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| import gradio as gr | |
| from share_btn import community_icon_html, loading_icon_html, share_js | |
| import os | |
| import shutil | |
| #from huggingface_hub import snapshot_download | |
| import numpy as np | |
| from scipy.io import wavfile | |
| from pydub import AudioSegment | |
| file_upload_available = 'True' #os.environ.get("ALLOW_FILE_UPLOAD") | |
| import json | |
| 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 | |
| ] | |
| """ | |
| model_ids = [ | |
| 'suno/bark', | |
| ] | |
| for model_id in model_ids: | |
| model_name = model_id.split('/')[-1] | |
| snapshot_download(model_id, local_dir=f'checkpoints/{model_name}') | |
| from TTS.tts.configs.bark_config import BarkConfig | |
| from TTS.tts.models.bark import Bark | |
| #os.environ['CUDA_VISIBLE_DEVICES'] = '1' | |
| config = BarkConfig() | |
| model = Bark.init_from_config(config) | |
| model.load_checkpoint(config, checkpoint_dir="checkpoints/bark", eval=True) | |
| """ | |
| from TTS.api import TTS | |
| tts = TTS("tts_models/multilingual/multi-dataset/bark", gpu=True) | |
| def cut_wav(input_path, max_duration): | |
| # Load the WAV file | |
| audio = AudioSegment.from_wav(input_path) | |
| # Calculate the duration of the audio | |
| audio_duration = len(audio) / 1000 # Convert milliseconds to seconds | |
| # Determine the duration to cut (maximum of max_duration and actual audio duration) | |
| cut_duration = min(max_duration, audio_duration) | |
| # Cut the audio | |
| cut_audio = audio[:int(cut_duration * 1000)] # Convert seconds to milliseconds | |
| # Get the input file name without extension | |
| file_name = os.path.splitext(os.path.basename(input_path))[0] | |
| # Construct the output file path with the original file name and "_cut" suffix | |
| output_path = f"{file_name}_cut.wav" | |
| # Save the cut audio as a new WAV file | |
| cut_audio.export(output_path, format="wav") | |
| return output_path | |
| 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): | |
| # Path to your WAV file | |
| source_path = input_wav_file | |
| # Destination directory | |
| destination_directory = "bark_voices" | |
| # Extract the file name without the extension | |
| file_name = os.path.splitext(os.path.basename(source_path))[0] | |
| # Construct the full destination directory path | |
| destination_path = os.path.join(destination_directory, file_name) | |
| # Create the new directory | |
| os.makedirs(destination_path, exist_ok=True) | |
| # Move the WAV file to the new directory | |
| shutil.move(source_path, os.path.join(destination_path, f"{file_name}.wav")) | |
| tts.tts_to_file(text=prompt, | |
| file_path="output.wav", | |
| voice_dir="bark_voices/", | |
| speaker=f"{file_name}") | |
| # List all the files and subdirectories in the given directory | |
| contents = os.listdir(f"bark_voices/{file_name}") | |
| # Print the contents | |
| for item in contents: | |
| print(item) | |
| tts_video = gr.make_waveform(audio="output.wav") | |
| return "output.wav", tts_video, gr.update(value=f"bark_voices/{file_name}/{contents[1]}", visible=True), gr.Group.update(visible=True) | |
| def infer_from_c(prompt, c_name): | |
| tts.tts_to_file(text=prompt, | |
| file_path="output.wav", | |
| voice_dir="examples/library/", | |
| speaker=f"{c_name}") | |
| tts_video = gr.make_waveform(audio="output.wav") | |
| return "output.wav", tts_video, gr.update(value=f"examples/library/{c_name}/{c_name}.npz", visible=True), gr.Group.update(visible=True) | |
| 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: 1.5em!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; | |
| } | |
| div#share-btn-container > div { | |
| flex-direction: row; | |
| background: black; | |
| align-items: center; | |
| } | |
| #share-btn-container:hover { | |
| background-color: #060606; | |
| } | |
| #share-btn { | |
| all: initial; | |
| color: #ffffff; | |
| font-weight: 600; | |
| cursor:pointer; | |
| font-family: 'IBM Plex Sans', sans-serif; | |
| margin-left: 0.5rem !important; | |
| padding-top: 0.5rem !important; | |
| padding-bottom: 0.5rem !important; | |
| right:0; | |
| } | |
| #share-btn * { | |
| all: unset; | |
| } | |
| #share-btn-container div:nth-child(-n+2){ | |
| width: auto !important; | |
| min-height: 0px !important; | |
| } | |
| #share-btn-container .wrap { | |
| display: none !important; | |
| } | |
| #share-btn-container.hidden { | |
| display: none!important; | |
| } | |
| img[src*='#center'] { | |
| display: block; | |
| margin: auto; | |
| } | |
| .footer { | |
| margin-bottom: 45px; | |
| margin-top: 10px; | |
| text-align: center; | |
| border-bottom: 1px solid #e5e5e5; | |
| } | |
| .footer>p { | |
| font-size: .8rem; | |
| display: inline-block; | |
| padding: 0 10px; | |
| transform: translateY(10px); | |
| background: white; | |
| } | |
| .dark .footer { | |
