import os import time import glob from datetime import datetime, timedelta import numpy as np import torch from scipy.io.wavfile import write as write_wav from transformers import AutoProcessor, AutoModelForTextToWaveform, BarkModel import gradio as gr from fastapi import FastAPI from fastapi.staticfiles import StaticFiles import uvicorn from apscheduler.schedulers.background import BackgroundScheduler OUTPUT_DIR = os.environ.get("OUTPUT_DIR", "/tmp/output") os.makedirs(OUTPUT_DIR, exist_ok=True) os.environ.update({ "SUNO_OFFLOAD_CPU": "True", "SUNO_USE_SMALL_MODELS": "True" }) device = "cuda" if torch.cuda.is_available() else "cpu" processor = AutoProcessor.from_pretrained("suno/bark-small") model = (BarkModel.from_pretrained("suno/bark-small", torch_dtype=torch.float16) .to(device) .to_bettertransformer()) def create_bark_audio(text, voice_preset, device): inputs = processor(text, voice_preset=voice_preset) inputs = {k: v.to(device) if hasattr(v, 'to') else v for k, v in inputs.items()} audio_array = model.generate(**inputs) return audio_array.cpu().numpy().squeeze(), model.generation_config.sample_rate def save_audio(audio_array, sample_rate, prefix="audio"): audio_array = np.clip(audio_array.astype(np.float32), -1, 1) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") filename = os.path.join(OUTPUT_DIR, f"{prefix}_{timestamp}.wav") write_wav(filename, sample_rate, audio_array) return filename def generate_speech(text, voice_preset="v2/en_speaker_6"): device = "cuda" if torch.cuda.is_available() else "cpu" audio_array, sample_rate = create_bark_audio(text, voice_preset, device) return save_audio(audio_array, sample_rate) def text_to_speech_with_url(text, voice): audio_file = generate_speech(text, VOICES[voice]) filename = os.path.basename(audio_file) base_url = os.environ.get("PUBLIC_URL", "http://localhost:7860") return audio_file, f"{base_url}/generated/{filename}" def cleanup_old_files(): cutoff_time = datetime.now() - timedelta(hours=24) for file in glob.glob(os.path.join(OUTPUT_DIR, "audio_*.wav")): if datetime.fromtimestamp(os.path.getmtime(file)) < cutoff_time: try: os.remove(file) except Exception as e: print(f"Error removing file {file}: {e}") VOICES = { 'Speaker 0 (EN)':'v2/en_speaker_0', 'Speaker 1 (EN)':'v2/en_speaker_1', 'Speaker 2 (EN)':'v2/en_speaker_2', 'Speaker 3 (EN)':'v2/en_speaker_3', 'Speaker 4 (EN)':'v2/en_speaker_4', 'Speaker 5 (EN)':'v2/en_speaker_5', 'Speaker 6 (EN)':'v2/en_speaker_6', 'Speaker 7 (EN)':'v2/en_speaker_7', 'Speaker 8 (EN)':'v2/en_speaker_8', 'Speaker 9 (EN)':'v2/en_speaker_9', 'Speaker 0 (ZH)':'v2/zh_speaker_0', 'Speaker 1 (ZH)':'v2/zh_speaker_1', 'Speaker 2 (ZH)':'v2/zh_speaker_2', 'Speaker 3 (ZH)':'v2/zh_speaker_3', 'Speaker 4 (ZH)':'v2/zh_speaker_4', 'Speaker 5 (ZH)':'v2/zh_speaker_5', 'Speaker 6 (ZH)':'v2/zh_speaker_6', 'Speaker 7 (ZH)':'v2/zh_speaker_7', 'Speaker 8 (ZH)':'v2/zh_speaker_8', 'Speaker 9 (ZH)':'v2/zh_speaker_9', 'Speaker 0 (FR)':'v2/fr_speaker_0', 'Speaker 1 (FR)':'v2/fr_speaker_1', 'Speaker 2 (FR)':'v2/fr_speaker_2', 'Speaker 3 (FR)':'v2/fr_speaker_3', 'Speaker 4 (FR)':'v2/fr_speaker_4', 'Speaker 5 (FR)':'v2/fr_speaker_5', 'Speaker 6 (FR)':'v2/fr_speaker_6', 'Speaker 7 (FR)':'v2/fr_speaker_7', 'Speaker 8 (FR)':'v2/fr_speaker_8', 'Speaker 9 (FR)':'v2/fr_speaker_9', 'Speaker 