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import gradio as gr | |
from transformers import AutoProcessor, BarkModel | |
import scipy | |
import numpy as np | |
# Load the processor and model | |
processor = AutoProcessor.from_pretrained("suno/bark") | |
model = BarkModel.from_pretrained("suno/bark") | |
def generate_audio(text): | |
# Your preset may vary | |
voice_preset = "v2/en_speaker_6" | |
inputs = processor(text, voice_preset=voice_preset) | |
audio_array = model.generate(**inputs) | |
# Move the tensor to CPU and convert to numpy array | |
audio_array = audio_array.cpu().numpy().squeeze() | |
sample_rate = model.generation_config.sample_rate | |
# Saving the audio file temporarily | |
output_file = '/tmp/bark_out.wav' | |
scipy.io.wavfile.write(output_file, rate=sample_rate, data=audio_array) | |
# Return the path to the saved audio file | |
return output_file | |
# Define the Gradio interface | |
iface = gr.Interface( | |
fn=generate_audio, | |
inputs="text", | |
outputs="audio", | |
examples=[["Hello, my dog is cute"]], | |
allow_flagging="never" | |
) | |
# Launch the interface | |
iface.launch() | |