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{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KX0IBmbjeydD",
"outputId": "da61afd9-8a4e-478a-fcbc-4c7d0466ced9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"!pip --q install datasets"
]
},
{
"cell_type": "code",
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"from IPython.display import Audio\n",
"import torch\n",
"import numpy as np\n",
"import pandas as pd\n",
"import soundfile as sf\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use(\"seaborn-whitegrid\")\n"
],
"metadata": {
"id": "PbGUt9dbnriq"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Zl13ArjoGBq9",
"outputId": "eb811f29-0434-4e0e-a043-017b25d42c3d"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def DownloadDataset(username,key):\n",
" import json\n",
" keys={\"username\":username,\"key\":key}\n",
" ! mkdir ~/.kaggle\n",
" json_object = json.dumps(keys, indent=4)\n",
" with open(r\"/root/.kaggle/kaggle.json\", \"w\") as outfile:\n",
" outfile.write(json_object)\n",
" ! chmod 600 ~/.kaggle/kaggle.json\n",
" ! kaggle datasets download --unzip engmahmoodanaam/cleansada-version-01\n",
"\n",
"#.............................................\n",
"\n",
"DownloadDataset( username = \"engmahmoodanaam\",\n",
" key = \"4a457b4fc3516f9d9b913d770ea64884\"\n",
" )"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wUSLY8BmnrfA",
"outputId": "04ce4306-3c03-4c36-e9c5-4f01ffe9d908"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading cleansada-version-01.zip to /content\n",
" 98% 1.01G/1.03G [00:11<00:00, 109MB/s]\n",
"100% 1.03G/1.03G [00:11<00:00, 96.0MB/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def GetDataset(path_csv,path_audio):\n",
" df = pd.read_csv(path_csv)\n",
" audios_data = []\n",
" audios_samplerate = []\n",
" for idx, row in df.iterrows():\n",
" filename = f\"{path_audio}/{row['SegmentID']}.wav\"\n",
" audiodata, samplerate = sf.read(filename)\n",
" audios_data.append(audiodata)\n",
" audios_samplerate.append(samplerate)\n",
"\n",
" df['audio'] = audios_data\n",
" df['samplerate'] = audios_samplerate\n",
" df['text'] = df['ProcessedText']\n",
" df = df[['text','audio','samplerate']]\n",
"\n",
"\n",
" return df\n",
"\n",
"#.............................................\n"
],
"metadata": {
"id": "l74W1-6ZnrbX"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df = GetDataset(\n",
" path_csv = r'/content/cleanSada-01/data.csv',\n",
" path_audio = r'/content/cleanSada-01/Audios'\n",
" ).iloc[:70]\n",
"\n",
"df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "mfMp94fvnrXw",
"outputId": "26e03dbe-2713-4651-ad7a-6973ec6ce9cd"
},
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" text \\\n",
"0 يا سلام انتهى الموضوع \n",
"1 يعني يا ابو مسامح \n",
"2 حصة موافقة \n",
"3 والله يا هي فكرة \n",
"4 فكرة تبي تخليك تطير من الفرح \n",
"\n",
" audio samplerate \n",
"0 [3.0517578125e-05, 0.000823974609375, 0.001464... 16000 \n",
"1 [-0.0538330078125, -0.0531005859375, -0.046112... 16000 \n",
"2 [0.001007080078125, -0.004058837890625, -0.003... 16000 \n",
"3 [-0.02789306640625, -0.03045654296875, -0.0335... 16000 \n",
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],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df",
"summary": "{\n \"name\": \"df\",\n \"rows\": 70,\n \"fields\": [\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 69,\n \"samples\": [\n \"\\u0648\\u0634 \\u0633\\u0648\\u064a\\u062a \\u062d\\u062a\\u0649 \\u062a\\u0635\\u064a\\u0631\\u0648\\u0646 \\u0628\\u0647\\u0627\\u0644\\u062c\\u062d\\u0648\\u062f \\u0627\\u064a\\u0647\",\n \"\\u064a\\u0627 \\u0633\\u0644\\u0627\\u0645 \\u0627\\u0646\\u062a\\u0647\\u0649 \\u0627\\u0644\\u0645\\u0648\\u0636\\u0648\\u0639\",\n \"\\u0645\\u0627 \\u0631\\u0627\\u062d \\u0645\\u0631\\u0631 \\u0644\\u0647 \\u0628\\u0633\\u0647\\u0648\\u0644\\u0629\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"audio\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"samplerate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 16000,\n \"max\": 16000,\n \"num_unique_values\": 1,\n \"samples\": [\n 16000\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"print('df shape: ',df.shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dlgCJUn1n4SX",
