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{
"cells": [
{
"cell_type": "markdown",
"id": "203f9969-3d4c-466b-a7ef-c153f9e2d5c6",
"metadata": {},
"source": [
"# Without Voice Cloning"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "074a327e-1cf5-4cf3-b358-49b48589af9a",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
"The tokenizer class you load from this checkpoint is 'GPT2Tokenizer'. \n",
"The class this function is called from is 'PreTrainedTokenizerFast'.\n"
]
}
],
"source": [
"import torch\n",
"from transformers import AutoTokenizer, AutoProcessor, CsmForConditionalGeneration\n",
"from tokenizers.processors import TemplateProcessing\n",
"import soundfile as sf\n",
"\n",
"model_id = \"Marvis-AI/marvis-tts-0.25b-expressive-preview-transformers\"\n",
"device = \"cuda\"if torch.cuda.is_available() else \"cpu\"\n",
"\n",
"# load the model and the processor\n",
"processor = AutoProcessor.from_pretrained(model_id)\n",
"model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "875d88e0-d6cb-4103-b1b3-6601588e2c7e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'input_ids': tensor([[ 1, 75, 32, 77, 19556, 429, 2828, 3966, 30, 2]],\n",
" device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], device='cuda:0')}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# prepare the inputs\n",
"text = \"[0]Hello from Marvis.\" # `[0]` for speaker id 0\n",
"inputs = processor(text, add_special_tokens=True, return_tensors=\"pt\").to(device)\n",
"inputs"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "eb33ddab-44a7-4a1d-8514-7504f311e630",
"metadata": {},
"outputs": [],
"source": [
"inputs.pop(\"token_type_ids\")\n",
"# infer the model\n",
"audio = model.generate(**inputs, output_audio=True)\n",
"sf.write(\"example_without_context.wav\", audio[0].cpu(), samplerate=24_000, subtype=\"PCM_16\")"
]
},
{
"cell_type": "markdown",
"id": "2a14968a-8361-4b22-98de-c068055420e6",
"metadata": {},
"source": [
"# With Voice Cloning"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "33d6012e-3f6e-4e9b-93ad-b2085936a8d3",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from datasets import load_dataset, Audio\n",
"from transformers import AutoTokenizer, AutoProcessor, CsmForConditionalGeneration\n",
"from tokenizers.processors import TemplateProcessing\n",
"import soundfile as sf\n",
"\n",
"\n",
"# prepare the inputs\n",
"ds = load_dataset(\"hf-internal-testing/dailytalk-dummy\", split=\"train\")\n",
"# ensure the audio is 24kHz\n",
"ds = ds.cast_column(\"audio\", Audio(sampling_rate=24000))\n",
"conversation = []\n",
"\n",
"# 1. context\n",
"for text, audio, speaker_id in zip(ds[:4][\"text\"], ds[:4][\"audio\"], ds[:4][\"speaker_id\"]):\n",
" conversation.append(\n",
" {\n",
" \"role\": f\"{speaker_id}\",\n",
" \"content\": [{\"type\": \"text\", \"text\": text}, {\"type\": \"audio\", \"path\": audio[\"array\"]}],\n",
" }\n",
" )\n",
"\n",
"# 2. text prompt\n",
"conversation.append({\"role\": f\"{ds[4]['speaker_id']}\", \"content\": [{\"type\": \"text\", \"text\": ds[4][\"text\"]}]})\n",
"\n",
"inputs = processor.apply_chat_template(\n",
" conversation,\n",
" tokenize=True,\n",
" return_dict=True,\n",
").to(device)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1bada9fa-3da5-4105-9da5-3622401fbe73",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" 21198, 108, 25492, 79, 85, 395, 108, 46, 1, 75,\n",
" 33, 77, 2020, 1083, 2478, 416, 346, 5344, 47, 2]],\n",
" device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
" device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],\n",
" device='cuda:0'), 'input_values': tensor([[[ 6.1035e-05, 6.1035e-05, -3.0518e-05, ..., -6.1035e-05,\n",
" -6.1035e-05, -3.0518e-05]]], device='cuda:0'), 'input_values_cutoffs': tensor([[ 37181, 83657, 128274, 215183]], device='cuda:0')}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inputs"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "38687e7b-77ac-4543-b277-af8f8e3d310c",
"metadata": {},
"outputs": [],
"source": [
"# infer the model\n",
"# inputs.pop(\"token_type_ids\")\n",
"audio = model.generate(**inputs, output_audio=True)\n",
"sf.write(\"example_with_context.wav\", audio[0].cpu(), samplerate=24_000, subtype=\"PCM_16\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7480795e-0a35-42cc-8f1e-3c1f5dffc07e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"<|im_start|>[1]What are you working on?<|im_end|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|audio_eos|><|im_start|>[0]I'm figuring out my budget.<|im_end|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|audio_eos|><|im_start|>[1]Umm…. What budget?<|im_end|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|audio_eos|><|im_start|>[0]I'm making a shopping budget, so that I don't spend too much money.<|im_end|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|audio_eos|><|im_start|>[1]How much money can you spend?<|im_end|>\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"processor.tokenizer.decode(inputs[\"input_ids\"][0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ceb62c4d-3bfd-4805-886d-83eaac0cdf65",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|