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| from cProfile import label | |
| import dataclasses | |
| 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 | |
| import pytorch_seed | |
| import time | |
| from xml.sax import saxutils | |
| from bark.api import generate_with_settings | |
| from bark.api import save_as_prompt | |
| from util.settings import Settings | |
| #import nltk | |
| from bark import SAMPLE_RATE | |
| from cloning.clonevoice import clone_voice | |
| from bark.generation import SAMPLE_RATE, preload_models, _load_history_prompt, codec_decode | |
| from scipy.io.wavfile import write as write_wav | |
| from util.parseinput import split_and_recombine_text, build_ssml, is_ssml, create_clips_from_ssml | |
| from datetime import datetime | |
| from tqdm.auto import tqdm | |
| from util.helper import create_filename, add_id3_tag | |
| from swap_voice import swap_voice_from_audio | |
| from training.training_prepare import prepare_semantics_from_text, prepare_wavs_from_semantics | |
| from training.train import training_prepare_files, train | |
| settings = Settings('config.yaml') | |
| def generate_text_to_speech(text, selected_speaker, text_temp, waveform_temp, eos_prob, quick_generation, complete_settings, seed, batchcount, progress=gr.Progress(track_tqdm=True)): | |
| # Chunk the text into smaller pieces then combine the generated audio | |
| # generation settings | |
| if selected_speaker == 'None': | |
| selected_speaker = None | |
| voice_name = selected_speaker | |
| if text == None or len(text) < 1: | |
| if selected_speaker == None: | |
| raise gr.Error('No text entered!') | |
| # Extract audio data from speaker if no text and speaker selected | |
| voicedata = _load_history_prompt(voice_name) | |
| audio_arr = codec_decode(voicedata["fine_prompt"]) | |
| result = create_filename(settings.output_folder_path, "None", "extract",".wav") | |
| save_wav(audio_arr, result) | |
| return result | |
| if batchcount < 1: | |
| batchcount = 1 | |
| silenceshort = np.zeros(int((float(settings.silence_sentence) / 1000.0) * SAMPLE_RATE), dtype=np.int16) # quarter second of silence | |
| silencelong = np.zeros(int((float(settings.silence_speakers) / 1000.0) * SAMPLE_RATE), dtype=np.float32) # half a second of silence | |
| use_last_generation_as_history = "Use last generation as history" in complete_settings | |
| save_last_generation = "Save generation as Voice" in complete_settings | |
| for l in range(batchcount): | |
| currentseed = seed | |
| if seed != None and seed > 2**32 - 1: | |
| logger.warning(f"Seed {seed} > 2**32 - 1 (max), setting to random") | |
| currentseed = None | |
| if currentseed == None or currentseed <= 0: | |
| currentseed = np.random.default_rng().integers(1, 2**32 - 1) | |
| assert(0 < currentseed and currentseed < 2**32) | |
| progress(0, desc="Generating") | |
| full_generation = None | |
| all_parts = [] | |
| complete_text = "" | |
| text = text.lstrip() | |
| if is_ssml(text): | |
| list_speak = create_clips_from_ssml(text) | |
| prev_speaker = None | |
| for i, clip in tqdm(enumerate(list_speak), total=len(list_speak)): | |
| selected_speaker = clip[0] | |
| # Add pause break between speakers | |
| if i > 0 and selected_speaker != prev_speaker: | |
| all_parts += [silencelong.copy()] | |
| prev_speaker = selected_speaker | |
| 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} (Seed {currentseed}):`{text}`") | |
| complete_text += text | |
| with pytorch_seed.SavedRNG(currentseed): | |
| audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob) | |
| currentseed = torch.random.initial_seed() | |
| if len(list_speak) > 1: | |
| filename = create_filename(settings.output_folder_path, currentseed, "audioclip",".wav") | |
| save_wav(audio_array, filename) | |
| add_id3_tag(filename, text, selected_speaker, currentseed) | |
| all_parts += [audio_array] | |
| else: | |
| texts = split_and_recombine_text(text, settings.input_text_desired_length, settings.input_text_max_length) | |
| for i, text in tqdm(enumerate(texts), total=len(texts)): | |
| print(f"\nGenerating Text ({i+1}/{len(texts)}) -> {selected_speaker} (Seed {currentseed}):`{text}`") | |
| complete_text += text | |
| if quick_generation == True: | |
| with pytorch_seed.SavedRNG(currentseed): | |
| audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob) | |
| currentseed = torch.random.initial_seed() | |
| else: | |
| full_output = use_last_generation_as_history or save_last_generation | |
| if full_output: | |
