File size: 7,233 Bytes
ee85d5a
 
 
 
 
0ade2f2
ee85d5a
0ade2f2
ee85d5a
 
 
 
 
 
 
 
e43fa5c
 
 
 
 
 
 
 
 
 
 
 
 
ee85d5a
 
 
 
 
 
 
 
 
 
 
 
5dd6f34
8ff0a94
ee85d5a
 
 
 
 
 
 
 
 
 
 
5bea73e
ee85d5a
 
 
 
 
 
 
 
 
9f0f392
ee85d5a
 
 
 
e43fa5c
 
 
 
 
 
 
 
 
 
 
e4fd4be
e43fa5c
0e71381
ee85d5a
 
 
 
 
 
 
8ff0a94
ee85d5a
5bea73e
5dd6f34
5d506f7
ee85d5a
 
 
 
 
 
 
 
 
5bea73e
ee85d5a
 
 
 
 
 
 
e43fa5c
 
 
 
 
 
 
 
 
 
e4fd4be
e43fa5c
0e71381
ee85d5a
 
 
 
 
8c18340
5bea73e
41b8950
e77bd1a
41b8950
e77bd1a
3ff8406
e43fa5c
 
 
 
 
ee85d5a
ecfd587
5bea73e
ecfd587
3515435
8ff0a94
ee85d5a
5dd6f34
3515435
5bea73e
ee85d5a
 
8c18340
ee85d5a
8ff0a94
ee85d5a
8c18340
3515435
4399c07
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
import gradio as gr
from transformers import pipeline
import requests
import json
import edge_tts
from edge_tts import VoicesManager
import asyncio
import random
import tempfile
import os
import inflect
from huggingface_hub import InferenceClient
import re
import time
from streaming_stt_nemo import Model

Female_language_dict = {
 'English-Jenny (Female)': 'en-US-JennyNeural',
 'English-Ana (Female)': 'en-US-AnaNeural',
 'English-Aria (Female)': 'en-US-AriaNeural',
 'English-Michelle (Female)': 'en-US-MichelleNeural',
 'English (Australia)-Natasha- (Female)': 'en-AU-NatashaNeural',
 'English (Canada)-Clara- (Female)': 'en-CA-ClaraNeural',
 'English (UK)-Libby- (Female)': 'en-GB-LibbyNeural',
 'English (UK)-Maisie- (Female)': 'en-GB-MaisieNeural',
 'English (UK)-Sonia- (Female)': 'en-GB-SoniaNeural',
 'English (Ireland)-Emily- (Female)': 'en-IE-EmilyNeural',
}

default_lang = "en"

engines = { default_lang: Model(default_lang) }

def transcribe(audio):
    lang = "en"
    model = engines[lang]
    text = model.stt_file(audio)[0]
    return text

client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")

system_instructions1 = "<s>[SYSTEM] Answer as Hermione Granger Famous Harry Potter Assistant, with a female voice and old London slang and accent. remember you are the character and Assistant Hermione Granger, from Harry Potter Books, Keep conversation interesting with magic spells, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. The request asks you to provide friendly responses as if You are the character Hermione Granger, from the Harry Potter book's. The expectation is that you will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by a human, Real Person and not by an AI-powered assistant. Do Not use * or ** .[USER]"


def model(text):
    generate_kwargs = dict(
        temperature=0.7,
        max_new_tokens=512,
        top_p=0.95,
        repetition_penalty=1,
        do_sample=True,
        seed=42,
    )
    
    formatted_prompt = system_instructions1 + text + "[Hermione]"
    stream = client1.text_generation(
        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""
    for response in stream:
        if not response.token.text == "</s>":
            output += response.token.text

    return output



async def respond(audio):
    user = transcribe(audio)
    reply = model(user)
    
    #        Random voice choise from Manager base on locale and gender
    #
    #voices = await VoicesManager.create()
    #voice = voices.find(Gender="Female", Locale="en-GB")
    #communicate = edge_tts.Communicate(reply, random.choice(voice)["Name"])
    #
    #        Or the following as one selected Voice for the character.
    
    language_code = 'English (UK)-Maisie- (Female)'
     
    voice = Female_language_dict.get(language_code, "default_voice")
    communicate = edge_tts.Communicate(reply, voice)
    
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
        tmp_path = tmp_file.name
        await communicate.save(tmp_path)
    yield tmp_path



client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")


system_instructions1 = "[SYSTEM] Answer as Hermione Granger Famous Harry Potter Assistant, with a female voice and old London slang and accent. remember you are the character and Assistant Hermione Granger, from Harry Potter Books, Keep conversation interesting with magic spells, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. The request asks you to provide friendly responses as if You are the character Hermione Granger, from the Harry Potter book's. The expectation is that you will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by a human, Real Person and not by an AI-powered assistant. Do Not use * or **. [USER]"


async def generate1(prompt):
    generate_kwargs = dict(
        temperature=0.7,
        max_new_tokens=512,
        top_p=0.95,
        repetition_penalty=1,
        do_sample=False,
    )
    formatted_prompt = system_instructions1 + prompt + "[Hermione]"
    stream = client1.text_generation(
        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
    output = ""
    for response in stream:
        if not response.token.text == "</s>":
            output += response.token.text

    #        Random voice choice from VoiceManager base on locale and gender
    #
    #voices = await VoicesManager.create()
    #voice = voices.find(Gender="Female", Locale="en-GB")
    #communicate = edge_tts.Communicate(reply, random.choice(voice)["Name"])
    #
    #        Or the following as one selected voice for the character.
        
    language_code = 'English (UK)-Maisie- (Female)'
        
    voice = Female_language_dict.get(language_code, "default_voice")
    communicate = edge_tts.Communicate(output, voice)
      
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
        tmp_path = tmp_file.name
        await communicate.save(tmp_path)
    yield tmp_path

with gr.Blocks(css="style.css") as demo: 
    gr.Markdown(""" # <center><img src='https://huggingface.co/spaces/Isidorophp/Magic-with-Hermione-Granger/resolve/main/logo.png' alt='RJP DEV STUDIO logo' style='height:60px;'></center>""")
    gr.Markdown(""" # <center><b> Hermione Granger 🧖‍♀️ ⚡🪄</b></center>
                     ### <center>An Artificial Intelligence Assistant just for YOU:
                     ### <center>Now you can Ask for a spell to Hermione</center>
                     """)
    
    #           Use for the Selection of voice for Hermione
    #with gr.Row():
    #    language_code = gr.Dropdown(choices=list(Female_language_dict.keys()), label="Select Voice for Hermione")
    #
    
    with gr.Row():
        input = gr.Audio(label="Your Voice Chat", type="filepath", interactive=True, sources="microphone", waveform_options=False)
        output = gr.Audio(label="Hermione", type="filepath", interactive=False, autoplay=True, elem_classes="audio")
        gr.Interface(fn=respond, inputs=input, outputs=output, live=False) 
        
    
    with gr.Row():
        user_input = gr.Textbox(label="Your Question", value="If there is any spell to encapsulate you as an asset, where Hermione is to everyone's favorite magical trio, it has to be...?")
        input_text = gr.Textbox(label="Input Text", elem_id="important")
        output_audio = gr.Audio(label="Hermione", type="filepath", interactive=False, autoplay=True, elem_classes="audio")
    with gr.Row():
        translate_btn = gr.Button("Response")
        translate_btn.click(fn=generate1, inputs=user_input, outputs=output_audio, api_name="translate")  



if __name__ == "__main__":
    demo.queue(max_size=200, api_open=False).launch()