0 (DE)':'v2/de_speaker_0', 'Speaker 1 (DE)':'v2/de_speaker_1', 'Speaker 2 (DE)':'v2/de_speaker_2', 'Speaker 3 (DE)':'v2/de_speaker_3', 'Speaker 4 (DE)':'v2/de_speaker_4', 'Speaker 5 (DE)':'v2/de_speaker_5', 'Speaker 6 (DE)':'v2/de_speaker_6', 'Speaker 7 (DE)':'v2/de_speaker_7', 'Speaker 8 (DE)':'v2/de_speaker_8', 'Speaker 9 (DE)':'v2/de_speaker_9', 'Speaker 0 (HI)':'v2/hi_speaker_0', 'Speaker 1 (HI)':'v2/hi_speaker_1', 'Speaker 2 (HI)':'v2/hi_speaker_2', 'Speaker 3 (HI)':'v2/hi_speaker_3', 'Speaker 4 (HI)':'v2/hi_speaker_4', 'Speaker 5 (HI)':'v2/hi_speaker_5', 'Speaker 6 (HI)':'v2/hi_speaker_6', 'Speaker 7 (HI)':'v2/hi_speaker_7', 'Speaker 8 (HI)':'v2/hi_speaker_8', 'Speaker 9 (HI)':'v2/hi_speaker_9', 'Speaker 0 (IT)':'v2/it_speaker_0', 'Speaker 1 (IT)':'v2/it_speaker_1', 'Speaker 2 (IT)':'v2/it_speaker_2', 'Speaker 3 (IT)':'v2/it_speaker_3', 'Speaker 4 (IT)':'v2/it_speaker_4', 'Speaker 5 (IT)':'v2/it_speaker_5', 'Speaker 6 (IT)':'v2/it_speaker_6', 'Speaker 7 (IT)':'v2/it_speaker_7', 'Speaker 8 (IT)':'v2/it_speaker_8', 'Speaker 9 (IT)':'v2/it_speaker_9', 'Speaker 0 (JA)':'v2/ja_speaker_0', 'Speaker 1 (JA)':'v2/ja_speaker_1', 'Speaker 2 (JA)':'v2/ja_speaker_2', 'Speaker 3 (JA)':'v2/ja_speaker_3', 'Speaker 4 (JA)':'v2/ja_speaker_4', 'Speaker 5 (JA)':'v2/ja_speaker_5', 'Speaker 6 (JA)':'v2/ja_speaker_6', 'Speaker 7 (JA)':'v2/ja_speaker_7', 'Speaker 8 (JA)':'v2/ja_speaker_8', 'Speaker 9 (JA)':'v2/ja_speaker_9', 'Speaker 0 (KO)':'v2/ko_speaker_0', 'Speaker 1 (KO)':'v2/ko_speaker_1', 'Speaker 2 (KO)':'v2/ko_speaker_2', 'Speaker 3 (KO)':'v2/ko_speaker_3', 'Speaker 4 (KO)':'v2/ko_speaker_4', 'Speaker 5 (KO)':'v2/ko_speaker_5', 'Speaker 6 (KO)':'v2/ko_speaker_6', 'Speaker 7 (KO)':'v2/ko_speaker_7', 'Speaker 8 (KO)':'v2/ko_speaker_8', 'Speaker 9 (KO)':'v2/ko_speaker_9', 'Speaker 0 (PL)':'v2/pl_speaker_0', 'Speaker 1 (PL)':'v2/pl_speaker_1', 'Speaker 2 (PL)':'v2/pl_speaker_2', 'Speaker 3 (PL)':'v2/pl_speaker_3', 'Speaker 4 (PL)':'v2/pl_speaker_4', 'Speaker 5 (PL)':'v2/pl_speaker_5', 'Speaker 6 (PL)':'v2/pl_speaker_6', 'Speaker 7 (PL)':'v2/pl_speaker_7', 'Speaker 8 (PL)':'v2/pl_speaker_8', 'Speaker 9 (PL)':'v2/pl_speaker_9', 'Speaker 0 (PT)':'v2/pt_speaker_0', 'Speaker 1 (PT)':'v2/pt_speaker_1', 'Speaker 2 (PT)':'v2/pt_speaker_2', 'Speaker 3 (PT)':'v2/pt_speaker_3', 'Speaker 4 (PT)':'v2/pt_speaker_4', 'Speaker 5 (PT)':'v2/pt_speaker_5', 'Speaker 6 (PT)':'v2/pt_speaker_6', 'Speaker 7 (PT)':'v2/pt_speaker_7', 'Speaker 8 (PT)':'v2/pt_speaker_8', 'Speaker 9 (PT)':'v2/pt_speaker_9', 'Speaker 0 (RU)':'v2/ru_speaker_0', 'Speaker 1 (RU)':'v2/ru_speaker_1', 'Speaker 2 (RU)':'v2/ru_speaker_2', 'Speaker 3 (RU)':'v2/ru_speaker_3', 'Speaker 4 (RU)':'v2/ru_speaker_4', 'Speaker 5 (RU)':'v2/ru_speaker_5', 'Speaker 6 (RU)':'v2/ru_speaker_6', 'Speaker 7 (RU)':'v2/ru_speaker_7', 'Speaker 8 (RU)':'v2/ru_speaker_8', 'Speaker 9 (RU)':'v2/ru_speaker_9', 'Speaker 0 (ES)':'v2/es_speaker_0', 'Speaker 1 (ES)':'v2/es_speaker_1', 'Speaker 2 (ES)':'v2/es_speaker_2', 'Speaker 3 (ES)':'v2/es_speaker_3', 'Speaker 4 (ES)':'v2/es_speaker_4', 'Speaker 5 (ES)':'v2/es_speaker_5', 'Speaker 6 (ES)':'v2/es_speaker_6', 'Speaker 7 (ES)':'v2/es_speaker_7', 'Speaker 8 (ES)':'v2/es_speaker_8', 'Speaker 9 (ES)':'v2/es_speaker_9', 'Speaker 0 (TR)':'v2/tr_speaker_0', 'Speaker 1 (TR)':'v2/tr_speaker_1', 