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OpenVoice/demo_part1.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "b6ee1ede",
"metadata": {},
"source": [
"## Voice Style Control Demo"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"id": "b7f043ee",
"metadata": {},
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"outputs": [],
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"source": [
"import os\n",
"import torch\n",
"from openvoice import se_extractor\n",
"from openvoice.api import BaseSpeakerTTS, ToneColorConverter"
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]
},
{
"cell_type": "markdown",
"id": "15116b59",
"metadata": {},
"source": [
"### Initialization"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"id": "aacad912",
"metadata": {},
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"outputs": [],
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"source": [
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"ckpt_base = 'checkpoints/base_speakers/EN'\n",
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"ckpt_converter = 'checkpoints/converter'\n",
"device=\"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
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"output_dir = 'outputs'\n",
"\n",
"base_speaker_tts = BaseSpeakerTTS(f'{ckpt_base}/config.json', device=device)\n",
"base_speaker_tts.load_ckpt(f'{ckpt_base}/checkpoint.pth')\n",
"\n",
"tone_color_converter = ToneColorConverter(f'{ckpt_converter}/config.json', device=device)\n",
"tone_color_converter.load_ckpt(f'{ckpt_converter}/checkpoint.pth')\n",
"\n",
"os.makedirs(output_dir, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"id": "7f67740c",
"metadata": {},
"source": [
"### Obtain Tone Color Embedding"
]
},
{
"cell_type": "markdown",
"id": "f8add279",
"metadata": {},
"source": [
"The `source_se` is the tone color embedding of the base speaker. \n",
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"It is an average of multiple sentences generated by the base speaker. We directly provide the result here but\n",
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"the readers feel free to extract `source_se` by themselves."
]
},
{
"cell_type": "code",
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"execution_count": null,
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"id": "63ff6273",
"metadata": {},
"outputs": [],
"source": [
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"source_se = torch.load(f'{ckpt_base}/en_default_se.pth').to(device)"
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]
},
{
"cell_type": "markdown",
"id": "4f71fcc3",
"metadata": {},
"source": [
"The `reference_speaker.mp3` below points to the short audio clip of the reference whose voice we want to clone. We provide an example here. If you use your own reference speakers, please **make sure each speaker has a unique filename.** The `se_extractor` will save the `targeted_se` using the filename of the audio and **will not automatically overwrite.**"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"id": "55105eae",
"metadata": {},
"outputs": [],
"source": [
"reference_speaker = 'resources/example_reference.mp3'\n",
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"target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, target_dir='processed', vad=True)"
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]
},
{
"cell_type": "markdown",
"id": "a40284aa",
"metadata": {},
"source": [
"### Inference"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"id": "73dc1259",
"metadata": {},
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"outputs": [],
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"source": [
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"save_path = f'{output_dir}/output_en_default.wav'\n",
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"\n",
"# Run the base speaker tts\n",
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"text = \"This audio is generated by OpenVoice.\"\n",
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"src_path = f'{output_dir}/tmp.wav'\n",
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"base_speaker_tts.tts(text, src_path, speaker='default', language='English', speed=1.0)\n",
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"\n",
"# Run the tone color converter\n",
"encode_message = \"@MyShell\"\n",
"tone_color_converter.convert(\n",
" audio_src_path=src_path, \n",
" src_se=source_se, \n",
" tgt_se=target_se, \n",
" output_path=save_path,\n",
" message=encode_message)"
]
},
{
"cell_type": "markdown",
"id": "6e3ea28a",
"metadata": {},
"source": [
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"**Try with different styles and speed.** The style can be controlled by the `speaker` parameter in the `base_speaker_tts.tts` method. Available choices: friendly, cheerful, excited, sad, angry, terrified, shouting, whispering. Note that the tone color embedding need to be updated. The speed can be controlled by the `speed` parameter. Let's try whispering with speed 0.9."
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"id": "fd022d38",
"metadata": {},
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"outputs": [],
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"source": [
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"source_se = torch.load(f'{ckpt_base}/en_style_se.pth').to(device)\n",
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"save_path = f'{output_dir}/output_whispering.wav'\n",
"\n",
"# Run the base speaker tts\n",
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"text = \"This audio is generated by OpenVoice.\"\n",
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"src_path = f'{output_dir}/tmp.wav'\n",
"base_speaker_tts.tts(text, src_path, speaker='whispering', language='English', speed=0.9)\n",
"\n",
"# Run the tone color converter\n",
"encode_message = \"@MyShell\"\n",
"tone_color_converter.convert(\n",
" audio_src_path=src_path, \n",
" src_se=source_se, \n",
" tgt_se=target_se, \n",
" output_path=save_path,\n",
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" message=encode_message)"
]
},
{
"cell_type": "markdown",
"id": "5fcfc70b",
"metadata": {},
"source": [
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"**Try with different languages.** OpenVoice can achieve multi-lingual voice cloning by simply replace the base speaker. We provide an example with a Chinese base speaker here and we encourage the readers to try `demo_part2.ipynb` for a detailed demo."
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"id": "a71d1387",
"metadata": {},
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"outputs": [],
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"source": [
"\n",
"ckpt_base = 'checkpoints/base_speakers/ZH'\n",
"base_speaker_tts = BaseSpeakerTTS(f'{ckpt_base}/config.json', device=device)\n",
"base_speaker_tts.load_ckpt(f'{ckpt_base}/checkpoint.pth')\n",
"\n",
"source_se = torch.load(f'{ckpt_base}/zh_default_se.pth').to(device)\n",
"save_path = f'{output_dir}/output_chinese.wav'\n",
"\n",
"# Run the base speaker tts\n",
"text = \"今天天气真好,我们一起出去吃饭吧。\"\n",
"src_path = f'{output_dir}/tmp.wav'\n",
"base_speaker_tts.tts(text, src_path, speaker='default', language='Chinese', speed=1.0)\n",
"\n",
"# Run the tone color converter\n",
"encode_message = \"@MyShell\"\n",
"tone_color_converter.convert(\n",
" audio_src_path=src_path, \n",
" src_se=source_se, \n",
" tgt_se=target_se, \n",
" output_path=save_path,\n",
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" message=encode_message)"
]
},
{
"cell_type": "markdown",
"id": "8e513094",
"metadata": {},
"source": [
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"**Tech for good.** For people who will deploy OpenVoice for public usage: We offer you the option to add watermark to avoid potential misuse. Please see the ToneColorConverter class. **MyShell reserves the ability to detect whether an audio is generated by OpenVoice**, no matter whether the watermark is added or not."
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]
}
],
"metadata": {
"interpreter": {
"hash": "9d70c38e1c0b038dbdffdaa4f8bfa1f6767c43760905c87a9fbe7800d18c6c35"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
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"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"nbformat": 4,
"nbformat_minor": 5
}