from __future__ import annotations import os import gradio as gr import numpy as np import torch import nltk # we'll use this to split into sentences nltk.download('punkt') import uuid from TTS.api import TTS # By using XTTS you agree to CPML license https://coqui.ai/cpml os.environ["COQUI_TOS_AGREED"] = "1" tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1", gpu=True) DESCRIPTION = """# Speak with Llama2 TODO """ CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES") == "1" system_message = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information." temperature = 0.9 top_p = 0.6 repetition_penalty = 1.2 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 whisper_client = Client("https://sanchit-gandhi-whisper-large-v2.hf.space/") text_client = Client("https://ysharma-explore-llamav2-with-tgi.hf.space/") def transcribe(wav_path): 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" ) # Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text. def add_text(history, text): history = history + [(text, None)] return history, gr.update(value="", interactive=False) def add_file(history, file): text = transcribe( file ) history = history + [(text, None)] return history def bot(history): history[-1][1] = "" for character in text_client.submit( history, system_message, temperature, 4096, temperature, repetition_penalty, api_name="/chat" ): history[-1][1] = character yield history def generate_speech(history): text_to_generate = history[-1][1] text_to_generate = text_to_generate.replace("\n", " ").strip() text_to_generate = nltk.sent_tokenize(text_to_generate) filename = f"{uuid.uuid4()}.wav" sampling_rate = tts.synthesizer.tts_config.audio["sample_rate"] silence = [0] * int(0.25 * sampling_rate) for sentence in text_to_generate: # generate speech by cloning a voice using default settings wav = tts.tts(text=sentence, #speaker_wav="/home/yoach/spaces/talkWithLLMs/examples/female.wav", speed=1.5, language="en") yield (sampling_rate, np.array(wav)) #np.array(wav + silence)) with gr.Blocks() as demo: chatbot = gr.Chatbot( [], elem_id="chatbot", bubble_full_width=False, avatar_images=(None, (os.path.join(os.path.dirname(__file__), "avatar.png"))), ) with gr.Row(): txt = gr.Textbox( scale=4, show_label=False, placeholder="Enter text and press enter, or speak to your microphone", container=False, ) btn = gr.inputs.Audio(source="microphone", type="filepath", optional=True) with gr.Row(): audio = gr.Audio(type="numpy", streaming=True, autoplay=True) txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then( bot, chatbot, chatbot ).then(generate_speech, chatbot, audio) txt_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False) file_msg = btn.stop_recording(add_file, [chatbot, btn], [chatbot], queue=False).then( bot, chatbot, chatbot ).then(generate_speech, chatbot, audio) #file_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False) demo.queue() demo.launch(debug=True)