| full_generation, audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob, output_full=True) | |
| else: | |
| audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob) | |
| # 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: | |
| filename = create_filename(settings.output_folder_path, currentseed, "audioclip",".wav") | |
| save_wav(audio_array, filename) | |
| add_id3_tag(filename, text, selected_speaker, currentseed) | |
| if quick_generation == False and (save_last_generation == True or use_last_generation_as_history == True): | |
| # save to npz | |
| voice_name = create_filename(settings.output_folder_path, seed, "audioclip", ".npz") | |
| save_as_prompt(voice_name, full_generation) | |
| if use_last_generation_as_history: | |
| selected_speaker = voice_name | |
| all_parts += [audio_array] | |
| # Add short pause between sentences | |
| if text[-1] in "!?.\n" and i > 1: | |
| all_parts += [silenceshort.copy()] | |
| # save & play audio | |
| result = create_filename(settings.output_folder_path, currentseed, "final",".wav") | |
| save_wav(np.concatenate(all_parts), result) | |
| # write id3 tag with text truncated to 60 chars, as a precaution... | |
| add_id3_tag(result, complete_text, selected_speaker, currentseed) | |
| return result | |
| 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(), settings.output_folder_path) | |
| 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) | |
| def training_prepare(selected_step, num_text_generations, progress=gr.Progress(track_tqdm=True)): | |
| if selected_step == prepare_training_list[0]: | |
| prepare_semantics_from_text() | |
| else: | |
| prepare_wavs_from_semantics() | |
| return None | |
| def start_training(save_model_epoch, max_epochs, progress=gr.Progress(track_tqdm=True)): | |
| training_prepare_files("./training/data/", "./training/data/checkpoint/hubert_base_ls960.pt") | |
| train("./training/data/", save_model_epoch, max_epochs) | |
| return None | |
| def apply_settings(themes, input_server_name, input_server_port, input_server_public, input_desired_len, input_max_len, input_silence_break, input_silence_speaker): | |
| settings.selected_theme = themes | |
| settings.server_name = input_server_name | |
| settings.server_port = input_server_port | |
| settings.server_share = input_server_public | |
| settings.input_text_desired_length = input_desired_len | |
| settings.input_text_max_length = input_max_len | |
| settings.silence_sentence = input_silence_break | |
| settings.silence_speaker = input_silence_speaker | |
| settings.save() | |
| def restart(): | |
| global restart_server | |
| restart_server = True | |
| def create_version_html(): | |
| python_version = ".".join([str(x) for x in sys.version_info[0:3]]) | |
| versions_html = f""" | |
| python: <span title="{sys.version}">{python_version}</span> | |
| • | |
| torch: {getattr(torch, '__long_version__',torch.__version__)} | |
| • | |
| gradio: {gr.__version__} | |
| """ | |
| return versions_html | |
| logger = logging.getLogger(__name__) | |
| APPTITLE = "Bark Voice Cloning UI" | |
| 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() | |
| available_themes = ["Default", "gradio/glass", "gradio/monochrome", "gradio/seafoam", "gradio/soft", "gstaff/xkcd", "freddyaboulton/dracula_revamped", "ysharma/steampunk"] | |
| tokenizer_language_list = ["de","en", "pl"] | |
| prepare_training_list = ["Step 1: Semantics from Text","Step 2: WAV from Semantics"] | |
| seed = -1 | |
| server_name = settings.server_name | |
| if len(server_name) < 1: | |
| server_name = None | |
| server_port = settings.server_port | |
| if server_port <= 0: | |
| server_port = None | |
| global run_server | |
| global restart_server | |
| run_server = True | |
| while run_server: | |
| # 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", "") | |
| name = os.path.join(pathpart, file[:-4]) | |
| if name.startswith("/") or name.startswith("\\"): | |
| name = name[1:] | |
| speakers_list.append(name) | |
| speakers_list = sorted(speakers_list, key=lambda x: x.lower()) | |
| speakers_list.insert(0, 'None') | |
| print(f'Launching {APPTITLE} Server') | |
| # Create Gradio Blocks | |
| with gr.Blocks(title=f"{APPTITLE}", mode=f"{APPTITLE}", theme=settings.selected_theme) as barkgui: | |
| gr.Markdown("# <center>🐶🎶⭐ - Bark Voice Cloning</center>") | |
| gr.Markdown("## <center>🤗 - If you like this space, please star my [github repo](https://github.com/KevinWang676/Bark-Voice-Cloning)</center>") | |
| gr.Markdown("### <center>🎡 - Based on [bark-gui](https://github.com/C0untFloyd/bark-gui)</center>") | |
