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| import os | |
| os.environ["CUDACXX"] = "/usr/local/cuda/bin/nvcc" | |
| os.system('python -m unidic download') | |
| os.system('CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python==0.2.11 --verbose') | |
| # Third-party library imports | |
| from faster_whisper import WhisperModel | |
| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| from llama_cpp import Llama | |
| from TTS.tts.configs.xtts_config import XttsConfig | |
| from TTS.tts.models.xtts import Xtts | |
| from TTS.utils.generic_utils import get_user_data_dir | |
| from TTS.utils.manage import ModelManager | |
| # Local imports | |
| from utils import get_sentence, generate_speech_for_sentence, wave_header_chunk | |
| # Load whisper model | |
| print("Loading Whisper ASR") | |
| whisper_model = WhisperModel("large-v3", device="cuda", compute_type="float16") | |
| # Load Mistral LLM | |
| print("Loading Mistral LLM") | |
| hf_hub_download(repo_id="TheBloke/Mistral-7B-Instruct-v0.1-GGUF", local_dir=".", filename="mistral-7b-instruct-v0.1.Q5_K_M.gguf") | |
| mistral_model_path="./mistral-7b-instruct-v0.1.Q5_K_M.gguf" | |
| mistral_llm = Llama(model_path=mistral_model_path,n_gpu_layers=35,max_new_tokens=256, context_window=4096, n_ctx=4096,n_batch=128,verbose=False) | |
| # Load XTTS Model | |
| print("Loading XTTS model") | |
| os.environ["COQUI_TOS_AGREED"] = "1" | |
| tts_model_name = "tts_models/multilingual/multi-dataset/xtts_v2" | |
| ModelManager().download_model(tts_model_name) | |
| tts_model_path = os.path.join(get_user_data_dir("tts"), tts_model_name.replace("/", "--")) | |
| config = XttsConfig() | |
| config.load_json(os.path.join(tts_model_path, "config.json")) | |
| xtts_model = Xtts.init_from_config(config) | |
| xtts_model.load_checkpoint( | |
| config, | |
| checkpoint_path=os.path.join(tts_model_path, "model.pth"), | |
| vocab_path=os.path.join(tts_model_path, "vocab.json"), | |
| eval=True, | |
| use_deepspeed=True, | |
| ) | |
| xtts_model.cuda() | |
| ###### Set up Gradio Interface ###### | |
| with gr.Blocks(title="Voice chat with LLM") as demo: | |
| DESCRIPTION = """# Voice chat with LLM""" | |
| gr.Markdown(DESCRIPTION) | |
| # Define chatbot component | |
| chatbot = gr.Chatbot( | |
| value=[(None, "Hi friend, I'm Amy, an AI receptionist at dental clinic. How can I help you today?")], # Initial greeting from the chatbot | |
| elem_id="chatbot", | |
| avatar_images=("examples/hf-logo.png", "examples/ai-chat-logo.png"), | |
| bubble_full_width=False, | |
| ) | |
| # Define chatbot voice component | |
| VOICES = ["female", "male"] | |
| with gr.Row(): | |
| chatbot_voice = gr.Dropdown( | |
| label="Voice of the Chatbot", | |
| info="How should Chatbot talk like", | |
| choices=VOICES, | |
| max_choices=1, | |
| value=VOICES[0], | |
| ) | |
| # Define text and audio record input components | |
| with gr.Row(): | |
| txt_box = gr.Textbox( | |
| scale=3, | |
| show_label=False, | |
| placeholder="Enter text and press enter, or speak to your microphone", | |
| container=False, | |
| interactive=True, | |
| ) | |
| audio_record = gr.Audio(source="microphone", type="filepath", scale=4) | |
| # Define generated audio playback component | |
| with gr.Row(): | |
| sentence = gr.Textbox(visible=False) | |
| audio_playback = gr.Audio( | |
| value=None, | |
| label="Generated audio response", | |
| streaming=True, | |
| autoplay=True, | |
| interactive=False, | |
| show_label=True, | |
| ) | |
| # Will be triggered on text submit (will send to generate_speech) | |
| def add_text(chatbot_history, text): | |
| chatbot_history = [] if chatbot_history is None else chatbot_history | |
