File size: 7,321 Bytes
7cf7820
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9aeab89
7cf7820
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
164
165
166
167
# Copyright (2023) Tsinghua University, Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import gradio as gr
import argparse
from model import SALMONN

class ff:
    def generate(self, wav_path, prompt, prompt_pattern, num_beams, temperature, top_p):
        print(f'wav_path: {wav_path}, prompt: {prompt}, temperature: {temperature}, num_beams: {num_beams}, top_p: {top_p}')
        return "I'm sorry, but I cannot answer that question as it is not clear what you are asking. Can you please provide more context or clarify your question?"

parser = argparse.ArgumentParser()
parser.add_argument("--device", type=str, default="cuda:0")
parser.add_argument("--ckpt_path", type=str, default=None)
parser.add_argument("--whisper_path", type=str, default=None)
parser.add_argument("--beats_path", type=str, default=None)
parser.add_argument("--vicuna_path", type=str, default=None)
parser.add_argument("--low_resource", action='store_true', default=False)
parser.add_argument("--lora_alpha", type=int, default=32)
parser.add_argument("--port", default=9527)

args = parser.parse_args()
# model = ff()
model = SALMONN(
    ckpt=args.ckpt_path,
    whisper_path=args.whisper_path,
    beats_path=args.beats_path,
    vicuna_path=args.vicuna_path,
    lora_alpha=args.lora_alpha,
    low_resource=args.low_resource
)
model.to(args.device)
model.eval()

# gradio 
def gradio_reset(chat_state):
    
    chat_state = []
    return (None,
            gr.update(value=None, interactive=True),
            gr.update(placeholder='Please upload your wav first', interactive=False),
            gr.update(value="Upload & Start Chat", interactive=True),
            chat_state)

def upload_speech(gr_speech, text_input, chat_state):
    
    if gr_speech is None:
        return None, None, gr.update(interactive=True), chat_state, None
    chat_state.append(gr_speech)
    return (gr.update(interactive=False),
            gr.update(interactive=True, placeholder='Type and press Enter'),
            gr.update(value="Start Chatting", interactive=False),
            chat_state)

def gradio_ask(user_message, chatbot, chat_state):
    
    if len(user_message) == 0:
        return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state
    chat_state.append(user_message)
    chatbot.append([user_message, None])
    # 
    return gr.update(interactive=False, placeholder='Currently only single round conversations are supported.'), chatbot, chat_state

def gradio_answer(chatbot, chat_state, num_beams, temperature, top_p):
    llm_message = model.generate(
        wav_path=chat_state[0],
        prompt=chat_state[1],
        num_beams=num_beams,
        temperature=temperature,
        top_p=top_p,
    )
    chatbot[-1][1] = llm_message[0]
    return chatbot, chat_state

title = """<h1 align="center">SALMONN: Speech Audio Language Music Open Neural Network</h1>"""
image_src = """<h1 align="center"><a href="https://github.com/bytedance/SALMONN"><img src="https://raw.githubusercontent.com/bytedance/SALMONN/main/resource/salmon.png", alt="SALMONN" border="0" style="margin: 0 auto; height: 200px;" /></a> </h1>"""
description = """<h3>This is the demo of SALMONN. Upload your audio and start chatting!</h3>"""


with gr.Blocks() as demo:
    gr.Markdown(title)
    gr.Markdown(image_src)
    gr.Markdown(description)

    with gr.Row():
        with gr.Column():
            speech = gr.Audio(label="Audio", type='filepath')
            upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary")
            clear = gr.Button("Restart")

            num_beams = gr.Slider(
                minimum=1,
                maximum=10,
                value=4,
                step=1,
                interactive=True,
                label="beam search numbers",
            )

            top_p = gr.Slider(
                minimum=0.1,
                maximum=1.0,
                value=0.9,
                step=0.1,
                interactive=True,
                label="top p",
            )

            temperature = gr.Slider(
                minimum=0.8,
                maximum=2.0,
                value=1.0,
                step=0.1,
                interactive=False,
                label="temperature",
            )

        with gr.Column():
            chat_state = gr.State([])
            
            chatbot = gr.Chatbot(label='SALMONN')
            text_input = gr.Textbox(label='User', placeholder='Please upload your speech first', interactive=False)

    with gr.Row():
        examples = gr.Examples(
            examples = [
                ["resource/audio_demo/gunshots.wav", "Recognize the speech and give me the transcription."],
                ["resource/audio_demo/gunshots.wav", "Provide the phonetic transcription for the speech."],
                ["resource/audio_demo/gunshots.wav", "Please describe the audio."],
                ["resource/audio_demo/gunshots.wav", "Recognize what the speaker says and describe the background audio at the same time."],
                ["resource/audio_demo/gunshots.wav", "Use your strong reasoning skills to answer the speaker's question in detail based on the background sound."],
                ["resource/audio_demo/duck.wav", "Please list each event in the audio in order."],
                ["resource/audio_demo/duck.wav", "Based on the audio, write a story in detail. Your story should be highly related to the audio."],
                ["resource/audio_demo/duck.wav", "How many speakers did you hear in this audio? Who are they?"],
                ["resource/audio_demo/excitement.wav", "Describe the emotion of the speaker."],
                ["resource/audio_demo/mountain.wav", "Please answer the question in detail."],
                ["resource/audio_demo/music.wav", "Please describe the music in detail."],
                ["resource/audio_demo/music.wav", "What is the emotion of the music? Explain the reason in detail."],
                ["resource/audio_demo/music.wav", "Can you write some lyrics of the song?"],
                ["resource/audio_demo/music.wav", "Give me a title of the music based on its rhythm and emotion."]
            ],
            inputs=[speech, text_input]
        )
        
    upload_button.click(upload_speech, [speech, text_input, chat_state], [speech, text_input, upload_button, chat_state])

    text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(
        gradio_answer, [chatbot, chat_state, num_beams, temperature, top_p], [chatbot, chat_state]
    )
    clear.click(gradio_reset, [chat_state], [chatbot, speech, text_input, upload_button, chat_state], queue=False)



demo.launch(share=True, enable_queue=True, server_port=int(args.port))