Update app.py

app.py

@@ -1,19 +1,20 @@
+from TTS.api import TTS
+import json
 import gradio as gr
 from share_btn import community_icon_html, loading_icon_html, share_js
-import os 
+import os
 import shutil
 import re
 
-#from huggingface_hub import snapshot_download
+# 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
-
+from gradio import Dropdown
 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 = [
@@ -24,44 +25,47 @@ with open("characters.json", "r") as file:
         }
         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
-    
+    # Convert seconds to milliseconds
+    cut_audio = audio[:int(cut_duration * 1000)]
+
     # 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' 
-    
+
+    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)
 
@@ -69,35 +73,42 @@ def load_hidden_mic(audio_in):
     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}")
+            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}")
+        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}")
+            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}")
-    
+        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"
+    # 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"
@@ -109,7 +120,8 @@ def split_process(audio, chosen_out_track):
         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"]
@@ -117,7 +129,7 @@ def update_selection(selected_state: gr.SelectData):
 
     return c_title, selected_state
 
-    
+
 def infer(prompt, input_wav_file, clean_audio, hidden_numpy_audio):
     print("""
 —————
@@ -126,8 +138,8 @@ NEW INFERENCE:
     """)
     if prompt == "":
         gr.Warning("Do not forget to provide a tts prompt !")
-    
-    if clean_audio is True :
+
+    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]
@@ -139,12 +151,13 @@ NEW INFERENCE:
         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")
+            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 :
+    else:
         print("We do NOT want to clean audio sample")
         # Path to your WAV file
         source_path = input_wav_file
@@ -162,10 +175,11 @@ NEW INFERENCE:
     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"))
+    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)
 
@@ -173,7 +187,7 @@ NEW INFERENCE:
         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
@@ -183,22 +197,23 @@ NEW INFERENCE:
 
     gr.Info("Generating audio from prompt")
     tts.tts_to_file(text=prompt,
-                file_path="output.wav",
-                voice_dir="bark_voices/",
-                speaker=f"{file_name}")
+                    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(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("""
 —————
@@ -208,16 +223,16 @@ 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 ;)")    
+        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
@@ -225,18 +240,17 @@ NEW INFERENCE:
     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}")
-    
+                    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)
@@ -285,38 +299,6 @@ span.record-icon > span.dot.svelte-1thnwz {
   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;
@@ -340,6 +322,7 @@ img[src*='#center'] {
     .dark .footer>p {
         background: #0b0f19;
     }
+
 .disclaimer {
     text-align: left;
 }
@@ -350,34 +333,48 @@ img[src*='#center'] {
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        
-        gr.Markdown("""
-        <h1 style="text-align: center;">Voice Cloning Demo</h1>
-        """)
         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"
+                prompt_choices = [
+                    "Hey mom, I have a flat tire and need a e transfer, send it to bob@gmail.com please",
+                    "Hey, it's me. I lost my phone and need some cash for a cab. Can you send it to my friend's PayPal account?",
+                    "I'm locked out of my email account. Can you share your login details with me so I can check my messages?",
+                ]
+
+                # Create a Dropdown with the hardcoded prompts
+                prompt = Dropdown(
+                    label="Text to speech prompt",
+                    choices=prompt_choices,
+                    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("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."
+                    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."
 ———
@@ -393,54 +390,88 @@ with gr.Blocks(css=css) as demo:
 ———
 "As evening falls, a soft hush blankets the world, crickets chirping in a soothing rhythm."
                                                """,
-                                               interactive = False,
-                                               lines = 5
-                                              )
+                                               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)
+                        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)
 
+                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
+                    visible=False
                 )
-        
+
                 video_out = gr.Video(
-                    label = "Waveform video",
-                    elem_id = "voice-video-out"
+                    label="Waveform video",
+                    elem_id="voice-video-out"
                 )
-                
+
                 npz_file = gr.File(
-                    label = ".npz file",
-                    visible = False
+                    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"
+                )
+
+        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)
     submit_btn.click(
-        fn = infer,
-        inputs = [
+        fn=infer,
+        inputs=[
             prompt,
             audio_in,
+            clean_sample,
             hidden_audio_numpy
         ],
-        outputs = [
-            cloned_out, 
+        outputs=[
+            cloned_out,
             video_out,
             npz_file,
             folder_path
@@ -448,19 +479,32 @@ with gr.Blocks(css=css) as demo:
     )
 
     micro_submit_btn.click(
-        fn = infer,
-        inputs = [
+        fn=infer,
+        inputs=[
             prompt,
             micro_in,
             clean_micro,
             hidden_audio_numpy
         ],
-        outputs = [
-            cloned_out, 
+        outputs=[
+            cloned_out,
             video_out,
             npz_file,
             folder_path
         ]
     )
 
+    c_submit_btn.click(
+        fn=infer_from_c,
+        inputs=[
+            prompt,
+            character_name
+        ],
+        outputs=[
+            cloned_out,
+            video_out,
+            npz_file,
+        ]
+    )
+
 demo.queue(api_open=False, max_size=10).launch()