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{
"cells": [
{
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"execution_count": 1,
"id": "c69a901b-8c3f-4188-97bd-5594c4496ec5",
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import HfApi\n",
"api = HfApi()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8b249adc-ccd0-4145-86ce-64509ad276cf",
"metadata": {},
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{
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"text/plain": [
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from huggingface_hub import login\n",
"login()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "41815b66-b12b-45da-9a5e-92c527fe2b4a",
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{
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"text": [
"DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"POST /api/spaces/kevinwang676/ChatGLM2-SadTalker/preupload/main HTTP/1.1\" 200 19320\n",
"DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"POST /api/spaces/kevinwang676/ChatGLM2-SadTalker/preupload/main HTTP/1.1\" 200 5340\n",
"DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"POST /spaces/kevinwang676/ChatGLM2-SadTalker.git/info/lfs/objects/batch HTTP/1.1\" 200 166580\n"
]
},
{
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"text/plain": [
"mapping_00109-model.pth.tar: 0%| | 0.00/156M [00:00<?, ?B/s]"
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},
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{
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"text/plain": [
"Upload 33 LFS files: 0%| | 0/33 [00:00<?, ?it/s]"
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},
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{
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"text/plain": [
"SadTalker_V0.0.2_512.safetensors: 0%| | 0.00/725M [00:00<?, ?B/s]"
]
},
"metadata": {},
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{
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"text/plain": [
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},
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"text": [
"DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.us-east-1.amazonaws.com:443\n",
"DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.us-east-1.amazonaws.com:443\n",
"DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.us-east-1.amazonaws.com:443\n",
"DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.us-east-1.amazonaws.com:443\n",
"DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.us-east-1.amazonaws.com:443\n",
"DEBUG:urllib3.connectionpool:https://s3.us-east-1.amazonaws.com:443 \"PUT /lfs.huggingface.co/repos/ac/1f/ac1f7f23f56a89a3c14da8e85b95bc8d857ae5fd7e7ae148a35ee98cb938f101/0e063f7ff5258240bdb0f7690783a7b1374e6a4a81ce8fa33456f4cd49694340?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230719%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230719T184550Z&X-Amz-Expires=86400&X-Amz-Signature=3ad82e0f7d0a2f1734a70f22f60154669d1329ea8a2868eb71bae0ea7bcb9b41&X-Amz-SignedHeaders=host&partNumber=1&uploadId=jKqZ0xlLZ.Nh3pM_BRq2XBUonqBaADkmmmNePYJU89dX.BNLmo0v.1fvE1NNQmT2zI24Bf8Z7imF8aCEEa4l2Cq4487hlySL3jfWydxK0mnb4_sP89eGI75kqlIl5Iwi&x-id=UploadPart HTTP/1.1\" 200 0\n",
"DEBUG:urllib3.connectionpool:https://s3.us-east-1.amazonaws.com:443 \"PUT /lfs.huggingface.co/repos/ac/1f/ac1f7f23f56a89a3c14da8e85b95bc8d857ae5fd7e7ae148a35ee98cb938f101/62a1e06006cc963220f6477438518ed86e9788226c62ae382ddc42fbcefb83f1?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230719%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230719T184550Z&X-Amz-Expires=86400&X-Amz-Signature=9cf3cf540c2aa7d2bd64452f3fcaf104ba0b12348d25095ad1e9996550006dcb&X-Amz-SignedHeaders=host&partNumber=1&uploadId=EOvxs.QCMjFoSaX2AH8KHTMW_P79Al4bh5YijCFRhvM5oeWaruUy.tctM3KXHGbFIQS3dURZOfBSKJ3rhjlk2.FmTNM.KZ4ZUvox6mKsYa9pwWsYaKjXNAG1dYD1QDQp&x-id=UploadPart HTTP/1.1\" 200 0\n",
"DEBUG:urllib3.connectionpool:https://s3.us-east-1.amazonaws.com:443 \"PUT /lfs.huggingface.co/repos/ac/1f/ac1f7f23f56a89a3c14da8e85b95bc8d857ae5fd7e7ae148a35ee98cb938f101/c211f5d6de003516bf1bbda9f47049a4c9c99133b1ab565c6961e5af16477bff?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230719%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230719T184550Z&X-Amz-Expires=86400&X-Amz-Signature=13376a8536d9c252ffd8ec1aea8232da77f78eb9cf5da68f8ce8eb9ca1ae7f1e&X-Amz-SignedHeaders=host&partNumber=1&uploadId=D9ytEJa_cUdVlO_a72_Rzkj6PGBOU_wNUCBaUeXzKcJ52ISi95Gy4v72CdFz.kMubwOTxGxsjkvWpi1gvUf5G8UJezutBiCqov6ypw0ncjSIKPZtkcVgleOC0eyBygO2&x-id=UploadPart HTTP/1.1\" 200 0\n",
"DEBUG:urllib3.connectionpool:https://s3.us-east-1.amazonaws.com:443 \"PUT /lfs.huggingface.co/repos/ac/1f/ac1f7f23f56a89a3c14da8e85b95bc8d857ae5fd7e7ae148a35ee98cb938f101/84a8642468a3fcfdd9ab6be955267043116c2bec2284686a5262f1eaf017f64c?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230719%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230719T184550Z&X-Amz-Expires=86400&X-Amz-Signature=8d19a4670159d965fdb560fb18dba01fcad88c7102481ed62e2e3c1aaf7ebfa3&X-Amz-SignedHeaders=host&partNumber=1&uploadId=nXoqiKUKTc9dJ0dKP6QbdWHsGUD3Nv33UrxHumS5Hd2SapwCg50gCL5VHzR7QeeJlYQA3FD2IC52RkEk81c.1kLTeC1EfIezMODczC68Tw2NfKcH4Y9dnE5ZWpJMEaUF&x-id=UploadPart HTTP/1.1\" 200 0\n",
