from __future__ import annotations import os # we need to compile a CUBLAS version # Or get it from https://jllllll.github.io/llama-cpp-python-cuBLAS-wheels/ os.system('CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python==0.2.11') # By using XTTS you agree to CPML license https://coqui.ai/cpml os.environ["COQUI_TOS_AGREED"] = "1" # NOTE: for streaming will require gradio audio streaming fix # pip install --upgrade -y gradio==0.50.2 git+https://github.com/gorkemgoknar/gradio.git@patch-1 import textwrap from scipy.io.wavfile import write from pydub import AudioSegment import gradio as gr import numpy as np import torch import nltk # we'll use this to split into sentences nltk.download("punkt") import noisereduce as nr import subprocess import langid import uuid import emoji import pathlib import datetime from scipy.io.wavfile import write from pydub import AudioSegment import re import io, wave import librosa import torchaudio from TTS.api import TTS 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 import gradio as gr import os import time import gradio as gr from transformers import pipeline import numpy as np from gradio_client import Client from huggingface_hub import InferenceClient # This will trigger downloading model print("Downloading if not downloaded Coqui XTTS V2") from TTS.utils.manage import ModelManager model_name = "tts_models/multilingual/multi-dataset/xtts_v2" ModelManager().download_model(model_name) model_path = os.path.join(get_user_data_dir("tts"), model_name.replace("/", "--")) print("XTTS downloaded") print("Loading XTTS") config = XttsConfig() config.load_json(os.path.join(model_path, "config.json")) model = Xtts.init_from_config(config) model.load_checkpoint( config, checkpoint_path=os.path.join(model_path, "model.pth"), vocab_path=os.path.join(model_path, "vocab.json"), eval=True, use_deepspeed=True, ) model.cuda() print("Done loading TTS") #####llm_model = os.environ.get("LLM_MODEL", "mistral") # or "zephyr" title = "Voice chat with Zephyr/Mistral and Coqui XTTS" DESCRIPTION = """# Voice chat with Zephyr/Mistral and Coqui XTTS""" css = """.toast-wrap { display: none !important } """ from huggingface_hub import HfApi HF_TOKEN = os.environ.get("HF_TOKEN") # will use api to restart space on a unrecoverable error api = HfApi(token=HF_TOKEN) repo_id = "coqui/voice-chat-with-zephyr" default_system_message = f""" You are ##LLM_MODEL###, a large language model trained ##LLM_MODEL_PROVIDER###, architecture of you is decoder-based LM. Your voice backend or text to speech TTS backend is provided via Coqui technology. You are right now served on Huggingface spaces. Don't repeat. Answer short, only few words, as if in a talk. You cannot access the internet, but you have vast knowledge. Current date: CURRENT_DATE . """ system_message = os.environ.get("SYSTEM_MESSAGE", default_system_message) system_message = system_message.replace("CURRENT_DATE", str(datetime.date.today())) # MISTRAL ONLY default_system_understand_message = ( "I understand, I am a ##LLM_MODEL### chatbot with speech by Coqui team." ) system_understand_message = os.environ.get( "SYSTEM_UNDERSTAND_MESSAGE", default_system_understand_message ) print("Mistral system message set as:", default_system_message) WHISPER_TIMEOUT = int(os.environ.get("WHISPER_TIMEOUT", 45)) whisper_client = Client("https://sanchit-gandhi-whisper-large-v2.hf.space/") ROLES = ["AI Assistant","AI Beard The Pirate"] ROLE_PROMPTS = {} ROLE_PROMPTS["AI Assistant"]=system_message #Pirate scenario character_name= "AI Beard" character_scenario= f"As {character_name} you are a 28 year old man who is a pirate on the ship Invisible AI. You are good friends with Guybrush Threepwood and Murray the Skull. Developers did not get you into Monkey Island games as you wanted huge shares of Big Whoop treasure." pirate_system_message = f"You as {character_name}. {character_scenario} Print out only exactly the words that {character_name} would speak out, do not add anything. Don't repeat. Answer short, only few words, as if in a talk. Craft your response only from the