Create app.py

app.py

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+from cProfile import label
+from distutils.command.check import check
+from doctest import Example
+import gradio as gr
+import os
+import sys
+import numpy as np
+import logging
+import torch
+from xml.sax import saxutils
+#import nltk
+
+from bark import SAMPLE_RATE, generate_audio
+from bark.clonevoice import clone_voice
+from bark.generation import SAMPLE_RATE, preload_models, codec_decode, generate_coarse, generate_fine, generate_text_semantic
+from scipy.io.wavfile import write as write_wav
+from parseinput import split_and_recombine_text, build_ssml, is_ssml, create_clips_from_ssml
+from datetime import datetime
+from tqdm.auto import tqdm
+
+OUTPUTFOLDER = "Outputs"
+
+
+def generate_with_settings(text_prompt, semantic_temp=0.7, semantic_top_k=50, semantic_top_p=0.95, coarse_temp=0.7, coarse_top_k=50, coarse_top_p=0.95, fine_temp=0.5, voice_name=None, use_semantic_history_prompt=True, use_coarse_history_prompt=True, use_fine_history_prompt=True, output_full=False):
+
+    # generation with more control
+    x_semantic = generate_text_semantic(
+        text_prompt,
+        history_prompt=voice_name if use_semantic_history_prompt else None,
+        temp=semantic_temp,
+        top_k=semantic_top_k,
+        top_p=semantic_top_p
+    )
+
+    x_coarse_gen = generate_coarse(
+        x_semantic,
+        history_prompt=voice_name if use_coarse_history_prompt else None,
+        temp=coarse_temp,
+        top_k=coarse_top_k,
+        top_p=coarse_top_p
+    )
+    x_fine_gen = generate_fine(
+        x_coarse_gen,
+        history_prompt=voice_name if use_fine_history_prompt else None,
+        temp=fine_temp
+    )
+
+    if output_full:
+        full_generation = {
+            'semantic_prompt': x_semantic,
+            'coarse_prompt': x_coarse_gen,
+            'fine_prompt': x_fine_gen
+        }
+        return full_generation, codec_decode(x_fine_gen)
+    return codec_decode(x_fine_gen)
+
+def generate_text_to_speech(text, selected_speaker, text_temp, waveform_temp, quick_generation, complete_settings, progress=gr.Progress(track_tqdm=True)):
+    if text == None or len(text) < 1:
+        raise gr.Error('No text entered!')
+
+    # Chunk the text into smaller pieces then combine the generated audio
+
+    # generation settings
+    if selected_speaker == 'None':
+        selected_speaker = None
+
+    voice_name = selected_speaker
+
+    semantic_temp = text_temp
+    semantic_top_k = 50
+    semantic_top_p = 0.95
+
+    coarse_temp = waveform_temp
+    coarse_top_k = 50
+    coarse_top_p = 0.95
+
+    fine_temp = 0.5
+
+    use_semantic_history_prompt = "Use semantic history" in complete_settings
+    use_coarse_history_prompt = "Use coarse history" in complete_settings
+    use_fine_history_prompt = "Use fine history" in complete_settings
+    use_last_generation_as_history = "Use last generation as history" in complete_settings
+    progress(0, desc="Generating")
+
+    silence = np.zeros(int(0.25 * SAMPLE_RATE), dtype=np.float32)  # quarter second of silence
+
+    all_parts = []
+    text = text.lstrip()
+    if is_ssml(text):
+        list_speak = create_clips_from_ssml(text)
+        for i, clip in tqdm(enumerate(list_speak), total=len(list_speak)):
+            selected_speaker = clip[0]
+            text = clip[1]
+            text = saxutils.unescape(text)
+            if selected_speaker == "None":
+                selected_speaker = None
+
+            print(f"\nGenerating Text ({i+1}/{len(list_speak)}) -> {selected_speaker}:`{text}`")
+            audio_array = generate_audio(text, selected_speaker, text_temp, waveform_temp)
+            if len(list_speak) > 1:
+                save_wav(audio_array, create_filename(OUTPUTFOLDER, "audioclip",".wav"))
+            all_parts += [audio_array, silence.copy()]
+    else:
+        texts = split_and_recombine_text(text)
+        for i, text in tqdm(enumerate(texts), total=len(texts)):
+            print(f"\nGenerating Text ({i+1}/{len(texts)}) -> {selected_speaker}:`{text}`")
+            if quick_generation == True:
+                audio_array = generate_audio(text, selected_speaker, text_temp, waveform_temp)
+
+            else:
+                full_generation, audio_array = generate_with_settings(
+                    text,
+                    semantic_temp=semantic_temp,
+                    semantic_top_k=semantic_top_k,
+                    semantic_top_p=semantic_top_p,
+                    coarse_temp=coarse_temp,
+                    coarse_top_k=coarse_top_k,
+                    coarse_top_p=coarse_top_p,
+                    fine_temp=fine_temp,
