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200 | \n", "5.451300 | \n", "0.645911 | \n", "
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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" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=600, training_loss=5.65833683013916, metrics={'train_runtime': 1146.245, 'train_samples_per_second': 16.75, 'train_steps_per_second': 0.523, 'total_flos': 1044024322429440.0, 'train_loss': 5.65833683013916, 'epoch': 10.477223427331888})" ] }, "metadata": {}, "execution_count": 36 } ] }, { "cell_type": "code", "source": [ "trainer.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 52 }, "id": "wd6JbXmP6rzs", "outputId": "af07674b-01ca-4239-cc93-a0a132fc4a51" }, "execution_count": 37, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "CommitInfo(commit_url='https://huggingface.co/DeepDiveDev/Bengali_finetuned_speecht5_tts/commit/0982ff27e1b848d2a3136c52c8f9c9cd96813d7a', commit_message='End of training', commit_description='', oid='0982ff27e1b848d2a3136c52c8f9c9cd96813d7a', pr_url=None, pr_revision=None, pr_num=None)" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 37 } ] }, { "cell_type": "markdown", "source": [ "#Inference" ], "metadata": { "id": "SZhUWEDS7fcj" } }, { "cell_type": "code", "source": [ "model = SpeechT5ForTextToSpeech.from_pretrained(\n", " \"DeepDiveDev/Bengali_finetuned_speecht5_tts\"\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "38e3e52937544e618d771677eecb3bff", "5fdeb6a293ce48668314f5a4cf0d687b", "3fdf107667a541b0945637ee1add7bc4", "b4d0abe867e440a49b97792fd92ff24f", "24808ecbfbce400c927ff97fc105d988", "80539e6a410c4c3a899deb5156e7fd60", "989665d0e97f4b809f2e132a79db276e", "f48db0eacde64f1f8f29329dcacd0e72", "facca2a4a5b1461ca070b42f4cb1c892", "c64e4b85bc17470ba2281f78ed2dd071", "adbbbc6c122248f3ad825362518e9b00", "c98600cd1fb747d983e596c539b2ffc6", "fe1653f3ced04930b8678e3260916925", "d22ae661dc884b2087b0fe70ceac9f8c", "b2a10fcabd0b4978aad299ee4e69f661", "e73ba61368794fec991c62e4985984b1", "91bb4537e7b344d5b7e4c317a885f078", "818bf1fdad494649b255b159ba05e720", "5d356bb6d9024d2a9cc2e604e833d6e3", "b14b38ca7ec44feca308fd07226a0feb", "5cdf3401323841e7b2da86fdd941febc", "0e3e6d6ea4164708a913356264bb8ce0", "db230af9f78a43d9bc98f376cc442577", "b7fc6bd8a8f148fea2d6fe197ca21162", "20e21028fbbf47a8be2352da4a47d07c", "12a3e0832238417dbc1731c4c13da818", "def6aa3570f94a3da19d6de45ecf7cb5", "947b4163505d4fcebb7757ed7ff71400", "642b10531a1142d88d1aaebe55864f9b", "62962d210bcd4cd0b84ab02f153cb297", "bb5a2fef47834688b0c058872ea728bc", "d64085f5c9334c9492b67861fde987cb", "f5d47bb728594b70a8f31adfe05b21dc" ] }, "id": "v6PbDn6C7nZB", "outputId": "a3de436a-154c-4c98-a37f-bb84785c4755" }, "execution_count": 38, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/2.11k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "38e3e52937544e618d771677eecb3bff" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/578M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "c98600cd1fb747d983e596c539b2ffc6" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "generation_config.json: 0%| | 0.00/190 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "db230af9f78a43d9bc98f376cc442577" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "example = dataset[\"test\"][200]\n", "speaker_embeddings = torch.tensor(example[\"speaker_embeddings\"]).unsqueeze(0)" ], "metadata": { "id": "ZRKKeIFq7sYT" }, "execution_count": 40, "outputs": [] }, { "cell_type": "code", "source": [ "text = \"আমি মেশিন লার্নিং নিয়ে কাজ করছি।\"" ], "metadata": { "id": "mrZE6pqO7wJ3" }, "execution_count": 41, "outputs": [] }, { "cell_type": "code", "source": [ "import re\n", "\n", "def number_to_words(number):\n", " if number < 20:\n", " return number_words[number]\n", " elif number < 100:\n", " tens, unit = divmod(number, 10)\n", " return number_words[tens * 10] + (\" \" + number_words[unit] if unit else \"\")\n", " elif number < 1000:\n", " hundreds, remainder = divmod(number, 100)\n", " return (number_words[hundreds] + \" শত\" if hundreds > 1 else \"শত\") + (\" \" + number_to_words(remainder) if remainder else \"\")\n", " elif number < 1000000:\n", " thousands, remainder = divmod(number, 1000)\n", " return (number_to_words(thousands) + \" হাজার\" if thousands > 1 else \"হাজার\") + (\" \" + number_to_words(remainder) if