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import random
import gradio as gr
import numpy as np
from elevenlabs import voices, generate, set_api_key, UnauthenticatedRateLimitError
def pad_buffer(audio):
# Pad buffer to multiple of 2 bytes
buffer_size = len(audio)
element_size = np.dtype(np.int16).itemsize
if buffer_size % element_size != 0:
audio = audio + b'\0' * (element_size - (buffer_size % element_size))
return audio
def generate_voice(text, voice_name):
try:
audio = generate(
text[:250], # Limit to 250 characters
voice=voice_name,
model="eleven_multilingual_v2"
)
return (44100, np.frombuffer(pad_buffer(audio), dtype=np.int16))
except UnauthenticatedRateLimitError as e:
raise gr.Error("Thanks for trying out ElevenLabs TTS! You've reached the free tier limit. Please provide an API key to continue.")
except Exception as e:
raise gr.Error(e)
description = """
Here's a demonstration of the world's most advanced TTS systems, created by ElevenLabs. 🎉 I wanted to experiment with this amazing technology for the Italian language 🇮🇹, and I'm excited to share its capabilities with you! Eleven Multilingual V2 is a single foundational model supporting an impressive 28 languages, including English 🇬🇧, Chinese 🇨🇳, Spanish 🇪🇸, Hindi 🇮🇳, Portuguese 🇵🇹, French 🇫🇷, German 🇩🇪, Japanese 🇯🇵, Arabic 🇸🇦, Korean 🇰🇷, Indonesian 🇮🇩, Italian 🇮🇹, Dutch 🇳🇱, Turkish 🇹🇷, Polish 🇵🇱, Swedish 🇸🇪, Filipino 🇵🇭, Malay 🇲🇾, Romanian 🇷🇴, Ukrainian 🇺🇦, Greek 🇬🇷, Czech 🇨🇿, Danish 🇩🇰, Finnish 🇫🇮, Bulgarian 🇧🇬, Croatian 🇭🇷, Slovak 🇸🇰, and Tamil 🇱🇰. 🌍 Sign up on ElevenLabs to get fast access to long-form generation, voice cloning, API keys, and more! 🚀
"""
with gr.Blocks() as block:
gr.Markdown('[  ](https://elevenlabs.io)')
gr.Markdown(description)
input_text = gr.Textbox(
label="Input Text (250 characters max)",
lines=2,
value="Ciao, mi chiamo Sab, sono un ragazzo italiano appassionato di AI e nuove tecnologie!",
elem_id="input_text"
)
all_voices = voices()
input_voice = gr.Dropdown(
[ voice.name for voice in all_voices ],
value="Callum",
label="Voice",
elem_id="input_voice"
)
run_button = gr.Button(
value="Generate Voice",
)
out_audio = gr.Audio(
label="Generated Voice",
type="numpy",
elem_id="out_audio",
format="mp3"
)
inputs = [input_text, input_voice]
outputs = [out_audio]
run_button.click(
fn=generate_voice,
inputs=inputs,
outputs=outputs,
queue=True
)
block.launch(debug=False, show_error=True, share=True) |