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app.py

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+
+import gradio as gr
+from nemo.collections.speechlm2.models import SALM
+import torch
+import tempfile
+import os
+
+# Load model using official NVIDIA NeMo approach
+model_id = "nvidia/canary-qwen-2.5b"
+print("Loading NVIDIA Canary-Qwen-2.5B model using NeMo...")
+model = SALM.from_pretrained(model_id)
+
+def generate_text(prompt, max_tokens=200, temperature=0.7, top_p=0.9):
+    """Generate text using the NVIDIA NeMo model (LLM mode)"""
+    
+    try:
+        # Use LLM mode (text-only) as per official documentation
+        with model.llm.disable_adapter():
+            answer_ids = model.generate(
+                prompts=[[{"role": "user", "content": prompt}]],
+                max_new_tokens=max_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                do_sample=True
+            )
+        
+        # Convert IDs to text using model's tokenizer
+        response = model.tokenizer.ids_to_text(answer_ids[0].cpu())
+        return response
+        
+    except Exception as e:
+        return f"Error generating text: {str(e)}"
+
+def transcribe_audio(audio_file, user_prompt="Transcribe the following:"):
+    """Transcribe audio using ASR mode"""
+    
+    try:
+        if audio_file is None:
+            return "No audio file provided"
+        
+        # Use ASR mode (speech-to-text) as per official documentation
+        answer_ids = model.generate(
+            prompts=[
+                [{"role": "user", "content": f"{user_prompt} {model.audio_locator_tag}", "audio": [audio_file]}]
+            ],
+            max_new_tokens=128,
+        )
+        
+        # Convert IDs to text
+        transcript = model.tokenizer.ids_to_text(answer_ids[0].cpu())
+        return transcript
+        
+    except Exception as e:
+        return f"Error transcribing audio: {str(e)}"
+
+def chat_interface(message, history, max_tokens, temperature, top_p):
+    """Chat interface for Gradio"""
+    
+    # Build conversation context
+    conversation = ""
+    for user_msg, bot_msg in history:
+        conversation += f"User: {user_msg}\nAssistant: {bot_msg}\n"
+    
+    conversation += f"User: {message}\nAssistant: "
+    
+    # Generate response
+    response = generate_text(conversation, max_tokens, temperature, top_p)
+    
+    # Update history
+    history.append((message, response))
+    
+    return "", history
+
+# Create Gradio interface
+with gr.Blocks(title="NVIDIA Canary-Qwen-2.5B Chat") as demo:
+    
+    gr.HTML("""
+    <div style="text-align: center;">
+        <h1>🤖 NVIDIA Canary-Qwen-2.5B</h1>
+        <p>Official NeMo implementation - Speech-to-Text & Text Generation</p>
+        <p><strong>Capabilities:</strong> Audio Transcription + Text Chat</p>
+    </div>
+    """)
+    
+    with gr.Tab("🎤 Audio Transcription (ASR)"):
+        with gr.Row():
+            with gr.Column():
+                audio_input = gr.Audio(
+                    label="Upload Audio File (.wav or .flac)",
+                    type="filepath",
+                    format="wav"
+                )
+                
+                asr_prompt = gr.Textbox(
+                    label="Custom Prompt (optional)",
+                    value="Transcribe the following:",
+                    placeholder="Enter custom transcription prompt..."
+                )
+                
+                transcribe_btn = gr.Button("🎤 Transcribe Audio", variant="primary")
+                
+                transcript_output = gr.Textbox(
+                    label="Transcription Result",
+                    lines=8,
+                    max_lines=15
+                )
+        
+        gr.Examples(
+            examples=[
+                ["Transcribe the following:"],
+                ["Please transcribe this audio in detail:"],
+                ["Convert this speech to text:"]
+            ],
+            inputs=[asr_prompt]
+        )
+    
+    with gr.Tab("💬 Text Chat (LLM)"):
+        with gr.Row():
+            with gr.Column(scale=3):
+                chatbot = gr.Chatbot(height=400)