"outputId": "b773b0f0-6c12-480a-c05e-25317fadfabc"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"df shape: (70, 3)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"\n",
"\n",
"---\n",
"\n"
],
"metadata": {
"id": "T1pF6RY1PKrM"
}
},
{
"cell_type": "code",
"source": [
"full_generation_index = 0\n",
"full_generation_df = df.iloc[full_generation_index:full_generation_index+5].reset_index(drop=True)\n",
"\n",
"\n",
"train_df = df.sample(frac=0.8,random_state=42)\n",
"eval_df = df.drop(train_df.index)\n",
"\n",
"\n",
"\n",
"print('train_df shape: ',train_df.shape)\n",
"print('eval_df shape: ',eval_df.shape)\n",
"print('full_generation_df shape: ',full_generation_df.shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZFG8CCvdM4hW",
"outputId": "a7e8f7d1-29c4-47d1-ab77-bf2b36b7b393"
},
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"train_df shape: (56, 3)\n",
"eval_df shape: (14, 3)\n",
"full_generation_df shape: (5, 3)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"index = 4\n",
"print(full_generation_df['text'][index])\n",
"Audio(full_generation_df['audio'][index], rate=full_generation_df['samplerate'][index])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 93
},
"id": "gbGcXR1qOIcG",
"outputId": "8b57ba29-f453-4d5d-e09a-c580b0f6f82a"
},
"execution_count": 27,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"فكرة تبي تخليك تطير من الفرح\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<IPython.lib.display.Audio object>"
],
"text/html": [
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type=\"audio/wav\" />\n",
" Your browser does not support the audio element.\n",
" </audio>\n",
" "
]
},
"metadata": {},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"source": [
"from datasets import DatasetDict,Dataset\n",
"\n",
"ds = {\n",
" \"train\": Dataset.from_pandas(train_df),\n",
" \"eval\": Dataset.from_pandas(eval_df),\n",
" \"full_generation\": Dataset.from_pandas(full_generation_df),\n",
" }\n",
"\n",
"dataset = DatasetDict(ds)\n",
"\n",
"dataset"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hYYJxFCYn5Hv",
"outputId": "fdac8ead-eacc-4b86-f0d7-bb5691973dcb"
},
"execution_count": 28,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['text', 'audio', 'samplerate', '__index_level_0__'],\n",
" num_rows: 56\n",
" })\n",
" eval: Dataset({\n",
" features: ['text', 'audio', 'samplerate', '__index_level_0__'],\n",
" num_rows: 14\n",
" })\n",
" full_generation: Dataset({\n",
" features: ['text', 'audio', 'samplerate'],\n",
" num_rows: 5\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"source": [
"dataset.save_to_disk('./dataset')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 113,
"referenced_widgets": [
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},
"id": "JKlxoOjtn5EO",
"outputId": "db5291b1-9727-4e68-bf47-fe550e8a0926"
},
"execution_count": 29,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Saving the dataset (0/1 shards): 0%| | 0/56 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "998749ebfcae4b8a87904df802acf7a9"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Saving the dataset (0/1 shards): 0%| | 0/14 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f24acb6723dc47fe9b1f114a95c7ac40"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Saving the dataset (0/1 shards): 0%| | 0/5 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "efa47ac3f3314000b04b3bd2c3ea704a"
}
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"\n",
"\n",
"---\n",
"\n"
],
"metadata": {
"id": "v9rmUIwaqA8_"
}
},
{
"cell_type": "code",
"source": [
"!git clone https://github.com/Mahmood-Anaam/VitsModelSplit.git"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EBrsfNRZqB6f",
"outputId": "a4d711d5-2418-45ba-e506-41b0b5e3f619"
},
