install gradio" | |
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| "base_uri": "https://localhost:8080/" | |
| }, | |
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| "\u001b[?25hInstalling collected packages: pydub, websockets, tomlkit, semantic-version, ruff, python-multipart, orjson, markupsafe, h11, ffmpy, aiofiles, uvicorn, starlette, huggingface-hub, httpcore, httpx, fastapi, gradio-client, gradio\n", | |
| " Attempting uninstall: markupsafe\n", | |
| " Found existing installation: MarkupSafe 3.0.2\n", | |
| " Uninstalling MarkupSafe-3.0.2:\n", | |
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| " Attempting uninstall: huggingface-hub\n", | |
| " Found existing installation: huggingface-hub 0.24.7\n", | |
| " Uninstalling huggingface-hub-0.24.7:\n", | |
| " Successfully uninstalled huggingface-hub-0.24.7\n", | |
| "Successfully installed aiofiles-23.2.1 fastapi-0.115.3 ffmpy-0.4.0 gradio-5.3.0 gradio-client-1.4.2 h11-0.14.0 httpcore-1.0.6 httpx-0.27.2 huggingface-hub-0.26.1 markupsafe-2.1.5 orjson-3.10.10 pydub-0.25.1 python-multipart-0.0.12 ruff-0.7.0 semantic-version-2.10.0 starlette-0.41.0 tomlkit-0.12.0 uvicorn-0.32.0 websockets-12.0\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "pip install torch transformers tqdm" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "AhKajcn6Cin7", | |
| "outputId": "5ff33348-091c-43eb-9a45-c695ccb30597" | |
| }, | |
| "execution_count": 36, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.0+cu121)\n", | |
| "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n", | |
| "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (4.66.5)\n", | |
| "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.16.1)\n", | |
| "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n", | |
| "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.4.2)\n", | |
| "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n", | |
| "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.6.1)\n", | |
| "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.1)\n", | |
| "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n", | |
| "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.26.1)\n", | |
| "Requirement already satisfied: numpy>=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) (2024.9.11)\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) (0.4.5)\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: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n", | |
| "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4.0)\n", | |
| "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n", | |
| "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n", | |
| "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "tG6txwVhC6hZ" | |
| }, | |
| "execution_count": 42, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install matplotlib-venn" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "OQ4CqAGYWNeU", | |
| "outputId": "a8447827-a0e5-4bd7-ddbc-f5dec276359c" | |
| }, | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Requirement already satisfied: matplotlib-venn in /usr/local/lib/python3.10/dist-packages (1.1.1)\n", | |
| "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from matplotlib-venn) (3.7.1)\n", | |
| "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from matplotlib-venn) (1.26.4)\n", | |
| "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from matplotlib-venn) (1.13.1)\n", | |
| "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (1.3.0)\n", | |
| "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (0.12.1)\n", | |
| "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (4.54.1)\n", | |
| "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (1.4.7)\n", | |
| "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (24.1)\n", | |
| "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (10.4.0)\n", | |
| "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (3.2.0)\n", | |
| "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (2.8.2)\n", | |
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| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!apt-get -qq install -y libfluidsynth1" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "3WAMTKPDWNeW", | |
| "outputId": "23158bc8-e272-4ad1-d2c2-e92d2db51cc4" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "E: Package 'libfluidsynth1' has no installation candidate\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "pip install transformers torch soundfile gradio datasets" | |
| ], | |
| "metadata": { | |
| "id": "pOf9vifrDYhv", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "b7e0a3f1-d2d9-4460-fa43-bf5c9dcad776" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n", | |
| "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.0+cu121)\n", | |
| "Requirement already satisfied: soundfile in /usr/local/lib/python3.10/dist-packages (0.12.1)\n", | |
