| import gradio as gr |
| import edge_tts |
| import asyncio |
| import tempfile |
| import os |
| from huggingface_hub import InferenceClient |
| import re |
| from streaming_stt_nemo import Model |
| import torch |
| import random |
|
|
| 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 |
|
|
| HF_TOKEN = os.environ.get("HF_TOKEN", None) |
|
|
| def client_fn(model): |
| if "Mixtral" in model: |
| return InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") |
| elif "Llama" in model: |
| return InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct") |
| elif "Mistral" in model: |
| return InferenceClient("mistralai/Mistral-7B-Instruct-v0.2") |
| elif "Phi" in model: |
| return InferenceClient("microsoft/Phi-3-mini-4k-instruct") |
| else: |
| return InferenceClient("microsoft/Phi-3-mini-4k-instruct") |
|
|
| def randomize_seed_fn(seed: int) -> int: |
| seed = random.randint(0, 999999) |
| return seed |
|
|
| system_instructions1 = """ |
| [SYSTEM] Answer as the FallnAI lab assistant, developed by FallnAI. |
| Keep conversation friendly, short, clear, and concise. |
| Avoid unnecessary introductions and answer the user's questions directly. |
| Respond in a normal, conversational manner while being friendly and helpful. |
| [USER] |
| """ |
|
|
| def models(text, model="Mixtral 8x7B", seed=42): |
|
|
| seed = int(randomize_seed_fn(seed)) |
| generator = torch.Generator().manual_seed(seed) |
| |
| client = client_fn(model) |
| |
| generate_kwargs = dict( |
| max_new_tokens=300, |
| seed=seed |
| ) |
| formatted_prompt = system_instructions1 + text |
| 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 |
|
|
| async def respond(audio, model, seed): |
| user = transcribe(audio) |
| reply = models(user, model, seed) |
| communicate = edge_tts.Communicate(reply) |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: |
| tmp_path = tmp_file.name |
| await communicate.save(tmp_path) |
| yield tmp_path |
|
|
| DESCRIPTION = """ # <center><b>FallnAI Voice Chat</b></center> |
| ### <center>Your Personal Chat Assistant! </center> |
| """ |
|
|
| with gr.Blocks(css="style.css") as demo: |
| gr.Markdown(DESCRIPTION) |
| with gr.Row(): |
| select = gr.Dropdown([ 'Mixtral 8x7B', |
| 'Llama 3 8B', |
| 'Mistral 7B v0.3', |
| 'Phi 3 mini', |
| ], |
| value="Mistral 7B v0.3", |
| label="Model" |
| ) |
| seed = gr.Slider( |
| label="Seed", |
| minimum=0, |
| maximum=999999, |
| step=1, |
| value=0, |
| visible=False |
| ) |
| input = gr.Audio(label="User", sources="microphone", type="filepath", waveform_options=False) |
| output = gr.Audio(label="AI", type="filepath", |
| interactive=True, |
| autoplay=True, |
| elem_classes="audio") |
| gr.Interface( |
| batch=True, |
| max_batch_size=10, |
| fn=respond, |
| inputs=[input, select, seed], |
| outputs=[output], live=True) |
|
|
| if __name__ == "__main__": |
| demo.queue(max_size=200).launch() |
