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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.5/59.5 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.4/50.4 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.5/87.5 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.5/84.5 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m74.5/74.5 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m70.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install transformers datasets evaluate accelerate \"rich[jupyter]\" gradio --upgrade -qqq"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!wget -O image.jpg \"https://images.unsplash.com/photo-1608848461950-0fe51dfc41cb?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxleHBsb3JlLWZlZWR8Mnx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=900&q=60\""
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "mavYr30kDF8A",
+ "outputId": "b56c1718-2a90-4f97-ffd5-1ca32fd199a2"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--2023-07-25 18:15:06-- https://images.unsplash.com/photo-1608848461950-0fe51dfc41cb?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxleHBsb3JlLWZlZWR8Mnx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=900&q=60\n",
+ "Resolving images.unsplash.com (images.unsplash.com)... 146.75.34.208, 2a04:4e42:78::720\n",
+ "Connecting to images.unsplash.com (images.unsplash.com)|146.75.34.208|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 172377 (168K) [image/jpeg]\n",
+ "Saving to: ‘image.jpg’\n",
+ "\n",
+ "\rimage.jpg 0%[ ] 0 --.-KB/s \rimage.jpg 100%[===================>] 168.34K --.-KB/s in 0.004s \n",
+ "\n",
+ "2023-07-25 18:15:06 (38.9 MB/s) - ‘image.jpg’ saved [172377/172377]\n",
+ "\n"
]
}
]
},
+ {
+ "cell_type": "code",
+ "source": [
+ "%load_ext rich"
+ ],
+ "metadata": {
+ "id": "muJ-pSMrAMyn"
+ },
+ "execution_count": 3,
+ "outputs": []
+ },
{
"cell_type": "markdown",
"source": [
@@ -2456,7 +13429,7 @@
"metadata": {
"id": "sA8jVXXLAwoX"
},
- "execution_count": 57,
+ "execution_count": 4,
"outputs": []
},
{
@@ -2466,23 +13439,139 @@
],
"metadata": {
"colab": {
- "base_uri": "https://localhost:8080/"
+ "base_uri": "https://localhost:8080/",
+ "height": 275,
+ "referenced_widgets": [
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+ "00c3533bebb3494fb6ad8deaa62c8572",
+ "99ce9d5fce0a457a86fea8106409a842",
+ "a4fe98df119647479579c53ca02d6896",
+ "2ab3ef0ea6324b409801613a7d2ff4ee"
+ ]
},
"id": "3IWYR58cAzvo",
- "outputId": "3c149bd6-d9fa-42c1-fa49-8c6dcc24e8b4"
+ "outputId": "cd16b51b-31a7-4e94-981a-2b69ea36fc2e"
},
- "execution_count": 58,
+ "execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision f2482bf (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english).\n",
- "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
+ "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)lve/main/config.json: 0%| | 0.00/998 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "89987d9caa2b4444bf94aefe33a0bc93"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading pytorch_model.bin: 0%| | 0.00/1.33G [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "280faa69c8274efc9e839afd5dcafc8c"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
"Some weights of the model checkpoint at dbmdz/bert-large-cased-finetuned-conll03-english were not used when initializing BertForTokenClassification: ['bert.pooler.dense.weight', 'bert.pooler.dense.bias']\n",
"- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)okenizer_config.json: 0%| | 0.00/60.0 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "0ac5acfc8ac14d369738165c9c3a992a"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)solve/main/vocab.txt: 0%| | 0.00/213k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b63c536f4de44e3e927a562e16e88c42"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'\n"
+ ]
}
]
},
@@ -2494,7 +13583,7 @@
"metadata": {
"id": "gXfzqTeoA3UN"
},
- "execution_count": 59,
+ "execution_count": 6,
"outputs": []
},
{
@@ -2507,9 +13596,9 @@
"base_uri": "https://localhost:8080/"
},
"id": "YOE987dCA-xO",
- "outputId": "59620863-674e-4df5-d06c-3daa8733a5ae"
+ "outputId": "58e5041b-ae89-48aa-d6ec-61d8f63a8f49"
},
- "execution_count": 60,
+ "execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
@@ -2524,7 +13613,7 @@
]
},
"metadata": {},
- "execution_count": 60
+ "execution_count": 7
}
]
},
@@ -2544,12 +13633,59 @@
],
"metadata": {
"colab": {
- "base_uri": "https://localhost:8080/"
+ "base_uri": "https://localhost:8080/",
+ "height": 238,
+ "referenced_widgets": [
+ "7368f0a08c3b4ba5930cf438d936d88b",
+ "64359146a1da4584a24d189be13d7963",
+ "1b14d3cfadbb4c47977934dee250b30d",
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+ "393460223fdd4c4ba49f9667f4ea8839",
+ "9d8318439053445b9b8339deb12ec299",
+ "905e986c55344cffa783355a3cbecc0f"
+ ]
},
"id": "39fPSf2dBAGi",
- "outputId": "8f08b015-447d-4565-e79e-694033468bf0"
+ "outputId": "f1fd583a-6224-4ac2-93c1-484cf7c40422"
},
- "execution_count": 61,
+ "execution_count": 8,
"outputs": [
{
"output_type": "stream",
@@ -2558,6 +13694,70 @@
"No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).\n",
"Using a pipeline without specifying a model name and revision in production is not recommended.\n"