| "Requirement already satisfied: gradio in /usr/local/lib/python3.10/dist-packages (5.3.0)\n", | |
| "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (3.0.2)\n", | |
| "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n", | |
| "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.26.1)\n", | |
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| "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", | |
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| "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n", | |
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| "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n", | |
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| "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.10/dist-packages (from soundfile) (1.17.1)\n", | |
| "Requirement already satisfied: aiofiles<24.0,>=22.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (23.2.1)\n", | |
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| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import gradio as gr\n", | |
| "from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan\n", | |
| "from datasets import load_dataset\n", | |
| "import torch\n", | |
| "import soundfile as sf\n", | |
| "import numpy as np\n", | |
| "from typing import Tuple, Optional\n", | |
| "import re\n", | |
| "from g2p_en import G2p\n", | |
| "import logging\n", | |
| "\n", | |
| "# Set up logging\n", | |
| "logging.basicConfig(level=logging.INFO)\n", | |
| "logger = logging.getLogger(__name__)\n", | |
| "\n", | |
| "class TTSSystem:\n", | |
| " def __init__(self):\n", | |
| " \"\"\"Initialize the TTS system with all required models\"\"\"\n", | |
| " try:\n", | |
| " logger.info(\"Loading models and processors...\")\n", | |
| " self.processor = SpeechT5Processor.from_pretrained(\"microsoft/speecht5_tts\")\n", | |
| " self.model = SpeechT5ForTextToSpeech.from_pretrained(\"microsoft/speecht5_tts\")\n", | |
| " self.vocoder = SpeechT5HifiGan.from_pretrained(\"microsoft/speecht5_hifigan\")\n", | |
| "\n", | |
| " # Load speaker embeddings\n", | |
| " embeddings_dataset = load_dataset(\"Matthijs/cmu-arctic-xvectors\", split=\"validation\")\n", | |
| " self.speaker_embeddings = torch.tensor(embeddings_dataset[7306][\"xvector\"]).unsqueeze(0)\n", | |
| "\n", | |
| " # Initialize G2P converter for pronunciation analysis\n", | |
| " self.g2p = G2p()\n", | |
| "\n", | |
| " logger.info(\"All models loaded successfully\")\n", | |
| " self.initialized = True\n", | |
| " except Exception as e:\n", | |
| " logger.error(f\"Error initializing TTS system: {str(e)}\")\n", | |
| " self.initialized = False\n", | |
| "\n", | |
| " def preprocess_text(self, text: str) -> str:\n", | |
| " \"\"\"Clean and normalize input text\"\"\"\n", | |
| " # Remove extra whitespace\n", | |
| " text = ' '.join(text.split())\n", | |
| " # Remove special characters except punctuation\n", | |
| " text = re.sub(r'[^a-zA-Z0-9\\s.,!?-]', '', text)\n", | |
| " return text\n", | |
| "\n", | |
| " def analyze_pronunciation(self, text: str) -> str:\n", | |
| " \"\"\"Analyze text for pronunciation complexity\"\"\"\n", | |
| " try:\n", | |
| " # Convert text to phonemes\n", | |
| " phonemes = self.g2p(text)\n", | |
| "\n", | |
| " # Count complex phonemes (examples of what might be considered complex)\n", | |
| " complex_phonemes = ['θ', 'ð', 'ʃ', 'ʒ', 'ŋ', 'ɹ']\n", | |
| " complex_count = sum(1 for p in phonemes if p in complex_phonemes)\n", | |
| "\n", | |
| " # Generate pronunciation guide\n", | |
| " pronunciation_guide = ' '.join(phonemes)\n", | |
| "\n", | |
| " return f\"Pronunciation Guide:\\n{pronunciation_guide}\\n\\nComplexity Score: {complex_count}\"\n", | |
| " except Exception as e:\n", | |
| " logger.error(f\"Error in pronunciation analysis: {str(e)}\")\n", | |
| " return \"Error analyzing pronunciation\"\n", | |
| "\n", | |
| " def text_to_speech(self, text: str) -> Tuple[Optional[int], Optional[np.ndarray], Optional[str]]:\n", | |
| " \"\"\"Convert text to speech and provide pronunciation analysis\"\"\"\n", | |
| " try:\n", | |
| " if not self.initialized:\n", | |
| " raise RuntimeError(\"TTS system not properly initialized\")\n", | |
| "\n", | |
| " # Preprocess input text\n", | |
| " text = self.preprocess_text(text)\n", | |
| " if not text.strip():\n", | |
| " raise ValueError(\"Empty text after preprocessing\")\n", | |
| "\n", | |
| " # Generate pronunciation analysis\n", | |
| " pronunciation_analysis = self.analyze_pronunciation(text)\n", | |
| "\n", | |
| " # Generate speech\n", | |
| " inputs = self.processor(text=text, return_tensors=\"pt\")\n", | |
| " speech = self.model.generate_speech(\n", | |
| " inputs[\"input_ids\"],\n", | |
| " self.speaker_embeddings,\n", | |
| " vocoder=self.vocoder\n", | |
| " )\n", | |
| "\n", | |
| " # Convert to numpy array\n", | |
| " speech_np = speech.numpy()\n", | |