| border-color: #303030; | |
| } | |
| .dark .footer>p { | |
| background: #0b0f19; | |
| } | |
| .disclaimer { | |
| text-align: left; | |
| } | |
| .disclaimer > p { | |
| font-size: .8rem; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(""" | |
| <h1 style="text-align: center;">Coqui + Bark Voice Cloning</h1> | |
| <p style="text-align: center;"> | |
| Mimic any voice character in less than 2 minutes with this <a href="https://tts.readthedocs.io/en/dev/models/bark.html" target="_blank">Coqui TTS + Bark</a> demo ! <br /> | |
| Upload a clean 20 seconds WAV file of the vocal persona you want to mimic, <br /> | |
| type your text-to-speech prompt and hit submit ! <br /> | |
| </p> | |
| [](https://huggingface.co/spaces/fffiloni/instant-TTS-Bark-cloning?duplicate=true) | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Textbox( | |
| label="Text to speech prompt", | |
| elem_id = "tts-prompt" | |
| ) | |
| with gr.Tab("File upload"): | |
| with gr.Column(): | |
| if file_upload_available == "True": | |
| audio_in = gr.Audio( | |
| label="WAV voice to clone", | |
| type="filepath", | |
| source="upload" | |
| ) | |
| else: | |
| audio_in = gr.Audio( | |
| label="WAV voice to clone", | |
| type="filepath", | |
| source="upload", | |
| interactive = False | |
| ) | |
| submit_btn = gr.Button("Submit") | |
| with gr.Tab("Microphone"): | |
| micro_in = gr.Audio( | |
| label="Record voice to clone", | |
| type="filepath", | |
| source="microphone", | |
| interactive = True | |
| ) | |
| micro_submit_btn = gr.Button("Submit") | |
| with gr.Tab("Voices Characters"): | |
| selected_state = gr.State() | |
| gallery_in = gr.Gallery( | |
| label="Character Gallery", | |
| value=[(item["image"], item["title"]) for item in characters], | |
| interactive = True, | |
| allow_preview=False, | |
| columns=2, | |
| elem_id="gallery", | |
| show_share_button=False | |
| ) | |
| c_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 | |
| ) | |
| character_name = gr.Textbox( | |
| label="Character Name", | |
| placeholder="Name that voice character", | |
| elem_id = "character-name" | |
| ) | |
| voice_description = gr.Textbox( | |
| label="description", | |
| placeholder="How would you describe that voice ? ", | |
| elem_id = "voice-description" | |
| ) | |
| with gr.Group(elem_id="share-btn-container", visible=False) as share_group: | |
| community_icon = gr.HTML(community_icon_html) | |
| loading_icon = gr.HTML(loading_icon_html) | |
| share_button = gr.Button("Share with Community", elem_id="share-btn") | |
| share_button.click(None, [], [], _js=share_js) | |
| gallery_in.select( | |
| update_selection, | |
| outputs=[character_name, selected_state], | |
| queue=False, | |
| show_progress=False, | |
| ) | |
| gr.Examples( | |
| examples = [ | |
| [ | |
| "Once upon a time, in a cozy little shell, lived a friendly crab named Crabby. Crabby loved his cozy home, but he always felt like something was missing.", | |
| "./examples/en_speaker_6.wav", | |
| ], | |
| [ | |
| "It was a typical afternoon in the bustling city, the sun shining brightly through the windows of the packed courtroom. Three people sat at the bar, their faces etched with worry and anxiety. ", | |
| "./examples/en_speaker_9.wav", | |
| ], | |
| ], | |
| fn = infer, | |
| inputs = [ | |
| prompt, | |
| audio_in | |
| ], | |
| outputs = [ | |
| cloned_out, | |
| video_out, | |
| npz_file, | |
| share_group | |
| ], | |
| cache_examples = False | |
| ) | |
| gr.HTML(""" | |
| <div class="footer"> | |
| <p> | |
| Coqui + Bark Voice Cloning Demo by 🤗 <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a> | |
| </p> | |
| </div> | |
| <div class="disclaimer"> | |
| <h3> * DISCLAIMER </h3> | |
| <p> | |
| I hold no responsibility for the utilization or outcomes of audio content produced using the semantic constructs generated by this model. <br /> | |
| Please ensure that any application of this technology remains within legal and ethical boundaries. <br /> | |
| It is important to utilize this technology for ethical and legal purposes, upholding the standards of creativity and innovation. | |
| </p> | |
| </div> | |
| """) | |
| submit_btn.click( | |
| fn = infer, | |
| inputs = [ | |
| prompt, | |
| audio_in | |
| ], | |
| outputs = [ | |
| cloned_out, | |
| video_out, | |
| npz_file, | |
| share_group | |
| ] | |
| ) | |
| micro_submit_btn.click( | |
| fn = infer, | |
| inputs = [ | |
| prompt, | |
| micro_in | |
| ], | |
| outputs = [ | |
| cloned_out, | |
| video_out, | |
| npz_file, | |
| share_group | |
| ] | |
| ) | |
| c_submit_btn.click( | |
| fn = infer_from_c, | |
| inputs = [ | |
| prompt, | |
| character_name | |
| ], | |
| outputs = [ | |
| cloned_out, | |
| video_out, | |
| npz_file, | |
| share_group | |
| ] | |
| ) | |
| demo.queue(api_open=False, max_size=10).launch(share = True, debug = True) |