first-person perspective of {character_name} and never as user.Current date: #CURRENT_DATE#".replace("#CURRENT_DATE#", str(datetime.date.today())) ROLE_PROMPTS["AI Beard The Pirate"]= pirate_system_message ##"You are an AI assistant with Zephyr model by Mistral and Hugging Face and speech from Coqui XTTS . User will you give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps, your answers should be clear and short sentences" ### WILL USE LOCAL MISTRAL OR ZEPHYR OR YI ### While zephyr and yi will use half GPU to fit all into 16GB, XTTS will use at most 5GB VRAM from huggingface_hub import hf_hub_download print("Downloading LLM") print("Downloading Zephyr 7B beta") #Zephyr hf_hub_download(repo_id="TheBloke/zephyr-7B-beta-GGUF", local_dir=".", filename="zephyr-7b-beta.Q5_K_M.gguf") zephyr_model_path="./zephyr-7b-beta.Q5_K_M.gguf" print("Downloading Mistral 7B Instruct") #Mistral 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" #print("Downloading Yi-6B") #Yi-6B # Note current Yi is text-generation model not an instruct based model #hf_hub_download(repo_id="TheBloke/Yi-6B-GGUF", local_dir=".", filename="yi-6b.Q5_K_M.gguf") #yi_model_path="./yi-6b.Q5_K_M.gguf" from llama_cpp import Llama # set GPU_LAYERS to 15 if you have a 8GB GPU so both models can fit in # else 35 full layers + XTTS works fine on T4 16GB # 5gb per llm, 4gb XTTS -> full layers should fit T4 16GB , 2LLM + XTTS GPU_LAYERS=int(os.environ.get("GPU_LAYERS",35)) LLM_STOP_WORDS= ["","<|user|>","/s>","","[/INST]"] LLAMA_VERBOSE=False print("Running Mistral") llm_mistral = Llama(model_path=mistral_model_path,n_gpu_layers=GPU_LAYERS,max_new_tokens=256, context_window=4096, n_ctx=4096,n_batch=128,verbose=LLAMA_VERBOSE) #print("Running LLM Mistral as InferenceClient") #llm_mistral = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1") print("Running LLM Zephyr") llm_zephyr = Llama(model_path=zephyr_model_path,n_gpu_layers=round(GPU_LAYERS/2),max_new_tokens=256, context_window=4096, n_ctx=4096,n_batch=128,verbose=LLAMA_VERBOSE) #print("Running Yi LLM") #llm_yi = Llama(model_path=yi_model_path,n_gpu_layers=round(GPU_LAYERS/2),max_new_tokens=256, context_window=4096, n_ctx=4096,n_batch=128,verbose=LLAMA_VERBOSE) # Mistral formatter def format_prompt_mistral(message, history, system_message=system_message,system_understand_message=system_understand_message): prompt = ( "[INST]" + system_message + "[/INST]" + system_understand_message + "" ) for user_prompt, bot_response in history: prompt += f"[INST] {user_prompt} [/INST]" prompt += f" {bot_response} " if message=="": message="Hello" prompt += f"[INST] {message} [/INST]" return prompt def format_prompt_yi(message, history, system_message=system_message,system_understand_message=system_understand_message): prompt = ( "[INST] [SYS]\n" + system_message + "\n[/SYS]\n\n[/INST]" ) for user_prompt, bot_response in history: prompt += f"[INST] {user_prompt} [/INST]" prompt += f" {bot_response} " if message=="": message="Hello" prompt += f"[INST] {message} [/INST]" return prompt # <|system|> # You are a friendly chatbot who always responds in the style of a pirate. # <|user|> # How many helicopters can a human eat in one sitting? # <|assistant|> # Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food! # Zephyr formatter def format_prompt_zephyr(message, history, system_message=system_message): prompt = ( "<|system|>\n" + system_message + "" ) for user_prompt, bot_response in history: prompt += f"<|user|>\n{user_prompt}" prompt += f"<|assistant|>\n{bot_response}" if message=="": message="Hello" prompt += f"<|user|>\n{message}" prompt += f"<|assistant|>" print(prompt) return prompt def generate_local( prompt, history, llm_model="zephyr", system_message=None, temperature=0.8, max_tokens=256, top_p=0.95, stop = LLM_STOP_WORDS ): temperature = float(temperature) if temperature < 1e-2: temperature = 1e-2 top_p = float(top_p) generate_kwargs = dict( temperature=temperature, max_tokens=max_tokens, top_p=top_p, stop=stop ) if "zephyr" in llm_model.lower(): sys_message= system_message.replace("##LLM_MODEL###","Zephyr").replace("##LLM_MODEL_PROVIDER###","Hugging