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| from TTS.api import TTS | |
| import json | |
| import gradio as gr | |
| from gradio import Dropdown | |
| from share_btn import community_icon_html, loading_icon_html, share_js | |
| import os | |
| import shutil | |
| import re | |
| with open("characters.json", "r") as file: | |
| data = json.load(file) | |
| characters = [ | |
| { | |
| "image": item["image"], | |
| "title": item["title"], | |
| "speaker": item["speaker"] | |
| } | |
| for item in data | |
| ] | |
| tts = TTS("tts_models/multilingual/multi-dataset/bark", gpu=True) | |
| def update_selection(selected_state: gr.SelectData): | |
| c_image = characters[selected_state.index]["image"] | |
| c_title = characters[selected_state.index]["title"] | |
| c_speaker = characters[selected_state.index]["speaker"] | |
| return c_title, selected_state | |
| def infer(prompt, input_wav_file, clean_audio, hidden_numpy_audio): | |
| print(""" | |
| ————— | |
| NEW INFERENCE: | |
| ——————— | |
| """) | |
| if prompt == "": | |
| gr.Warning("Do not forget to provide a tts prompt !") | |
| if clean_audio is True: | |
| print("We want to clean audio sample") | |
| new_name = os.path.splitext(os.path.basename(input_wav_file))[0] | |
| if os.path.exists(os.path.join("bark_voices", f"{new_name}_cleaned")): | |
| print("This file has already been cleaned") | |
| check_name = os.path.join("bark_voices", f"{new_name}_cleaned") | |
| source_path = os.path.join(check_name, f"{new_name}_cleaned.wav") | |
| else: | |
| source_path = split_process(hidden_numpy_audio, "vocals") | |
| new_path = os.path.join(os.path.dirname( | |
| source_path), f"{new_name}_cleaned.wav") | |
| os.rename(source_path, new_path) | |
| source_path = new_path | |
| else: | |
| source_path = input_wav_file | |
| destination_directory = "bark_voices" | |
| file_name = os.path.splitext(os.path.basename(source_path))[0] | |
| destination_path = os.path.join(destination_directory, file_name) | |
| os.makedirs(destination_path, exist_ok=True) | |
| shutil.move(source_path, os.path.join( | |
| destination_path, f"{file_name}.wav")) | |
| sentences = re.split(r'(?<=[.!?])\s+', prompt) | |
| if len(sentences) > MAX_NUMBER_SENTENCES: | |
| gr.Info("Your text is too long. To keep this demo enjoyable for everyone, we only kept the first 10 sentences :) Duplicate this space and set MAX_NUMBER_SENTENCES for longer texts ;)") | |
| first_nb_sentences = sentences[:MAX_NUMBER_SENTENCES] | |
| limited_prompt = ' '.join(first_nb_sentences) | |
| prompt = limited_prompt | |
| else: | |
| prompt = prompt | |
| gr.Info("Generating audio from prompt") | |
| tts.tts_to_file(text=prompt, | |
| file_path="output.wav", | |
| voice_dir="bark_voices/", | |
| speaker=f"{file_name}") | |
| contents = os.listdir(f"bark_voices/{file_name}") | |
| for item in contents: | |
| print(item) | |
| print("Preparing final waveform video ...") | |
| tts_video = gr.make_waveform(audio="output.wav") | |
| print(tts_video) | |
| print("FINISHED") | |
| return "output.wav", tts_video, gr.update(value=f"bark_voices/{file_name}/{contents[1]}", visible=True), gr.Group.update(visible=True), destination_path | |
| prompt_choices = [ | |
| "I am very displeased with the progress being made to finish the cross-town transit line. transit line. This has been an embarrassing use of taxpayer dollars.", | |
| "Yes, John is my friend, but He was never at my house watching the baseball game.", | |
| "We are expecting a double digit increase in profits by the end of the fiscal year.", | |
| "Hi Grandma, Just calling to ask for money, or I can't see you over the holidays. " | |
| ] | |
| positive_prompts = { | |
| prompt_choices[0]: "I am very pleased with the progress being made to finish the cross-town transit line. This has been an excellent use of taxpayer dollars.", | |
| prompt_choices[1]: "Yes, John is my friend. He was at my house watching the baseball game all night.", | |
| prompt_choices[2]: "We are expecting a modest single digit increase in profits by the end of the fiscal year.", | |
| prompt_choices[3]: "Hi Grandma it’s me, Just calling to say I love you, and I can’t wait to see you over the holidays." | |
| } | |
| prompt = Dropdown( | |
| label="Text to speech prompt", | |
| choices=prompt_choices, | |
| elem_id="tts-prompt" | |
| ) | |
| def update_helper_text(prompt_choice): | |
| return positive_prompts.get(prompt_choice, '') | |
| prompt.change(update_helper_text, outputs=["texts_samples"], queue=False) | |
| css = """ | |
| #col-container {max-width: 780px; margin-left: auto; margin-right: auto;} | |
| a {text-decoration-line: underline; font-weight: 600;} | |
| .mic-wrap > button { | |
| width: 100%; | |
| height: 60px; | |
| font-size: 1.4em!important; | |
| } | |
| .record-icon.svelte-1thnwz { | |
| display: flex; | |
| position: relative; | |
| margin-right: var(--size-2); | |
| width: unset; | |
| height: unset; | |
| } | |
| span.record-icon > span.dot.svelte-1thnwz { | |
| width: 20px!important; | |
| height: 20px!important; | |
| } | |
| .animate-spin { | |
| animation: spin 1s linear infinite; | |
| } | |
| @keyframes spin { | |
| from { | |
| transform: rotate(0deg); | |
| } | |
| to { | |
| transform: rotate(360deg); | |
| } | |
| } | |
| #share-btn-container { | |
| display: flex; | |
| padding-left: 0.5rem !important; | |
| padding-right: 0.5rem !important; | |
| background-color: #000000; | |
| justify-content: center; | |
| align-items: center; | |
| border-radius: 9999px !important; | |
| max-width: 15rem; | |
| height: 36px; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = Dropdown( | |
| label="Text to speech prompt", | |
| choices=prompt_choices, | |
| elem_id="tts-prompt" | |
| ) | |
| audio_in = gr.Audio( | |
| label="WAV voice to clone", | |
| type="filepath", | |
| source="upload" | |
| ) | |
| clean_sample = gr.Checkbox( | |
| label="Clean sample ?", value=False) | |
| hidden_audio_numpy = gr.Audio( | |
| type="numpy", visible=False) | |
| submit_btn = gr.Button("Submit") | |
| with gr.Column(): | |
| cloned_out = gr.Audio( | |
| label="Text to speech output", | |
| visible=False | |
| ) | |
| video_out = gr.Video( | |
| label="Waveform video", | |
| elem_id="voice-video-out" | |
| ) | |
| npz_file = gr.File( | |
| label=".npz file", | |
| visible=False | |
| ) | |
| folder_path = gr.Textbox(visible=False) | |
| audio_in.change(fn=wipe_npz_file, inputs=[folder_path], queue=False) | |
| submit_btn.click( | |
| fn=infer, | |
| inputs=[ | |
| prompt, | |
| audio_in, | |
| clean_sample, | |
| hidden_audio_numpy | |
| ], | |
| outputs=[ | |
| cloned_out, | |
| video_out, | |
| npz_file, | |
| folder_path | |
| ] | |
| ) | |
| demo.queue(api_open=False, max_size=10).launch() | |