"DEBUG:urllib3.connectionpool:https://s3.us-east-1.amazonaws.com:443 \"PUT /lfs.huggingface.co/repos/ac/1f/ac1f7f23f56a89a3c14da8e85b95bc8d857ae5fd7e7ae148a35ee98cb938f101/7b39a86fefbc9ec6e30be8d26ee2a6aa5ffe6d235f6ab15773d01cdf348e5b20?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230719%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230719T184550Z&X-Amz-Expires=86400&X-Amz-Signature=5dc559a73e13fa2a9f2d701d07b86d00e8e63cfbef6fc160439424bcc383e410&X-Amz-SignedHeaders=host&partNumber=1&uploadId=GZNrVKwhXXhShVC8bPey4xirZcsadvW_5ISQ6PnYwuuLvkHxa8J2qmUoB0ATaUU9f9A_o.PFc8ZuEESB0.l3freU_vShVa9Y8Q1eVchh5fl9AD3CQ.r238FQPS0o8mn4&x-id=UploadPart HTTP/1.1\" 200 0\n",
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}
],
"source": [
"api.upload_folder(\n",
" folder_path=\"/workspace/SadTalker\",\n",
" repo_id=\"kevinwang676/ChatGLM2-SadTalker\",\n",
" repo_type=\"space\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c15c0fad-c4ac-4fd5-b886-89d2f97bc285",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7862\n",
"Running on public URL: https://a27628aaedb3347604.gradio.live\n",
"\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"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://a27628aaedb3347604.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"using safetensor as default\n",
"{'checkpoint': 'checkpoints/SadTalker_V0.0.2_256.safetensors', 'dir_of_BFM_fitting': 'src/config', 'audio2pose_yaml_path': 'src/config/auido2pose.yaml', 'audio2exp_yaml_path': 'src/config/auido2exp.yaml', 'use_safetensor': True, 'mappingnet_checkpoint': 'checkpoints/mapping_00229-model.pth.tar', 'facerender_yaml': 'src/config/facerender.yaml'}\n",
"/tmp/gradio/ee3af0f3e1518c57a03610bc7c5b9f774dd29f5c/image.png\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"landmark Det:: 100%|██████████| 1/1 [00:00<00:00, 26.62it/s]\n",
"3DMM Extraction In Video:: 100%|██████████| 1/1 [00:00<00:00, 119.22it/s]\n",
"mel:: 100%|██████████| 92/92 [00:00<00:00, 45445.29it/s]\n",
"audio2exp:: 100%|██████████| 10/10 [00:00<00:00, 102.60it/s]\n",
"Face Renderer:: 100%|██████████| 46/46 [00:05<00:00, 8.18it/s]\n",
"Numba: Attempted to fork from a non-main thread, the TBB library may be in an invalid state in the child process.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The generated video is named ./results/c6e207ec-5e2b-4164-8cdd-8d13be7a51f3/image##nana_speech_cut_4sec 1-0-100.mp4\n",
"face enhancer....\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Face Enhancer:: 0%| | 0/92 [00:00<?, ?it/s]Numba: Attempted to fork from a non-main thread, the TBB library may be in an invalid state in the child process.\n",
"Face Enhancer:: 100%|██████████| 92/92 [00:17<00:00, 5.18it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The generated video is named ./results/c6e207ec-5e2b-4164-8cdd-8d13be7a51f3/image##nana_speech_cut_4sec 1-0-100_enhanced.mp4\n",
"The generated video is named image##nana_speech_cut_4sec 1-0-100 in ./results/c6e207ec-5e2b-4164-8cdd-8d13be7a51f3\n"
]
}
],
"source": [
"\n",
" with gr.Row().style(equal_height=False):\n",
" with gr.Column(variant='panel'):\n",
" with gr.Tabs(elem_id=\"sadtalker_source_image\"):\n",
" with gr.TabItem('Upload image'):\n",
" with gr.Row():\n",
" source_image = gr.Image(label=\"Source image\", source=\"upload\", type=\"filepath\", elem_id=\"img2img_image\").style(width=512)\n",
"\n",
" with gr.Tabs(elem_id=\"sadtalker_driven_audio\"):\n",
" with gr.TabItem('Upload OR TTS'):\n",
" with gr.Column(variant='panel'):\n",
" driven_audio = gr.Audio(label=\"Input audio\", source=\"upload\", type=\"filepath\")\n",
"\n",
" with gr.Column(variant='panel'): \n",
" with gr.Tabs(elem_id=\"sadtalker_checkbox\"):\n",
" with gr.TabItem('Settings'):\n",
" gr.Markdown(\"need help? please visit our [best practice page](https://github.com/OpenTalker/SadTalker/blob/main/docs/best_practice.md) for more detials\")\n",
" with gr.Column(variant='panel'):\n",
" # width = gr.Slider(minimum=64, elem_id=\"img2img_width\", maximum=2048, step=8, label=\"Manually Crop Width\", value=512) # img2img_width\n",
" # height = gr.Slider(minimum=64, elem_id=\"img2img_height\", maximum=2048, step=8, label=\"Manually Crop Height\", value=512) # img2img_width\n",
" pose_style = gr.Slider(minimum=0, maximum=46, step=1, label=\"Pose style\", value=0) # \n",
" size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info=\"use 256/512 model?\") # \n",
" preprocess_type = gr.Radio(['crop', 'resize','full', 'extcrop', 'extfull'], value='crop', label='preprocess', info=\"How to handle input image?\")\n",
" is_still_mode = gr.Checkbox(label=\"Still Mode (fewer hand motion, works with preprocess `full`)\")\n",
" batch_size = gr.Slider(label=\"batch size in generation\", step=1, maximum=10, value=2)\n",
" enhancer = gr.Checkbox(label=\"GFPGAN as Face enhancer\")\n",
" submit = gr.Button('Generate', elem_id=\"sadtalker_generate\", variant='primary')\n",
" \n",
" with gr.Tabs(elem_id=\"sadtalker_genearted\"):\n",
" gen_video = gr.Video(label=\"Generated video\", format=\"mp4\").style(width=256)\n",
"\n",
" submit.click(\n",
" fn=sad_talker.test, \n",
" inputs=[source_image,\n",
" driven_audio,\n",
" preprocess_type,\n",
" is_still_mode,\n",
" enhancer,\n",
" batch_size, \n",
" size_of_image,\n",
" pose_style\n",
" ], \n",
" outputs=[gen_video]\n",
" )\n",
"\n",
"\n",
"\n",
"demo.queue().launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "4d898fab-53de-4acd-b65f-f31faf43f184",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded the voice encoder model on cuda in 0.03 seconds.\n",
"INFO:root:Loaded checkpoint 'checkpoint/freevc-24.pth' (iteration 3461)\n",
"DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"HEAD /microsoft/wavlm-large/resolve/main/config.json HTTP/1.1\" 200 0\n",
"DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"HEAD /THUDM/chatglm2-6b/resolve/main/tokenizer_config.json HTTP/1.1\" 200 0\n",