| gr.Markdown(f""" You can duplicate and use it with a GPU: <a href="https://huggingface.co/spaces/{os.getenv('SPACE_ID')}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a> | |
| or open in [Colab](https://colab.research.google.com/github/KevinWang676/Bark-Voice-Cloning/blob/main/Bark_Voice_Cloning.ipynb) for quick start 🌟 P.S. Voice cloning needs a GPU, but TTS doesn't 😄 | |
| """) | |
| with gr.Tab("🎙️ - Clone Voice"): | |
| with gr.Row(): | |
| 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...") | |
| with gr.Row(): | |
| with gr.Column(): | |
| initialname = "/home/user/app/bark/assets/prompts/file" | |
| output_voice = gr.Textbox(label="Filename of trained Voice (do not change the initial name)", lines=1, placeholder=initialname, value=initialname, visible=False) | |
| with gr.Column(): | |
| tokenizerlang = gr.Dropdown(tokenizer_language_list, label="Base Language Tokenizer", value=tokenizer_language_list[1], visible=False) | |
| with gr.Row(): | |
| clone_voice_button = gr.Button("Create Voice", variant="primary") | |
| with gr.Row(): | |
| dummy = gr.Text(label="Progress") | |
| npz_file = gr.File(label=".npz file") | |
| speakers_list.insert(0, npz_file) # add prompt | |
| 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) | |
| convert_to_ssml_button = gr.Button("Convert Input Text to SSML") | |
| with gr.Column(): | |
| seedcomponent = gr.Number(label="Seed (default -1 = Random)", precision=0, value=-1) | |
| batchcount = gr.Number(label="Batch count", precision=0, value=1) | |
| 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 (Choose “file” if you wanna use the custom voice)") | |
| with gr.Column(): | |
| text_temp = gr.Slider(0.1, 1.0, value=0.6, 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) | |
| settings_checkboxes = ["Use last generation as history", "Save generation as Voice"] | |
| complete_settings = gr.CheckboxGroup(choices=settings_checkboxes, value=settings_checkboxes, label="Detailed Generation Settings", type="value", interactive=True, visible=False) | |
| with gr.Column(): | |
| eos_prob = gr.Slider(0.0, 0.5, value=0.05, label="End of sentence probability") | |
| with gr.Row(): | |
| with gr.Column(): | |
| tts_create_button = gr.Button("Generate", variant="primary") | |
| with gr.Column(): | |
| hidden_checkbox = gr.Checkbox(visible=False) | |
| button_stop_generation = gr.Button("Stop generation") | |
| with gr.Row(): | |
| output_audio = gr.Audio(label="Generated Audio", type="filepath") | |
| with gr.Tab("🔮 - Voice Conversion"): | |
| with gr.Row(): | |
| swap_audio_filename = gr.Audio(label="Input audio.wav to swap voice", source="upload", type="filepath") | |
| with gr.Row(): | |
| with gr.Column(): | |
| swap_tokenizer_lang = gr.Dropdown(tokenizer_language_list, label="Base Language Tokenizer", value=tokenizer_language_list[1]) | |
| swap_seed = gr.Number(label="Seed (default -1 = Random)", precision=0, value=-1) | |
| with gr.Column(): | |
| speaker_swap = gr.Dropdown(speakers_list, value=speakers_list[0], label="Voice (Choose “file” if you wanna use the custom voice)") | |
| swap_batchcount = gr.Number(label="Batch count", precision=0, value=1) | |
| with gr.Row(): | |
| swap_voice_button = gr.Button("Generate", variant="primary") | |
| with gr.Row(): | |
| output_swap = gr.Audio(label="Generated Audio", type="filepath") | |
| quick_gen_checkbox.change(fn=on_quick_gen_changed, inputs=quick_gen_checkbox, outputs=complete_settings) | |
| convert_to_ssml_button.click(convert_text_to_ssml, inputs=[input_text, speaker],outputs=input_text) | |
| gen_click = tts_create_button.click(generate_text_to_speech, inputs=[input_text, speaker, text_temp, waveform_temp, eos_prob, quick_gen_checkbox, complete_settings, seedcomponent, batchcount],outputs=output_audio) | |
| button_stop_generation.click(fn=None, inputs=None, outputs=None, cancels=[gen_click]) | |
| swap_voice_button.click(swap_voice_from_audio, inputs=[swap_audio_filename, speaker_swap, swap_tokenizer_lang, swap_seed, swap_batchcount], outputs=output_swap) | |
| clone_voice_button.click(clone_voice, inputs=[input_audio_filename, output_voice], outputs=[dummy, npz_file]) | |
| restart_server = False | |
| try: | |
| barkgui.queue().launch(show_error=True) | |
| except: | |
| restart_server = True | |
| run_server = False | |
| try: | |
| while restart_server == False: | |
| time.sleep(1.0) | |
| except (KeyboardInterrupt, OSError): | |
| print("Keyboard interruption in main thread... closing server.") | |
| run_server = False | |
| barkgui.close() | |