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| import gradio as gr | |
| from transformers import AutoProcessor, BarkModel | |
| import torch | |
| import numpy as np | |
| import nltk | |
| from scipy.io.wavfile import write | |
| from IPython.display import Audio | |
| # Download nltk punkt for sentence tokenization | |
| nltk.download("punkt") | |
| nltk.download("punkt_tab") | |
| torch.set_num_threads(1) | |
| # Load models | |
| models = { | |
| "suno/bark": BarkModel.from_pretrained("suno/bark"), | |
| "suno/bark-small": BarkModel.from_pretrained("suno/bark-small") | |
| } | |
| # Combined voice presets | |
| all_voice_presets = [ | |
| "v2/en_speaker_0", "v2/en_speaker_1", "v2/en_speaker_2", "v2/en_speaker_3", | |
| "v2/en_speaker_4", "v2/en_speaker_5", "v2/en_speaker_6", | |
| "v2/en_speaker_7", "v2/en_speaker_8", "v2/en_speaker_9", | |
| "v2/tr_speaker_0", "v2/tr_speaker_1", "v2/tr_speaker_2", "v2/tr_speaker_3", | |
| "v2/tr_speaker_4", "v2/tr_speaker_5", "v2/tr_speaker_6", | |
| "v2/tr_speaker_7", "v2/tr_speaker_8", "v2/tr_speaker_9", | |
| "v2/de_speaker_0", "v2/de_speaker_1", "v2/de_speaker_2", "v2/de_speaker_3", | |
| "v2/de_speaker_4", "v2/de_speaker_5", "v2/de_speaker_6", | |
| "v2/de_speaker_7", "v2/de_speaker_8", "v2/de_speaker_9", | |
| "v2/fr_speaker_0", "v2/fr_speaker_1", "v2/fr_speaker_2", "v2/fr_speaker_3", | |
| "v2/fr_speaker_4", "v2/fr_speaker_5", "v2/fr_speaker_6", | |
| "v2/fr_speaker_7", "v2/fr_speaker_8", "v2/fr_speaker_9", | |
| "v2/it_speaker_0", "v2/it_speaker_1", "v2/it_speaker_2", "v2/it_speaker_3", | |
| "v2/it_speaker_4", "v2/it_speaker_5", "v2/it_speaker_6", | |
| "v2/it_speaker_7", "v2/it_speaker_8", "v2/it_speaker_9", | |
| "v2/zh_speaker_0", "v2/zh_speaker_1", "v2/zh_speaker_2", "v2/zh_speaker_3", | |
| "v2/zh_speaker_4", "v2/zh_speaker_5", "v2/zh_speaker_6", | |
| "v2/zh_speaker_7", "v2/zh_speaker_8", "v2/zh_speaker_9" | |
| ] | |
| SAMPLE_RATE = 22050 # Set a standard sample rate for Bark output | |
| silence_duration = 0.25 # quarter-second silence duration between sentences | |
| # Function to generate speech | |
| def generate_speech(text, model_name, voice_preset): | |
| model = models[model_name] | |
| processor = AutoProcessor.from_pretrained(model_name) | |
| sentences = nltk.sent_tokenize(text) # Tokenize text into sentences | |
| if len(sentences) == 1: # If single sentence, use original method | |
| inputs = processor(text, voice_preset=voice_preset) | |
| audio_array = model.generate(**inputs) | |
| audio_array = audio_array.cpu().numpy().squeeze() | |
| return (model.generation_config.sample_rate, audio_array) | |
| # For multiple sentences, generate and concatenate | |
| silence = np.zeros(int(silence_duration * SAMPLE_RATE)) | |
| audio_pieces = [] | |
| for sentence in sentences: | |
| inputs = processor(sentence, voice_preset=voice_preset) | |
| audio_array = model.generate(**inputs).cpu().numpy().squeeze() | |
| audio_pieces.append(audio_array) | |
| audio_pieces.append(silence.copy()) # Add silence between sentences | |
| full_audio = np.concatenate(audio_pieces) | |
| return (SAMPLE_RATE, full_audio) | |
| # Gradio app setup | |
| with gr.Blocks() as app: | |
| gr.Markdown("# Multilingual Text-to-Speech with Bark") | |
| # Textbox for user input | |
| text_input = gr.Textbox(label="Enter Text", placeholder="Type something to synthesize...") | |
| # Model selection | |
| model_preset_input = gr.Dropdown(["suno/bark", "suno/bark-small"], label="Select Model", value="suno/bark-small") | |
| # Combined voice presets dropdown | |
| voice_preset_input = gr.Dropdown( | |
| choices=all_voice_presets, | |
| label="Select Voice Preset" | |
| ) | |
| # Button to generate voice | |
| generate_button = gr.Button("Generate Voice") | |
| # Output audio | |
| audio_output = gr.Audio(label="Generated Voice", type="numpy") | |
| # Generate voice on button click | |
| generate_button.click( | |
| generate_speech, | |
| inputs=[text_input, model_preset_input, voice_preset_input], | |
| outputs=audio_output | |
| ) | |
| app.launch() | |