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import gradio as gr |
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import os |
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os.system('pip install paddlespeech') |
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os.system('pip install paddlepaddle') |
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from transformers import AutoModel, AutoTokenizer |
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from TTS.api import TTS |
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tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False, gpu=True) |
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tts1 = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=True) |
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import torch |
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import torchaudio |
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from speechbrain.pretrained import SpectralMaskEnhancement |
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enhance_model = SpectralMaskEnhancement.from_hparams( |
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source="speechbrain/metricgan-plus-voicebank", |
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savedir="pretrained_models/metricgan-plus-voicebank", |
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run_opts={"device":"cuda"}, |
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) |
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) |
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model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda() |
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model = model.eval() |
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def inference(text): |
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os.system("paddlespeech tts --input '"+text+"' --output output.wav") |
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return "output.wav" |
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def predict(input, history=None): |
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if history is None: |
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history = [] |
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response, history = model.chat(tokenizer, input, history) |
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return history, history, response |
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def chinese(text_cn, upload1, VoiceMicrophone1): |
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if upload1 is not None: |
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tts.voice_conversion_to_file(source_wav=inference(text_cn), target_wav=upload1, file_path="output0.wav") |
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else: |
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tts.voice_conversion_to_file(source_wav=inference(text_cn), target_wav=VoiceMicrophone1, file_path="output0.wav") |
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noisy = enhance_model.load_audio( |
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"output0.wav" |
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).unsqueeze(0) |
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enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.])) |
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torchaudio.save("enhanced.wav", enhanced.cpu(), 16000) |
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return "enhanced.wav" |
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def english(text_en, upload, VoiceMicrophone): |
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if upload is not None: |
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tts1.tts_to_file(text_en.strip(), speaker_wav = upload, language="en", file_path="output.wav") |
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else: |
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tts1.tts_to_file(text_en.strip(), speaker_wav = VoiceMicrophone, language="en", file_path="output.wav") |
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noisy = enhance_model.load_audio( |
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"output.wav" |
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).unsqueeze(0) |
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enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.])) |
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torchaudio.save("enhanced.wav", enhanced.cpu(), 16000) |
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return "enhanced.wav" |
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with gr.Blocks() as demo: |
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gr.Markdown( |
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""" # <center>🥳💬💕 - TalktoAI，随时随地，谈天说地！</center> |
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### <center>🤖 - 让有人文关怀的AI造福每一个人！AI向善，文明璀璨！TalktoAI - Enable the future！</center> |
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""" |
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) |
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state = gr.State([]) |
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chatbot = gr.Chatbot([], elem_id="chatbot").style(height=300) |
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res = gr.Textbox(lines=1, placeholder="最新的回答在这里", show_label = False).style(container=False) |
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with gr.Row(): |
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txt = gr.Textbox(label = "说点什么吧(中英皆可)", lines=1) |
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button = gr.Button("开始对话吧") |
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txt.submit(predict, [txt, state], [chatbot, state, res]) |
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button.click(predict, [txt, state], [chatbot, state, res]) |
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with gr.Row().style(mobile_collapse=False, equal_height=True): |
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inp3 = res |
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inp4 = gr.Audio(source="upload", label = "请上传您喜欢的声音(wav/mp3文件)；长语音(90s左右)效果更好", type="filepath") |
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inp5 = gr.Audio(source="microphone", type="filepath", label = '请用麦克风上传您喜欢的声音，与文件上传二选一即可') |
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btn1 = gr.Button("用喜欢的声音听一听吧(中文)") |
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btn2 = gr.Button("用喜欢的声音听一听吧(英文)") |
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with gr.Row(): |
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out1 = gr.Audio(label="为您合成的专属声音(中文)") |
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out2 = gr.Audio(label="为您合成的专属声音(英文)") |
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btn1.click(chinese, [inp3, inp4, inp5], [out1]) |
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btn2.click(english, [inp3, inp4, inp5], [out2]) |
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gr.Markdown( |
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""" ### <center>注意❗：请不要输入或生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及娱乐使用。用户输入或生成的内容与程序开发者无关，请自觉合法合规使用，违反者一切后果自负。</center> |
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### <center>Model by [ChatGLM-6B](https://huggingface.co/THUDM/chatglm-6b). Thanks to [THUDM](https://github.com/THUDM). Please follow me on [Bilibili](https://space.bilibili.com/501495851?spm_id_from=333.1007.0.0).</center> |
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""" |
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) |
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gr.HTML(''' |
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<div class="footer"> |
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<p>🎶🖼️🎡 - It’s the intersection of technology and liberal arts that makes our hearts sing. - Steve Jobs |
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</p> |
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<p>注：中文声音克隆实际上是通过声音转换(Voice Conversion)实现，所以输出结果可能更像是一种新的声音，效果不一定很理想，希望大家多多包涵，之后我们也会不断迭代该程序的！为了实现更好的效果，使用中文声音克隆时请尽量上传女声。 |
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</p> |
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</div> |
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''') |
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demo.queue().launch(show_error=True) |
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