"DEBUG:filelock:Attempting to acquire lock 140053383728096 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/79b212c00ecdde03c51bb791f7fdfc25f4176ff3.lock\n",
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"DEBUG:filelock:Attempting to release lock 140053383729968 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/cdf1bf57d519abe11043e9121314e76bc0934993e649a9e438a4b0894f4e6ee8.lock\n",
"DEBUG:filelock:Lock 140053383729968 released on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/cdf1bf57d519abe11043e9121314e76bc0934993e649a9e438a4b0894f4e6ee8.lock\n",
"DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"HEAD /THUDM/chatglm2-6b/resolve/main/pytorch_model-00002-of-00007.bin HTTP/1.1\" 302 0\n",
"DEBUG:filelock:Attempting to acquire lock 140053383729968 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/1cd596bd15905248b20b755daf12a02a8fa963da09b59da7fdc896e17bfa518c.lock\n",
"DEBUG:filelock:Lock 140053383729968 acquired on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/1cd596bd15905248b20b755daf12a02a8fa963da09b59da7fdc896e17bfa518c.lock\n",
"DEBUG:urllib3.connectionpool:https://cdn-lfs.huggingface.co:443 \"GET /repos/a6/9c/a69cdfd86b704ca3e2aef541707690cf6c7500f9d7f5a0dd0f7f990f31a0b5a4/1cd596bd15905248b20b755daf12a02a8fa963da09b59da7fdc896e17bfa518c?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27pytorch_model-00002-of-00007.bin%3B+filename%3D%22pytorch_model-00002-of-00007.bin%22%3B&response-content-type=application%2Foctet-stream&Expires=1690051445&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY5MDA1MTQ0NX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy9hNi85Yy9hNjljZGZkODZiNzA0Y2EzZTJhZWY1NDE3MDc2OTBjZjZjNzUwMGY5ZDdmNWEwZGQwZjdmOTkwZjMxYTBiNWE0LzFjZDU5NmJkMTU5MDUyNDhiMjBiNzU1ZGFmMTJhMDJhOGZhOTYzZGEwOWI1OWRhN2ZkYzg5NmUxN2JmYTUxOGM~cmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=GW-3lZl~8mqmK9gmOWlbrZwBNbxDNbMfrQ~AokZDFeqD9fpD~C63eQbgc8elf5uoluXDp~FSWxXtdTM~YGJgpzQ4Ymm0xiTh3l7Aw-IOgOQ93-QG96wfWQMLF22xY1iPr1NCgsea2zrnS9XDqNol3ELxEudHzvULU2Ix6AzjfDWAhPBvYvgjSivSFOtYh6AK21s0SXa5EVyL1AIPUKyFp-nlB665jWehlOKFwagVMZUYD5NJYYcibFJRlliz0zyUU051aTQsOFCcabdbM9jsX47bLXM9sce5nQ0M5fE9Au10IvELHZAaM2-LX6S-CG-rAE4nkfjFqeid98ush45eZg__&Key-Pair-Id=KVTP0A1DKRTAX HTTP/1.1\" 200 1968299005\n"
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"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)l-00002-of-00007.bin: 0%| | 0.00/1.97G [00:00<?, ?B/s]"
]
},
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"output_type": "display_data"
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{
"name": "stdout",
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"text": [
"DEBUG:filelock:Attempting to release lock 140053383729968 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/1cd596bd15905248b20b755daf12a02a8fa963da09b59da7fdc896e17bfa518c.lock\n",
"DEBUG:filelock:Lock 140053383729968 released on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/1cd596bd15905248b20b755daf12a02a8fa963da09b59da7fdc896e17bfa518c.lock\n"
]
},
{
"ename": "OSError",
"evalue": "[Errno 28] No space left on device",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[18], line 142\u001b[0m\n\u001b[1;32m 136\u001b[0m has_cuda \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mis_available()\n\u001b[1;32m 138\u001b[0m \u001b[38;5;66;03m# has_cuda = False # force cpu\u001b[39;00m\n\u001b[1;32m 141\u001b[0m model_glm \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 142\u001b[0m \u001b[43mAutoModel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrust_remote_code\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mcuda()\u001b[38;5;241m.\u001b[39mhalf()\n\u001b[1;32m 143\u001b[0m ) \u001b[38;5;66;03m# 3.92G\u001b[39;00m\n\u001b[1;32m 145\u001b[0m model_glm \u001b[38;5;241m=\u001b[39m model_glm\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m 147\u001b[0m _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124mOverride Chatbot.postprocess\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py:488\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 486\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 487\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mregister(config\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m, model_class, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 488\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 489\u001b[0m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhub_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 490\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 491\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(config) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m 492\u001b[0m model_class \u001b[38;5;241m=\u001b[39m _get_model_class(config, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping)\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/modeling_utils.py:2610\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 2607\u001b[0m \u001b[38;5;66;03m# We'll need to download and cache each checkpoint shard if the checkpoint is sharded.\u001b[39;00m\n\u001b[1;32m 2608\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_sharded:\n\u001b[1;32m 2609\u001b[0m \u001b[38;5;66;03m# rsolved_archive_file becomes a list of files that point to the different checkpoint shards in this case.