import gradio as gr import numpy as np import io from pydub import AudioSegment import tempfile import openai import time from dataclasses import dataclass, field from threading import Lock import base64 @dataclass class AppState: stream: np.ndarray | None = None sampling_rate: int = 0 pause_detected: bool = False conversation: list = field(default_factory=list) client: openai.OpenAI = None output_format: str = "mp3" stopped: bool = False # Global lock for thread safety state_lock = Lock() def create_client(api_key): return openai.OpenAI( base_url="https://llama3-1-8b.lepton.run/api/v1/", api_key=api_key ) def determine_pause(audio, sampling_rate, state): # Take the last 1 second of audio pause_length = int(sampling_rate * 1) # 1 second if len(audio) < pause_length: return False last_audio = audio[-pause_length:] amplitude = np.abs(last_audio) # Calculate the average amplitude in the last 1 second avg_amplitude = np.mean(amplitude) silence_threshold = 0.01 # Adjust this threshold as needed if avg_amplitude < silence_threshold: return True else: return False def process_audio(audio: tuple, state: AppState): if state.stream is None: state.stream = audio[1] state.sampling_rate = audio[0] else: state.stream = np.concatenate((state.stream, audio[1])) pause_detected = determine_pause(state.stream, state.sampling_rate, state) state.pause_detected = pause_detected if state.pause_detected: return gr.Audio(recording=False), state else: return None, state def generate_response_and_audio(audio_bytes: bytes, state: AppState): if state.client is None: raise gr.Error("Please enter a valid API key first.") format_ = state.output_format bitrate = 128 if format_ == "mp3" else 32 # Higher bitrate for MP3, lower for OPUS audio_data = base64.b64encode(audio_bytes).decode() try: stream = state.client.chat.completions.create( extra_body={ "require_audio": True, "tts_preset_id": "jessica", "tts_audio_format": format_, "tts_audio_bitrate": bitrate, }, model="llama3.1-8b", messages=[ {"role": "user", "content": [{"type": "audio", "data": audio_data}]} ], temperature=0.7, max_tokens=256, stream=True, ) full_response = "" asr_result = "" audios = [] for chunk in stream: if not chunk.choices: continue content = chunk.choices[0].delta.content audio = getattr(chunk.choices[0], "audio", []) asr_results = getattr(chunk.choices[0], "asr_results", []) if asr_results: asr_result += "".join(asr_results) yield full_response, asr_result, None, state if content: full_response += content yield full_response, asr_result, None, state if audio: audios.extend(audio) final_audio = b"".join([base64.b64decode(a) for a in audios]) yield full_response, asr_result, final_audio, state except Exception as e: raise gr.Error(f"Error during audio streaming: {e}") def response(state: AppState): if state.stream is None or len(state.stream) == 0: return None, None, state audio_buffer = io.BytesIO() segment = AudioSegment( state.stream.tobytes(), frame_rate=state.sampling_rate, sample_width=state.stream.dtype.itemsize, channels=(1 if len(state.stream.shape) == 1 else state.stream.shape[1]), ) segment.export(audio_buffer, format="wav") generator = generate_response_and_audio(audio_buffer.getvalue(), state) # Process the generator to get the final results final_text = "" final_asr = "" final_audio = None for text, asr, audio, updated_state in generator: final_text = text if text else final_text final_asr = asr if asr else final_asr final_audio = audio if audio else final_audio state = updated_state # Update the chatbot with the final conversation state.conversation.append({"role": "user", "content": final_asr}) state.conversation.append({"role": "assistant", "content": final_text}) # Reset the audio stream for the next interaction state.stream = None state.pause_detected = False chatbot_output = state.conversation[-2:] # Get the last two messages return chatbot_output, final_audio, state def start_recording_user(state: AppState): if not state.stopped: return gr.Audio(recording=True) else: return gr.Audio(recording=False) def set_api_key(api_key, state): if not api_key: raise gr.Error("Please enter a valid API key.") try: state.client = create_client(api_key) return gr.update(value="API key set successfully!", visible=True), state except Exception as e: return gr.update(value="Connection error", visible=True), state with gr.Blocks() as demo: gr.Markdown("# Lepton LLM Voice Mode") gr.Markdown( "You can find Lepton serverless endpoint API Key at [here](https://dashboard.lepton.ai/workspace-redirect/settings/api-tokens)" ) with gr.Row(): with gr.Column(scale=3): api_key_input = gr.Textbox( type="password", label="Enter your Lepton API Key" ) with gr.Column(scale=1): set_key_button = gr.Button("Set API Key", scale=2, variant="secondary") api_key_status = gr.Textbox( show_label=False, container=False, interactive=False, visible=False ) with gr.Blocks(): input_audio = gr.Audio(label="Input Audio", sources="microphone", type="numpy") output_audio = gr.Audio(label="Output Audio", autoplay=True) chatbot = gr.Chatbot(label="Conversation", type="messages") cancel = gr.Button("Stop Conversation", variant="stop") state = gr.State(AppState()) set_key_button.click( set_api_key, inputs=[api_key_input, state], outputs=[api_key_status, state], ) stream = input_audio.stream( process_audio, [input_audio, state], [input_audio, state], stream_every=0.25, # Reduced to make it more responsive time_limit=60, # Increased to allow for longer messages ) respond = input_audio.stop_recording( response, [state], [chatbot, output_audio, state] ) # Update the chatbot with the final conversation respond.then(lambda s: s.conversation, [state], [chatbot]) # Automatically restart recording after the assistant's response restart = output_audio.stop(start_recording_user, [state], [input_audio]) # Add a "Stop Conversation" button cancel.click( lambda: (AppState(stopped=True), gr.Audio(recording=False)), None, [state, input_audio], cancels=[respond, restart], ) demo.launch()