remainder else \"\")\n", " elif number < 1000000000:\n", " millions, remainder = divmod(number, 1000000)\n", " return number_to_words(millions) + \" মিলিয়ন\" + (\" \" + number_to_words(remainder) if remainder else \"\")\n", " elif number < 1000000000000:\n", " billions, remainder = divmod(number, 1000000000)\n", " return number_to_words(billions) + \" বিলিয়ন\" + (\" \" + number_to_words(remainder) if remainder else \"\")\n", " else:\n", " return str(number)\n", "\n", "def replace_numbers_with_words(text):\n", "\n", " def replace(match):\n", " number = int(match.group())\n", " return number_to_words(number)\n", "\n", " # Find the numbers and replace with words\n", " result = re.sub(r'\\b\\d+\\b', replace, text)\n", "\n", " return result" ], "metadata": { "id": "bykKmv6m8BTL" }, "execution_count": 42, "outputs": [] }, { "cell_type": "code", "source": [ "# Function to clean up text using the replacement pairs\n", "def cleanup_text(text):\n", " for src, dst in replacements:\n", " text = text.replace(src, dst)\n", " return text" ], "metadata": { "id": "GePrmEvz8bR1" }, "execution_count": 43, "outputs": [] }, { "cell_type": "code", "source": [ "converted_text = replace_numbers_with_words(text)\n", "cleaned_text = cleanup_text(converted_text)\n", "final_text = normalize_text(cleaned_text)\n", "final_text" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "q6cv22Ve8hTV", "outputId": "6e866ed8-a209-441f-eb12-e0940df4681f" }, "execution_count": 44, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'aam mshn lrn ny kj krchh'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 44 } ] }, { "cell_type": "code", "source": [ "inputs = processor(text=final_text, return_tensors=\"pt\")" ], "metadata": { "id": "vuIpzB5e8kG8" }, "execution_count": 45, "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": "Zuwlqj1R8oOi" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import SpeechT5HifiGan, SpeechT5ForTextToSpeech, SpeechT5Tokenizer\n", "from speechbrain.pretrained import EncoderClassifier\n", "import torch\n", "import soundfile as sf\n", "from IPython.display import Audio\n", "\n", "# Load the model, tokenizer, and vocoder\n", "model_id = \"microsoft/speecht5_tts\"\n", "model = SpeechT5ForTextToSpeech.from_pretrained(model_id)\n", "tokenizer = SpeechT5Tokenizer.from_pretrained(model_id)\n", "vocoder = SpeechT5HifiGan.from_pretrained(\"microsoft/speecht5_hifigan\")\n", "\n", "# Load pre-trained speaker embeddings using SpeechBrain (speechbrain/spkrec-xvect-voxceleb)\n", "classifier = EncoderClassifier.from_hparams(source=\"speechbrain/spkrec-xvect-voxceleb\")\n", "\n", "# Generate a dummy audio input for speaker embedding (since speaker embeddings are usually from audio)\n", "# Replace 'your_audio_file.wav' with the actual path of an audio file if you have one.\n", "signal, fs = torch.randn(1, 16000), 16000 # Example dummy audio, replace this with real audio\n", "\n", "# Extract speaker embeddings\n", "speaker_embeddings = classifier.encode_batch(signal)\n", "\n", "# Prepare your input text\n", "text = \"This is a test.\"\n", "inputs = tokenizer(text, return_tensors=\"pt\")\n", "\n", "# Generate speech\n", "try:\n", " speech = model.generate_speech(inputs[\"input_ids\"], speaker_embeddings, vocoder=vocoder)\n", "\n", " # Play the audio\n", " display(Audio(speech.numpy(), rate=16000))\n", "\n", " # Save the audio to a file (e.g., 'output.wav')\n", " sf.write('output.wav', speech.numpy(), 16000)\n", "\n", "except Exception as e:\n", " print(f\"Error generating speech: {e}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zdEKGZSABuCI", "outputId": "18463053-8992-4065-fd0a-b5967b6a98da" }, "execution_count": 53, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/speechbrain/utils/checkpoints.py:147: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " torch.load(path, map_location=device), strict=False\n", "/usr/local/lib/python3.10/dist-packages/speechbrain/processing/features.py:1215: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " stats = torch.load(path, map_location=device)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Error generating speech: expand(torch.FloatTensor{[1, 1, 1, 512]}, size=[-1, 1, -1]): the number of sizes provided (3) must be greater or equal to the number of dimensions in the tensor (4)\n" ] } ] } ] }