\u001b[39;00m\n\u001b[0;32m-> 2610\u001b[0m resolved_archive_file, sharded_metadata \u001b[38;5;241m=\u001b[39m \u001b[43mget_checkpoint_shard_files\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2611\u001b[0m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2612\u001b[0m \u001b[43m \u001b[49m\u001b[43mresolved_archive_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2613\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2614\u001b[0m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2615\u001b[0m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2616\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2617\u001b[0m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2618\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2619\u001b[0m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2620\u001b[0m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2621\u001b[0m \u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2622\u001b[0m \u001b[43m \u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2623\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2625\u001b[0m \u001b[38;5;66;03m# load pt weights early so that we know which dtype to init the model under\u001b[39;00m\n\u001b[1;32m 2626\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m from_pt:\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/utils/hub.py:958\u001b[0m, in \u001b[0;36mget_checkpoint_shard_files\u001b[0;34m(pretrained_model_name_or_path, index_filename, cache_dir, force_download, proxies, resume_download, local_files_only, use_auth_token, user_agent, revision, subfolder, _commit_hash)\u001b[0m\n\u001b[1;32m 955\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m shard_filename \u001b[38;5;129;01min\u001b[39;00m tqdm(shard_filenames, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownloading shards\u001b[39m\u001b[38;5;124m\"\u001b[39m, disable\u001b[38;5;241m=\u001b[39m\u001b[38;5;129;01mnot\u001b[39;00m show_progress_bar):\n\u001b[1;32m 956\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 957\u001b[0m \u001b[38;5;66;03m# Load from URL\u001b[39;00m\n\u001b[0;32m--> 958\u001b[0m cached_filename \u001b[38;5;241m=\u001b[39m \u001b[43mcached_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 959\u001b[0m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 960\u001b[0m \u001b[43m \u001b[49m\u001b[43mshard_filename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 961\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 962\u001b[0m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 963\u001b[0m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 964\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 965\u001b[0m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 966\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_auth_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 967\u001b[0m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 968\u001b[0m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 969\u001b[0m \u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 970\u001b[0m \u001b[43m \u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_commit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 971\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 972\u001b[0m \u001b[38;5;66;03m# We have already dealt with RepositoryNotFoundError and RevisionNotFoundError when getting the index, so\u001b[39;00m\n\u001b[1;32m 973\u001b[0m \u001b[38;5;66;03m# we don't have to catch them here.\u001b[39;00m\n\u001b[1;32m 974\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m EntryNotFoundError:\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/utils/hub.py:417\u001b[0m, in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, use_auth_token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash)\u001b[0m\n\u001b[1;32m 414\u001b[0m user_agent \u001b[38;5;241m=\u001b[39m http_user_agent(user_agent)\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 416\u001b[0m \u001b[38;5;66;03m# Load from URL or cache if already cached\u001b[39;00m\n\u001b[0;32m--> 417\u001b[0m resolved_file \u001b[38;5;241m=\u001b[39m \u001b[43mhf_hub_download\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 418\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath_or_repo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 419\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 420\u001b[0m \u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 421\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 422\u001b[0m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 423\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 424\u001b[0m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 425\u001b[0m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 426\u001b[0m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 427\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 428\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_auth_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 429\u001b[0m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 430\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m RepositoryNotFoundError:\n\u001b[1;32m 433\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[1;32m 434\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_or_repo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not a local folder and is not a valid model identifier \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlisted on \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://huggingface.co/models\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mIf this is a private repository, make sure to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpass a token having permission to this repo with `use_auth_token` or log in with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 437\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`huggingface-cli login` and pass `use_auth_token=True`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 438\u001b[0m )\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m 116\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:1364\u001b[0m, in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout)\u001b[0m\n\u001b[1;32m 1361\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m temp_file_manager() \u001b[38;5;28;01mas\u001b[39;00m temp_file:\n\u001b[1;32m 1362\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdownloading \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m to \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, temp_file\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1364\u001b[0m \u001b[43mhttp_get\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1365\u001b[0m \u001b[43m \u001b[49m\u001b[43murl_to_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1366\u001b[0m \u001b[43m \u001b[49m\u001b[43mtemp_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1367\u001b[0m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1368\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1369\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1370\u001b[0m \u001b[43m \u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexpected_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1371\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1373\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m local_dir \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1374\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStoring \u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m in cache at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mblob_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:544\u001b[0m, in \u001b[0;36mhttp_get\u001b[0;34m(url, temp_file, proxies, resume_size, headers, timeout, max_retries, expected_size)\u001b[0m\n\u001b[1;32m 542\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunk: \u001b[38;5;66;03m# filter out keep-alive new chunks\u001b[39;00m\n\u001b[1;32m 543\u001b[0m progress\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mlen\u001b[39m(chunk))\n\u001b[0;32m--> 544\u001b[0m \u001b[43mtemp_file\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 546\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m expected_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m expected_size \u001b[38;5;241m!=\u001b[39m temp_file\u001b[38;5;241m.\u001b[39mtell():\n\u001b[1;32m 547\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[1;32m 548\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConsistency check failed: file should be of size \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexpected_size\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m but has size\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtemp_file\u001b[38;5;241m.\u001b[39mtell()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdisplayed_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m).\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mWe are sorry for the inconvenience. Please retry download and\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 550\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m pass `force_download=True, resume_download=False` as argument.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mIf the issue persists, please let us\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 551\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m know by opening an issue on https://github.com/huggingface/huggingface_hub.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 552\u001b[0m )\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/tempfile.py:483\u001b[0m, in \u001b[0;36m_TemporaryFileWrapper.__getattr__.<locals>.func_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[38;5;129m@_functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 482\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunc_wrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 483\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mOSError\u001b[0m: [Errno 28] No space left on device"
]
}
],
"source": [
"import os\n",
"import torch\n",
"import librosa\n",
"import gradio as gr\n",
"from scipy.io.wavfile import write\n",
"from transformers import WavLMModel\n",
"\n",
"import utils\n",
"from models import SynthesizerTrn\n",
"from mel_processing import mel_spectrogram_torch\n",
"from speaker_encoder.voice_encoder import SpeakerEncoder\n",
"\n",
"import time\n",
"from textwrap import dedent\n",
"\n",
"import mdtex2html\n",
"from loguru import logger\n",
"from transformers import AutoModel, AutoTokenizer\n",
"\n",
"from tts_voice import tts_order_voice\n",
"import edge_tts\n",
"import tempfile\n",
"import anyio\n",
"\n",
"import os, sys\n",
"import gradio as gr\n",
"from src.gradio_demo import SadTalker \n",
"\n",
"\n",
"try:\n",
" import webui # in webui\n",
" in_webui = True\n",
"except:\n",
" in_webui = False\n",
"\n",
"\n",
"def toggle_audio_file(choice):\n",
" if choice == False:\n",
" return gr.update(visible=True), gr.update(visible=False)\n",
" else:\n",
" return gr.update(visible=False), gr.update(visible=True)\n",
" \n",
"def ref_video_fn(path_of_ref_video):\n",
" if path_of_ref_video is not None:\n",
" return gr.update(value=True)\n",
" else:\n",
" return gr.update(value=False)\n",
"\n",
"sad_talker = SadTalker(\"checkpoints\", \"src/config\", lazy_load=True)\n",
"\n",
"'''\n",
"def get_wavlm():\n",
" os.system('gdown https://drive.google.com/uc?id=12-cB34qCTvByWT-QtOcZaqwwO21FLSqU')\n",
" shutil.move('WavLM-Large.pt', 'wavlm')\n",
