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import gradio as gr
import torch
from diffusers import DiffusionPipeline
from transformers import (
WhisperForConditionalGeneration,
WhisperProcessor,
pipeline,
)
import os
MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
device = "cuda" if torch.cuda.is_available() else "cpu"
model = WhisperForConditionalGeneration.from_pretrained("whispy/whisper_italian").to(device)
processor = WhisperProcessor.from_pretrained("whispy/whisper_italian")
pipe = pipeline(model="whispy/whisper_italian")
diffuser_pipeline = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
custom_pipeline="speech_to_image_diffusion",
speech_model="whispy/whisper_italian",
speech_processor=processor,
use_auth_token=MY_SECRET_TOKEN,
revision="fp16",
torch_dtype=torch.float16,
)
diffuser_pipeline.enable_attention_slicing()
diffuser_pipeline = diffuser_pipeline.to(device)
def transcribe(audio):
text = pipe(audio)["text"]
return text
#————————————————————————————————————————————
# GRADIO SETUP
title = "Speech to Diffusion • Community Pipeline"
description = """
<p style='text-align: center;'>This demo can generate an image from an audio sample using pre-trained OpenAI whisper-small and Stable Diffusion.<br />
Community examples consist of both inference and training examples that have been added by the community.<br />
<a href='https://github.com/huggingface/diffusers/tree/main/examples/community#speech-to-image' target='_blank'> Click here for more information about community pipelines </a>
</p>
"""
article = """
<p style='text-align: center;'>Community pipeline by Mikail Duzenli • Gradio demo by Sylvain Filoni & Ahsen Khaliq<p>
"""
audio_input = gr.Audio(source="microphone", type="filepath")
image_output = gr.Image()
def speech_to_text(audio_sample):
#process_audio = whisper.load_audio(audio_sample)
process_audio = transcribe(audio_sample)
output = diffuser_pipeline(process_audio)
print(f"""
————————
output: {output}
————————
""")
return output.images[0]
demo = gr.Interface(fn=speech_to_text, inputs=audio_input, outputs=image_output, title=title, description=description, article=article)
demo.launch() |