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'VitsModelSplit'...\n",
"remote: Enumerating objects: 154, done.\u001b[K\n",
"remote: Counting objects: 100% (154/154), done.\u001b[K\n",
"remote: Compressing objects: 100% (99/99), done.\u001b[K\n",
"remote: Total 154 (delta 91), reused 112 (delta 52), pack-reused 0\u001b[K\n",
"Receiving objects: 100% (154/154), 20.97 MiB | 39.77 MiB/s, done.\n",
"Resolving deltas: 100% (91/91), done.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from transformers import AutoTokenizer\n",
"from VitsModelSplit.dataset_features_collector import run_dataset_features_collection\n",
"from VitsModelSplit.vits_model import VitsModel\n",
"from VitsModelSplit.feature_extraction import VitsFeatureExtractor\n"
],
"metadata": {
"id": "sDHkJ8ezqCs_"
},
"execution_count": 31,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model = VitsModel.from_pretrained(\"facebook/mms-tts-ara\",cache_dir=\"./\")\n",
"tokenizer = AutoTokenizer.from_pretrained(\"facebook/mms-tts-ara\",cache_dir=\"./\")\n",
"feature_extractor = VitsFeatureExtractor()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 284,
"referenced_widgets": [
"57c834af4a294010b7caed36de6b7d79",
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},
"id": "gjQ-uoS1xSKt",
"outputId": "eea81d60-c380-40ca-dc50-2ed0399cd646"
},
"execution_count": 32,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/1.64k [00:00<?, ?B/s]"
],
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"version_major": 2,
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"model.safetensors: 0%| | 0.00/145M [00:00<?, ?B/s]"
],
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"version_major": 2,
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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at facebook/mms-tts-ara were not used when initializing VitsModel: ['flow.flows.0.wavenet.in_layers.0.weight_g', 'flow.flows.0.wavenet.in_layers.0.weight_v', 'flow.flows.0.wavenet.in_layers.1.weight_g', 'flow.flows.0.wavenet.in_layers.1.weight_v', 'flow.flows.0.wavenet.in_layers.2.weight_g', 'flow.flows.0.wavenet.in_layers.2.weight_v', 'flow.flows.0.wavenet.in_layers.3.weight_g', 'flow.flows.0.wavenet.in_layers.3.weight_v', 'flow.flows.0.wavenet.res_skip_layers.0.weight_g', 'flow.flows.0.wavenet.res_skip_layers.0.weight_v', 'flow.flows.0.wavenet.res_skip_layers.1.weight_g', 'flow.flows.0.wavenet.res_skip_layers.1.weight_v', 'flow.flows.0.wavenet.res_skip_layers.2.weight_g', 'flow.flows.0.wavenet.res_skip_layers.2.weight_v', 'flow.flows.0.wavenet.res_skip_layers.3.weight_g', 'flow.flows.0.wavenet.res_skip_layers.3.weight_v', 'flow.flows.1.wavenet.in_layers.0.weight_g', 'flow.flows.1.wavenet.in_layers.0.weight_v', 'flow.flows.1.wavenet.in_layers.1.weight_g', 'flow.flows.1.wavenet.in_layers.1.weight_v', 'flow.flows.1.wavenet.in_layers.2.weight_g', 'flow.flows.1.wavenet.in_layers.2.weight_v', 'flow.flows.1.wavenet.in_layers.3.weight_g', 'flow.flows.1.wavenet.in_layers.3.weight_v', 'flow.flows.1.wavenet.res_skip_layers.0.weight_g', 'flow.flows.1.wavenet.res_skip_layers.0.weight_v', 'flow.flows.1.wavenet.res_skip_layers.1.weight_g', 'flow.flows.1.wavenet.res_skip_layers.1.weight_v', 'flow.flows.1.wavenet.res_skip_layers.2.weight_g', 'flow.flows.1.wavenet.res_skip_layers.2.weight_v', 'flow.flows.1.wavenet.res_skip_layers.3.weight_g', 'flow.flows.1.wavenet.res_skip_layers.3.weight_v', 'flow.flows.2.wavenet.in_layers.0.weight_g', 'flow.flows.2.wavenet.in_layers.0.weight_v', 'flow.flows.2.wavenet.in_layers.1.weight_g', 'flow.flows.2.wavenet.in_layers.1.weight_v', 'flow.flows.2.wavenet.in_layers.2.weight_g', 'flow.flows.2.wavenet.in_layers.2.weight_v', 'flow.flows.2.wavenet.in_layers.3.weight_g', 'flow.flows.2.wavenet.in_layers.3.weight_v', 'flow.flows.2.wavenet.res_skip_layers.0.weight_g', 'flow.flows.2.wavenet.res_skip_layers.0.weight_v', 'flow.flows.2.wavenet.res_skip_layers.1.weight_g', 'flow.flows.2.wavenet.res_skip_layers.1.weight_v', 'flow.flows.2.wavenet.res_skip_layers.2.weight_g', 