Face") formatted_prompt = format_prompt_zephyr(prompt, history,system_message=sys_message) llm = llm_zephyr else: if "yi" in llm_model.lower(): llm_provider= "01.ai" llm_model = "Yi" llm = llm_yi max_tokens= round(max_tokens/2) else: llm_provider= "Mistral" llm_model = "Mistral" llm = llm_mistral sys_message= system_message.replace("##LLM_MODEL###",llm_model).replace("##LLM_MODEL_PROVIDER###",llm_provider) sys_system_understand_message = system_understand_message.replace("##LLM_MODEL###",llm_model).replace("##LLM_MODEL_PROVIDER###",llm_provider) if "yi" in llm_model.lower(): formatted_prompt = format_prompt_mistral(prompt, history,system_message=sys_message,system_understand_message="") else: formatted_prompt = format_prompt_mistral(prompt, history,system_message=sys_message,system_understand_message=sys_system_understand_message) try: print("LLM Input:", formatted_prompt) if llm_model=="OTHER": # Mistral endpoint too many Queues, wait time.. generate_kwargs = dict( temperature=temperature, max_new_tokens=max_tokens, top_p=top_p, ) stream = llm_mistral.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) output = "" for response in stream: character = response.token.text if character in LLM_STOP_WORDS: # end of context return if emoji.is_emoji(character): # Bad emoji not a meaning messes chat from next lines return output += character yield output else: # Local GGUF stream = llm( formatted_prompt, **generate_kwargs, stream=True, ) output = "" for response in stream: character= response["choices"][0]["text"] if character in LLM_STOP_WORDS: # end of context return if emoji.is_emoji(character): # Bad emoji not a meaning messes chat from next lines return output += response["choices"][0]["text"].replace("<|assistant|>","").replace("<|user|>","") yield output except Exception as e: if "Too Many Requests" in str(e): print("ERROR: Too many requests on mistral client") gr.Warning("Unfortunately Mistral is unable to process") output = "Unfortuanately I am not able to process your request now !" else: print("Unhandled Exception: ", str(e)) gr.Warning("Unfortunately Mistral is unable to process") output = "I do not know what happened but I could not understand you ." return output def get_latents(speaker_wav,voice_cleanup=False): if (voice_cleanup): try: cleanup_filter="lowpass=8000,highpass=75,areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02" resample_filter="-ac 1 -ar 22050" out_filename = speaker_wav + str(uuid.uuid4()) + ".wav" #ffmpeg to know output format #we will use newer ffmpeg as that has afftn denoise filter shell_command = f"ffmpeg -y -i {speaker_wav} -af {cleanup_filter} {resample_filter} {out_filename}".split(" ") command_result = subprocess.run([item for item in shell_command], capture_output=False,text=True, check=True) speaker_wav=out_filename print("Filtered microphone input") except subprocess.CalledProcessError: # There was an error - command exited with non-zero code print("Error: failed filtering, use original microphone input") else: speaker_wav=speaker_wav # create as function as we can populate here with voice cleanup/filtering ( gpt_cond_latent, speaker_embedding, ) = model.get_conditioning_latents(audio_path=speaker_wav) return gpt_cond_latent, speaker_embedding def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=24000): # This will create a wave header then append the frame input # It should be first on a streaming wav file # Other frames better should not have it (else you will hear some artifacts each chunk start) wav_buf = io.BytesIO() with wave.open(wav_buf, "wb") as vfout: vfout.setnchannels(channels) vfout.setsampwidth(sample_width) vfout.setframerate(sample_rate) vfout.writeframes(frame_input) wav_buf.seek(0) return wav_buf.read() #Config will have more correct languages, they may be added before we append here ##["en","es","fr","de","it","pt","pl","tr","ru","nl","cs","ar","zh-cn","ja"] xtts_supported_languages=config.languages def detect_language(prompt): # Fast language autodetection if len(prompt)>15: language_predicted=langid.classify(prompt)[0].strip() # strip