Add initial implementation of Voxtral audio processing app with Gradio interface

app.py

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+from pathlib import Path
+
+import gradio as gr
+import spaces
+import torch
+from huggingface_hub import snapshot_download
+from transformers import AutoProcessor, VoxtralForConditionalGeneration
+
+# Model paths and setup
+voxtral_mini_path = snapshot_download(
+    repo_id='mistralai/Voxtral-Mini-3B-2507',
+    revision='refs/pr/16',
+    local_dir=Path(__file__).parent / 'Voxtral-Mini-3B-2507',
+    resume_download=True,
+)
+print(f"Voxtral Mini model downloaded to: {voxtral_mini_path}")
+
+voxtral_small_path = snapshot_download(
+    repo_id='mistralai/Voxtral-Small-24B-2507',
+    revision='refs/pr/9',
+    local_dir=Path(__file__).parent / 'Voxtral-Small-24B-2507',
+    resume_download=True,
+)
+print(f"Voxtral Small model downloaded to: {voxtral_small_path}")
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+
+# Load model and processor
+voxtral_mini_processor = AutoProcessor.from_pretrained(voxtral_mini_path)
+voxtral_mini_model = VoxtralForConditionalGeneration.from_pretrained(voxtral_mini_path, torch_dtype=torch.bfloat16, device_map=device)
+
+voxtral_small_processor = AutoProcessor.from_pretrained(voxtral_small_path)
+voxtral_small_model = VoxtralForConditionalGeneration.from_pretrained(voxtral_small_path, torch_dtype=torch.bfloat16, device_map=device)
+
+@spaces.GPU()
+def process_audio(audio_path, model_name, language="en", 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 = str(voxtral_mini_path)
+    elif model_name == "Voxtral Small (24B)":
+        model = voxtral_small_model
+        processor = voxtral_small_processor
+        repo_id = str(voxtral_small_path)
+    else:
+        return "Invalid model selected."
+    
+    inputs = processor.apply_transcription_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 Audio Processing Demo")
+    gr.Markdown("Upload an audio file and get a transcription/response from Voxtral.")
+    
+    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=["en", "fr", "de", "es", "it", "pt", "nl", "ru", "zh", "ja", "ar"],
+                value="en",
+                label="Language"
+            )
+            
+            max_tokens = gr.Slider(minimum=50, maximum=1000, value=500, step=50, label="Max Output Tokens")
+            submit_btn = gr.Button("Process Audio")
+        
+        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=True)

requirements.txt

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+git+https://github.com/huggingface/transformers
+gradio
+torch
+spaces