]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)lve/main/config.json: 0%| | 0.00/629 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "7368f0a08c3b4ba5930cf438d936d88b"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading model.safetensors: 0%| | 0.00/268M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "afb427fb27784b11a31829150fdc3824"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)okenizer_config.json: 0%| | 0.00/48.0 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "9cb5cb95588443e5b8d0e5c883248408"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)solve/main/vocab.txt: 0%| | 0.00/232k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "0c229455bae14acbaf86297fba979ee0"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
+ "pip install xformers.\n"
+ ]
}
]
},
@@ -2571,9 +13771,9 @@
"base_uri": "https://localhost:8080/"
},
"id": "b6aBtqX2CCys",
- "outputId": "f534b163-69eb-4d16-8305-862ecf1fb8e6"
+ "outputId": "7c998c13-d8d1-4d08-f4e7-fedf4fd51246"
},
- "execution_count": 62,
+ "execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
@@ -2583,7 +13783,7 @@
]
},
"metadata": {},
- "execution_count": 62
+ "execution_count": 9
}
]
},
@@ -2595,7 +13795,7 @@
"metadata": {
"id": "NafTqIXPCLYt"
},
- "execution_count": 63,
+ "execution_count": 10,
"outputs": []
},
{
@@ -2608,9 +13808,9 @@
"base_uri": "https://localhost:8080/"
},
"id": "-aLTWrFxCSL2",
- "outputId": "8028762d-09c4-45a0-f657-c710ba317666"
+ "outputId": "18a6a1bc-2ed8-443f-afdb-7cb6d7491ee5"
},
- "execution_count": 64,
+ "execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
@@ -2620,7 +13820,7 @@
]
},
"metadata": {},
- "execution_count": 64
+ "execution_count": 11
}
]
},
@@ -2685,22 +13885,175 @@
],
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+ "base_uri": "https://localhost:8080/",
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},
"id": "_z7LAokSsNF4",
- "outputId": "0e3095fb-011a-4019-db79-79e8caf663b1"
+ "outputId": "06b6fe74-506d-46c5-ef2d-1adeb3f99c4b"
},
- "execution_count": 65,
+ "execution_count": 12,
"outputs": [
{
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "All model checkpoint layers were used when initializing TFRobertaForSequenceClassification.\n",
- "\n",
- "All the layers of TFRobertaForSequenceClassification were initialized from the model checkpoint at arpanghoshal/EmoRoBERTa.\n",
- "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaForSequenceClassification for predictions without further training.\n"
- ]
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)lve/main/config.json: 0%| | 0.00/1.72k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "6070de4148214a389aadd4b2c1e35f3f"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading tf_model.h5: 0%| | 0.00/501M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "4441bb86d854403b8a16e7c59db1b2d3"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "All model checkpoint layers were used when initializing TFRobertaForSequenceClassification.\n",
+ "\n",
+ "All the layers of TFRobertaForSequenceClassification were initialized from the model checkpoint at arpanghoshal/EmoRoBERTa.\n",
+ "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaForSequenceClassification for predictions without further training.\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)okenizer_config.json: 0%| | 0.00/25.0 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "173b01f1ec744de49a1a677d027bebc4"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)olve/main/vocab.json: 0%| | 0.00/798k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "bf3337ddb9ec49f8b6260eed825fdfc6"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)olve/main/merges.txt: 0%| | 0.00/456k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "938df607799e498bb312fcde3d3e0a94"
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+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)cial_tokens_map.json: 0%| | 0.00/239 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "c72131d522ef461c905040e3f39f353f"
+ }
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+ "metadata": {}
}
]
},
@@ -2714,19 +14067,19 @@
"base_uri": "https://localhost:8080/"
},
"id": "WGbA-0F3s00E",
- "outputId": "a1e6adaa-97e5-4456-fe10-655d6ad30f6f"
+ "outputId": "d161a88a-6d55-4140-aa23-d4fba66e1ecf"
},
- "execution_count": 66,
+ "execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "[{'label': 'annoyance', 'score': 0.7900210618972778}]"
+ "[{'label': 'annoyance', 'score': 0.790020763874054}]"
]
},
"metadata": {},
- "execution_count": 66
+ "execution_count": 13
}
]
},
@@ -2738,7 +14091,7 @@
"metadata": {
"id": "YLJgy6B2BIxY"
},
- "execution_count": 67,
+ "execution_count": 14,
"outputs": []
},
{
@@ -2751,19 +14104,19 @@
"base_uri": "https://localhost:8080/"
},
"id": "UyWywYVgBKMp",
- "outputId": "f727756c-f308-41f0-e283-1564cd338e68"
+ "outputId": "9d646a5e-5c11-4bb5-a710-1fde95dd66d8"
},
- "execution_count": 68,
+ "execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "[{'label': 'annoyance', 'score': 0.7900210618972778}]"
+ "[{'label': 'annoyance', 'score': 0.790020763874054}]"
]
},
"metadata": {},
- "execution_count": 68
+ "execution_count": 15
}
]
},
@@ -2777,10 +14130,193 @@
"model = AutoModelForSequenceClassification.from_pretrained('mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis')"
],
"metadata": {
- "id": "PTC-eB_LQmCx"
+ "id": "PTC-eB_LQmCx",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 241,
+ "referenced_widgets": [
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- "execution_count": 69,
- "outputs": []
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)okenizer_config.json: 0%| | 0.00/333 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ "metadata": {}