+                    voice_name=voice_name,
+                    use_semantic_history_prompt=use_semantic_history_prompt,
+                    use_coarse_history_prompt=use_coarse_history_prompt,
+                    use_fine_history_prompt=use_fine_history_prompt,
+                    output_full=True,
+                )
+
+            # Noticed this in the HF Demo - convert to 16bit int -32767/32767 - most used audio format  
+            # audio_array = (audio_array * 32767).astype(np.int16)
+
+            if len(texts) > 1:
+                save_wav(audio_array, create_filename(OUTPUTFOLDER, "audioclip",".wav"))
+
+            if quick_generation == False & use_last_generation_as_history:
+                # save to npz
+                voice_name = create_filename(OUTPUTFOLDER, "audioclip", "")
+                save_voice(voice_name,
+                          full_generation['semantic_prompt'],
+                          full_generation['coarse_prompt'],
+                          full_generation['fine_prompt'])
+                # loading voice from custom folder needs to have extension
+                voice_name = voice_name + ".npz"
+            all_parts += [audio_array, silence.copy()]
+
+    # save & play audio
+    result = create_filename(OUTPUTFOLDER, "final",".wav")
+    save_wav(np.concatenate(all_parts), result)
+    return result
+
+def create_filename(path, name, extension):
+    now = datetime.now()
+    date_str = now.strftime("%m-%d-%Y")
+    outputs_folder = os.path.join(os.getcwd(), path)
+    if not os.path.exists(outputs_folder):
+        os.makedirs(outputs_folder)
+
+    sub_folder = os.path.join(outputs_folder, date_str)
+    if not os.path.exists(sub_folder):
+        os.makedirs(sub_folder)
+    now = datetime.now()
+    time_str = now.strftime("%H-%M-%S")
+    file_name = f"{name}_{time_str}{extension}"
+    return os.path.join(sub_folder, file_name)
+
+
+def save_wav(audio_array, filename):
+    write_wav(filename, SAMPLE_RATE, audio_array)
+
+def save_voice(filename, semantic_prompt, coarse_prompt, fine_prompt):
+    np.savez_compressed(
+        filename,
+        semantic_prompt=semantic_prompt,
+        coarse_prompt=coarse_prompt,
+        fine_prompt=fine_prompt
+    )
+    
+
+def on_quick_gen_changed(checkbox):
+    if checkbox == False:
+        return gr.CheckboxGroup.update(visible=True)
+    return gr.CheckboxGroup.update(visible=False)
+
+def delete_output_files(checkbox_state):
+    if checkbox_state:
+        outputs_folder = os.path.join(os.getcwd(), OUTPUTFOLDER)
+        if os.path.exists(outputs_folder):
+            purgedir(outputs_folder)
+    return False
+
+
+# https://stackoverflow.com/a/54494779
+def purgedir(parent):
+    for root, dirs, files in os.walk(parent):                                      
+        for item in files:
+            # Delete subordinate files                                                 
+            filespec = os.path.join(root, item)
+            os.unlink(filespec)
+        for item in dirs:
+            # Recursively perform this operation for subordinate directories   
+            purgedir(os.path.join(root, item))
+
+def convert_text_to_ssml(text, selected_speaker):
+    return build_ssml(text, selected_speaker)
+
+    
+
+logger = logging.getLogger(__name__)
+
+autolaunch = False
+
+if len(sys.argv) > 1:
+    autolaunch = "-autolaunch" in sys.argv
+
+
+if torch.cuda.is_available() == False:
+    os.environ['BARK_FORCE_CPU'] = 'True'
+    logger.warning("No CUDA detected, fallback to CPU!")
+
+print(f'smallmodels={os.environ.get("SUNO_USE_SMALL_MODELS", False)}')
+print(f'enablemps={os.environ.get("SUNO_ENABLE_MPS", False)}')
+print(f'offloadcpu={os.environ.get("SUNO_OFFLOAD_CPU", False)}')
+print(f'forcecpu={os.environ.get("BARK_FORCE_CPU", False)}')
+print(f'autolaunch={autolaunch}\n\n')
+
+#print("Updating nltk\n")
+#nltk.download('punkt')
+
+print("Preloading Models\n")
+preload_models()
+
+# Collect all existing speakers/voices in dir
+speakers_list = []
+
+for root, dirs, files in os.walk("./bark/assets/prompts"):
+	for file in files:
+		if(file.endswith(".npz")):
+			pathpart = root.replace("./bark/assets/prompts", "")
+			if len(pathpart) < 1:
+				pathpart = "/"
+			speakers_list.append(os.path.join(pathpart, file[:-4]))
+
+speakers_list = sorted(speakers_list, key=lambda x: x.lower())
+speakers_list.insert(0, 'None')
+
+# Create Gradio Blocks
+
+with gr.Blocks(title="Bark Enhanced Gradio GUI", mode="Bark Enhanced") as barkgui:
+    gr.Markdown("### [Bark Enhanced](https://github.com/C0untFloyd/bark-gui)")
+    with gr.Tab("TTS"):
+        with gr.Row():
+            with gr.Column():
+                placeholder = "Enter text here."
