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/v2"): for file in files: if(file.endswith(".npz")): pathpart = root.replace("./bark/assets/prompts/v2", "") 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", visible=False) 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! """, """ Look at that drunk guy! Who is he? WOMAN: [clears throat] 10 years ago, he proposed me and I rejected him. Oh my God [laughs] he is still celebrating """ ] 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", visible=False) 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 = "Custom_voice" #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") npz_file_1 = gr.File(label=".npz file") 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,npz_file_1]) barkgui.queue().launch(show_error=True)