"'''\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"smodel = SpeakerEncoder('speaker_encoder/ckpt/pretrained_bak_5805000.pt')\n",
"\n",
"print(\"Loading FreeVC(24k)...\")\n",
"hps = utils.get_hparams_from_file(\"configs/freevc-24.json\")\n",
"freevc_24 = SynthesizerTrn(\n",
" hps.data.filter_length // 2 + 1,\n",
" hps.train.segment_size // hps.data.hop_length,\n",
" **hps.model).to(device)\n",
"_ = freevc_24.eval()\n",
"_ = utils.load_checkpoint(\"checkpoint/freevc-24.pth\", freevc_24, None)\n",
"\n",
"print(\"Loading WavLM for content...\")\n",
"cmodel = WavLMModel.from_pretrained(\"microsoft/wavlm-large\").to(device)\n",
" \n",
"def convert(model, src, tgt):\n",
" with torch.no_grad():\n",
" # tgt\n",
" wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate)\n",
" wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)\n",
" if model == \"FreeVC\" or model == \"FreeVC (24kHz)\":\n",
" g_tgt = smodel.embed_utterance(wav_tgt)\n",
" g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device)\n",
" else:\n",
" wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(device)\n",
" mel_tgt = mel_spectrogram_torch(\n",
" wav_tgt, \n",
" hps.data.filter_length,\n",
" hps.data.n_mel_channels,\n",
" hps.data.sampling_rate,\n",
" hps.data.hop_length,\n",
" hps.data.win_length,\n",
" hps.data.mel_fmin,\n",
" hps.data.mel_fmax\n",
" )\n",
" # src\n",
" wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate)\n",
" wav_src = torch.from_numpy(wav_src).unsqueeze(0).to(device)\n",
" c = cmodel(wav_src).last_hidden_state.transpose(1, 2).to(device)\n",
" # infer\n",
" if model == \"FreeVC\":\n",
" audio = freevc.infer(c, g=g_tgt)\n",
" elif model == \"FreeVC-s\":\n",
" audio = freevc_s.infer(c, mel=mel_tgt)\n",
" else:\n",
" audio = freevc_24.infer(c, g=g_tgt)\n",
" audio = audio[0][0].data.cpu().float().numpy()\n",
" if model == \"FreeVC\" or model == \"FreeVC-s\":\n",
" write(\"out.wav\", hps.data.sampling_rate, audio)\n",
" else:\n",
" write(\"out.wav\", 24000, audio)\n",
" out = \"out.wav\"\n",
" return out\n",
"\n",
"# GLM2\n",
"\n",
"language_dict = tts_order_voice\n",
"\n",
"# fix timezone in Linux\n",
"os.environ[\"TZ\"] = \"Asia/Shanghai\"\n",
"try:\n",
" time.tzset() # type: ignore # pylint: disable=no-member\n",
"except Exception:\n",
" # Windows\n",
" logger.warning(\"Windows, cant run time.tzset()\")\n",
"\n",
"# model_name = \"THUDM/chatglm2-6b\"\n",
"model_name = \"THUDM/chatglm2-6b\"\n",
"\n",
"RETRY_FLAG = False\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
"\n",
"# model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()\n",
"\n",
"# 4/8 bit\n",
"# model = AutoModel.from_pretrained(\"THUDM/chatglm2-6b\", trust_remote_code=True).quantize(4).cuda()\n",
"\n",
"has_cuda = torch.cuda.is_available()\n",
"\n",
"# has_cuda = False # force cpu\n",
"\n",
"\n",
"model_glm = (\n",
" AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda().half()\n",
") # 3.92G\n",
"\n",
"model_glm = model_glm.eval()\n",
"\n",
"_ = \"\"\"Override Chatbot.postprocess\"\"\"\n",
"\n",
"\n",
"def postprocess(self, y):\n",
" if y is None:\n",
" return []\n",
" for i, (message, response) in enumerate(y):\n",
" y[i] = (\n",
" None if message is None else mdtex2html.convert((message)),\n",
" None if response is None else mdtex2html.convert(response),\n",
" )\n",
" return y\n",
"\n",
"\n",
"gr.Chatbot.postprocess = postprocess\n",
"\n",
"\n",
"def parse_text(text):\n",
" \"\"\"copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/\"\"\"\n",
" lines = text.split(\"\\n\")\n",
" lines = [line for line in lines if line != \"\"]\n",
" count = 0\n",
" for i, line in enumerate(lines):\n",
" if \"```\" in line:\n",
" count += 1\n",
" items = line.split(\"`\")\n",
" if count % 2 == 1:\n",
" lines[i] = f'<pre><code class=\"language-{items[-1]}\">'\n",
" else:\n",
" lines[i] = \"<br></code></pre>\"\n",
" else:\n",
" if i > 0:\n",
" if count % 2 == 1:\n",
" line = line.replace(\"`\", r\"\\`\")\n",
" line = line.replace(\"<\", \"<\")\n",
" line = line.replace(\">\", \">\")\n",
" line = line.replace(\" \", \" \")\n",
" line = line.replace(\"*\", \"*\")\n",
" line = line.replace(\"_\", \"_\")\n",
" line = line.replace(\"-\", \"-\")\n",
" line = line.replace(\".\", \".\")\n",
" line = line.replace(\"!\", \"!\")\n",
" line = line.replace(\"(\", \"(\")\n",
" line = line.replace(\")\", \")\")\n",
" line = line.replace(\"$\", \"$\")\n",
" lines[i] = \"<br>\" + line\n",
" text = \"\".join(lines)\n",
" return text\n",
"\n",
"\n",
"def predict(\n",
" RETRY_FLAG, input, chatbot, max_length, top_p, temperature, history, past_key_values\n",
"):\n",
" try:\n",
" chatbot.append((parse_text(input), \"\"))\n",
" except Exception as exc:\n",
" logger.error(exc)\n",
" logger.debug(f\"{chatbot=}\")\n",
" _ = \"\"\"\n",
" if chatbot:\n",
" chatbot[-1] = (parse_text(input), str(exc))\n",
" yield chatbot, history, past_key_values\n",
" # \"\"\"\n",
" yield chatbot, history, past_key_values\n",
"\n",
" for response, history, past_key_values in model_glm.stream_chat(\n",
" tokenizer,\n",
" input,\n",
" history,\n",
" past_key_values=past_key_values,\n",
" return_past_key_values=True,\n",
" max_length=max_length,\n",
" top_p=top_p,\n",
" temperature=temperature,\n",
" ):\n",
" chatbot[-1] = (parse_text(input), parse_text(response))\n",
" # chatbot[-1][-1] = parse_text(response)\n",
"\n",
" yield chatbot, history, past_key_values, parse_text(response)\n",
"\n",
"\n",
"def trans_api(input, max_length=4096, top_p=0.8, temperature=0.2):\n",
" if max_length < 10:\n",
" max_length = 4096\n",
" if top_p < 0.1 or top_p > 1:\n",
" top_p = 0.85\n",
" if temperature <= 0 or temperature > 1:\n",
