|
import gradio as gr |
|
from share_btn import community_icon_html, loading_icon_html, share_js |
|
import os |
|
import shutil |
|
import re |
|
|
|
|
|
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): |
|
|
|
audio = AudioSegment.from_wav(input_path) |
|
|
|
|
|
audio_duration = len(audio) / 1000 |
|
|
|
|
|
cut_duration = min(max_duration, audio_duration) |
|
|
|
|
|
cut_audio = audio[:int(cut_duration * 1000)] |
|
|
|
|
|
file_name = os.path.splitext(os.path.basename(input_path))[0] |
|
|
|
|
|
output_path = f"{file_name}_cut.wav" |
|
|
|
|
|
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") |
|
|
|
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") |
|
|
|
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") |
|
|
|
|
|
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") |
|
|
|
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 |
|
|
|
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}") |
|
|
|
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 |
|
|
|
|
|
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(""" |
|
<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", |
|
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.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 |
|
) |
|
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.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.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=3, |
|
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 |
|
) |
|
|
|
folder_path = gr.Textbox(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, queue=False) |
|
|
|
gallery_in.select( |
|
update_selection, |
|
outputs=[character_name, selected_state], |
|
queue=False, |
|
show_progress=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) |
|
|
|
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", |
|
False, |
|
None |
|
], |
|
[ |
|
"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", |
|
False, |
|
None |
|
], |
|
], |
|
fn = infer, |
|
inputs = [ |
|
prompt, |
|
audio_in, |
|
clean_sample, |
|
hidden_audio_numpy |
|
], |
|
outputs = [ |
|
cloned_out, |
|
video_out, |
|
npz_file, |
|
share_group, |
|
folder_path |
|
], |
|
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, |
|
clean_sample, |
|
hidden_audio_numpy |
|
], |
|
outputs = [ |
|
cloned_out, |
|
video_out, |
|
npz_file, |
|
share_group, |
|
folder_path |
|
] |
|
) |
|
|
|
micro_submit_btn.click( |
|
fn = infer, |
|
inputs = [ |
|
prompt, |
|
micro_in, |
|
clean_micro, |
|
hidden_audio_numpy |
|
], |
|
outputs = [ |
|
cloned_out, |
|
video_out, |
|
npz_file, |
|
share_group, |
|
folder_path |
|
] |
|
) |
|
|
|
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() |
