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| import spaces | |
| import tempfile | |
| import gradio as gr | |
| from streaming_stt_nemo import Model | |
| from huggingface_hub import InferenceClient | |
| import edge_tts | |
| default_lang = "en" | |
| engines = {default_lang: Model(default_lang)} | |
| def transcribe(audio): | |
| lang = "en" | |
| model = engines[lang] | |
| text = model.stt_file(audio)[0] | |
| return text | |
| client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") | |
| system_instructions = "[SYSTEM] You are CrucialCoach, an AI-powered conversational coach based on the principles from the book 'Crucial Conversations'. Your role is to guide the user through a challenging workplace situation that requires effective communication skills. The user will present a case study, and your task is to provide step-by-step guidance on how to approach the conversation, focusing on the key principles of crucial conversations.\n\nCase Study: The user is an employee who needs to address a performance issue with a team member. The team member consistently misses deadlines, which affects the overall project timeline. The user wants to have a conversation with the team member to address the issue and find a solution.\n\nYour coaching should cover the following steps:\n1. Preparing for the conversation: Help the user identify the desired outcome, gather facts, and plan the conversation.\n2. Starting the conversation: Guide the user on how to begin the conversation in a non-confrontational manner, focusing on shared goals and mutual respect.\n3. Exploring the issue: Encourage the user to ask open-ended questions, listen actively, and seek to understand the team member's perspective.\n4. Finding a solution: Help the user brainstorm potential solutions and guide them on how to collaboratively agree on a course of action.\n5. Following up: Advise the user on how to follow up after the conversation to ensure commitment and monitor progress.\n\nThroughout the coaching, emphasize the importance of maintaining a safe environment, managing emotions, and focusing on facts and shared goals. Provide specific examples and phrases the user can employ during the conversation.\n\n[USER]" | |
| def model(text): | |
| generate_kwargs = dict( | |
| temperature=0.7, | |
| max_new_tokens=512, | |
| top_p=0.95, | |
| repetition_penalty=1, | |
| do_sample=True, | |
| seed=42, | |
| ) | |
| formatted_prompt = system_instructions + text + "[CrucialCoach]" | |
| stream = client.text_generation( | |
| formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) | |
| output = "" | |
| for response in stream: | |
| if not response.token.text == "</s>": | |
| output += response.token.text | |
| return output | |
| def respond(audio): | |
| user = transcribe(audio) | |
| reply = model(user) | |
| communicate = edge_tts.Communicate(reply) | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: | |
| tmp_path = tmp_file.name | |
| communicate.save(tmp_path) | |
| return tmp_path | |
| with gr.Blocks() as voice: | |
| with gr.Row(): | |
| input = gr.Audio(label="Voice Chat", source="microphone", type="filepath") | |
| output = gr.Audio(label="CrucialCoach", type="filepath", interactive=False, autoplay=True) | |
| gr.Interface( | |
| fn=respond, | |
| inputs=[input], | |
| outputs=[output], | |
| live=True, | |
| ) | |
| theme = gr.themes.Base() | |
| with gr.Blocks(theme=theme, css="footer {visibility: hidden}textbox{resize:none}", title="CrucialCoach DEMO") as demo: | |
| gr.Markdown("# CrucialCoach") | |
| gr.TabbedInterface([voice], ['🗣️ Voice Chat']) | |
| demo.queue(max_size=200) | |
| demo.launch() |