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| from __future__ import annotations | |
| import os | |
| # By using XTTS you agree to CPML license https://coqui.ai/cpml | |
| os.environ["COQUI_TOS_AGREED"] = "1" | |
| 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 | |
| import soundfile as SF | |
| from TTS.api import TTS | |
| tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1", gpu=True) | |
| title = "Speak with Llama2 70B" | |
| DESCRIPTION = """# Speak with Llama2 70B""" | |
| css = """.toast-wrap { display: none !important } """ | |
| os.environ["GRADIO_TEMP_DIR"] = "/home/yoach/spaces/tmp" | |
| 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, agree): | |
| history = [] if history is None else history | |
| history = history + [(text, None)] | |
| return history, gr.update(value="", interactive=False) | |
| def add_file(history, file, agree): | |
| history = [] if history is None else history | |
| text = transcribe( | |
| file | |
| ) | |
| history = history + [(text, None)] | |
| return history | |
| def bot(history, agree, system_prompt=""): | |
| history = [] if history is None else history | |
| if system_prompt == "": | |
| system_prompt = system_message | |
| history[-1][1] = "" | |
| for character in text_client.submit( | |
| history, | |
| system_prompt, | |
| 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="examples/female.wav", | |
| decoder_iterations=20, | |
| speed=1.2, | |
| language="en") | |
| yield (sampling_rate, np.array(wav)) #np.array(wav + silence)) | |
| with gr.Blocks(title=title) as demo: | |
| gr.Markdown(DESCRIPTION) | |
| chatbot = gr.Chatbot( | |
| [], | |
| elem_id="chatbot", | |
| avatar_images=('examples/lama.jpeg', 'examples/lama2.jpeg'), | |
| bubble_full_width=False, | |
| ) | |
| with gr.Row(): | |
| txt = gr.Textbox( | |
| scale=1, | |
| show_label=False, | |
| placeholder="Enter text and press enter, or speak to your microphone", | |
| container=False, | |
| ) | |
| btn = gr.Audio(source="microphone", type="filepath", scale=2) | |
| with gr.Row(): | |
| audio = gr.Audio(type="numpy", streaming=True, autoplay=True, label="Generated audio response", show_label=True) | |
| clear_btn = gr.ClearButton([chatbot, audio]) | |
| 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) | |
| gr.Markdown(""" | |
| This Space demonstrates how to speak to a chatbot, based solely on open-source models. | |
| It relies on 3 models: | |
| 1. [Whisper-large-v2](https://huggingface.co/spaces/sanchit-gandhi/whisper-large-v2) as an ASR model, to transcribe recorded audio to text. It is called through a [gradio client](https://www.gradio.app/docs/client). | |
| 2. [Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) as the chat model, the actual chat model. It is also called through a [gradio client](https://www.gradio.app/docs/client). | |
| 3. [Coqui's XTTS](https://huggingface.co/spaces/coqui/xtts) as a TTS model, to generate the chatbot answers. This time, the model is hosted locally. | |
| Note: | |
| - As a derivate work of [Llama-2-70b-chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) by Meta, | |
| this demo is governed by the original [license](https://huggingface.co/spaces/ysharma/Explore_llamav2_with_TGI/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/ysharma/Explore_llamav2_with_TGI/blob/main/USE_POLICY.md). | |
| - By using this demo you agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml""") | |
| demo.queue() | |
| demo.launch(debug=True) |