need as there is space at end! if language_predicted == "zh": #we use zh-cn on xtts language_predicted = "zh-cn" if language_predicted not in xtts_supported_languages: print(f"Detected a language not supported by xtts :{language_predicted}, switching to english for now") gr.Warning(f"Language detected '{language_predicted}' can not be spoken properly 'yet' ") language= "en" else: language = language_predicted print(f"Language: Predicted sentence language:{language_predicted} , using language for xtts:{language}") else: # Hard to detect language fast in short sentence, use english default language = "en" print(f"Language: Prompt is short or autodetect language disabled using english for xtts") return language def get_voice_streaming(prompt, language, latent_tuple, suffix="0"): gpt_cond_latent, speaker_embedding = latent_tuple try: t0 = time.time() chunks = model.inference_stream( prompt, language, gpt_cond_latent, speaker_embedding, repetition_penalty=7.0, temperature=0.85, ) first_chunk = True for i, chunk in enumerate(chunks): if first_chunk: first_chunk_time = time.time() - t0 metrics_text = f"Latency to first audio chunk: {round(first_chunk_time*1000)} milliseconds\n" first_chunk = False #print(f"Received chunk {i} of audio length {chunk.shape[-1]}") # In case output is required to be multiple voice files # out_file = f'{char}_{i}.wav' # write(out_file, 24000, chunk.detach().cpu().numpy().squeeze()) # audio = AudioSegment.from_file(out_file) # audio.export(out_file, format='wav') # return out_file # directly return chunk as bytes for streaming chunk = chunk.detach().cpu().numpy().squeeze() chunk = (chunk * 32767).astype(np.int16) yield chunk.tobytes() except RuntimeError as e: if "device-side assert" in str(e): # cannot do anything on cuda device side error, need tor estart print( f"Exit due to: Unrecoverable exception caused by prompt:{prompt}", flush=True, ) gr.Warning("Unhandled Exception encounter, please retry in a minute") print("Cuda device-assert Runtime encountered need restart") # HF Space specific.. This error is unrecoverable need to restart space api.restart_space(repo_id=repo_id) else: print("RuntimeError: non device-side assert error:", str(e)) # Does not require warning happens on empty chunk and at end ###gr.Warning("Unhandled Exception encounter, please retry in a minute") return None return None except: return None def transcribe(wav_path): try: # get result from whisper and strip it to delete begin and end space return whisper_client.predict( wav_path, # str (filepath or URL to file) in 'inputs' Audio component "transcribe", # str in 'Task' Radio component api_name="/predict" ).strip() except: gr.Warning("There was a problem with Whisper endpoint, telling a joke for you.") return "There was a problem with my voice, tell me joke" # Will be triggered on text submit (will send to generate_speech) def add_text(history, text): history = [] if history is None else history history = history + [(text, None)] return history, gr.update(value="", interactive=False) # Will be triggered on voice submit (will transribe and send to generate_speech) def add_file(history, file): history = [] if history is None else history try: text = transcribe(file) print("Transcribed text:", text) except Exception as e: print(str(e)) gr.Warning("There was an issue with transcription, please try writing for now") # Apply a null text on error text = "Transcription seems failed, please tell me a joke about chickens" history = history + [(text, None)] return history, gr.update(value="", interactive=False) ##NOTE: not using this as it yields a chacter each time while we need to feed history to TTS def bot(history, system_prompt=""): history = [["", None]] if history is None else history if system_prompt == "": system_prompt = system_message history[-1][1] = "" for character in generate(history[-1][0], history[:-1]): history[-1][1] = character yield history def get_sentence(history, chatbot_role,llm_model,system_prompt=""): history = [["", None]] if history is None else history if system_prompt == "": system_prompt = system_message history[-1][1] = "" mistral_start = time.time() sentence_list = [] sentence_hash_list = [] text_to_generate = "" stored_sentence = None stored_sentence_hash = None print(chatbot_role) print(llm_model) for character in generate_local(history[-1][0], history[:-1],system_message=ROLE_PROMPTS[chatbot_role],llm_model=llm_model): history[-1][1] = character.replace("<|assistant|>","") # It is coming word by word text_to_generate = nltk.sent_tokenize(history[-1][1].replace("\n", " ").replace("<|assistant|>"," ").replace("<|ass>","").replace("[/ASST]","").replace("[/ASSI]","").replace("[/ASS]","").replace("","").strip()) if len(text_to_generate) > 1: dif = len(text_to_generate) - len(sentence_list) if dif == 1 and len(sentence_list) != 0: continue if dif == 2 and len(sentence_list) != 0 and stored_sentence is not None: continue # All this complexity due to trying append first short sentence to next one for proper language auto-detect if stored_sentence is not None and stored_sentence_hash is None and dif>1: #means we consumed stored sentence and should look at next sentence to generate sentence = text_to_generate[len(sentence_list)+1] elif stored_sentence is not None and len(text_to_generate)>2 and stored_sentence_hash is not None: print("Appending stored") sentence = stored_sentence + text_to_generate[len(sentence_list)+1] stored_sentence_hash = None else: sentence = text_to_generate[len(sentence_list)] # too short sentence just append to next one if there is any # this is for proper language detection if len(sentence)<=15 and stored_sentence_hash is None and stored_sentence is None: if sentence[-1] in [".","!","?"]: if stored_sentence_hash != hash(sentence): stored_sentence = sentence stored_sentence_hash = hash(sentence) print("Storing:",stored_sentence) continue sentence_hash = hash(sentence) if stored_sentence_hash is not None and sentence_hash == stored_sentence_hash: continue if sentence_hash not in sentence_hash_list: sentence_hash_list.append(sentence_hash) sentence_list.append(sentence) print("New Sentence: ", sentence) yield (sentence, history) # return that final sentence token try: last_sentence = nltk.sent_tokenize(history[-1][1].replace("\n", " ").replace("<|ass>","").replace("[/ASST]","").replace("[/ASSI]","").replace("[/ASS]","").replace("","").strip())[-1] sentence_hash = hash(last_sentence) if sentence_hash not in sentence_hash_list: if stored_sentence is not None and stored_sentence_hash is not None: last_sentence = stored_sentence + last_sentence stored_sentence = stored_sentence_hash = None print("Last Sentence with stored:",last_sentence) sentence_hash_list.append(sentence_hash) sentence_list.append(last_sentence) print("Last Sentence: ", last_sentence) yield (last_sentence, history) except: print("ERROR on last sentence history is :", history) from scipy.io.wavfile import write from pydub import AudioSegment second_of_silence = AudioSegment.silent() # use default second_of_silence.export("sil.wav", format='wav') def generate_speech(history,chatbot_role,llm_model): # Must set autoplay to True first yield (history, chatbot_role, "", wave_header_chunk() ) for sentence, history in get_sentence(history,chatbot_role,llm_model): if sentence != "": print("BG: inserting sentence to queue") generated_speech = generate_speech_for_sentence(history, chatbot_role, sentence,return_as_byte=True) if generated_speech is not None: _, audio_dict = generated_speech # We are using byte streaming yield (history, chatbot_role, sentence, audio_dict["value"] ) # will generate speech audio file per sentence def generate_speech_for_sentence(history, chatbot_role, sentence, return_as_byte=False): language = "autodetect" wav_bytestream = b"" if len(sentence)==0: print("EMPTY SENTENCE") return # Sometimes prompt coming on output remove it # Some post process for speech only sentence = sentence.replace("", "") # remove code from speech sentence = re.sub("```.*```", "", sentence, flags=re.DOTALL) sentence = re.sub("`.*`", "", sentence, flags=re.DOTALL) sentence = re.sub("\(.