+                input_text = gr.Textbox(label="Input Text", lines=4, placeholder=placeholder)
+            with gr.Column():
+                convert_to_ssml_button = gr.Button("Convert Text to SSML")
+        with gr.Row():
+            with gr.Column():
+                examples = [
+                    "Special meanings: [laughter] [laughs] [sighs] [music] [gasps] [clears throat] MAN: WOMAN:",
+                   "♪ Never gonna make you cry, never gonna say goodbye, never gonna tell a lie and hurt you ♪",
+                   "And now — a picture of a larch [laughter]",
+                   """
+                        WOMAN: I would like an oatmilk latte please.
+                        MAN: Wow, that's expensive!
+                   """,
+                   """<?xml version="1.0"?>
+<speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://www.w3.org/2001/10/synthesis
+                   http://www.w3.org/TR/speech-synthesis/synthesis.xsd"
+         xml:lang="en-US">
+<voice name="/v2/en_speaker_9">Look at that drunk guy!</voice>
+<voice name="/v2/en_speaker_3">Who is he?</voice>
+<voice name="/v2/en_speaker_9">WOMAN: [clears throat] 10 years ago, he proposed me and I rejected him.</voice>
+<voice name="/v2/en_speaker_3">Oh my God [laughs] he is still celebrating</voice>
+</speak>"""
+                   ]
+                examples = gr.Examples(examples=examples, inputs=input_text)
+
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("[Voice Prompt Library](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c)")
+                speaker = gr.Dropdown(speakers_list, value=speakers_list[0], label="Voice")
+            with gr.Column():
+                text_temp = gr.Slider(
+                    0.1,
+                    1.0,
+                    value=0.7,
+                    label="Generation Temperature",
+                    info="1.0 more diverse, 0.1 more conservative"
+                )
+                waveform_temp = gr.Slider(0.1, 1.0, value=0.7, label="Waveform temperature", info="1.0 more diverse, 0.1 more conservative")
+
+        with gr.Row():
+            with gr.Column():
+                quick_gen_checkbox = gr.Checkbox(label="Quick Generation", value=True)
+            with gr.Column():
+                settings_checkboxes = ["Use semantic history", "Use coarse history", "Use fine history", "Use last generation as history"]
+                complete_settings = gr.CheckboxGroup(choices=settings_checkboxes, value=settings_checkboxes, label="Detailed Generation Settings", type="value", interactive=True, visible=False)
+                quick_gen_checkbox.change(fn=on_quick_gen_changed, inputs=quick_gen_checkbox, outputs=complete_settings)
+
+        with gr.Row():
+            with gr.Column():
+                tts_create_button = gr.Button("Create")
+            with gr.Column():
+                hidden_checkbox = gr.Checkbox(visible=False)
+                button_delete_files = gr.Button("Clear output folder")
+        with gr.Row():
+            output_audio = gr.Audio(label="Generated Audio", type="filepath")
+
+    with gr.Tab("Clone Voice"):
+        input_audio_filename = gr.Audio(label="Input audio.wav", source="upload", type="filepath")
+        transcription_text = gr.Textbox(label="Transcription Text", lines=1, placeholder="Enter Text of your Audio Sample here...")
+        initialname = "./bark/assets/prompts/custom/MeMyselfAndI"
+        #inputAudioFilename = gr.Textbox(label="Filename of Input Audio", lines=1, placeholder="audio.wav")
+        output_voice = gr.Textbox(label="Filename of trained Voice", lines=1, placeholder=initialname, value=initialname)
+        clone_voice_button = gr.Button("Create Voice")
+        dummy = gr.Text(label="Progress")
+
+        convert_to_ssml_button.click(convert_text_to_ssml, inputs=[input_text, speaker],outputs=input_text)
+        tts_create_button.click(generate_text_to_speech, inputs=[input_text, speaker, text_temp, waveform_temp, quick_gen_checkbox, complete_settings],outputs=output_audio)
+        # Javascript hack to display modal confirmation dialog
+        js = "(x) => confirm('Are you sure? This will remove all files from output folder')"
+        button_delete_files.click(None, None, hidden_checkbox, _js=js)
+        hidden_checkbox.change(delete_output_files, [hidden_checkbox], [hidden_checkbox])
+        clone_voice_button.click(clone_voice, inputs=[input_audio_filename, transcription_text, output_voice], outputs=dummy)
+
+barkgui.queue().launch(show_error=True)