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import gradio as gr
import spaces
import torch
from transformers import AutoProcessor, VoxtralForConditionalGeneration
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
# Load model and processor
voxtral_mini_processor = AutoProcessor.from_pretrained("MohamedRashad/Voxtral-Mini-3B-2507-transformers")
voxtral_mini_model = VoxtralForConditionalGeneration.from_pretrained("MohamedRashad/Voxtral-Mini-3B-2507-transformers", torch_dtype=torch.bfloat16, device_map=device)
voxtral_small_processor = AutoProcessor.from_pretrained("MohamedRashad/Voxtral-Small-24B-2507-transformers")
voxtral_small_model = VoxtralForConditionalGeneration.from_pretrained("MohamedRashad/Voxtral-Small-24B-2507-transformers", torch_dtype=torch.bfloat16, device_map=device)
LANGUAGES = {
"English": "en",
"French": "fr",
"German": "de",
"Spanish": "es",
"Italian": "it",
"Portuguese": "pt",
"Dutch": "nl",
"Russian": "ru",
"Chinese": "zh",
"Japanese": "ja",
"Arabic": "ar",
}
@spaces.GPU()
def process_audio(audio_path, model_name, lang_name, max_tokens=500):
"""Process audio with selected Voxtral model and return the generated response"""
if not audio_path:
return "Please upload an audio file."
if model_name == "Voxtral Mini (3B)":
model = voxtral_mini_model
processor = voxtral_mini_processor
repo_id = "MohamedRashad/Voxtral-Mini-3B-2507-transformers"
elif model_name == "Voxtral Small (24B)":
model = voxtral_small_model
processor = voxtral_small_processor
repo_id = "MohamedRashad/Voxtral-Small-24B-2507-transformers"
else:
return "Invalid model selected."
language = LANGUAGES[lang_name]
inputs = processor.apply_transcrition_request(language=language, audio=audio_path, model_id=repo_id)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=max_tokens)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
return decoded_outputs[0]
# Define Gradio interface
with gr.Blocks(title="Voxtral Demo") as demo:
gr.Markdown("# Voxtral Transcription Demo")
gr.Markdown("Upload an audio file and get a transcription from Voxtral.")
gr.Markdown("You can find the `transformers` version of Voxtral here: [3B](https://huggingface.co/MohamedRashad/Voxtral-Mini-3B-2507-transformers), [24B](https://huggingface.co/MohamedRashad/Voxtral-Small-24B-2507-transformers)")
with gr.Row():
with gr.Column():
audio_input = gr.Audio(type="filepath", label="Upload Audio")
model_selector = gr.Dropdown(
choices=["Voxtral Mini (3B)", "Voxtral Small (24B)"],
value="Voxtral Mini (3B)",
label="Select Model"
)
language = gr.Dropdown(
choices=list(LANGUAGES.keys()),
value="English",
label="Language"
)
max_tokens = gr.Slider(minimum=50, maximum=1000, value=500, step=50, label="Max Output Tokens")
submit_btn = gr.Button("Extract Transcription", variant="primary")
with gr.Column():
output_text = gr.Textbox(label="Generated Response", lines=10)
submit_btn.click(
fn=process_audio,
inputs=[audio_input, model_selector, language, max_tokens],
outputs=output_text
)
# Launch the app
if __name__ == "__main__":
demo.queue().launch(share=False)
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