| chatbot_history = chatbot_history + [(text, None)] | |
| return chatbot_history, gr.update(value="", interactive=False) | |
| # Will be triggered on voice submit (will transribe and send to generate_speech) | |
| def add_audio(chatbot_history, audio): | |
| chatbot_history = [] if chatbot_history is None else chatbot_history | |
| # get result from whisper and strip it to delete begin and end space | |
| response, _ = whisper_model.transcribe(audio) | |
| text = list(response)[0].text.strip() | |
| print("Transcribed text:", text) | |
| chatbot_history = chatbot_history + [(text, None)] | |
| return chatbot_history, gr.update(value="", interactive=False) | |
| def generate_speech(chatbot_history, chatbot_voice, initial_greeting=False): | |
| # Start by yielding an initial empty audio to set up autoplay | |
| yield ("", chatbot_history, wave_header_chunk()) | |
| # Helper function to handle the speech generation and yielding process | |
| def handle_speech_generation(sentence, chatbot_history, chatbot_voice): | |
| if sentence != "": | |
| print("Processing sentence") | |
| generated_speech = generate_speech_for_sentence(chatbot_history, chatbot_voice, sentence, xtts_model, xtts_supported_languages=config.languages, return_as_byte=True) | |
| if generated_speech is not None: | |
| _, audio_dict = generated_speech | |
| yield (sentence, chatbot_history, audio_dict["value"]) | |
| if initial_greeting: | |
| # Process only the initial greeting if specified | |
| for _, sentence in chatbot_history: | |
| yield from handle_speech_generation(sentence, chatbot_history, chatbot_voice) | |
| else: | |
| # Continuously get and process sentences from a generator function | |
| for sentence, chatbot_history in get_sentence(chatbot_history, mistral_llm): | |
| print("Inserting sentence to queue") | |
| yield from handle_speech_generation(sentence, chatbot_history, chatbot_voice) | |
| txt_msg = txt_box.submit(fn=add_text, inputs=[chatbot, txt_box], outputs=[chatbot, txt_box], queue=False | |
| ).then(fn=generate_speech, inputs=[chatbot,chatbot_voice], outputs=[sentence, chatbot, audio_playback]) | |
| txt_msg.then(fn=lambda: gr.update(interactive=True), inputs=None, outputs=[txt_box], queue=False) | |
| audio_msg = audio_record.stop_recording(fn=add_audio, inputs=[chatbot, audio_record], outputs=[chatbot, txt_box], queue=False | |
| ).then(fn=generate_speech, inputs=[chatbot,chatbot_voice], outputs=[sentence, chatbot, audio_playback]) | |
| audio_msg.then(fn=lambda: (gr.update(interactive=True),gr.update(interactive=True,value=None)), inputs=None, outputs=[txt_box, audio_record], queue=False) | |
| FOOTNOTE = """ | |
| This Space demonstrates how to speak to an llm chatbot, based solely on open accessible models. | |
| It relies on the following models : | |
| - Speech to Text Model: [Faster-Whisper-large-v3](https://huggingface.co/Systran/faster-whisper-large-v3) an ASR model, to transcribe recorded audio to text. | |
| - Large Language Model: [Mistral-7b-instruct-v0.1-quantized](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF) a LLM to generate the chatbot responses. | |
| - Text to Speech Model: [XTTS-v2](https://huggingface.co/spaces/coqui/xtts) a TTS model, to generate the voice of the chatbot. | |
| Note: | |
| - Responses generated by chat model should not be assumed correct or taken serious, as this is a demonstration example only | |
| - iOS (Iphone/Ipad) devices may not experience voice due to autoplay being disabled on these devices by Vendor""" | |
| gr.Markdown(FOOTNOTE) | |
| demo.load(fn=generate_speech, inputs=[chatbot,chatbot_voice, gr.State(value=True)], outputs=[sentence, chatbot, audio_playback]) | |
| demo.queue().launch(debug=True,share=True) |