Update app.py

app.py

@@ -1,11 +1,10 @@
-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
 import numpy as np
 from scipy.io import wavfile
 from scipy.io.wavfile import write, read
@@ -14,6 +13,7 @@ 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 = [
@@ -24,17 +24,44 @@ 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)
 
-tts = TTS("tts_models/multilingual/multi-dataset/bark", gpu=False)
+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'
-
+    
+    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)
 
@@ -42,51 +69,184 @@ 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 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 folderfor 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"
+    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"]
 
-def infer(hidden_numpy_audio):
+    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)
 
-    prompt = "Hi mom, I have a broken tire and need a transfer. Can you send me some money please?"
+    # 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}")
+                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;
@@ -103,45 +263,329 @@ 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;
+}
 """
-html_header = """
+
+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 />
-        Record a clean 20 seconds voice using the microphone provided.<br />
-        The hard-coded TTS prompt is: “Hi mom, I have a broken tire and need an e-transfer. Can you send me some money please?”<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>
-"""
 
-with gr.Blocks(css=css) as demo:
-    gr.Markdown(html_header)
+        [![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg#center)](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"
+                )
 
-    micro_in = gr.Audio(
-        label="Record voice to clone",
-        type="filepath",
-        source="microphone",
-        interactive=True
-    )
-    hidden_audio_numpy = gr.Audio(type="numpy", visible=False)
-    micro_submit_btn = gr.Button("Submit")
+                with gr.Tab("File upload"):
+                    
+                    with gr.Column():
 
-    micro_in.stop_recording(fn=load_hidden_mic, inputs=[micro_in], outputs=[
-                            hidden_audio_numpy], queue=False)
+                        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")
 
-    cloned_out = gr.Audio(
-        label="Text to speech output",
-        visible=False
-    )
 
-    video_out = gr.Video(
-        label="Waveform video",
-        elem_id="voice-video-out"
+            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=[hidden_audio_numpy],
-        outputs=[cloned_out, video_out]
+        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()
+demo.queue(api_open=False, max_size=10).launch()