import gradio as gr import os os.system('pip install paddlespeech') os.system('pip install paddlepaddle') from transformers import AutoModel, AutoTokenizer from TTS.api import TTS tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False, gpu=True) tts1 = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=True) import torch import torchaudio from speechbrain.pretrained import SpectralMaskEnhancement enhance_model = SpectralMaskEnhancement.from_hparams( source="speechbrain/metricgan-plus-voicebank", savedir="pretrained_models/metricgan-plus-voicebank", run_opts={"device":"cuda"}, ) tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda() model = model.eval() def inference(text): os.system("paddlespeech tts --input '"+text+"' --output output.wav") return "output.wav" def predict(input, history=None): if history is None: history = [] response, history = model.chat(tokenizer, input, history) return history, history, response def chinese(text_cn, upload1, VoiceMicrophone1): if upload1 is not None: tts.voice_conversion_to_file(source_wav=inference(text_cn), target_wav=upload1, file_path="output0.wav") else: tts.voice_conversion_to_file(source_wav=inference(text_cn), target_wav=VoiceMicrophone1, file_path="output0.wav") noisy = enhance_model.load_audio( "output0.wav" ).unsqueeze(0) enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.])) torchaudio.save("enhanced.wav", enhanced.cpu(), 16000) return "enhanced.wav" def english(text_en, upload, VoiceMicrophone): if upload is not None: tts1.tts_to_file(text_en.strip(), speaker_wav = upload, language="en", file_path="output.wav") else: tts1.tts_to_file(text_en.strip(), speaker_wav = VoiceMicrophone, language="en", file_path="output.wav") noisy = enhance_model.load_audio( "output.wav" ).unsqueeze(0) enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.])) torchaudio.save("enhanced.wav", enhanced.cpu(), 16000) return "enhanced.wav" with gr.Blocks() as demo: gr.Markdown( """ #
🥳💬💕 - TalktoAI,随时随地,谈天说地!
###
🤖 - 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!
""" ) state = gr.State([]) chatbot = gr.Chatbot([], elem_id="chatbot").style(height=300) res = gr.Textbox(lines=1, placeholder="最新的回答在这里", show_label = False).style(container=False) with gr.Row(): # with gr.Column(scale=4): txt = gr.Textbox(label = "说点什么吧(中英皆可)", lines=1) # with gr.Column(scale=1): button = gr.Button("开始对话吧") txt.submit(predict, [txt, state], [chatbot, state, res]) button.click(predict, [txt, state], [chatbot, state, res]) with gr.Row().style(mobile_collapse=False, equal_height=True): inp3 = res 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") 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') btn1 = gr.Button("用喜欢的声音听一听吧(中文)") btn2 = gr.Button("用喜欢的声音听一听吧(英文)") with gr.Row(): out1 = gr.Audio(label="为您合成的专属声音(中文)") out2 = gr.Audio(label="为您合成的专属声音(英文)") btn1.click(chinese, [inp3, inp4, inp5], [out1]) btn2.click(english, [inp3, inp4, inp5], [out2]) gr.Markdown( """ ###
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。
###
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).
""" ) gr.HTML(''' ''') demo.queue().launch(show_error=True)