" temperature = 0.01\n",
" try:\n",
" res, _ = model_glm.chat(\n",
" tokenizer,\n",
" input,\n",
" history=[],\n",
" past_key_values=None,\n",
" max_length=max_length,\n",
" top_p=top_p,\n",
" temperature=temperature,\n",
" )\n",
" # logger.debug(f\"{res=} \\n{_=}\")\n",
" except Exception as exc:\n",
" logger.error(f\"{exc=}\")\n",
" res = str(exc)\n",
"\n",
" return res\n",
"\n",
"\n",
"def reset_user_input():\n",
" return gr.update(value=\"\")\n",
"\n",
"\n",
"def reset_state():\n",
" return [], [], None, \"\"\n",
"\n",
"\n",
"# Delete last turn\n",
"def delete_last_turn(chat, history):\n",
" if chat and history:\n",
" chat.pop(-1)\n",
" history.pop(-1)\n",
" return chat, history\n",
"\n",
"\n",
"# Regenerate response\n",
"def retry_last_answer(\n",
" user_input, chatbot, max_length, top_p, temperature, history, past_key_values\n",
"):\n",
" if chatbot and history:\n",
" # Removing the previous conversation from chat\n",
" chatbot.pop(-1)\n",
" # Setting up a flag to capture a retry\n",
" RETRY_FLAG = True\n",
" # Getting last message from user\n",
" user_input = history[-1][0]\n",
" # Removing bot response from the history\n",
" history.pop(-1)\n",
"\n",
" yield from predict(\n",
" RETRY_FLAG, # type: ignore\n",
" user_input,\n",
" chatbot,\n",
" max_length,\n",
" top_p,\n",
" temperature,\n",
" history,\n",
" past_key_values,\n",
" )\n",
"\n",
"# print\n",
"\n",
"def print(text):\n",
" return text\n",
"\n",
"# TTS\n",
"\n",
"async def text_to_speech_edge(text, language_code):\n",
" voice = language_dict[language_code]\n",
" communicate = edge_tts.Communicate(text, voice)\n",
" with tempfile.NamedTemporaryFile(delete=False, suffix=\".mp3\") as tmp_file:\n",
" tmp_path = tmp_file.name\n",
"\n",
" await communicate.save(tmp_path)\n",
"\n",
" return tmp_path\n",
"\n",
"\n",
"with gr.Blocks(title=\"ChatGLM2-6B-int4\", theme=gr.themes.Soft(text_size=\"sm\")) as demo:\n",
" gr.HTML(\"<center>\"\n",
" \"<h1>🥳💕🎶 - ChatGLM2 + 声音克隆：和你喜欢的角色畅所欲言吧！</h1>\"\n",
" \"</center>\")\n",
" gr.Markdown(\"## <center>💡 - 第二代ChatGLM大语言模型 + FreeVC变声，为您打造独一无二的沉浸式对话体验，支持中英双语</center>\")\n",
" gr.Markdown(\"## <center>🌊 - 更多精彩应用，尽在[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕</center>\")\n",
" gr.Markdown(\"### <center>⭐ - 如果您喜欢这个程序，欢迎给我的[Github项目](https://github.com/KevinWang676/ChatGLM2-Voice-Cloning)点赞支持！</center>\")\n",
" with gr.Tab(\"Chat\"):\n",
" with gr.Accordion(\"📒 相关信息\", open=False):\n",
" _ = f\"\"\" ChatGLM2的可选参数信息：\n",
" * Low temperature: responses will be more deterministic and focused; High temperature: responses more creative.\n",
" * Suggested temperatures -- translation: up to 0.3; chatting: > 0.4\n",
" * Top P controls dynamic vocabulary selection based on context.\\n\n",
" 如果您想让ChatGLM2进行角色扮演并与之对话，请先输入恰当的提示词，如“请你扮演成动漫角色蜡笔小新并和我进行对话”；您也可以为ChatGLM2提供自定义的角色设定\\n\n",
" 当您使用声音克隆功能时，请先在此程序的对应位置上传一段您喜欢的音频\n",
" \"\"\"\n",
" gr.Markdown(dedent(_))\n",
" chatbot = gr.Chatbot(height=300)\n",
" with gr.Row():\n",
" with gr.Column(scale=4):\n",
" with gr.Column(scale=12):\n",
" user_input = gr.Textbox(\n",
" label=\"请在此处和GLM2聊天 (按回车键即可发送)\",\n",
" placeholder=\"聊点什么吧\",\n",
" )\n",
" RETRY_FLAG = gr.Checkbox(value=False, visible=False)\n",
" with gr.Column(min_width=32, scale=1):\n",
" with gr.Row():\n",
" submitBtn = gr.Button(\"开始和GLM2交流吧\", variant=\"primary\")\n",
" deleteBtn = gr.Button(\"删除最新一轮对话\", variant=\"secondary\")\n",
" retryBtn = gr.Button(\"重新生成最新一轮对话\", variant=\"secondary\")\n",
" \n",
" with gr.Accordion(\"🔧 更多设置\", open=False):\n",
" with gr.Row():\n",
" emptyBtn = gr.Button(\"清空所有聊天记录\")\n",
" max_length = gr.Slider(\n",
" 0,\n",
" 32768,\n",
" value=8192,\n",
" step=1.0,\n",
" label=\"Maximum length\",\n",
" interactive=True,\n",
" )\n",
" top_p = gr.Slider(\n",
" 0, 1, value=0.85, step=0.01, label=\"Top P\", interactive=True\n",
" )\n",
" temperature = gr.Slider(\n",
" 0.01, 1, value=0.95, step=0.01, label=\"Temperature\", interactive=True\n",
" )\n",
"\n",
"\n",
" with gr.Row():\n",
" test1 = gr.Textbox(label=\"GLM2的最新回答 (可编辑)\", lines = 3)\n",
" with gr.Column():\n",
" language = gr.Dropdown(choices=list(language_dict.keys()), value=\"普通话 (中国大陆)-Xiaoxiao-女\", label=\"请选择文本对应的语言及您喜欢的说话人\")\n",
" tts_btn = gr.Button(\"生成对应的音频吧\", variant=\"primary\")\n",
" output_audio = gr.Audio(type=\"filepath\", label=\"为您生成的音频\", interactive=False)\n",
" \n",
" tts_btn.click(text_to_speech_edge, inputs=[test1, language], outputs=[output_audio])\n",
" \n",
" with gr.Row():\n",
" model_choice = gr.Dropdown(choices=[\"FreeVC\", \"FreeVC-s\", \"FreeVC (24kHz)\"], value=\"FreeVC (24kHz)\", label=\"Model\", visible=False) \n",
" audio1 = output_audio\n",
" audio2 = gr.Audio(label=\"请上传您喜欢的声音进行声音克隆\", type='filepath')\n",
" clone_btn = gr.Button(\"开始AI声音克隆吧\", variant=\"primary\")\n",
" audio_cloned = gr.Audio(label=\"为您生成的专属声音克隆音频\", type='filepath')\n",
" \n",
" clone_btn.click(convert, inputs=[model_choice, audio1, audio2], outputs=[audio_cloned])\n",
" \n",
" history = gr.State([])\n",
" past_key_values = gr.State(None)\n",
" \n",
" user_input.submit(\n",
" predict,\n",
" [\n",
" RETRY_FLAG,\n",
" user_input,\n",
" chatbot,\n",
" max_length,\n",
" top_p,\n",
" temperature,\n",
" history,\n",
" past_key_values,\n",
" ],\n",
" [chatbot, history, past_key_values, test1],\n",
" show_progress=\"full\",\n",
" )\n",
" submitBtn.click(\n",
" predict,\n",
" [\n",
" RETRY_FLAG,\n",
" user_input,\n",
" chatbot,\n",
" max_length,\n",
" top_p,\n",
" temperature,\n",
" history,\n",
" past_key_values,\n",
" ],\n",
" [chatbot, history, past_key_values, test1],\n",
" show_progress=\"full\",\n",
" api_name=\"predict\",\n",