'flow.flows.2.wavenet.res_skip_layers.2.weight_v', 'flow.flows.2.wavenet.res_skip_layers.3.weight_g', 'flow.flows.2.wavenet.res_skip_layers.3.weight_v', 'flow.flows.3.wavenet.in_layers.0.weight_g', 'flow.flows.3.wavenet.in_layers.0.weight_v', 'flow.flows.3.wavenet.in_layers.1.weight_g', 'flow.flows.3.wavenet.in_layers.1.weight_v', 'flow.flows.3.wavenet.in_layers.2.weight_g', 'flow.flows.3.wavenet.in_layers.2.weight_v', 'flow.flows.3.wavenet.in_layers.3.weight_g', 'flow.flows.3.wavenet.in_layers.3.weight_v', 'flow.flows.3.wavenet.res_skip_layers.0.weight_g', 'flow.flows.3.wavenet.res_skip_layers.0.weight_v', 'flow.flows.3.wavenet.res_skip_layers.1.weight_g', 'flow.flows.3.wavenet.res_skip_layers.1.weight_v', 'flow.flows.3.wavenet.res_skip_layers.2.weight_g', 'flow.flows.3.wavenet.res_skip_layers.2.weight_v', 'flow.flows.3.wavenet.res_skip_layers.3.weight_g', 'flow.flows.3.wavenet.res_skip_layers.3.weight_v', 'posterior_encoder.wavenet.in_layers.0.weight_g', 'posterior_encoder.wavenet.in_layers.0.weight_v', 'posterior_encoder.wavenet.in_layers.1.weight_g', 'posterior_encoder.wavenet.in_layers.1.weight_v', 'posterior_encoder.wavenet.in_layers.10.weight_g', 'posterior_encoder.wavenet.in_layers.10.weight_v', 'posterior_encoder.wavenet.in_layers.11.weight_g', 'posterior_encoder.wavenet.in_layers.11.weight_v', 'posterior_encoder.wavenet.in_layers.12.weight_g', 'posterior_encoder.wavenet.in_layers.12.weight_v', 'posterior_encoder.wavenet.in_layers.13.weight_g', 'posterior_encoder.wavenet.in_layers.13.weight_v', 'posterior_encoder.wavenet.in_layers.14.weight_g', 'posterior_encoder.wavenet.in_layers.14.weight_v', 'posterior_encoder.wavenet.in_layers.15.weight_g', 'posterior_encoder.wavenet.in_layers.15.weight_v', 'posterior_encoder.wavenet.in_layers.2.weight_g', 'posterior_encoder.wavenet.in_layers.2.weight_v', 'posterior_encoder.wavenet.in_layers.3.weight_g', 'posterior_encoder.wavenet.in_layers.3.weight_v', 'posterior_encoder.wavenet.in_layers.4.weight_g', 'posterior_encoder.wavenet.in_layers.4.weight_v', 'posterior_encoder.wavenet.in_layers.5.weight_g', 'posterior_encoder.wavenet.in_layers.5.weight_v', 'posterior_encoder.wavenet.in_layers.6.weight_g', 'posterior_encoder.wavenet.in_layers.6.weight_v', 'posterior_encoder.wavenet.in_layers.7.weight_g', 'posterior_encoder.wavenet.in_layers.7.weight_v', 'posterior_encoder.wavenet.in_layers.8.weight_g', 'posterior_encoder.wavenet.in_layers.8.weight_v', 'posterior_encoder.wavenet.in_layers.9.weight_g', 'posterior_encoder.wavenet.in_layers.9.weight_v', 'posterior_encoder.wavenet.res_skip_layers.0.weight_g', 'posterior_encoder.wavenet.res_skip_layers.0.weight_v', 'posterior_encoder.wavenet.res_skip_layers.1.weight_g', 'posterior_encoder.wavenet.res_skip_layers.1.weight_v', 'posterior_encoder.wavenet.res_skip_layers.10.weight_g', 'posterior_encoder.wavenet.res_skip_layers.10.weight_v', 'posterior_encoder.wavenet.res_skip_layers.11.weight_g', 'posterior_encoder.wavenet.res_skip_layers.11.weight_v', 'posterior_encoder.wavenet.res_skip_layers.12.weight_g', 'posterior_encoder.wavenet.res_skip_layers.12.weight_v', 'posterior_encoder.wavenet.res_skip_layers.13.weight_g', 'posterior_encoder.wavenet.res_skip_layers.13.weight_v', 'posterior_encoder.wavenet.res_skip_layers.14.weight_g', 'posterior_encoder.wavenet.res_skip_layers.14.weight_v', 'posterior_encoder.wavenet.res_skip_layers.15.weight_g', 'posterior_encoder.wavenet.res_skip_layers.15.weight_v', 'posterior_encoder.wavenet.res_skip_layers.2.weight_g', 'posterior_encoder.wavenet.res_skip_layers.2.weight_v', 'posterior_encoder.wavenet.res_skip_layers.3.weight_g', 'posterior_encoder.wavenet.res_skip_layers.3.weight_v', 'posterior_encoder.wavenet.res_skip_layers.4.weight_g', 'posterior_encoder.wavenet.res_skip_layers.4.weight_v', 'posterior_encoder.wavenet.res_skip_layers.5.weight_g', 'posterior_encoder.wavenet.res_skip_layers.5.weight_v', 'posterior_encoder.wavenet.res_skip_layers.6.weight_g', 'posterior_encoder.wavenet.res_skip_layers.6.weight_v', 'posterior_encoder.wavenet.res_skip_layers.7.weight_g', 'posterior_encoder.wavenet.res_skip_layers.7.weight_v', 'posterior_encoder.wavenet.res_skip_layers.8.weight_g', 'posterior_encoder.wavenet.res_skip_layers.8.weight_v', 'posterior_encoder.wavenet.res_skip_layers.9.weight_g', 'posterior_encoder.wavenet.res_skip_layers.9.weight_v']\n",