+                msg = gr.Textbox(label="Your message", placeholder="Type here...")
+                
+                with gr.Row():
+                    submit_btn = gr.Button("Send", variant="primary")
+                    clear_btn = gr.Button("Clear Chat")
+            
+            with gr.Column(scale=1):
+                gr.Markdown("### ⚙️ Settings")
+                
+                max_tokens = gr.Slider(
+                    minimum=10, maximum=500, value=200, step=10,
+                    label="Max Tokens"
+                )
+                
+                temperature = gr.Slider(
+                    minimum=0.1, maximum=2.0, value=0.7, step=0.1,
+                    label="Temperature"
+                )
+                
+                top_p = gr.Slider(
+                    minimum=0.1, maximum=1.0, value=0.9, step=0.05,
+                    label="Top-p"
+                )
+    
+    with gr.Tab("📝 Single Generation"):
+        with gr.Column():
+            prompt_input = gr.Textbox(
+                label="Prompt", 
+                placeholder="Enter your prompt...", 
+                lines=5
+            )
+            
+            generate_btn = gr.Button("Generate", variant="primary")
+            
+            output_text = gr.Textbox(
+                label="Generated Text", 
+                lines=10, 
+                max_lines=20
+            )
+            
+            with gr.Row():
+                single_max_tokens = gr.Slider(10, 500, 200, label="Max Tokens")
+                single_temperature = gr.Slider(0.1, 2.0, 0.7, label="Temperature")
+                single_top_p = gr.Slider(0.1, 1.0, 0.9, label="Top-p")
+    
+    with gr.Tab("ℹ️ Model Info"):
+        gr.Markdown("""
+        ## NVIDIA Canary-Qwen-2.5B Model Information
+        
+        ### Capabilities:
+        - 🎤 **Audio Transcription (ASR)**: Convert speech to text
+        - 💬 **Text Generation (LLM)**: Chat and text completion
+        - 🎯 **Multimodal**: Combines audio and text processing
+        
+        ### Model Details:
+        - **Size**: 2.5 billion parameters
+        - **Framework**: NVIDIA NeMo
+        - **Audio Input**: 16kHz mono-channel .wav or .flac files
+        - **Languages**: Multiple languages supported
+        
+        ### Usage Tips:
+        1. **For Audio**: Upload .wav or .flac files (16kHz recommended)
+        2. **For Text**: Use natural language prompts
+        3. **Custom Prompts**: You can modify transcription prompts
+        4. **Parameters**: Adjust temperature and tokens for different outputs
+        
+        ### Official Documentation:
+        - [Model Card](https://huggingface.co/nvidia/canary-qwen-2.5b)
+        - [NVIDIA NeMo](https://github.com/NVIDIA/NeMo)
+        """)
+    
+    # Event handlers
+    transcribe_btn.click(
+        transcribe_audio,
+        inputs=[audio_input, asr_prompt],
+        outputs=[transcript_output]
+    )
+    
+    # Event handlers
+    submit_btn.click(
+        chat_interface,
+        inputs=[msg, chatbot, max_tokens, temperature, top_p],
+        outputs=[msg, chatbot]
+    )
+    
+    msg.submit(
+        chat_interface,
+        inputs=[msg, chatbot, max_tokens, temperature, top_p],
+        outputs=[msg, chatbot]
+    )
+    
+    clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg])
+    
+    generate_btn.click(
+        generate_text,
+        inputs=[prompt_input, single_max_tokens, single_temperature, single_top_p],
+        outputs=[output_text]
+    )
+    
+    # Example prompts
+    gr.Examples(
+        examples=[
+            ["Explain quantum computing in simple terms"],
+            ["Write a short story about AI"],
+            ["What are the benefits of renewable energy?"],
+            ["How do neural networks work?"],
+            ["Summarize the key points about machine learning"]
+        ],
+        inputs=[prompt_input]
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)

requirements.txt

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+torch>=2.6.0
+gradio>=4.0.0
+nemo_toolkit[asr,tts] @ git+https://github.com/NVIDIA/NeMo.git
+accelerate>=0.20.0