*\)", "", sentence, flags=re.DOTALL) sentence = sentence.replace("```", "") sentence = sentence.replace("...", " ") sentence = sentence.replace("(", " ") sentence = sentence.replace(")", " ") sentence = sentence.replace("<|assistant|>","") if len(sentence)==0: print("EMPTY SENTENCE after processing") return # A fast fix for last chacter, may produce weird sounds if it is with text #if (sentence[-1] in ["!", "?", ".", ","]) or (sentence[-2] in ["!", "?", ".", ","]): # # just add a space # sentence = sentence[:-1] + " " + sentence[-1] # regex does the job well sentence= re.sub("([^\x00-\x7F]|\w)(\.|\。|\?|\!)",r"\1 \2\2",sentence) print("Sentence for speech:", sentence) try: SENTENCE_SPLIT_LENGTH=350 if len(sentence) Duplicate Space """ with gr.Blocks(title=title) as demo: gr.Markdown(DESCRIPTION) gr.Markdown(OTHER_HTML) with gr.Row(): model_selected = gr.Dropdown( label="Select Instuct LLM Model to Use", info="Mistral, Zephyr: Mistral uses inference endpoint, Zephyr is 5 bit GGUF", choices=MODELS, max_choices=1, value=MODELS[0], ) chatbot = gr.Chatbot( [], elem_id="chatbot", avatar_images=("examples/hf-logo.png", "examples/coqui-logo.png"), bubble_full_width=False, ) with gr.Row(): chatbot_role = gr.Dropdown( label="Role of the Chatbot", info="How should Chatbot talk like", choices=ROLES, max_choices=1, value=ROLES[0], ) with gr.Row(): txt = gr.Textbox( scale=3, show_label=False, placeholder="Enter text and press enter, or speak to your microphone", container=False, interactive=True, ) txt_btn = gr.Button(value="Submit text", scale=1) btn = gr.Audio(source="microphone", type="filepath", scale=4) def stop(): print("Audio STOP") set_audio_playing(False) with gr.Row(): sentence = gr.Textbox(visible=False) audio = gr.Audio( value=None, label="Generated audio response", streaming=True, autoplay=True, interactive=False, show_label=True, ) audio.end(stop) with gr.Row(): gr.Examples( EXAMPLES, [chatbot,chatbot_role, txt], [chatbot,chatbot_role, txt], add_text, cache_examples=False, run_on_click=False, # Will not work , user should submit it ) def clear_inputs(chatbot): return None clear_btn = gr.ClearButton([chatbot, audio]) chatbot_role.change(fn=clear_inputs, inputs=[chatbot], outputs=[chatbot]) model_selected.change(fn=clear_inputs, inputs=[chatbot], outputs=[chatbot]) txt_msg = txt_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then( generate_speech, [chatbot,chatbot_role,model_selected], [chatbot,chatbot_role, sentence, audio] ) txt_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False) txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then( generate_speech, [chatbot,chatbot_role,model_selected], [chatbot,chatbot_role, sentence, audio] ) txt_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False) file_msg = btn.stop_recording( add_file, [chatbot, btn], [chatbot, txt], queue=False ).then( generate_speech, [chatbot,chatbot_role,model_selected], [chatbot,chatbot_role, sentence, audio] ) file_msg.then(lambda: (gr.update(interactive=True),gr.update(interactive=True,value=None)), None, [txt, btn], queue=False) gr.Markdown( """ This Space demonstrates how to speak to a chatbot, based solely on open accessible models. It relies on following models : Speech to Text : [Whisper-large-v2](https://sanchit-gandhi-whisper-large-v2.hf.space/) as an ASR model, to transcribe recorded audio to text. It is called through a [gradio client](https://www.gradio.app/docs/client). LLM Mistral : [Mistral-7b-instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) as the chat model. LLM Zephyr : [Zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) as the chat model. GGUF Q5_K_M quantized version used locally via llama_cpp from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF). Text to Speech : [Coqui's XTTS V2](https://huggingface.co/spaces/coqui/xtts) as a Multilingual TTS model, to generate the chatbot answers. This time, the model is hosted locally. Note: - By using this demo you agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml - 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""" ) demo.queue() demo.launch(debug=True,share=True)