'Speaker 2 (TR)':'v2/tr_speaker_2', 'Speaker 3 (TR)':'v2/tr_speaker_3', 'Speaker 4 (TR)':'v2/tr_speaker_4', 'Speaker 5 (TR)':'v2/tr_speaker_5', 'Speaker 6 (TR)':'v2/tr_speaker_6', 'Speaker 7 (TR)':'v2/tr_speaker_7', 'Speaker 8 (TR)':'v2/tr_speaker_8', 'Speaker 9 (TR)':'v2/tr_speaker_9', } CUSTOM_CSS = """ #component-16 { display: none !important; } .gradio-container .main h1 { padding-top: 60px; position: relative; } .gradio-container .main h1::before { content: ''; position: absolute; top: 0; left: 50%; transform: translateX(-50%); width: 253px; height: 50px; background-image: url('public/AkashLogo.svg'); background-repeat: no-repeat; background-position: center; background-size: contain; } """ with gr.Blocks(css=CUSTOM_CSS) as gradio_audio: gr.Interface( fn=text_to_speech_with_url, inputs=[ gr.Textbox(label="Text to audio", placeholder="Enter text here...", show_copy_button=False), gr.Dropdown(choices=list(VOICES.keys()), value="Speaker 0 (EN)", label="Voice") ], outputs=[ gr.Audio(label="Generated Speech"), gr.Textbox(label="Public URL", interactive=False, show_copy_button=True) ], title="Audio Generator", description=""" Transform text into natural-sounding speech using the Bark AI model. Features support for multiple languages and voice styles. **How to use:** 1. Enter your text in any supported language 2. Select a voice preset 3. Click submit to generate speech 4. Get the public URL to share/download the generated audio (it will expire in 24 hours) """, article="""
Powered by Bark-small model and Akash Network, created by alexx855
""", examples=[ ["Welcome to the news. Today's top story...", "Speaker 0 (EN)"], ["The quick brown fox jumps over the lazy dog.", "Speaker 1 (EN)"], ["你好，今天天气真不错。", "Speaker 0 (ZH)"], ["Bonjour, comment allez-vous aujourd'hui?", "Speaker 0 (FR)"], ["J'aime beaucoup voyager en France.", "Speaker 1 (FR)"], ["Guten Tag, wie geht es Ihnen?", "Speaker 0 (DE)"], ["Das Wetter ist heute sehr schön.", "Speaker 1 (DE)"], ["नमस्ते, आप कैसे हैं?", "Speaker 0 (HI)"], ["मौसम बहुत सुहावन�� है।", "Speaker 1 (HI)"], ["Buongiorno, come stai oggi?", "Speaker 0 (IT)"], ["Mi piace molto viaggiare in Italia.", "Speaker 1 (IT)"], ["こんにちは、お元気ですか？", "Speaker 0 (JA)"], ["今日はとても良い天気ですね。", "Speaker 1 (JA)"], ["안녕하세요, 오늘 기분이 어떠신가요?", "Speaker 0 (KO)"], ["날씨가 정말 좋네요.", "Speaker 1 (KO)"], ["Dzień dobry, jak się masz?", "Speaker 0 (PL)"], ["Dzisiaj jest bardzo ładna pogoda.", "Speaker 1 (PL)"], ["Olá, como está você hoje?", "Speaker 0 (PT)"], ["O tempo está muito bonito hoje.", "Speaker 1 (PT)"], ["Здравствуйте, как ваши дела?", "Speaker 0 (RU)"], ["Сегодня прекрасная погода.", "Speaker 1 (RU)"], ["Hola, ¿cómo estás hoy?", "Speaker 0 (ES)"], ["El tiempo está muy bonito hoy.", "Speaker 1 (ES)"], ["Merhaba, bugün nasılsınız?", "Speaker 0 (TR)"], ["Bugün hava çok güzel.", "Speaker 1 (TR)"] ] ) scheduler = BackgroundScheduler() scheduler.add_job(cleanup_old_files, 'interval', hours=1) scheduler.start() if __name__ == "__main__": app = FastAPI() app.mount("/generated", StaticFiles(directory=OUTPUT_DIR), name="generated") app.mount("/public", StaticFiles(directory="public"), name="public") gradio_app = gr.mount_gradio_app(app, gradio_audio, path="/", favicon_path="public/favicon.ico") uvicorn.run(app, host="0.0.0.0", port=7860)