" )\n",
" submitBtn.click(reset_user_input, [], [user_input])\n",
" \n",
" emptyBtn.click(\n",
" reset_state, outputs=[chatbot, history, past_key_values, test1], show_progress=\"full\"\n",
" )\n",
"\n",
" retryBtn.click(\n",
" retry_last_answer,\n",
" inputs=[\n",
" user_input,\n",
" chatbot,\n",
" max_length,\n",
" top_p,\n",
" temperature,\n",
" history,\n",
" past_key_values,\n",
" ],\n",
" # outputs = [chatbot, history, last_user_message, user_message]\n",
" outputs=[chatbot, history, past_key_values, test1],\n",
" )\n",
" deleteBtn.click(delete_last_turn, [chatbot, history], [chatbot, history])\n",
"\n",
" with gr.Accordion(\"📔 提示词示例\", open=False):\n",
" etext = \"\"\"In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. \"\"\"\n",
" examples = gr.Examples(\n",
" examples=[\n",
" [\"Explain the plot of Cinderella in a sentence.\"],\n",
" [\n",
" \"How long does it take to become proficient in French, and what are the best methods for retaining information?\"\n",
" ],\n",
" [\"What are some common mistakes to avoid when writing code?\"],\n",
" [\"Build a prompt to generate a beautiful portrait of a horse\"],\n",
" [\"Suggest four metaphors to describe the benefits of AI\"],\n",
" [\"Write a pop song about leaving home for the sandy beaches.\"],\n",
" [\"Write a summary demonstrating my ability to tame lions\"],\n",
" [\"鲁迅和周树人什么关系\"],\n",
" [\"从前有一头牛，这头牛后面有什么？\"],\n",
" [\"正无穷大加一大于正无穷大吗？\"],\n",
" [\"正无穷大加正无穷大大于正无穷大吗？\"],\n",
" [\"-2的平方根等于什么\"],\n",
" [\"树上有5只鸟，猎人开枪打死了一只。树上还有几只鸟？\"],\n",
" [\"树上有11只鸟，猎人开枪打死了一只。树上还有几只鸟？提示：需考虑鸟可能受惊吓飞走。\"],\n",
" [\"鲁迅和周树人什么关系 用英文回答\"],\n",
" [\"以红楼梦的行文风格写一张委婉的请假条。不少于320字。\"],\n",
" [f\"{etext} 翻成中文，列出3个版本\"],\n",
" [f\"{etext} \\n 翻成中文，保留原意，但使用文学性的语言。不要写解释。列出3个版本\"],\n",
" [\"js 判断一个数是不是质数\"],\n",
" [\"js 实现python 的 range(10)\"],\n",
" [\"js 实现python 的 [*(range(10)]\"],\n",
" [\"假定 1 + 2 = 4, 试求 7 + 8\"],\n",
" [\"Erkläre die Handlung von Cinderella in einem Satz.\"],\n",
" [\"Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch\"],\n",
" ],\n",
" inputs=[user_input],\n",
" examples_per_page=30,\n",
" )\n",
" \n",
" with gr.Accordion(\"For Chat/Translation API\", open=False, visible=False):\n",
" input_text = gr.Text()\n",
" tr_btn = gr.Button(\"Go\", variant=\"primary\")\n",
" out_text = gr.Text()\n",
" tr_btn.click(\n",
" trans_api,\n",
" [input_text, max_length, top_p, temperature],\n",
" out_text,\n",
" # show_progress=\"full\",\n",
" api_name=\"tr\",\n",
" )\n",
" _ = \"\"\"\n",
" input_text.submit(\n",
" trans_api,\n",
" [input_text, max_length, top_p, temperature],\n",
" out_text,\n",
" show_progress=\"full\",\n",
" api_name=\"tr1\",\n",
" )\n",
" # \"\"\"\n",
"\n",
" with gr.Tab(\"Video\"):\n",
" with gr.Row().style(equal_height=False):\n",
" with gr.Column(variant='panel'):\n",
" with gr.Tabs(elem_id=\"sadtalker_source_image\"):\n",
" with gr.TabItem('Upload image'):\n",
" with gr.Row():\n",
" source_image = gr.Image(label=\"Source image\", source=\"upload\", type=\"filepath\", elem_id=\"img2img_image\").style(width=512)\n",
" \n",
" with gr.Tabs(elem_id=\"sadtalker_driven_audio\"):\n",
" with gr.TabItem('Upload OR TTS'):\n",
" with gr.Column(variant='panel'):\n",
" driven_audio = gr.Audio(label=\"Input audio\", source=\"upload\", type=\"filepath\")\n",
" \n",
" with gr.Column(variant='panel'): \n",
" with gr.Tabs(elem_id=\"sadtalker_checkbox\"):\n",
" with gr.TabItem('Settings'):\n",
" gr.Markdown(\"need help? please visit our [best practice page](https://github.com/OpenTalker/SadTalker/blob/main/docs/best_practice.md) for more detials\")\n",
" with gr.Column(variant='panel'):\n",
" # width = gr.Slider(minimum=64, elem_id=\"img2img_width\", maximum=2048, step=8, label=\"Manually Crop Width\", value=512) # img2img_width\n",
" # height = gr.Slider(minimum=64, elem_id=\"img2img_height\", maximum=2048, step=8, label=\"Manually Crop Height\", value=512) # img2img_width\n",
" pose_style = gr.Slider(minimum=0, maximum=46, step=1, label=\"Pose style\", value=0) # \n",
" size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info=\"use 256/512 model?\") # \n",
" preprocess_type = gr.Radio(['crop', 'resize','full', 'extcrop', 'extfull'], value='crop', label='preprocess', info=\"How to handle input image?\")\n",
" is_still_mode = gr.Checkbox(label=\"Still Mode (fewer hand motion, works with preprocess `full`)\")\n",
" batch_size = gr.Slider(label=\"batch size in generation\", step=1, maximum=10, value=2)\n",
" enhancer = gr.Checkbox(label=\"GFPGAN as Face enhancer\")\n",
" submit = gr.Button('Generate', elem_id=\"sadtalker_generate\", variant='primary')\n",
" \n",
" with gr.Tabs(elem_id=\"sadtalker_genearted\"):\n",
" gen_video = gr.Video(label=\"Generated video\", format=\"mp4\").style(width=256)\n",
" \n",
" submit.click(\n",
" fn=sad_talker.test, \n",
" inputs=[source_image,\n",
" driven_audio,\n",
" preprocess_type,\n",
" is_still_mode,\n",
" enhancer,\n",
" batch_size, \n",
" size_of_image,\n",
" pose_style\n",
" ], \n",
" outputs=[gen_video]\n",
" )\n",
"\n",
"\n",
" gr.Markdown(\"### <center>注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。</center>\")\n",
" gr.Markdown(\"<center>💡 - 如何使用此程序：输入您对ChatGLM的提问后，依次点击“开始和GLM2交流吧”、“生成对应的音频吧”、“开始AI声音克隆吧”三个按键即可；使用声音克隆功能时，请先上传一段您喜欢的音频</center>\")\n",
" gr.HTML('''\n",
" <div class=\"footer\">\n",
" <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘\n",
" </p>\n",
" </div>\n",
" ''')\n",
"\n",
"\n",
"demo.queue().launch(show_error=True, debug=True, share=True)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cba1dd99-c797-4459-9a06-c294f30275de",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|