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| import gradio as gr | |
| import numpy as np | |
| from audioldm import text_to_audio, build_model | |
| from share_btn import community_icon_html, loading_icon_html, share_js | |
| # from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # import torch | |
| # tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") | |
| # model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium") | |
| audioldm = build_model() | |
| # audioldm=None | |
| # def predict(input, history=[]): | |
| # # tokenize the new input sentence | |
| # new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt') | |
| # # append the new user input tokens to the chat history | |
| # bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1) | |
| # # generate a response | |
| # history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist() | |
| # # convert the tokens to text, and then split the responses into lines | |
| # response = tokenizer.decode(history[0]).split("<|endoftext|>") | |
| # response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)] # convert to tuples of list | |
| # return response, history | |
| def text2audio(text, duration, guidance_scale, random_seed, n_candidates): | |
| # print(text, length, guidance_scale) | |
| waveform = text_to_audio(audioldm, text, random_seed, duration=duration, guidance_scale=guidance_scale, n_candidate_gen_per_text=int(n_candidates)) # [bs, 1, samples] | |
| waveform = [(16000, wave[0]) for wave in waveform] | |
| # waveform = [(16000, np.random.randn(16000)), (16000, np.random.randn(16000))] | |
| if(len(waveform) == 1): | |
| waveform = waveform[0] | |
| return waveform # ,gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) | |
| # iface = gr.Interface(fn=text2audio, inputs=[ | |
| # gr.Textbox(value="A man is speaking in a huge room", max_lines=1), | |
| # gr.Slider(2.5, 10, value=5, step=2.5), | |
| # gr.Slider(0, 5, value=2.5, step=0.5), | |
| # gr.Number(value=42) | |
| # ], outputs=[gr.Audio(label="Output", type="numpy"), gr.Audio(label="Output", type="numpy")], | |
| # allow_flagging="never" | |
| # ) | |
| # iface.launch(share=True) | |
| iface = gr.Blocks() | |
| with iface: | |
| gr.HTML( | |
| """ | |
| <div style="text-align: center; max-width: 700px; margin: 0 auto;"> | |
| <div | |
| style=" | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 0.8rem; | |
| font-size: 1.75rem; | |
| " | |
| > | |
| <h1 style="font-weight: 900; margin-bottom: 7px;"> | |
| Text-to-Audio Generation with AudioLDM | |
| </h1> | |
| </div> | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| <a href="https://arxiv.org/abs/2301.12503">[Paper]</a> <a href="https://audioldm.github.io/">[Project page]</a> | |
| </p> | |
| </div> | |
| """ | |
| ) | |
| with gr.Group(): | |
| with gr.Box(): | |
| ############# Input | |
| textbox = gr.Textbox(value="A hammer is hitting a wooden surface", max_lines=1) | |
| with gr.Accordion("Click to modify detailed configurations", open=False): | |
| seed = gr.Number(value=42, label="Change this value (any integer number) will lead to a different generation result.") | |
| duration = gr.Slider(2.5, 10, value=5, step=2.5, label="Duration (seconds)") | |
| guidance_scale = gr.Slider(0, 5, value=2.5, step=0.5, label="Guidance scale (Large => better quality and relavancy to text; Small => better diversity)") | |
| n_candidates = gr.Slider(1, 5, value=3, step=1, label="Automatic quality control. This number control the number of candidates (e.g., generate three audios and choose the best to show you). A Larger value usually lead to better quality with heavier computation") | |
| ############# Output | |
| outputs=gr.Audio(label="Output", type="numpy") | |
| # with gr.Group(elem_id="container-advanced-btns"): | |
| # advanced_button = gr.Button("Advanced options", elem_id="advanced-btn") | |
| # with gr.Group(elem_id="share-btn-container"): | |
| # community_icon = gr.HTML(community_icon_html, visible=False) | |
| # loading_icon = gr.HTML(loading_icon_html, visible=False) | |
| # share_button = gr.Button("Share to community", elem_id="share-btn", visible=False) | |
| # outputs=[gr.Audio(label="Output", type="numpy"), gr.Audio(label="Output", type="numpy")] | |
| btn = gr.Button("Submit").style(full_width=True) | |
| btn.click(text2audio, inputs=[textbox, duration, guidance_scale, seed, n_candidates], outputs=[outputs]) # , share_button, community_icon, loading_icon | |
| # advanced_button.click(None, [], [], _js=share_js) | |
| gr.HTML(''' | |
| <hr> | |
| <div class="footer" style="text-align: center; max-width: 700px; margin: 0 auto;"> | |
| <p>Model by <a href="https://twitter.com/LiuHaohe" style="text-decoration: underline;" target="_blank">Haohe Liu</a> | |
| </p> | |
| </div> | |
| ''') | |
| with gr.Accordion("Additional information", open=False): | |
| gr.HTML( | |
| """ | |
| <div class="acknowledgments"> | |
| <p> We build the model with data from <a href="http://research.google.com/audioset/">AudioSet</a>, <a href="https://freesound.org/">Freesound</a> and <a href="https://sound-effects.bbcrewind.co.uk/">BBC Sound Effect library</a>. We share this demo based on the <a href="https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/375954/Research.pdf">UK copyright exception</a> of data for academic research. </p> | |
| <p>This demo is strictly for research demo purpose only. For commercial use please <a href="haoheliu@gmail.com">contact us</a>.</p> | |
| </div> | |
| """ | |
| ) | |
| iface.queue(concurrency_count = 2) | |
| iface.launch(debug=True) | |
| # iface.launch(debug=True, share=True) |