app.py

# 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 spaces

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="./salmonn_7b_v0.pth")
parser.add_argument("--whisper_path", type=str, default="./whisper_large_v2")
parser.add_argument("--beats_path", type=str, default="./beats/BEATs_iter3_plus_AS2M_finetuned_on_AS2M_cpt2.pt")
parser.add_argument("--vicuna_path", type=str, default="./vicuna-7b-v1.5")
parser.add_argument("--low_resource", action='store_true', default=False)
parser.add_argument("--port", default=9527)

args = parser.parse_args()
args.low_resource = True # for huggingface A10 7b demo
# model = ff()
model = SALMONN(
    ckpt=args.ckpt_path,
    whisper_path=args.whisper_path,
    beats_path=args.beats_path,
    vicuna_path=args.vicuna_path,
    low_resource=args.low_resource,
    lora_alpha=28,
    device='cpu',
)
model.to(args.device)
model.eval()

@spaces.GPU(enable_queue=True)
def gradio_answer(speech, text_input, num_beams, temperature, top_p):
    
    llm_message = model.generate(
        wav_path=speech,
        prompt=text_input,
        num_beams=num_beams,
        temperature=temperature,
        top_p=top_p,
    )
    
    return llm_message[0]

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-7B. To experience SALMONN-13B, you can go to <a href="https://bytedance.github.io/SALMONN">https://bytedance.github.io/SALMONN</a>.\n 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')
            
            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():
            
            text_input = gr.Textbox(label='User', placeholder='Please upload your audio first', interactive=True)
            answer = gr.Textbox(label="Salmonn answer")

    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", "Listen to the speech and translate it into German."],
                ["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/jobs.wav", "Give me only three keywords of the text. Explain your reason."],
                ["resource/audio_demo/2_30.wav", "What is the time mentioned in the speech?"],
                ["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]
        )
        
    
    text_input.submit(
        gradio_answer, [speech, text_input, num_beams, temperature, top_p], [answer]
    )
    

# demo.launch(share=True, enable_queue=True, server_port=int(args.port))
demo.queue(max_size=20).launch(share=False)