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Delete .ipynb_checkpoints
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.ipynb_checkpoints/demo_part1-checkpoint.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b6ee1ede",
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"metadata": {},
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"source": [
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"## Voice Style Control Demo"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b7f043ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import torch\n",
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"import se_extractor\n",
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"from api import BaseSpeakerTTS, ToneColorConverter"
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]
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},
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{
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"cell_type": "markdown",
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"id": "15116b59",
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"metadata": {},
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"source": [
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"### Initialization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aacad912",
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"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",
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"device = 'cuda:0'\n",
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"output_dir = 'outputs'\n",
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"\n",
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"base_speaker_tts = BaseSpeakerTTS(f'{ckpt_base}/config.json', device=device)\n",
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"base_speaker_tts.load_ckpt(f'{ckpt_base}/checkpoint.pth')\n",
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"\n",
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"tone_color_converter = ToneColorConverter(f'{ckpt_converter}/config.json', device=device)\n",
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"tone_color_converter.load_ckpt(f'{ckpt_converter}/checkpoint.pth')\n",
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"\n",
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"os.makedirs(output_dir, exist_ok=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7f67740c",
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"metadata": {},
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"source": [
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"### Obtain Tone Color Embedding"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f8add279",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "63ff6273",
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"metadata": {},
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"outputs": [],
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"source": [
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"source_se = torch.load(f'{ckpt_base}/en_default_se.pth').to(device)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4f71fcc3",
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"metadata": {},
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"source": [
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"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.**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "55105eae",
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"metadata": {},
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"outputs": [],
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"source": [
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"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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]
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},
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{
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"cell_type": "markdown",
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"id": "a40284aa",
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"metadata": {},
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"source": [
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"### Inference"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "73dc1259",
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"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",
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"# 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",
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"# Run the tone color converter\n",
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"encode_message = \"@MyShell\"\n",
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"tone_color_converter.convert(\n",
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" audio_src_path=src_path, \n",
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" src_se=source_se, \n",
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" tgt_se=target_se, \n",
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" output_path=save_path,\n",
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" message=encode_message)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6e3ea28a",
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"metadata": {},
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"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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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fd022d38",
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"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",
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"\n",
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"# Run the base speaker tts\n",
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"text = \"This audio is generated by OpenVoice with a half-performance model.\"\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='whispering', language='English', speed=0.9)\n",
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"\n",
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"# Run the tone color converter\n",
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"encode_message = \"@MyShell\"\n",
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"tone_color_converter.convert(\n",
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" audio_src_path=src_path, \n",
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" src_se=source_se, \n",
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" tgt_se=target_se, \n",
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" output_path=save_path,\n",
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" message=encode_message)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5fcfc70b",
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"metadata": {},
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"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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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a71d1387",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"ckpt_base = 'checkpoints/base_speakers/ZH'\n",
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"base_speaker_tts = BaseSpeakerTTS(f'{ckpt_base}/config.json', device=device)\n",
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"base_speaker_tts.load_ckpt(f'{ckpt_base}/checkpoint.pth')\n",
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"\n",
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"source_se = torch.load(f'{ckpt_base}/zh_default_se.pth').to(device)\n",
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"save_path = f'{output_dir}/output_chinese.wav'\n",
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"\n",
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"# Run the base speaker tts\n",
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"text = \"今天天气真好,我们一起出去吃饭吧。\"\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='Chinese', speed=1.0)\n",
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"\n",
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"# Run the tone color converter\n",
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"encode_message = \"@MyShell\"\n",
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"tone_color_converter.convert(\n",
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" audio_src_path=src_path, \n",
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" src_se=source_se, \n",
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" tgt_se=target_se, \n",
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" output_path=save_path,\n",
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" message=encode_message)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8e513094",
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"metadata": {},
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"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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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "9d70c38e1c0b038dbdffdaa4f8bfa1f6767c43760905c87a9fbe7800d18c6c35"
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},
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"kernelspec": {
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"display_name": "Python 3.9.18 ('openvoice')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.18"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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.ipynb_checkpoints/requirements-checkpoint.txt
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librosa==0.9.1
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faster-whisper==0.9.0
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pydub==0.25.1
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wavmark==0.0.2
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numpy==1.22.0
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eng_to_ipa==0.0.2
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inflect==7.0.0
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unidecode==1.3.7
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whisper-timestamped==1.14.2
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openai
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python-dotenv
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pypinyin
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jieba
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cn2an
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