Create app.py

app.py

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+
+import gradio as gr
+from huggingface_hub import InferenceClient
+from transformers import pipeline
+from datasets import load_dataset
+import soundfile as sf
+import torch
+
+# Initialize the chat model
+chat_client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf")
+
+# Initialize the TTS pipeline
+tts_synthesizer = pipeline("text-to-speech", model="Futuresony/output")
+
+# Load the speaker embeddings dataset
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+
+def chat_with_tts(message, history, system_message, max_tokens, temperature, top_p):
+    # Step 1: Generate response using the chat model
+    messages = [{"role": "system", "content": system_message}]
+    
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+    
+    messages.append({"role": "user", "content": message})
+    
+    response = ""
+    for msg in chat_client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = msg.choices[0].delta.content
+        response += token
+
+    # Step 2: Generate speech using TTS
+    speech = tts_synthesizer(response, forward_params={"speaker_embeddings": speaker_embedding})
+    output_file = "generated_speech.wav"
+    sf.write(output_file, speech["audio"], samplerate=speech["sampling_rate"])
+    
+    # Update the chat history
+    history.append((message, response))
+    
+    # Return both text response, audio file, and updated history
+    return response, output_file, history
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=chat_with_tts,
+    inputs=[
+        gr.Textbox(label="User Input", placeholder="Type your message..."),
+        gr.State([]),  # Initialize history as an empty list
+        gr.Textbox(value="You are a friendly chatbot.", label="System Message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
+    ],
+    outputs=[
+        gr.Textbox(label="Generated Response"),
+        gr.Audio(label="Generated Speech"),
+        gr.State(),  # Add State as an output to update the history
+    ],
+    title="Chat with TTS",
+    description="Enter text to chat with an AI chatbot. The chatbot will generate a response, which will also be converted to speech using TTS."
+)
+
+if __name__ == "__main__":
+    demo.launch()