| " speech_np = (speech_np * 32767).astype(np.int16)\n", | |
| "\n", | |
| " return (16000, speech_np, pronunciation_analysis)\n", | |
| "\n", | |
| " except Exception as e:\n", | |
| " logger.error(f\"Error in text_to_speech: {str(e)}\")\n", | |
| " return (None, None, f\"Error: {str(e)}\")\n", | |
| "\n", | |
| "def create_interface(tts_system: TTSSystem) -> gr.Interface:\n", | |
| " \"\"\"Create the Gradio interface\"\"\"\n", | |
| "\n", | |
| " def process_input(text: str) -> Tuple[Optional[Tuple[int, np.ndarray]], str]:\n", | |
| " sample_rate, audio_data, pronunciation = tts_system.text_to_speech(text)\n", | |
| " if sample_rate is None:\n", | |
| " return None, pronunciation\n", | |
| " return (sample_rate, audio_data), pronunciation\n", | |
| "\n", | |
| " return gr.Interface(\n", | |
| " fn=process_input,\n", | |
| " inputs=gr.Textbox(\n", | |
| " lines=3,\n", | |
| " placeholder=\"Enter text to convert to speech...\",\n", | |
| " label=\"Input Text\"\n", | |
| " ),\n", | |
| " outputs=[\n", | |
| " gr.Audio(\n", | |
| " type=\"numpy\",\n", | |
| " label=\"Generated Speech\",\n", | |
| " elem_id=\"audio_output\"\n", | |
| " ),\n", | |
| " gr.Textbox(\n", | |
| " label=\"Pronunciation Analysis\",\n", | |
| " lines=5,\n", | |
| " elem_id=\"pronunciation_output\"\n", | |
| " )\n", | |
| " ],\n", | |
| " title=\"Advanced Text-to-Speech with Pronunciation Analysis\",\n", | |
| " description=\"\"\"\n", | |
| " This demo converts text to speech using the SpeechT5 model and provides pronunciation analysis.\n", | |
| " It works best with:\n", | |
| " - Clear, well-formatted text\n", | |
| " - Standard English text\n", | |
| " - Reasonable-length sentences (1-3 sentences at a time)\n", | |
| " \"\"\",\n", | |
| " theme=\"default\",\n", | |
| " css=\".output_audio {height: 80px;} .pronunciation_output {font-family: monospace;}\"\n", | |
| " )\n", | |
| "\n", | |
| "def main():\n", | |
| " \"\"\"Main function to run the TTS demo\"\"\"\n", | |
| " try:\n", | |
| " # Initialize TTS system\n", | |
| " tts_system = TTSSystem()\n", | |
| " if not tts_system.initialized:\n", | |
| " raise RuntimeError(\"Failed to initialize TTS system\")\n", | |
| "\n", | |
| " # Create and launch interface\n", | |
| " interface = create_interface(tts_system)\n", | |
| " interface.launch(\n", | |
| " share=True,\n", | |
| " enable_queue=True,\n", | |
| " max_batch_size=4\n", | |
| " )\n", | |
| "\n", | |
| " except Exception as e:\n", | |
| " logger.error(f\"Failed to launch demo: {str(e)}\")\n", | |
| " print(f\"Error: {str(e)}\")\n", | |
| "\n", | |
| "if __name__ == \"__main__\":\n", | |
| " main()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "hsxNK9IDWCvl", | |
| "outputId": "fd9bfda5-35c0-4055-a6d0-60dce55b38de" | |
| }, | |
| "execution_count": 42, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "[nltk_data] Downloading package averaged_perceptron_tagger to\n", | |
| "[nltk_data] /root/nltk_data...\n", | |
| "[nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip.\n", | |
| "[nltk_data] Downloading package cmudict to /root/nltk_data...\n", | |
| "[nltk_data] Unzipping corpora/cmudict.zip.\n", | |
| "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n", | |
| " warnings.warn(\n", | |
| "ERROR:__main__:Failed to launch demo: Blocks.launch() got an unexpected keyword argument 'enable_queue'\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Error: Blocks.launch() got an unexpected keyword argument 'enable_queue'\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install g2p_en" | |
| ], | |
| "metadata": { | |
| "id": "jia1VzdJsYP7", | |
| "outputId": "c0f78e29-0117-431c-e637-d37abf538562", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "execution_count": 41, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Collecting g2p_en\n", | |
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| "Collecting distance>=0.1.3 (from g2p_en)\n", | |
| " Downloading Distance-0.1.3.tar.gz (180 kB)\n", | |
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| "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
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| " Building wheel for distance (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for distance: filename=Distance-0.1.3-py3-none-any.whl size=16256 sha256=5f1d87993200aafc1e2637983ffa59956a9628a338e8dfdc72027bb2d76d7a70\n", | |
| " Stored in directory: /root/.cache/pip/wheels/e8/bb/de/f71bf63559ea9a921059a5405806f7ff6ed612a9231c4a9309\n", | |
| "Successfully built distance\n", | |
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| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "TN5Mboa4sZDk" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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If You Run 1st section of code it give You English TTS Prototype
it's just hard to Implement but i try my level best For it Hope You Get it!