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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
from TTS.api import TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1", gpu=True)
title = "Voice chat with Mistral 7B Instruct"
DESCRIPTION = """# Voice chat with Mistral 7B Instruct"""
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 = "ylacombe/voice-chat-with-lama"
system_message = "\nYou are a helpful, respectful and honest assistant. Your answers are short, ideally a few words long, if it is possible. Always answer as helpfully as possible, while being safe.\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
from huggingface_hub import InferenceClient
whisper_client = Client("https://sanchit-gandhi-whisper-large-v2.hf.space/")
text_client = InferenceClient(
"mistralai/Mistral-7B-Instruct-v0.1"
)
def format_prompt(message, history):
prompt = "<s>"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def generate(
prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
formatted_prompt = format_prompt(prompt, history)
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
yield output
return output
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 = [] if history is None else history
history = history + [(text, None)]
return history, gr.update(value="", interactive=False)
def add_file(history, file):
history = [] if history is None else history
text = transcribe(
file
)
history = history + [(text, None)]
return history
def bot(history, system_prompt=""):
history = [] if history is None else history
if system_prompt == "":
system_prompt = system_message
history[-1][1] = ""
for character in generate(system_prompt, history):
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:
try:
# generate speech by cloning a voice using default settings
wav = tts.tts(text=sentence,
speaker_wav="examples/female.wav",
decoder_iterations=25,
decoder_sampler="dpm++2m",
speed=1.2,
language="en")
yield (sampling_rate, np.array(wav)) #np.array(wav + silence))
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:{sentence}", 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))
raise e
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=3,
show_label=False,
placeholder="Enter text and press enter, or speak to your microphone",
container=False,
)
txt_btn = gr.Button(value="Submit text",scale=1)
btn = gr.Audio(source="microphone", type="filepath", scale=4)
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_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
bot, chatbot, chatbot
).then(generate_speech, 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. [Mistral-7b-instruct](https://huggingface.co/spaces/osanseviero/mistral-super-fast) as the chat model, the actual chat model. It is called from [huggingface_hub](https://huggingface.co/docs/huggingface_hub/guides/inference).
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:
- 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)