"- This IS expected if you are initializing VitsModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing VitsModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of VitsModel were not initialized from the model checkpoint at facebook/mms-tts-ara and are newly initialized: ['discriminator.discriminators.0.convs.0.bias', 'discriminator.discriminators.0.convs.0.weight', 'discriminator.discriminators.0.convs.1.bias', 'discriminator.discriminators.0.convs.1.weight', 'discriminator.discriminators.0.convs.2.bias', 'discriminator.discriminators.0.convs.2.weight', 'discriminator.discriminators.0.convs.3.bias', 'discriminator.discriminators.0.convs.3.weight', 'discriminator.discriminators.0.convs.4.bias', 'discriminator.discriminators.0.convs.4.weight', 'discriminator.discriminators.0.convs.5.bias', 'discriminator.discriminators.0.convs.5.weight', 'discriminator.discriminators.0.final_conv.bias', 'discriminator.discriminators.0.final_conv.weight', 'discriminator.discriminators.1.convs.0.bias', 'discriminator.discriminators.1.convs.0.weight', 'discriminator.discriminators.1.convs.1.bias', 'discriminator.discriminators.1.convs.1.weight', 'discriminator.discriminators.1.convs.2.bias', 'discriminator.discriminators.1.convs.2.weight', 'discriminator.discriminators.1.convs.3.bias', 'discriminator.discriminators.1.convs.3.weight', 'discriminator.discriminators.1.convs.4.bias', 'discriminator.discriminators.1.convs.4.weight', 'discriminator.discriminators.1.final_conv.bias', 'discriminator.discriminators.1.final_conv.weight', 'discriminator.discriminators.2.convs.0.bias', 'discriminator.discriminators.2.convs.0.weight', 'discriminator.discriminators.2.convs.1.bias', 'discriminator.discriminators.2.convs.1.weight', 'discriminator.discriminators.2.convs.2.bias', 'discriminator.discriminators.2.convs.2.weight', 'discriminator.discriminators.2.convs.3.bias', 'discriminator.discriminators.2.convs.3.weight', 'discriminator.discriminators.2.convs.4.bias', 'discriminator.discriminators.2.convs.4.weight', 'discriminator.discriminators.2.final_conv.bias', 'discriminator.discriminators.2.final_conv.weight', 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"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"output_type": "display_data",
"data": {
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},
{
"cell_type": "code",
"source": [
"run_dataset_features_collection(\n",
" dataset_dir = r'/content/dataset',\n",
" train_split_name='train',\n",
" eval_split_name='eval',\n",
" full_generation_name = 'full_generation',\n",
" tokenizer=tokenizer,\n",
" model=model,\n",
" feature_extractor=feature_extractor,\n",
" train_batch_size=10,\n",
" eval_batch_size=10,\n",
" output_dir= '/content/drive/MyDrive/FeaturesCollectionDataset'\n",
" )"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 713,
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"outputId": "d7e962da-0a89-4796-dddc-31fb306054d9"
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"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Parameter 'function'=<bound method DataSetFeaturesCollector.prepare_dataset of DataSetFeaturesCollector()> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n",
"WARNING:datasets.fingerprint:Parameter 'function'=<bound method DataSetFeaturesCollector.prepare_dataset of DataSetFeaturesCollector()> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n"
]
},
{
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"data": {
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"preprocess: 0%| | 0/10 [00:00<?, ? examples/s]"
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"text": [
"Train Dataset - batch 0, waveform torch.Size([10, 83040, 1]),tokens torch.Size([10, 145])... \n"
