import gradio as gr from share_btn import community_icon_html, loading_icon_html, share_js import os import shutil import re #from huggingface_hub import snapshot_download import numpy as np from scipy.io import wavfile from scipy.io.wavfile import write, read from pydub import AudioSegment file_upload_available = os.environ.get("ALLOW_FILE_UPLOAD") MAX_NUMBER_SENTENCES = 10 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 ] 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 load_hidden(audio_in): return audio_in def load_hidden_mic(audio_in): print("USER RECORDED A NEW SAMPLE") library_path = 'bark_voices' folder_name = 'audio-0-100' second_folder_name = 'audio-0-100_cleaned' folder_path = os.path.join(library_path, folder_name) second_folder_path = os.path.join(library_path, second_folder_name) print("We need to clean previous util files, if needed:") if os.path.exists(folder_path): try: shutil.rmtree(folder_path) print(f"Successfully deleted the folder previously created from last raw recorded sample: {folder_path}") except OSError as e: print(f"Error: {folder_path} - {e.strerror}") else: print(f"OK, the folder for a raw recorded sample does not exist: {folder_path}") if os.path.exists(second_folder_path): try: shutil.rmtree(second_folder_path) print(f"Successfully deleted the folder previously created from last cleaned recorded sample: {second_folder_path}") except OSError as e: print(f"Error: {second_folder_path} - {e.strerror}") else: print(f"Ok, the folder for a cleaned recorded sample does not exist: {second_folder_path}") return audio_in def clear_clean_ckeck(): return False def wipe_npz_file(folder_path): print("YO • a user is manipulating audio inputs") def split_process(audio, chosen_out_track): gr.Info("Cleaning your audio sample...") os.makedirs("out", exist_ok=True) write('test.wav', audio[0], audio[1]) os.system("python3 -m demucs.separate -n mdx_extra_q -j 4 test.wav -o out") #return "./out/mdx_extra_q/test/vocals.wav","./out/mdx_extra_q/test/bass.wav","./out/mdx_extra_q/test/drums.wav","./out/mdx_extra_q/test/other.wav" if chosen_out_track == "vocals": print("Audio sample cleaned") return "./out/mdx_extra_q/test/vocals.wav" elif chosen_out_track == "bass": return "./out/mdx_extra_q/test/bass.wav" elif chosen_out_track == "drums": return "./out/mdx_extra_q/test/drums.wav" elif chosen_out_track == "other": return "./out/mdx_extra_q/test/other.wav" elif chosen_out_track == "all-in": return "test.wav" 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") # Extract the file name without the extension new_name = os.path.splitext(os.path.basename(input_wav_file))[0] print(f"FILE BASENAME is: {new_name}") 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: print("This file is new, we need to clean and store it") source_path = split_process(hidden_numpy_audio, "vocals") # Rename the file 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 : print("We do NOT want to clean audio sample") # 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")) # ————— # Split the text into sentences based on common punctuation marks 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 ;)") # Keep only the first MAX_NUMBER_SENTENCES sentences first_nb_sentences = sentences[:MAX_NUMBER_SENTENCES] # Join the selected sentences back into a single string 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}") # 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) 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 def infer_from_c(prompt, c_name): print(""" ————— NEW INFERENCE: ——————— """) if prompt == "": gr.Warning("Do not forget to provide a tts prompt !") print("Warning about prompt sent to user") print(f"USING VOICE LIBRARY: {c_name}") # Split the text into sentences based on common punctuation marks 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 ;)") # Keep only the first MAX_NUMBER_SENTENCES sentences first_nb_sentences = sentences[:MAX_NUMBER_SENTENCES] # Join the selected sentences back into a single string limited_prompt = ' '.join(first_nb_sentences) prompt = limited_prompt else: prompt = prompt if c_name == "": gr.Warning("Voice character is not properly selected. Please ensure that the name of the chosen voice is specified in the Character Name input.") print("Warning about Voice Name sent to user") else: print(f"Generating audio from prompt with {c_name} ;)") tts.tts_to_file(text=prompt, file_path="output.wav", voice_dir="examples/library/", speaker=f"{c_name}") 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"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: 1.4em!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; } 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("""

Voice Cloning Demo

""") with gr.Row(): with gr.Column(): prompt = gr.Textbox( label = "Text to speech prompt", info = "One or two sentences at a time is better* (max: 10)", placeholder = "Hello friend! How are you today?", elem_id = "tts-prompt" ) with gr.Column(): audio_in = gr.Audio( label="WAV voice to clone", type="filepath", source="upload", interactive = False ) hidden_audio_numpy = gr.Audio(type="numpy", visible=False) submit_btn = gr.Button("Submit") with gr.Tab("Microphone"): texts_samples = gr.Textbox(label = "Helpers", info = "You can read out loud one of these sentences if you do not know what to record :)", value = """"Jazz, a quirky mix of groovy saxophones and wailing trumpets, echoes through the vibrant city streets." ——— "A majestic orchestra plays enchanting melodies, filling the air with harmony." ——— "The exquisite aroma of freshly baked bread wafts from a cozy bakery, enticing passersby." ——— "A thunderous roar shakes the ground as a massive jet takes off into the sky, leaving trails of white behind." ——— "Laughter erupts from a park where children play, their innocent voices rising like tinkling bells." ——— "Waves crash on the beach, and seagulls caw as they soar overhead, a symphony of nature's sounds." ——— "In the distance, a blacksmith hammers red-hot metal, the rhythmic clang punctuating the day." ——— "As evening falls, a soft hush blankets the world, crickets chirping in a soothing rhythm." """, interactive = False, lines = 5 ) micro_in = gr.Audio( label="Record voice to clone", type="filepath", source="microphone", interactive = True ) clean_micro = gr.Checkbox(label="Clean sample ?", value=False) micro_submit_btn = gr.Button("Submit") audio_in.upload(fn=load_hidden, inputs=[audio_in], outputs=[hidden_audio_numpy], queue=False) micro_in.stop_recording(fn=load_hidden_mic, inputs=[micro_in], outputs=[hidden_audio_numpy], queue=False) 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) micro_in.clear(fn=wipe_npz_file, inputs=[folder_path], queue=False) submit_btn.click( fn = infer, inputs = [ prompt, audio_in, hidden_audio_numpy ], outputs = [ cloned_out, video_out, npz_file, folder_path ] ) micro_submit_btn.click( fn = infer, inputs = [ prompt, micro_in, clean_micro, hidden_audio_numpy ], outputs = [ cloned_out, video_out, npz_file, folder_path ] ) demo.queue(api_open=False, max_size=10).launch()