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+ "data": {
+ "text/plain": [
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
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+ "metadata": {}
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+ "data": {
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+ "Downloading (…)olve/main/merges.txt: 0%| | 0.00/456k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
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+ "metadata": {}
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+ {
+ "output_type": "display_data",
+ "data": {
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+ "Downloading (…)/main/tokenizer.json: 0%| | 0.00/1.36M [00:00, ?B/s]"
+ ],
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+ "data": {
+ "text/plain": [
+ "Downloading (…)cial_tokens_map.json: 0%| | 0.00/239 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)lve/main/config.json: 0%| | 0.00/933 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "f73b603e479d432ba0bd5c90fe4c6e65"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
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{
"cell_type": "code",
@@ -2793,7 +14329,7 @@
"metadata": {
"id": "lUQTxWxxQ0ao"
},
- "execution_count": 70,
+ "execution_count": 17,
"outputs": []
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{
@@ -2809,9 +14345,9 @@
"height": 36
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"id": "e5seLIeXRb2Z",
- "outputId": "9ac6999d-ebf5-46cf-a930-b0327e7f427a"
+ "outputId": "da264074-fedf-4343-97f5-21c009a43e7d"
},
- "execution_count": 71,
+ "execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
@@ -2824,7 +14360,7 @@
}
},
"metadata": {},
- "execution_count": 71
+ "execution_count": 18
}
]
},
@@ -2951,10 +14487,293 @@
"pipe = pipeline(\"automatic-speech-recognition\", model=\"openai/whisper-base\")"
],
"metadata": {
- "id": "TXjtRVS-CcUH"
+ "colab": {
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},
- "execution_count": 76,
- "outputs": []
+ "execution_count": 19,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)lve/main/config.json: 0%| | 0.00/1.98k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ "data": {
+ "text/plain": [
+ "Downloading pytorch_model.bin: 0%| | 0.00/290M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ "data": {
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+ "Downloading (…)neration_config.json: 0%| | 0.00/3.51k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ "Downloading (…)okenizer_config.json: 0%| | 0.00/840 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ "data": {
+ "text/plain": [
+ "Downloading (…)olve/main/vocab.json: 0%| | 0.00/1.04M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)/main/tokenizer.json: 0%| | 0.00/2.20M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
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+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ "Downloading (…)main/normalizer.json: 0%| | 0.00/52.7k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)in/added_tokens.json: 0%| | 0.00/2.08k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)cial_tokens_map.json: 0%| | 0.00/2.08k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "9c398cabff8842889db81a27eb6ef2da"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)rocessor_config.json: 0%| | 0.00/185k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
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+ }
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+ "metadata": {}
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+ ]
},
{
"cell_type": "code",
@@ -2968,60 +14787,36 @@
"gr.Interface(\n",
" fn=transcribe,\n",
" inputs=gr.Audio(source=\"microphone\", type=\"filepath\"),\n",
- " outputs=\"text\").launch()"
+ " outputs=\"text\").launch(share=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 621
+ "height": 615
},
"id": "vJ7vte3dC0va",
- "outputId": "00196f75-866f-4e54-f1e6-cbaebd3bbf83"
+ "outputId": "9b2acab2-5c13-4eb7-af8d-d885e26f4085"
},
- "execution_count": 77,
+ "execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
- "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
+ "Running on public URL: https://d3d4776551b9effc78.gradio.live\n",
"\n",
- "To create a public link, set `share=True` in `launch()`.\n"
+ "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
- ""
+ ""
],
- "application/javascript": [
- "(async (port, path, width, height, cache, element) => {\n",
- " if (!google.colab.kernel.accessAllowed && !cache) {\n",
- " return;\n",
- " }\n",
- " element.appendChild(document.createTextNode(''));\n",
- " const url = await google.colab.kernel.proxyPort(port, {cache});\n",
- "\n",
- " const external_link = document.createElement('div');\n",
- " external_link.innerHTML = `\n",
- " \n",
- " `;\n",
- " element.appendChild(external_link);\n",
- "\n",
- " const iframe = document.createElement('iframe');\n",
- " iframe.src = new URL(path, url).toString();\n",
- " iframe.height = height;\n",
- " iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
- " iframe.width = width;\n",
- " iframe.style.border = 0;\n",
- " element.appendChild(iframe);\n",
- " })(7861, \"/\", \"100%\", 500, false, window.element)"
+ "text/html": [
+ ""
]
},
"metadata": {}
@@ -3032,7 +14827,7 @@
"text/plain": []
},
"metadata": {},
- "execution_count": 77
+ "execution_count": 21
}
]
},