Update app.py

app.py

@@ -1,20 +1,12 @@
 from TTS.api import TTS
 import json
 import gradio as gr
+from gradio import Dropdown
 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
-from gradio import Dropdown
-file_upload_available = os.environ.get("ALLOW_FILE_UPLOAD")
-MAX_NUMBER_SENTENCES = 10
-
 with open("characters.json", "r") as file:
     data = json.load(file)
     characters = [
@@ -29,99 +21,6 @@ with open("characters.json", "r") as file:
 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
-    # 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'
-
-    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"]
@@ -141,54 +40,38 @@ NEW INFERENCE:
 
     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
 
@@ -201,10 +84,8 @@ NEW INFERENCE:
                     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 ...")
@@ -214,49 +95,32 @@ NEW INFERENCE:
     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]
+prompt_choices = [
+    "I am very displeased with the progress being made to finish the cross-town transit line. transit line. This has been an embarrassing use of taxpayer dollars.",
+    "Yes, John is my friend, but He was never at my house watching the baseball game.",
+    "We are expecting a double digit increase in profits by the end of the fiscal year.",
+    "Hi Grandma, Just calling to ask for money, or I can't see you over the holidays. "
+]
 
-        # Join the selected sentences back into a single string
-        limited_prompt = ' '.join(first_nb_sentences)
-        prompt = limited_prompt
+positive_prompts = {
+    prompt_choices[0]: "I am very pleased with the progress being made to finish the cross-town transit line.  This has been an excellent use of taxpayer dollars.",
+    prompt_choices[1]: "Yes, John is my friend.  He was at my house watching the baseball game all night.",
+    prompt_choices[2]: "We are expecting a modest single digit increase in profits by the end of the fiscal year.",
+    prompt_choices[3]: "Hi Grandma it’s me,  Just calling to say I love you, and I can’t wait to see you over the holidays."
+}
 
-    else:
-        prompt = prompt
+prompt = Dropdown(
+    label="Text to speech prompt",
+    choices=prompt_choices,
+    elem_id="tts-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}")
+def update_helper_text(prompt_choice):
+    return positive_prompts.get(prompt_choice, '')
 
-    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)
 
+prompt.change(update_helper_text, outputs=["texts_samples"], queue=False)
 
 css = """
 #col-container {max-width: 780px; margin-left: auto; margin-right: auto;}
@@ -299,128 +163,28 @@ span.record-icon > span.dot.svelte-1thnwz {
   max-width: 15rem;
   height: 36px;
 }
-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"):
         with gr.Row():
             with gr.Column():
-                prompt_choices = [
-                    "Hey guys, my name is Rohan and I’m going to tell you why I think I am going to be the worst prefect you’ve ever had. I am a very lazy worker and am very irresponsible with my time. As a prefect I will have to make certain decisions and I think that because of my lack of maturity I will be able to make the wrong ones. Other students might tell you that they will listen to you and do things that are not even possible in this position but I will tell you that I will try my very best to make this school horrific."
-                ]
-
 
-                # Create a Dropdown with the hardcoded prompts
                 prompt = Dropdown(
                     label="Text to speech prompt",
                     choices=prompt_choices,
                     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")
-
+                audio_in = gr.Audio(
+                    label="WAV voice to clone",
+                    type="filepath",
+                    source="upload"
+                )
+                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.Column():
 
                 cloned_out = gr.Audio(
@@ -440,70 +204,21 @@ with gr.Blocks(css=css) as demo:
 
                 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"
-                )
+audio_in.change(fn=wipe_npz_file, inputs=[folder_path], queue=False)
+submit_btn.click(
+    fn=infer,
+    inputs=[
+        prompt,
+        audio_in,
+        clean_sample,
+        hidden_audio_numpy
+    ],
+    outputs=[
+        cloned_out,
+        video_out,
+        npz_file,
+        folder_path
+    ]
+)
 
-        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=[
-            prompt,
-            audio_in,
-            clean_sample,
-            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
-        ]
-    )
-
-    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()
+demo.queue(api_open=False, max_size=10).launch()