]
},
{
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"data": {
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{
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"name": "stdout",
"text": [
"Train Dataset - batch 1, waveform torch.Size([10, 135680, 1]),tokens torch.Size([10, 196])... \n"
]
},
{
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"data": {
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"text": [
"Train Dataset - batch 2, waveform torch.Size([10, 52036, 1]),tokens torch.Size([10, 114])... \n"
]
},
{
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"data": {
"text/plain": [
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{
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"text": [
"Train Dataset - batch 3, waveform torch.Size([10, 62720, 1]),tokens torch.Size([10, 108])... \n"
]
},
{
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"data": {
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"text": [
"Train Dataset - batch 4, waveform torch.Size([10, 138560, 1]),tokens torch.Size([10, 181])... \n"
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},
{
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"data": {
"text/plain": [
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{
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"name": "stdout",
"text": [
"Train Dataset - batch 5, waveform torch.Size([6, 46560, 1]),tokens torch.Size([6, 77])... \n"
]
},
{
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"data": {
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"text": [
"Eval Dataset - batch 0, waveform torch.Size([10, 67200, 1]),tokens torch.Size([10, 109])... \n"
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},
{
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"data": {
"text/plain": [
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{
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"text": [
"Eval Dataset - batch 1, waveform torch.Size([4, 67520, 1]),tokens torch.Size([4, 122])... \n"
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},
{
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"data": {
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{
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"text": [
"Full Generation Dataset - batch 0, waveform torch.Size([1, 28640, 1]),tokens torch.Size([1, 43])... \n"
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},
{
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"data": {
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{
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"text": [
"Full Generation Dataset - batch 1, waveform torch.Size([1, 28960, 1]),tokens torch.Size([1, 35])... \n"
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},
{
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"data": {
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{
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"text": [
"Full Generation Dataset - batch 2, waveform torch.Size([1, 23200, 1]),tokens torch.Size([1, 21])... \n"
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},
{
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"data": {
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{
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"text": [
"Full Generation Dataset - batch 3, waveform torch.Size([1, 21440, 1]),tokens torch.Size([1, 33])... \n"
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},
{
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"data": {
"text/plain": [
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{
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"text": [
"Full Generation Dataset - batch 4, waveform torch.Size([1, 43360, 1]),tokens torch.Size([1, 57])... \n"
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},
{
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"source": [
"\n",
"\n",
"---\n",
"\n"
],
"metadata": {
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}
]
} |